The Concepts section helps you learn about the parts of the Kubernetes system and the abstractions Kubernetes uses to represent your cluster, and helps you obtain a deeper understanding of how Kubernetes works.

1 - Overview

Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.

This page is an overview of Kubernetes.

The name Kubernetes originates from Greek, meaning helmsman or pilot. K8s as an abbreviation results from counting the eight letters between the "K" and the "s". Google open-sourced the Kubernetes project in 2014. Kubernetes combines over 15 years of Google's experience running production workloads at scale with best-of-breed ideas and practices from the community.

Going back in time

Let's take a look at why Kubernetes is so useful by going back in time.

Deployment evolution

Traditional deployment era: Early on, organizations ran applications on physical servers. There was no way to define resource boundaries for applications in a physical server, and this caused resource allocation issues. For example, if multiple applications run on a physical server, there can be instances where one application would take up most of the resources, and as a result, the other applications would underperform. A solution for this would be to run each application on a different physical server. But this did not scale as resources were underutilized, and it was expensive for organizations to maintain many physical servers.

Virtualized deployment era: As a solution, virtualization was introduced. It allows you to run multiple Virtual Machines (VMs) on a single physical server's CPU. Virtualization allows applications to be isolated between VMs and provides a level of security as the information of one application cannot be freely accessed by another application.

Virtualization allows better utilization of resources in a physical server and allows better scalability because an application can be added or updated easily, reduces hardware costs, and much more. With virtualization you can present a set of physical resources as a cluster of disposable virtual machines.

Each VM is a full machine running all the components, including its own operating system, on top of the virtualized hardware.

Container deployment era: Containers are similar to VMs, but they have relaxed isolation properties to share the Operating System (OS) among the applications. Therefore, containers are considered lightweight. Similar to a VM, a container has its own filesystem, share of CPU, memory, process space, and more. As they are decoupled from the underlying infrastructure, they are portable across clouds and OS distributions.

Containers have become popular because they provide extra benefits, such as:

Agile application creation and deployment: increased ease and efficiency of container image creation compared to VM image use.
Continuous development, integration, and deployment: provides for reliable and frequent container image build and deployment with quick and efficient rollbacks (due to image immutability).
Dev and Ops separation of concerns: create application container images at build/release time rather than deployment time, thereby decoupling applications from infrastructure.
Observability: not only surfaces OS-level information and metrics, but also application health and other signals.
Environmental consistency across development, testing, and production: Runs the same on a laptop as it does in the cloud.
Cloud and OS distribution portability: Runs on Ubuntu, RHEL, CoreOS, on-premises, on major public clouds, and anywhere else.
Application-centric management: Raises the level of abstraction from running an OS on virtual hardware to running an application on an OS using logical resources.
Loosely coupled, distributed, elastic, liberated micro-services: applications are broken into smaller, independent pieces and can be deployed and managed dynamically – not a monolithic stack running on one big single-purpose machine.
Resource isolation: predictable application performance.
Resource utilization: high efficiency and density.

Why you need Kubernetes and what it can do

Containers are a good way to bundle and run your applications. In a production environment, you need to manage the containers that run the applications and ensure that there is no downtime. For example, if a container goes down, another container needs to start. Wouldn't it be easier if this behavior was handled by a system?

That's how Kubernetes comes to the rescue! Kubernetes provides you with a framework to run distributed systems resiliently. It takes care of scaling and failover for your application, provides deployment patterns, and more. For example, Kubernetes can easily manage a canary deployment for your system.

Kubernetes provides you with:

Service discovery and load balancing Kubernetes can expose a container using the DNS name or using their own IP address. If traffic to a container is high, Kubernetes is able to load balance and distribute the network traffic so that the deployment is stable.
Storage orchestration Kubernetes allows you to automatically mount a storage system of your choice, such as local storages, public cloud providers, and more.
Automated rollouts and rollbacks You can describe the desired state for your deployed containers using Kubernetes, and it can change the actual state to the desired state at a controlled rate. For example, you can automate Kubernetes to create new containers for your deployment, remove existing containers and adopt all their resources to the new container.
Automatic bin packing You provide Kubernetes with a cluster of nodes that it can use to run containerized tasks. You tell Kubernetes how much CPU and memory (RAM) each container needs. Kubernetes can fit containers onto your nodes to make the best use of your resources.
Self-healing Kubernetes restarts containers that fail, replaces containers, kills containers that don't respond to your user-defined health check, and doesn't advertise them to clients until they are ready to serve.
Secret and configuration management Kubernetes lets you store and manage sensitive information, such as passwords, OAuth tokens, and SSH keys. You can deploy and update secrets and application configuration without rebuilding your container images, and without exposing secrets in your stack configuration.

What Kubernetes is not

Kubernetes is not a traditional, all-inclusive PaaS (Platform as a Service) system. Since Kubernetes operates at the container level rather than at the hardware level, it provides some generally applicable features common to PaaS offerings, such as deployment, scaling, load balancing, and lets users integrate their logging, monitoring, and alerting solutions. However, Kubernetes is not monolithic, and these default solutions are optional and pluggable. Kubernetes provides the building blocks for building developer platforms, but preserves user choice and flexibility where it is important.

Kubernetes:

Does not limit the types of applications supported. Kubernetes aims to support an extremely diverse variety of workloads, including stateless, stateful, and data-processing workloads. If an application can run in a container, it should run great on Kubernetes.
Does not deploy source code and does not build your application. Continuous Integration, Delivery, and Deployment (CI/CD) workflows are determined by organization cultures and preferences as well as technical requirements.
Does not provide application-level services, such as middleware (for example, message buses), data-processing frameworks (for example, Spark), databases (for example, MySQL), caches, nor cluster storage systems (for example, Ceph) as built-in services. Such components can run on Kubernetes, and/or can be accessed by applications running on Kubernetes through portable mechanisms, such as the Open Service Broker.
Does not dictate logging, monitoring, or alerting solutions. It provides some integrations as proof of concept, and mechanisms to collect and export metrics.
Does not provide nor mandate a configuration language/system (for example, Jsonnet). It provides a declarative API that may be targeted by arbitrary forms of declarative specifications.
Does not provide nor adopt any comprehensive machine configuration, maintenance, management, or self-healing systems.
Additionally, Kubernetes is not a mere orchestration system. In fact, it eliminates the need for orchestration. The technical definition of orchestration is execution of a defined workflow: first do A, then B, then C. In contrast, Kubernetes comprises a set of independent, composable control processes that continuously drive the current state towards the provided desired state. It shouldn't matter how you get from A to C. Centralized control is also not required. This results in a system that is easier to use and more powerful, robust, resilient, and extensible.

What's next

Take a look at the Kubernetes Components
Take a look at the The Kubernetes API
Take a look at the Cluster Architecture
Ready to Get Started?

1.1 - Kubernetes Components

A Kubernetes cluster consists of the components that are a part of the control plane and a set of machines called nodes.

When you deploy Kubernetes, you get a cluster.

A Kubernetes cluster consists of a set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node.

The worker node(s) host the Pods that are the components of the application workload. The control plane manages the worker nodes and the Pods in the cluster. In production environments, the control plane usually runs across multiple computers and a cluster usually runs multiple nodes, providing fault-tolerance and high availability.

This document outlines the various components you need to have for a complete and working Kubernetes cluster.

Components of Kubernetes — The components of a Kubernetes cluster

Control Plane Components

The control plane's components make global decisions about the cluster (for example, scheduling), as well as detecting and responding to cluster events (for example, starting up a new pod when a deployment's replicas field is unsatisfied).

Control plane components can be run on any machine in the cluster. However, for simplicity, set up scripts typically start all control plane components on the same machine, and do not run user containers on this machine. See Creating Highly Available clusters with kubeadm for an example control plane setup that runs across multiple machines.

kube-apiserver

The API server is a component of the Kubernetes control plane that exposes the Kubernetes API. The API server is the front end for the Kubernetes control plane.

The main implementation of a Kubernetes API server is kube-apiserver. kube-apiserver is designed to scale horizontally—that is, it scales by deploying more instances. You can run several instances of kube-apiserver and balance traffic between those instances.

etcd

Consistent and highly-available key value store used as Kubernetes' backing store for all cluster data.

If your Kubernetes cluster uses etcd as its backing store, make sure you have a back up plan for those data.

You can find in-depth information about etcd in the official documentation.

kube-scheduler

Control plane component that watches for newly created Pods with no assigned node, and selects a node for them to run on.

Factors taken into account for scheduling decisions include: individual and collective resource requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, and deadlines.

kube-controller-manager

Control plane component that runs controller processes.

Logically, each controller is a separate process, but to reduce complexity, they are all compiled into a single binary and run in a single process.

Some types of these controllers are:

Node controller: Responsible for noticing and responding when nodes go down.
Job controller: Watches for Job objects that represent one-off tasks, then creates Pods to run those tasks to completion.
Endpoints controller: Populates the Endpoints object (that is, joins Services & Pods).
Service Account & Token controllers: Create default accounts and API access tokens for new namespaces.

cloud-controller-manager

A Kubernetes component that embeds cloud-specific control logic. The cloud controller manager lets you link your cluster into your cloud provider's API, and separates out the components that interact with that cloud platform from components that only interact with your cluster.

The cloud-controller-manager only runs controllers that are specific to your cloud provider. If you are running Kubernetes on your own premises, or in a learning environment inside your own PC, the cluster does not have a cloud controller manager.

As with the kube-controller-manager, the cloud-controller-manager combines several logically independent control loops into a single binary that you run as a single process. You can scale horizontally (run more than one copy) to improve performance or to help tolerate failures.

The following controllers can have cloud provider dependencies:

Node controller: For checking the cloud provider to determine if a node has been deleted in the cloud after it stops responding
Route controller: For setting up routes in the underlying cloud infrastructure
Service controller: For creating, updating and deleting cloud provider load balancers

Node Components

Node components run on every node, maintaining running pods and providing the Kubernetes runtime environment.

kubelet

An agent that runs on each node in the cluster. It makes sure that containers are running in a Pod.

The kubelet takes a set of PodSpecs that are provided through various mechanisms and ensures that the containers described in those PodSpecs are running and healthy. The kubelet doesn't manage containers which were not created by Kubernetes.

kube-proxy

kube-proxy is a network proxy that runs on each node in your cluster, implementing part of the Kubernetes Service concept.

kube-proxy maintains network rules on nodes. These network rules allow network communication to your Pods from network sessions inside or outside of your cluster.

kube-proxy uses the operating system packet filtering layer if there is one and it's available. Otherwise, kube-proxy forwards the traffic itself.

Container runtime

The container runtime is the software that is responsible for running containers.

Kubernetes supports container runtimes such as containerd, CRI-O, and any other implementation of the Kubernetes CRI (Container Runtime Interface).

Addons

Addons use Kubernetes resources (DaemonSet, Deployment, etc) to implement cluster features. Because these are providing cluster-level features, namespaced resources for addons belong within the kube-system namespace.

Selected addons are described below; for an extended list of available addons, please see Addons.

DNS

While the other addons are not strictly required, all Kubernetes clusters should have cluster DNS, as many examples rely on it.

Cluster DNS is a DNS server, in addition to the other DNS server(s) in your environment, which serves DNS records for Kubernetes services.

Containers started by Kubernetes automatically include this DNS server in their DNS searches.

Web UI (Dashboard)

Dashboard is a general purpose, web-based UI for Kubernetes clusters. It allows users to manage and troubleshoot applications running in the cluster, as well as the cluster itself.

Container Resource Monitoring

Container Resource Monitoring records generic time-series metrics about containers in a central database, and provides a UI for browsing that data.

Cluster-level Logging

A cluster-level logging mechanism is responsible for saving container logs to a central log store with search/browsing interface.

What's next

Learn about Nodes
Learn about Controllers
Learn about kube-scheduler
Read etcd's official documentation

1.2 - The Kubernetes API

The Kubernetes API lets you query and manipulate the state of objects in Kubernetes. The core of Kubernetes' control plane is the API server and the HTTP API that it exposes. Users, the different parts of your cluster, and external components all communicate with one another through the API server.

The core of Kubernetes' control plane is the API server. The API server exposes an HTTP API that lets end users, different parts of your cluster, and external components communicate with one another.

The Kubernetes API lets you query and manipulate the state of API objects in Kubernetes (for example: Pods, Namespaces, ConfigMaps, and Events).

Most operations can be performed through the kubectl command-line interface or other command-line tools, such as kubeadm, which in turn use the API. However, you can also access the API directly using REST calls.

Consider using one of the client libraries if you are writing an application using the Kubernetes API.

OpenAPI specification

Complete API details are documented using OpenAPI.

OpenAPI V2

The Kubernetes API server serves an aggregated OpenAPI v2 spec via the /openapi/v2 endpoint. You can request the response format using request headers as follows:

Valid request header values for OpenAPI v2 queries
Header	Possible values	Notes
`Accept-Encoding`	`gzip`	not supplying this header is also acceptable
`Accept`	`application/com.github.proto-openapi.spec.v2@v1.0+protobuf`	mainly for intra-cluster use
	`application/json`	default
	`*`	serves `application/json`

Kubernetes implements an alternative Protobuf based serialization format that is primarily intended for intra-cluster communication. For more information about this format, see the Kubernetes Protobuf serialization design proposal and the Interface Definition Language (IDL) files for each schema located in the Go packages that define the API objects.

OpenAPI V3

FEATURE STATE: Kubernetes v1.24 [beta]

Kubernetes v1.25 offers beta support for publishing its APIs as OpenAPI v3; this is a beta feature that is enabled by default. You can disable the beta feature by turning off the feature gate named OpenAPIV3 for the kube-apiserver component.

A discovery endpoint /openapi/v3 is provided to see a list of all group/versions available. This endpoint only returns JSON. These group/versions are provided in the following format:

{
    "paths": {
        ...,
        "api/v1": {
            "serverRelativeURL": "/openapi/v3/api/v1?hash=CC0E9BFD992D8C59AEC98A1E2336F899E8318D3CF4C68944C3DEC640AF5AB52D864AC50DAA8D145B3494F75FA3CFF939FCBDDA431DAD3CA79738B297795818CF"
        },
        "apis/admissionregistration.k8s.io/v1": {
            "serverRelativeURL": "/openapi/v3/apis/admissionregistration.k8s.io/v1?hash=E19CC93A116982CE5422FC42B590A8AFAD92CDE9AE4D59B5CAAD568F083AD07946E6CB5817531680BCE6E215C16973CD39003B0425F3477CFD854E89A9DB6597"
        },
        ....
    }
}

The relative URLs are pointing to immutable OpenAPI descriptions, in order to improve client-side caching. The proper HTTP caching headers are also set by the API server for that purpose (Expires to 1 year in the future, and Cache-Control to immutable). When an obsolete URL is used, the API server returns a redirect to the newest URL.

The Kubernetes API server publishes an OpenAPI v3 spec per Kubernetes group version at the /openapi/v3/apis/<group>/<version>?hash=<hash> endpoint.

Refer to the table below for accepted request headers.

Valid request header values for OpenAPI v3 queries
Header	Possible values	Notes
`Accept-Encoding`	`gzip`	not supplying this header is also acceptable
`Accept`	`application/com.github.proto-openapi.spec.v3@v1.0+protobuf`	mainly for intra-cluster use
	`application/json`	default
	`*`	serves `application/json`

Persistence

Kubernetes stores the serialized state of objects by writing them into etcd.

API groups and versioning

To make it easier to eliminate fields or restructure resource representations, Kubernetes supports multiple API versions, each at a different API path, such as /api/v1 or /apis/rbac.authorization.k8s.io/v1alpha1.

Versioning is done at the API level rather than at the resource or field level to ensure that the API presents a clear, consistent view of system resources and behavior, and to enable controlling access to end-of-life and/or experimental APIs.

To make it easier to evolve and to extend its API, Kubernetes implements API groups that can be enabled or disabled.

API resources are distinguished by their API group, resource type, namespace (for namespaced resources), and name. The API server handles the conversion between API versions transparently: all the different versions are actually representations of the same persisted data. The API server may serve the same underlying data through multiple API versions.

For example, suppose there are two API versions, v1 and v1beta1, for the same resource. If you originally created an object using the v1beta1 version of its API, you can later read, update, or delete that object using either the v1beta1 or the v1 API version, until the v1beta1 version is deprecated and removed. At that point you can continue accessing and modifying the object using the v1 API.

API changes

Any system that is successful needs to grow and change as new use cases emerge or existing ones change. Therefore, Kubernetes has designed the Kubernetes API to continuously change and grow. The Kubernetes project aims to not break compatibility with existing clients, and to maintain that compatibility for a length of time so that other projects have an opportunity to adapt.

In general, new API resources and new resource fields can be added often and frequently. Elimination of resources or fields requires following the API deprecation policy.

Kubernetes makes a strong commitment to maintain compatibility for official Kubernetes APIs once they reach general availability (GA), typically at API version v1. Additionally, Kubernetes maintains compatibility with data persisted via beta API versions of official Kubernetes APIs, and ensures that data can be converted and accessed via GA API versions when the feature goes stable.

If you adopt a beta API version, you will need to transition to a subsequent beta or stable API version once the API graduates. The best time to do this is while the beta API is in its deprecation period, since objects are simultaneously accessible via both API versions. Once the beta API completes its deprecation period and is no longer served, the replacement API version must be used.

Note: Although Kubernetes also aims to maintain compatibility for alpha APIs versions, in some circumstances this is not possible. If you use any alpha API versions, check the release notes for Kubernetes when upgrading your cluster, in case the API did change in incompatible ways that require deleting all existing alpha objects prior to upgrade.

Refer to API versions reference for more details on the API version level definitions.

API Extension

The Kubernetes API can be extended in one of two ways:

Custom resources let you declaratively define how the API server should provide your chosen resource API.
You can also extend the Kubernetes API by implementing an aggregation layer.

What's next

Learn how to extend the Kubernetes API by adding your own CustomResourceDefinition.
Controlling Access To The Kubernetes API describes how the cluster manages authentication and authorization for API access.
Learn about API endpoints, resource types and samples by reading API Reference.
Learn about what constitutes a compatible change, and how to change the API, from API changes.

1.3 - Working with Kubernetes Objects

Kubernetes objects are persistent entities in the Kubernetes system. Kubernetes uses these entities to represent the state of your cluster. Learn about the Kubernetes object model and how to work with these objects.

1.3.1 - Understanding Kubernetes Objects

This page explains how Kubernetes objects are represented in the Kubernetes API, and how you can express them in .yaml format.

Understanding Kubernetes objects

Kubernetes objects are persistent entities in the Kubernetes system. Kubernetes uses these entities to represent the state of your cluster. Specifically, they can describe:

What containerized applications are running (and on which nodes)
The resources available to those applications
The policies around how those applications behave, such as restart policies, upgrades, and fault-tolerance

A Kubernetes object is a "record of intent"--once you create the object, the Kubernetes system will constantly work to ensure that object exists. By creating an object, you're effectively telling the Kubernetes system what you want your cluster's workload to look like; this is your cluster's desired state.

To work with Kubernetes objects--whether to create, modify, or delete them--you'll need to use the Kubernetes API. When you use the kubectl command-line interface, for example, the CLI makes the necessary Kubernetes API calls for you. You can also use the Kubernetes API directly in your own programs using one of the Client Libraries.

Object Spec and Status

Almost every Kubernetes object includes two nested object fields that govern the object's configuration: the object spec and the object status. For objects that have a spec, you have to set this when you create the object, providing a description of the characteristics you want the resource to have: its desired state.

The status describes the current state of the object, supplied and updated by the Kubernetes system and its components. The Kubernetes control plane continually and actively manages every object's actual state to match the desired state you supplied.

For example: in Kubernetes, a Deployment is an object that can represent an application running on your cluster. When you create the Deployment, you might set the Deployment spec to specify that you want three replicas of the application to be running. The Kubernetes system reads the Deployment spec and starts three instances of your desired application--updating the status to match your spec. If any of those instances should fail (a status change), the Kubernetes system responds to the difference between spec and status by making a correction--in this case, starting a replacement instance.

For more information on the object spec, status, and metadata, see the Kubernetes API Conventions.

Describing a Kubernetes object

When you create an object in Kubernetes, you must provide the object spec that describes its desired state, as well as some basic information about the object (such as a name). When you use the Kubernetes API to create the object (either directly or via kubectl), that API request must include that information as JSON in the request body. Most often, you provide the information to kubectl in a .yaml file. kubectl converts the information to JSON when making the API request.

Here's an example .yaml file that shows the required fields and object spec for a Kubernetes Deployment:

application/deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2 # tells deployment to run 2 pods matching the template
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

One way to create a Deployment using a .yaml file like the one above is to use the kubectl apply command in the kubectl command-line interface, passing the .yaml file as an argument. Here's an example:

kubectl apply -f https://k8s.io/examples/application/deployment.yaml

The output is similar to this:

deployment.apps/nginx-deployment created

Required Fields

In the .yaml file for the Kubernetes object you want to create, you'll need to set values for the following fields:

apiVersion - Which version of the Kubernetes API you're using to create this object
kind - What kind of object you want to create
metadata - Data that helps uniquely identify the object, including a name string, UID, and optional namespace
spec - What state you desire for the object

The precise format of the object spec is different for every Kubernetes object, and contains nested fields specific to that object. The Kubernetes API Reference can help you find the spec format for all of the objects you can create using Kubernetes.

For example, see the spec field for the Pod API reference. For each Pod, the .spec field specifies the pod and its desired state (such as the container image name for each container within that pod). Another example of an object specification is the spec field for the StatefulSet API. For StatefulSet, the .spec field specifies the StatefulSet and its desired state. Within the .spec of a StatefulSet is a template for Pod objects. That template describes Pods that the StatefulSet controller will create in order to satisfy the StatefulSet specification. Different kinds of object can also have different .status; again, the API reference pages detail the structure of that .status field, and its content for each different type of object.

What's next

Learn about the most important basic Kubernetes objects, such as Pod.
Learn about controllers in Kubernetes.
Using the Kubernetes API explains some more API concepts.

1.3.2 - Kubernetes Object Management

The kubectl command-line tool supports several different ways to create and manage Kubernetes objects. This document provides an overview of the different approaches. Read the Kubectl book for details of managing objects by Kubectl.

Management techniques

Warning: A Kubernetes object should be managed using only one technique. Mixing and matching techniques for the same object results in undefined behavior.

Management technique	Operates on	Recommended environment	Supported writers	Learning curve
Imperative commands	Live objects	Development projects	1+	Lowest
Imperative object configuration	Individual files	Production projects	1	Moderate
Declarative object configuration	Directories of files	Production projects	1+	Highest

Imperative commands

When using imperative commands, a user operates directly on live objects in a cluster. The user provides operations to the kubectl command as arguments or flags.

This is the recommended way to get started or to run a one-off task in a cluster. Because this technique operates directly on live objects, it provides no history of previous configurations.

Examples

Run an instance of the nginx container by creating a Deployment object:

kubectl create deployment nginx --image nginx

Trade-offs

Advantages compared to object configuration:

Commands are expressed as a single action word.
Commands require only a single step to make changes to the cluster.

Disadvantages compared to object configuration:

Commands do not integrate with change review processes.
Commands do not provide an audit trail associated with changes.
Commands do not provide a source of records except for what is live.
Commands do not provide a template for creating new objects.

Imperative object configuration

In imperative object configuration, the kubectl command specifies the operation (create, replace, etc.), optional flags and at least one file name. The file specified must contain a full definition of the object in YAML or JSON format.

See the API reference for more details on object definitions.

Warning: The imperative replace command replaces the existing spec with the newly provided one, dropping all changes to the object missing from the configuration file. This approach should not be used with resource types whose specs are updated independently of the configuration file. Services of type LoadBalancer, for example, have their externalIPs field updated independently from the configuration by the cluster.

Examples

Create the objects defined in a configuration file:

kubectl create -f nginx.yaml

Delete the objects defined in two configuration files:

kubectl delete -f nginx.yaml -f redis.yaml

Update the objects defined in a configuration file by overwriting the live configuration:

kubectl replace -f nginx.yaml

Trade-offs

Advantages compared to imperative commands:

Object configuration can be stored in a source control system such as Git.
Object configuration can integrate with processes such as reviewing changes before push and audit trails.
Object configuration provides a template for creating new objects.

Disadvantages compared to imperative commands:

Object configuration requires basic understanding of the object schema.
Object configuration requires the additional step of writing a YAML file.

Advantages compared to declarative object configuration:

Imperative object configuration behavior is simpler and easier to understand.
As of Kubernetes version 1.5, imperative object configuration is more mature.

Disadvantages compared to declarative object configuration:

Imperative object configuration works best on files, not directories.
Updates to live objects must be reflected in configuration files, or they will be lost during the next replacement.

Declarative object configuration

When using declarative object configuration, a user operates on object configuration files stored locally, however the user does not define the operations to be taken on the files. Create, update, and delete operations are automatically detected per-object by kubectl. This enables working on directories, where different operations might be needed for different objects.

Note: Declarative object configuration retains changes made by other writers, even if the changes are not merged back to the object configuration file. This is possible by using the patch API operation to write only observed differences, instead of using the replace API operation to replace the entire object configuration.

Examples

Process all object configuration files in the configs directory, and create or patch the live objects. You can first diff to see what changes are going to be made, and then apply:

kubectl diff -f configs/
kubectl apply -f configs/

Recursively process directories:

kubectl diff -R -f configs/
kubectl apply -R -f configs/

Trade-offs

Advantages compared to imperative object configuration:

Changes made directly to live objects are retained, even if they are not merged back into the configuration files.
Declarative object configuration has better support for operating on directories and automatically detecting operation types (create, patch, delete) per-object.

Disadvantages compared to imperative object configuration:

Declarative object configuration is harder to debug and understand results when they are unexpected.
Partial updates using diffs create complex merge and patch operations.

What's next

1.3.3 - Object Names and IDs

Each object in your cluster has a Name that is unique for that type of resource. Every Kubernetes object also has a UID that is unique across your whole cluster.

For example, you can only have one Pod named myapp-1234 within the same namespace, but you can have one Pod and one Deployment that are each named myapp-1234.

For non-unique user-provided attributes, Kubernetes provides labels and annotations.

Names

A client-provided string that refers to an object in a resource URL, such as /api/v1/pods/some-name.

Only one object of a given kind can have a given name at a time. However, if you delete the object, you can make a new object with the same name.

Note: In cases when objects represent a physical entity, like a Node representing a physical host, when the host is re-created under the same name without deleting and re-creating the Node, Kubernetes treats the new host as the old one, which may lead to inconsistencies.

Below are four types of commonly used name constraints for resources.

DNS Subdomain Names

Most resource types require a name that can be used as a DNS subdomain name as defined in RFC 1123. This means the name must:

contain no more than 253 characters
contain only lowercase alphanumeric characters, '-' or '.'
start with an alphanumeric character
end with an alphanumeric character

RFC 1123 Label Names

Some resource types require their names to follow the DNS label standard as defined in RFC 1123. This means the name must:

contain at most 63 characters
contain only lowercase alphanumeric characters or '-'
start with an alphanumeric character
end with an alphanumeric character

RFC 1035 Label Names

Some resource types require their names to follow the DNS label standard as defined in RFC 1035. This means the name must:

contain at most 63 characters
contain only lowercase alphanumeric characters or '-'
start with an alphabetic character
end with an alphanumeric character

Path Segment Names

Some resource types require their names to be able to be safely encoded as a path segment. In other words, the name may not be "." or ".." and the name may not contain "/" or "%".

Here's an example manifest for a Pod named nginx-demo.

apiVersion: v1
kind: Pod
metadata:
  name: nginx-demo
spec:
  containers:
  - name: nginx
    image: nginx:1.14.2
    ports:
    - containerPort: 80

Note: Some resource types have additional restrictions on their names.

UIDs

A Kubernetes systems-generated string to uniquely identify objects.

Every object created over the whole lifetime of a Kubernetes cluster has a distinct UID. It is intended to distinguish between historical occurrences of similar entities.

Kubernetes UIDs are universally unique identifiers (also known as UUIDs). UUIDs are standardized as ISO/IEC 9834-8 and as ITU-T X.667.

What's next

Read about labels in Kubernetes.
See the Identifiers and Names in Kubernetes design document.

1.3.4 - Namespaces

In Kubernetes, namespaces provides a mechanism for isolating groups of resources within a single cluster. Names of resources need to be unique within a namespace, but not across namespaces. Namespace-based scoping is applicable only for namespaced objects (e.g. Deployments, Services, etc) and not for cluster-wide objects (e.g. StorageClass, Nodes, PersistentVolumes, etc).

When to Use Multiple Namespaces

Namespaces are intended for use in environments with many users spread across multiple teams, or projects. For clusters with a few to tens of users, you should not need to create or think about namespaces at all. Start using namespaces when you need the features they provide.

Namespaces provide a scope for names. Names of resources need to be unique within a namespace, but not across namespaces. Namespaces cannot be nested inside one another and each Kubernetes resource can only be in one namespace.

Namespaces are a way to divide cluster resources between multiple users (via resource quota).

It is not necessary to use multiple namespaces to separate slightly different resources, such as different versions of the same software: use labels to distinguish resources within the same namespace.

Working with Namespaces

Creation and deletion of namespaces are described in the Admin Guide documentation for namespaces.

Note: Avoid creating namespaces with the prefix kube-, since it is reserved for Kubernetes system namespaces.

Viewing namespaces

You can list the current namespaces in a cluster using:

kubectl get namespace

NAME              STATUS   AGE
default           Active   1d
kube-node-lease   Active   1d
kube-public       Active   1d
kube-system       Active   1d

Kubernetes starts with four initial namespaces:

default The default namespace for objects with no other namespace
kube-system The namespace for objects created by the Kubernetes system
kube-public This namespace is created automatically and is readable by all users (including those not authenticated). This namespace is mostly reserved for cluster usage, in case that some resources should be visible and readable publicly throughout the whole cluster. The public aspect of this namespace is only a convention, not a requirement.
kube-node-lease This namespace holds Lease objects associated with each node. Node leases allow the kubelet to send heartbeats so that the control plane can detect node failure.

Setting the namespace for a request

To set the namespace for a current request, use the --namespace flag.

For example:

kubectl run nginx --image=nginx --namespace=<insert-namespace-name-here>
kubectl get pods --namespace=<insert-namespace-name-here>

Setting the namespace preference

You can permanently save the namespace for all subsequent kubectl commands in that context.

kubectl config set-context --current --namespace=<insert-namespace-name-here>
# Validate it
kubectl config view --minify | grep namespace:

Namespaces and DNS

When you create a Service, it creates a corresponding DNS entry. This entry is of the form <service-name>.<namespace-name>.svc.cluster.local, which means that if a container only uses <service-name>, it will resolve to the service which is local to a namespace. This is useful for using the same configuration across multiple namespaces such as Development, Staging and Production. If you want to reach across namespaces, you need to use the fully qualified domain name (FQDN).

As a result, all namespace names must be valid RFC 1123 DNS labels.

Warning:

By creating namespaces with the same name as public top-level domains, Services in these namespaces can have short DNS names that overlap with public DNS records. Workloads from any namespace performing a DNS lookup without a trailing dot will be redirected to those services, taking precedence over public DNS.

To mitigate this, limit privileges for creating namespaces to trusted users. If required, you could additionally configure third-party security controls, such as admission webhooks, to block creating any namespace with the name of public TLDs.

Not All Objects are in a Namespace

Most Kubernetes resources (e.g. pods, services, replication controllers, and others) are in some namespaces. However namespace resources are not themselves in a namespace. And low-level resources, such as nodes and persistentVolumes, are not in any namespace.

To see which Kubernetes resources are and aren't in a namespace:

# In a namespace
kubectl api-resources --namespaced=true

# Not in a namespace
kubectl api-resources --namespaced=false

Automatic labelling

FEATURE STATE: Kubernetes 1.21 [beta]

The Kubernetes control plane sets an immutable label kubernetes.io/metadata.name on all namespaces, provided that the NamespaceDefaultLabelName feature gate is enabled. The value of the label is the namespace name.

What's next

Learn more about creating a new namespace.
Learn more about deleting a namespace.

1.3.5 - Labels and Selectors

Labels are key/value pairs that are attached to objects, such as pods. Labels are intended to be used to specify identifying attributes of objects that are meaningful and relevant to users, but do not directly imply semantics to the core system. Labels can be used to organize and to select subsets of objects. Labels can be attached to objects at creation time and subsequently added and modified at any time. Each object can have a set of key/value labels defined. Each Key must be unique for a given object.

"metadata": {
  "labels": {
    "key1" : "value1",
    "key2" : "value2"
  }
}

Labels allow for efficient queries and watches and are ideal for use in UIs and CLIs. Non-identifying information should be recorded using annotations.

Motivation

Labels enable users to map their own organizational structures onto system objects in a loosely coupled fashion, without requiring clients to store these mappings.

Service deployments and batch processing pipelines are often multi-dimensional entities (e.g., multiple partitions or deployments, multiple release tracks, multiple tiers, multiple micro-services per tier). Management often requires cross-cutting operations, which breaks encapsulation of strictly hierarchical representations, especially rigid hierarchies determined by the infrastructure rather than by users.

Example labels:

"release" : "stable", "release" : "canary"
"environment" : "dev", "environment" : "qa", "environment" : "production"
"tier" : "frontend", "tier" : "backend", "tier" : "cache"
"partition" : "customerA", "partition" : "customerB"
"track" : "daily", "track" : "weekly"

These are examples of commonly used labels; you are free to develop your own conventions. Keep in mind that label Key must be unique for a given object.

Syntax and character set

Labels are key/value pairs. Valid label keys have two segments: an optional prefix and name, separated by a slash (/). The name segment is required and must be 63 characters or less, beginning and ending with an alphanumeric character ([a-z0-9A-Z]) with dashes (-), underscores (_), dots (.), and alphanumerics between. The prefix is optional. If specified, the prefix must be a DNS subdomain: a series of DNS labels separated by dots (.), not longer than 253 characters in total, followed by a slash (/).

If the prefix is omitted, the label Key is presumed to be private to the user. Automated system components (e.g. kube-scheduler, kube-controller-manager, kube-apiserver, kubectl, or other third-party automation) which add labels to end-user objects must specify a prefix.

The kubernetes.io/ and k8s.io/ prefixes are reserved for Kubernetes core components.

Valid label value:

must be 63 characters or less (can be empty),
unless empty, must begin and end with an alphanumeric character ([a-z0-9A-Z]),
could contain dashes (-), underscores (_), dots (.), and alphanumerics between.

For example, here's the configuration file for a Pod that has two labels environment: production and app: nginx :


apiVersion: v1
kind: Pod
metadata:
  name: label-demo
  labels:
    environment: production
    app: nginx
spec:
  containers:
  - name: nginx
    image: nginx:1.14.2
    ports:
    - containerPort: 80

Label selectors

Unlike names and UIDs, labels do not provide uniqueness. In general, we expect many objects to carry the same label(s).

Via a label selector, the client/user can identify a set of objects. The label selector is the core grouping primitive in Kubernetes.

The API currently supports two types of selectors: equality-based and set-based. A label selector can be made of multiple requirements which are comma-separated. In the case of multiple requirements, all must be satisfied so the comma separator acts as a logical AND (&&) operator.

The semantics of empty or non-specified selectors are dependent on the context, and API types that use selectors should document the validity and meaning of them.

Note: For some API types, such as ReplicaSets, the label selectors of two instances must not overlap within a namespace, or the controller can see that as conflicting instructions and fail to determine how many replicas should be present.

Caution: For both equality-based and set-based conditions there is no logical OR (||) operator. Ensure your filter statements are structured accordingly.

Equality-based requirement

Equality- or inequality-based requirements allow filtering by label keys and values. Matching objects must satisfy all of the specified label constraints, though they may have additional labels as well. Three kinds of operators are admitted =,==,!=. The first two represent equality (and are synonyms), while the latter represents inequality. For example:

environment = production
tier != frontend

The former selects all resources with key equal to environment and value equal to production. The latter selects all resources with key equal to tier and value distinct from frontend, and all resources with no labels with the tier key. One could filter for resources in production excluding frontend using the comma operator: environment=production,tier!=frontend

One usage scenario for equality-based label requirement is for Pods to specify node selection criteria. For example, the sample Pod below selects nodes with the label "accelerator=nvidia-tesla-p100".

apiVersion: v1
kind: Pod
metadata:
  name: cuda-test
spec:
  containers:
    - name: cuda-test
      image: "registry.k8s.io/cuda-vector-add:v0.1"
      resources:
        limits:
          nvidia.com/gpu: 1
  nodeSelector:
    accelerator: nvidia-tesla-p100

Set-based requirement

Set-based label requirements allow filtering keys according to a set of values. Three kinds of operators are supported: in,notin and exists (only the key identifier). For example:

environment in (production, qa)
tier notin (frontend, backend)
partition
!partition

The first example selects all resources with key equal to environment and value equal to production or qa.
The second example selects all resources with key equal to tier and values other than frontend and backend, and all resources with no labels with the tier key.
The third example selects all resources including a label with key partition; no values are checked.
The fourth example selects all resources without a label with key partition; no values are checked.

Similarly the comma separator acts as an AND operator. So filtering resources with a partition key (no matter the value) and with environment different than qa can be achieved using partition,environment notin (qa). The set-based label selector is a general form of equality since environment=production is equivalent to environment in (production); similarly for != and notin.

Set-based requirements can be mixed with equality-based requirements. For example: partition in (customerA, customerB),environment!=qa.

API

LIST and WATCH filtering

LIST and WATCH operations may specify label selectors to filter the sets of objects returned using a query parameter. Both requirements are permitted (presented here as they would appear in a URL query string):

equality-based requirements: ?labelSelector=environment%3Dproduction,tier%3Dfrontend
set-based requirements: ?labelSelector=environment+in+%28production%2Cqa%29%2Ctier+in+%28frontend%29

Both label selector styles can be used to list or watch resources via a REST client. For example, targeting apiserver with kubectl and using equality-based one may write:

kubectl get pods -l environment=production,tier=frontend

or using set-based requirements:

kubectl get pods -l 'environment in (production),tier in (frontend)'

As already mentioned set-based requirements are more expressive. For instance, they can implement the OR operator on values:

kubectl get pods -l 'environment in (production, qa)'

or restricting negative matching via exists operator:

kubectl get pods -l 'environment,environment notin (frontend)'

Set references in API objects

Some Kubernetes objects, such as services and replicationcontrollers, also use label selectors to specify sets of other resources, such as pods.

Service and ReplicationController

The set of pods that a service targets is defined with a label selector. Similarly, the population of pods that a replicationcontroller should manage is also defined with a label selector.

Labels selectors for both objects are defined in json or yaml files using maps, and only equality-based requirement selectors are supported:

"selector": {
    "component" : "redis",
}

selector:
    component: redis

this selector (respectively in json or yaml format) is equivalent to component=redis or component in (redis).

Resources that support set-based requirements

Newer resources, such as Job, Deployment, ReplicaSet, and DaemonSet, support set-based requirements as well.

selector:
  matchLabels:
    component: redis
  matchExpressions:
    - {key: tier, operator: In, values: [cache]}
    - {key: environment, operator: NotIn, values: [dev]}

matchLabels is a map of {key,value} pairs. A single {key,value} in the matchLabels map is equivalent to an element of matchExpressions, whose key field is "key", the operator is "In", and the values array contains only "value". matchExpressions is a list of pod selector requirements. Valid operators include In, NotIn, Exists, and DoesNotExist. The values set must be non-empty in the case of In and NotIn. All of the requirements, from both matchLabels and matchExpressions are ANDed together -- they must all be satisfied in order to match.

Selecting sets of nodes

One use case for selecting over labels is to constrain the set of nodes onto which a pod can schedule. See the documentation on node selection for more information.

1.3.6 - Annotations

You can use Kubernetes annotations to attach arbitrary non-identifying metadata to objects. Clients such as tools and libraries can retrieve this metadata.

Attaching metadata to objects

You can use either labels or annotations to attach metadata to Kubernetes objects. Labels can be used to select objects and to find collections of objects that satisfy certain conditions. In contrast, annotations are not used to identify and select objects. The metadata in an annotation can be small or large, structured or unstructured, and can include characters not permitted by labels.

Annotations, like labels, are key/value maps:

"metadata": {
  "annotations": {
    "key1" : "value1",
    "key2" : "value2"
  }
}

Note: The keys and the values in the map must be strings. In other words, you cannot use numeric, boolean, list or other types for either the keys or the values.

Here are some examples of information that could be recorded in annotations:

Fields managed by a declarative configuration layer. Attaching these fields as annotations distinguishes them from default values set by clients or servers, and from auto-generated fields and fields set by auto-sizing or auto-scaling systems.
Build, release, or image information like timestamps, release IDs, git branch, PR numbers, image hashes, and registry address.
Pointers to logging, monitoring, analytics, or audit repositories.
Client library or tool information that can be used for debugging purposes: for example, name, version, and build information.
User or tool/system provenance information, such as URLs of related objects from other ecosystem components.
Lightweight rollout tool metadata: for example, config or checkpoints.
Phone or pager numbers of persons responsible, or directory entries that specify where that information can be found, such as a team web site.
Directives from the end-user to the implementations to modify behavior or engage non-standard features.

Instead of using annotations, you could store this type of information in an external database or directory, but that would make it much harder to produce shared client libraries and tools for deployment, management, introspection, and the like.

Syntax and character set

Annotations are key/value pairs. Valid annotation keys have two segments: an optional prefix and name, separated by a slash (/). The name segment is required and must be 63 characters or less, beginning and ending with an alphanumeric character ([a-z0-9A-Z]) with dashes (-), underscores (_), dots (.), and alphanumerics between. The prefix is optional. If specified, the prefix must be a DNS subdomain: a series of DNS labels separated by dots (.), not longer than 253 characters in total, followed by a slash (/).

If the prefix is omitted, the annotation Key is presumed to be private to the user. Automated system components (e.g. kube-scheduler, kube-controller-manager, kube-apiserver, kubectl, or other third-party automation) which add annotations to end-user objects must specify a prefix.

The kubernetes.io/ and k8s.io/ prefixes are reserved for Kubernetes core components.

For example, here's the configuration file for a Pod that has the annotation imageregistry: https://hub.docker.com/ :


apiVersion: v1
kind: Pod
metadata:
  name: annotations-demo
  annotations:
    imageregistry: "https://hub.docker.com/"
spec:
  containers:
  - name: nginx
    image: nginx:1.14.2
    ports:
    - containerPort: 80

What's next

Learn more about Labels and Selectors.

1.3.7 - Field Selectors

Field selectors let you select Kubernetes resources based on the value of one or more resource fields. Here are some examples of field selector queries:

metadata.name=my-service
metadata.namespace!=default
status.phase=Pending

This kubectl command selects all Pods for which the value of the status.phase field is Running:

kubectl get pods --field-selector status.phase=Running

Note: Field selectors are essentially resource filters. By default, no selectors/filters are applied, meaning that all resources of the specified type are selected. This makes the kubectl queries kubectl get pods and kubectl get pods --field-selector "" equivalent.

Supported fields

Supported field selectors vary by Kubernetes resource type. All resource types support the metadata.name and metadata.namespace fields. Using unsupported field selectors produces an error. For example:

kubectl get ingress --field-selector foo.bar=baz

Error from server (BadRequest): Unable to find "ingresses" that match label selector "", field selector "foo.bar=baz": "foo.bar" is not a known field selector: only "metadata.name", "metadata.namespace"

Supported operators

You can use the =, ==, and != operators with field selectors (= and == mean the same thing). This kubectl command, for example, selects all Kubernetes Services that aren't in the default namespace:

kubectl get services  --all-namespaces --field-selector metadata.namespace!=default

Chained selectors

As with label and other selectors, field selectors can be chained together as a comma-separated list. This kubectl command selects all Pods for which the status.phase does not equal Running and the spec.restartPolicy field equals Always:

kubectl get pods --field-selector=status.phase!=Running,spec.restartPolicy=Always

Multiple resource types

You can use field selectors across multiple resource types. This kubectl command selects all Statefulsets and Services that are not in the default namespace:

kubectl get statefulsets,services --all-namespaces --field-selector metadata.namespace!=default

1.3.8 - Finalizers

Finalizers are namespaced keys that tell Kubernetes to wait until specific conditions are met before it fully deletes resources marked for deletion. Finalizers alert controllers to clean up resources the deleted object owned.

When you tell Kubernetes to delete an object that has finalizers specified for it, the Kubernetes API marks the object for deletion by populating .metadata.deletionTimestamp, and returns a 202 status code (HTTP "Accepted"). The target object remains in a terminating state while the control plane, or other components, take the actions defined by the finalizers. After these actions are complete, the controller removes the relevant finalizers from the target object. When the metadata.finalizers field is empty, Kubernetes considers the deletion complete and deletes the object.

You can use finalizers to control garbage collection of resources. For example, you can define a finalizer to clean up related resources or infrastructure before the controller deletes the target resource.

You can use finalizers to control garbage collection of resources by alerting controllers to perform specific cleanup tasks before deleting the target resource.

Finalizers don't usually specify the code to execute. Instead, they are typically lists of keys on a specific resource similar to annotations. Kubernetes specifies some finalizers automatically, but you can also specify your own.

How finalizers work

When you create a resource using a manifest file, you can specify finalizers in the metadata.finalizers field. When you attempt to delete the resource, the API server handling the delete request notices the values in the finalizers field and does the following:

Modifies the object to add a metadata.deletionTimestamp field with the time you started the deletion.
Prevents the object from being removed until its metadata.finalizers field is empty.
Returns a 202 status code (HTTP "Accepted")

The controller managing that finalizer notices the update to the object setting the metadata.deletionTimestamp, indicating deletion of the object has been requested. The controller then attempts to satisfy the requirements of the finalizers specified for that resource. Each time a finalizer condition is satisfied, the controller removes that key from the resource's finalizers field. When the finalizers field is emptied, an object with a deletionTimestamp field set is automatically deleted. You can also use finalizers to prevent deletion of unmanaged resources.

A common example of a finalizer is kubernetes.io/pv-protection, which prevents accidental deletion of PersistentVolume objects. When a PersistentVolume object is in use by a Pod, Kubernetes adds the pv-protection finalizer. If you try to delete the PersistentVolume, it enters a Terminating status, but the controller can't delete it because the finalizer exists. When the Pod stops using the PersistentVolume, Kubernetes clears the pv-protection finalizer, and the controller deletes the volume.

Owner references, labels, and finalizers

Like labels, owner references describe the relationships between objects in Kubernetes, but are used for a different purpose. When a controller manages objects like Pods, it uses labels to track changes to groups of related objects. For example, when a Job creates one or more Pods, the Job controller applies labels to those pods and tracks changes to any Pods in the cluster with the same label.

The Job controller also adds owner references to those Pods, pointing at the Job that created the Pods. If you delete the Job while these Pods are running, Kubernetes uses the owner references (not labels) to determine which Pods in the cluster need cleanup.

Kubernetes also processes finalizers when it identifies owner references on a resource targeted for deletion.

In some situations, finalizers can block the deletion of dependent objects, which can cause the targeted owner object to remain for longer than expected without being fully deleted. In these situations, you should check finalizers and owner references on the target owner and dependent objects to troubleshoot the cause.

Note: In cases where objects are stuck in a deleting state, avoid manually removing finalizers to allow deletion to continue. Finalizers are usually added to resources for a reason, so forcefully removing them can lead to issues in your cluster. This should only be done when the purpose of the finalizer is understood and is accomplished in another way (for example, manually cleaning up some dependent object).

What's next

Read Using Finalizers to Control Deletion on the Kubernetes blog.

1.3.9 - Owners and Dependents

In Kubernetes, some objects are owners of other objects. For example, a ReplicaSet is the owner of a set of Pods. These owned objects are dependents of their owner.

Ownership is different from the labels and selectors mechanism that some resources also use. For example, consider a Service that creates EndpointSlice objects. The Service uses labels to allow the control plane to determine which EndpointSlice objects are used for that Service. In addition to the labels, each EndpointSlice that is managed on behalf of a Service has an owner reference. Owner references help different parts of Kubernetes avoid interfering with objects they don’t control.

Owner references in object specifications

Dependent objects have a metadata.ownerReferences field that references their owner object. A valid owner reference consists of the object name and a UID within the same namespace as the dependent object. Kubernetes sets the value of this field automatically for objects that are dependents of other objects like ReplicaSets, DaemonSets, Deployments, Jobs and CronJobs, and ReplicationControllers. You can also configure these relationships manually by changing the value of this field. However, you usually don't need to and can allow Kubernetes to automatically manage the relationships.

Dependent objects also have an ownerReferences.blockOwnerDeletion field that takes a boolean value and controls whether specific dependents can block garbage collection from deleting their owner object. Kubernetes automatically sets this field to true if a controller (for example, the Deployment controller) sets the value of the metadata.ownerReferences field. You can also set the value of the blockOwnerDeletion field manually to control which dependents block garbage collection.

A Kubernetes admission controller controls user access to change this field for dependent resources, based on the delete permissions of the owner. This control prevents unauthorized users from delaying owner object deletion.

Note:

Cross-namespace owner references are disallowed by design. Namespaced dependents can specify cluster-scoped or namespaced owners. A namespaced owner must exist in the same namespace as the dependent. If it does not, the owner reference is treated as absent, and the dependent is subject to deletion once all owners are verified absent.

Cluster-scoped dependents can only specify cluster-scoped owners. In v1.20+, if a cluster-scoped dependent specifies a namespaced kind as an owner, it is treated as having an unresolvable owner reference, and is not able to be garbage collected.

In v1.20+, if the garbage collector detects an invalid cross-namespace ownerReference, or a cluster-scoped dependent with an ownerReference referencing a namespaced kind, a warning Event with a reason of OwnerRefInvalidNamespace and an involvedObject of the invalid dependent is reported. You can check for that kind of Event by running kubectl get events -A --field-selector=reason=OwnerRefInvalidNamespace.

Ownership and finalizers

When you tell Kubernetes to delete a resource, the API server allows the managing controller to process any finalizer rules for the resource. Finalizers prevent accidental deletion of resources your cluster may still need to function correctly. For example, if you try to delete a PersistentVolume that is still in use by a Pod, the deletion does not happen immediately because the PersistentVolume has the kubernetes.io/pv-protection finalizer on it. Instead, the volume remains in the Terminating status until Kubernetes clears the finalizer, which only happens after the PersistentVolume is no longer bound to a Pod.

Kubernetes also adds finalizers to an owner resource when you use either foreground or orphan cascading deletion. In foreground deletion, it adds the foreground finalizer so that the controller must delete dependent resources that also have ownerReferences.blockOwnerDeletion=true before it deletes the owner. If you specify an orphan deletion policy, Kubernetes adds the orphan finalizer so that the controller ignores dependent resources after it deletes the owner object.

What's next

Learn more about Kubernetes finalizers.
Learn about garbage collection.
Read the API reference for object metadata.

1.3.10 - Recommended Labels

You can visualize and manage Kubernetes objects with more tools than kubectl and the dashboard. A common set of labels allows tools to work interoperably, describing objects in a common manner that all tools can understand.

In addition to supporting tooling, the recommended labels describe applications in a way that can be queried.

The metadata is organized around the concept of an application. Kubernetes is not a platform as a service (PaaS) and doesn't have or enforce a formal notion of an application. Instead, applications are informal and described with metadata. The definition of what an application contains is loose.

Note: These are recommended labels. They make it easier to manage applications but aren't required for any core tooling.

Shared labels and annotations share a common prefix: app.kubernetes.io. Labels without a prefix are private to users. The shared prefix ensures that shared labels do not interfere with custom user labels.

Labels

In order to take full advantage of using these labels, they should be applied on every resource object.

Key	Description	Example	Type
`app.kubernetes.io/name`	The name of the application	`mysql`	string
`app.kubernetes.io/instance`	A unique name identifying the instance of an application	`mysql-abcxzy`	string
`app.kubernetes.io/version`	The current version of the application (e.g., a semantic version, revision hash, etc.)	`5.7.21`	string
`app.kubernetes.io/component`	The component within the architecture	`database`	string
`app.kubernetes.io/part-of`	The name of a higher level application this one is part of	`wordpress`	string
`app.kubernetes.io/managed-by`	The tool being used to manage the operation of an application	`helm`	string

To illustrate these labels in action, consider the following StatefulSet object:

# This is an excerpt
apiVersion: apps/v1
kind: StatefulSet
metadata:
  labels:
    app.kubernetes.io/name: mysql
    app.kubernetes.io/instance: mysql-abcxzy
    app.kubernetes.io/version: "5.7.21"
    app.kubernetes.io/component: database
    app.kubernetes.io/part-of: wordpress
    app.kubernetes.io/managed-by: helm

Applications And Instances Of Applications

An application can be installed one or more times into a Kubernetes cluster and, in some cases, the same namespace. For example, WordPress can be installed more than once where different websites are different installations of WordPress.

The name of an application and the instance name are recorded separately. For example, WordPress has a app.kubernetes.io/name of wordpress while it has an instance name, represented as app.kubernetes.io/instance with a value of wordpress-abcxzy. This enables the application and instance of the application to be identifiable. Every instance of an application must have a unique name.

Examples

To illustrate different ways to use these labels the following examples have varying complexity.

A Simple Stateless Service

Consider the case for a simple stateless service deployed using Deployment and Service objects. The following two snippets represent how the labels could be used in their simplest form.

The Deployment is used to oversee the pods running the application itself.

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app.kubernetes.io/name: myservice
    app.kubernetes.io/instance: myservice-abcxzy
...

The Service is used to expose the application.

apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: myservice
    app.kubernetes.io/instance: myservice-abcxzy
...

Web Application With A Database

Consider a slightly more complicated application: a web application (WordPress) using a database (MySQL), installed using Helm. The following snippets illustrate the start of objects used to deploy this application.

The start to the following Deployment is used for WordPress:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app.kubernetes.io/name: wordpress
    app.kubernetes.io/instance: wordpress-abcxzy
    app.kubernetes.io/version: "4.9.4"
    app.kubernetes.io/managed-by: helm
    app.kubernetes.io/component: server
    app.kubernetes.io/part-of: wordpress
...

The Service is used to expose WordPress:

apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: wordpress
    app.kubernetes.io/instance: wordpress-abcxzy
    app.kubernetes.io/version: "4.9.4"
    app.kubernetes.io/managed-by: helm
    app.kubernetes.io/component: server
    app.kubernetes.io/part-of: wordpress
...

MySQL is exposed as a StatefulSet with metadata for both it and the larger application it belongs to:

apiVersion: apps/v1
kind: StatefulSet
metadata:
  labels:
    app.kubernetes.io/name: mysql
    app.kubernetes.io/instance: mysql-abcxzy
    app.kubernetes.io/version: "5.7.21"
    app.kubernetes.io/managed-by: helm
    app.kubernetes.io/component: database
    app.kubernetes.io/part-of: wordpress
...

The Service is used to expose MySQL as part of WordPress:

apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: mysql
    app.kubernetes.io/instance: mysql-abcxzy
    app.kubernetes.io/version: "5.7.21"
    app.kubernetes.io/managed-by: helm
    app.kubernetes.io/component: database
    app.kubernetes.io/part-of: wordpress
...

With the MySQL StatefulSet and Service you'll notice information about both MySQL and WordPress, the broader application, are included.

2 - Cluster Architecture

The architectural concepts behind Kubernetes.

2.1 - Nodes

Kubernetes runs your workload by placing containers into Pods to run on Nodes. A node may be a virtual or physical machine, depending on the cluster. Each node is managed by the control plane and contains the services necessary to run Pods.

Typically you have several nodes in a cluster; in a learning or resource-limited environment, you might have only one node.

The components on a node include the kubelet, a container runtime, and the kube-proxy.

Management

There are two main ways to have Nodes added to the API server:

The kubelet on a node self-registers to the control plane
You (or another human user) manually add a Node object

After you create a Node object, or the kubelet on a node self-registers, the control plane checks whether the new Node object is valid. For example, if you try to create a Node from the following JSON manifest:

{
  "kind": "Node",
  "apiVersion": "v1",
  "metadata": {
    "name": "10.240.79.157",
    "labels": {
      "name": "my-first-k8s-node"
    }
  }
}

Kubernetes creates a Node object internally (the representation). Kubernetes checks that a kubelet has registered to the API server that matches the metadata.name field of the Node. If the node is healthy (i.e. all necessary services are running), then it is eligible to run a Pod. Otherwise, that node is ignored for any cluster activity until it becomes healthy.

Note:

Kubernetes keeps the object for the invalid Node and continues checking to see whether it becomes healthy.

You, or a controller, must explicitly delete the Node object to stop that health checking.

The name of a Node object must be a valid DNS subdomain name.

Node name uniqueness

The name identifies a Node. Two Nodes cannot have the same name at the same time. Kubernetes also assumes that a resource with the same name is the same object. In case of a Node, it is implicitly assumed that an instance using the same name will have the same state (e.g. network settings, root disk contents) and attributes like node labels. This may lead to inconsistencies if an instance was modified without changing its name. If the Node needs to be replaced or updated significantly, the existing Node object needs to be removed from API server first and re-added after the update.

Self-registration of Nodes

When the kubelet flag --register-node is true (the default), the kubelet will attempt to register itself with the API server. This is the preferred pattern, used by most distros.

For self-registration, the kubelet is started with the following options:

--kubeconfig - Path to credentials to authenticate itself to the API server.
--cloud-provider - How to talk to a cloud provider to read metadata about itself.
--register-node - Automatically register with the API server.
--register-with-taints - Register the node with the given list of taints (comma separated <key>=<value>:<effect>).

No-op if register-node is false.
--node-ip - IP address of the node.
--node-labels - Labels to add when registering the node in the cluster (see label restrictions enforced by the NodeRestriction admission plugin).
--node-status-update-frequency - Specifies how often kubelet posts its node status to the API server.

When the Node authorization mode and NodeRestriction admission plugin are enabled, kubelets are only authorized to create/modify their own Node resource.

Note:

As mentioned in the Node name uniqueness section, when Node configuration needs to be updated, it is a good practice to re-register the node with the API server. For example, if the kubelet being restarted with the new set of --node-labels, but the same Node name is used, the change will not take an effect, as labels are being set on the Node registration.

Pods already scheduled on the Node may misbehave or cause issues if the Node configuration will be changed on kubelet restart. For example, already running Pod may be tainted against the new labels assigned to the Node, while other Pods, that are incompatible with that Pod will be scheduled based on this new label. Node re-registration ensures all Pods will be drained and properly re-scheduled.

Manual Node administration

You can create and modify Node objects using kubectl.

When you want to create Node objects manually, set the kubelet flag --register-node=false.

You can modify Node objects regardless of the setting of --register-node. For example, you can set labels on an existing Node or mark it unschedulable.

You can use labels on Nodes in conjunction with node selectors on Pods to control scheduling. For example, you can constrain a Pod to only be eligible to run on a subset of the available nodes.

Marking a node as unschedulable prevents the scheduler from placing new pods onto that Node but does not affect existing Pods on the Node. This is useful as a preparatory step before a node reboot or other maintenance.

To mark a Node unschedulable, run:

kubectl cordon $NODENAME

See Safely Drain a Node for more details.

Note: Pods that are part of a tolerate being run on an unschedulable Node. DaemonSets typically provide node-local services that should run on the Node even if it is being drained of workload applications.

Node status

A Node's status contains the following information:

Addresses
Conditions
Capacity and Allocatable
Info

You can use kubectl to view a Node's status and other details:

kubectl describe node <insert-node-name-here>

Each section of the output is described below.

Addresses

The usage of these fields varies depending on your cloud provider or bare metal configuration.

HostName: The hostname as reported by the node's kernel. Can be overridden via the kubelet --hostname-override parameter.
ExternalIP: Typically the IP address of the node that is externally routable (available from outside the cluster).
InternalIP: Typically the IP address of the node that is routable only within the cluster.

Conditions

The conditions field describes the status of all Running nodes. Examples of conditions include:

Node conditions, and a description of when each condition applies.
Node Condition	Description
`Ready`	`True` if the node is healthy and ready to accept pods, `False` if the node is not healthy and is not accepting pods, and `Unknown` if the node controller has not heard from the node in the last `node-monitor-grace-period` (default is 40 seconds)
`DiskPressure`	`True` if pressure exists on the disk size—that is, if the disk capacity is low; otherwise `False`
`MemoryPressure`	`True` if pressure exists on the node memory—that is, if the node memory is low; otherwise `False`
`PIDPressure`	`True` if pressure exists on the processes—that is, if there are too many processes on the node; otherwise `False`
`NetworkUnavailable`	`True` if the network for the node is not correctly configured, otherwise `False`

Note: If you use command-line tools to print details of a cordoned Node, the Condition includes SchedulingDisabled. SchedulingDisabled is not a Condition in the Kubernetes API; instead, cordoned nodes are marked Unschedulable in their spec.

In the Kubernetes API, a node's condition is represented as part of the .status of the Node resource. For example, the following JSON structure describes a healthy node:

"conditions": [
  {
    "type": "Ready",
    "status": "True",
    "reason": "KubeletReady",
    "message": "kubelet is posting ready status",
    "lastHeartbeatTime": "2019-06-05T18:38:35Z",
    "lastTransitionTime": "2019-06-05T11:41:27Z"
  }
]

If the status of the Ready condition remains Unknown or False for longer than the pod-eviction-timeout (an argument passed to the kube-controller-manager), then the node controller triggers API-initiated eviction for all Pods assigned to that node. The default eviction timeout duration is five minutes. In some cases when the node is unreachable, the API server is unable to communicate with the kubelet on the node. The decision to delete the pods cannot be communicated to the kubelet until communication with the API server is re-established. In the meantime, the pods that are scheduled for deletion may continue to run on the partitioned node.

The node controller does not force delete pods until it is confirmed that they have stopped running in the cluster. You can see the pods that might be running on an unreachable node as being in the Terminating or Unknown state. In cases where Kubernetes cannot deduce from the underlying infrastructure if a node has permanently left a cluster, the cluster administrator may need to delete the node object by hand. Deleting the node object from Kubernetes causes all the Pod objects running on the node to be deleted from the API server and frees up their names.

When problems occur on nodes, the Kubernetes control plane automatically creates taints that match the conditions affecting the node. The scheduler takes the Node's taints into consideration when assigning a Pod to a Node. Pods can also have tolerations that let them run on a Node even though it has a specific taint.

See Taint Nodes by Condition for more details.

Capacity and Allocatable

Describes the resources available on the node: CPU, memory, and the maximum number of pods that can be scheduled onto the node.

The fields in the capacity block indicate the total amount of resources that a Node has. The allocatable block indicates the amount of resources on a Node that is available to be consumed by normal Pods.

You may read more about capacity and allocatable resources while learning how to reserve compute resources on a Node.

Info

Describes general information about the node, such as kernel version, Kubernetes version (kubelet and kube-proxy version), container runtime details, and which operating system the node uses. The kubelet gathers this information from the node and publishes it into the Kubernetes API.

Heartbeats

Heartbeats, sent by Kubernetes nodes, help your cluster determine the availability of each node, and to take action when failures are detected.

For nodes there are two forms of heartbeats:

updates to the .status of a Node
Lease objects within the kube-node-lease namespace. Each Node has an associated Lease object.

Compared to updates to .status of a Node, a Lease is a lightweight resource. Using Leases for heartbeats reduces the performance impact of these updates for large clusters.

The kubelet is responsible for creating and updating the .status of Nodes, and for updating their related Leases.

The kubelet updates the node's .status either when there is change in status or if there has been no update for a configured interval. The default interval for .status updates to Nodes is 5 minutes, which is much longer than the 40 second default timeout for unreachable nodes.
The kubelet creates and then updates its Lease object every 10 seconds (the default update interval). Lease updates occur independently from updates to the Node's .status. If the Lease update fails, the kubelet retries, using exponential backoff that starts at 200 milliseconds and capped at 7 seconds.

Node controller

The node controller is a Kubernetes control plane component that manages various aspects of nodes.

The node controller has multiple roles in a node's life. The first is assigning a CIDR block to the node when it is registered (if CIDR assignment is turned on).

The second is keeping the node controller's internal list of nodes up to date with the cloud provider's list of available machines. When running in a cloud environment and whenever a node is unhealthy, the node controller asks the cloud provider if the VM for that node is still available. If not, the node controller deletes the node from its list of nodes.

The third is monitoring the nodes' health. The node controller is responsible for:

In the case that a node becomes unreachable, updating the Ready condition in the Node's .status field. In this case the node controller sets the Ready condition to Unknown.
If a node remains unreachable: triggering API-initiated eviction for all of the Pods on the unreachable node. By default, the node controller waits 5 minutes between marking the node as Unknown and submitting the first eviction request.

By default, the node controller checks the state of each node every 5 seconds. This period can be configured using the --node-monitor-period flag on the kube-controller-manager component.

Rate limits on eviction

In most cases, the node controller limits the eviction rate to --node-eviction-rate (default 0.1) per second, meaning it won't evict pods from more than 1 node per 10 seconds.

The node eviction behavior changes when a node in a given availability zone becomes unhealthy. The node controller checks what percentage of nodes in the zone are unhealthy (the Ready condition is Unknown or False) at the same time:

If the fraction of unhealthy nodes is at least --unhealthy-zone-threshold (default 0.55), then the eviction rate is reduced.
If the cluster is small (i.e. has less than or equal to --large-cluster-size-threshold nodes - default 50), then evictions are stopped.
Otherwise, the eviction rate is reduced to --secondary-node-eviction-rate (default 0.01) per second.

The reason these policies are implemented per availability zone is because one availability zone might become partitioned from the control plane while the others remain connected. If your cluster does not span multiple cloud provider availability zones, then the eviction mechanism does not take per-zone unavailability into account.

A key reason for spreading your nodes across availability zones is so that the workload can be shifted to healthy zones when one entire zone goes down. Therefore, if all nodes in a zone are unhealthy, then the node controller evicts at the normal rate of --node-eviction-rate. The corner case is when all zones are completely unhealthy (none of the nodes in the cluster are healthy). In such a case, the node controller assumes that there is some problem with connectivity between the control plane and the nodes, and doesn't perform any evictions. (If there has been an outage and some nodes reappear, the node controller does evict pods from the remaining nodes that are unhealthy or unreachable).

The node controller is also responsible for evicting pods running on nodes with NoExecute taints, unless those pods tolerate that taint. The node controller also adds taints corresponding to node problems like node unreachable or not ready. This means that the scheduler won't place Pods onto unhealthy nodes.

Resource capacity tracking

Node objects track information about the Node's resource capacity: for example, the amount of memory available and the number of CPUs. Nodes that self register report their capacity during registration. If you manually add a Node, then you need to set the node's capacity information when you add it.

The Kubernetes scheduler ensures that there are enough resources for all the Pods on a Node. The scheduler checks that the sum of the requests of containers on the node is no greater than the node's capacity. That sum of requests includes all containers managed by the kubelet, but excludes any containers started directly by the container runtime, and also excludes any processes running outside of the kubelet's control.

Note: If you want to explicitly reserve resources for non-Pod processes, see reserve resources for system daemons.

Node topology

FEATURE STATE: Kubernetes v1.18 [beta]

If you have enabled the TopologyManager feature gate, then the kubelet can use topology hints when making resource assignment decisions. See Control Topology Management Policies on a Node for more information.

Graceful node shutdown

FEATURE STATE: Kubernetes v1.21 [beta]

The kubelet attempts to detect node system shutdown and terminates pods running on the node.

Kubelet ensures that pods follow the normal pod termination process during the node shutdown.

The Graceful node shutdown feature depends on systemd since it takes advantage of systemd inhibitor locks to delay the node shutdown with a given duration.

Graceful node shutdown is controlled with the GracefulNodeShutdown feature gate which is enabled by default in 1.21.

Note that by default, both configuration options described below, shutdownGracePeriod and shutdownGracePeriodCriticalPods are set to zero, thus not activating the graceful node shutdown functionality. To activate the feature, the two kubelet config settings should be configured appropriately and set to non-zero values.

During a graceful shutdown, kubelet terminates pods in two phases:

Terminate regular pods running on the node.
Terminate critical pods running on the node.

Graceful node shutdown feature is configured with two KubeletConfiguration options:

shutdownGracePeriod:
- Specifies the total duration that the node should delay the shutdown by. This is the total grace period for pod termination for both regular and critical pods.
shutdownGracePeriodCriticalPods:
- Specifies the duration used to terminate critical pods during a node shutdown. This value should be less than shutdownGracePeriod.

For example, if shutdownGracePeriod=30s, and shutdownGracePeriodCriticalPods=10s, kubelet will delay the node shutdown by 30 seconds. During the shutdown, the first 20 (30-10) seconds would be reserved for gracefully terminating normal pods, and the last 10 seconds would be reserved for terminating critical pods.

Note:

When pods were evicted during the graceful node shutdown, they are marked as shutdown. Running kubectl get pods shows the status of the the evicted pods as Terminated. And kubectl describe pod indicates that the pod was evicted because of node shutdown:

Reason:         Terminated
Message:        Pod was terminated in response to imminent node shutdown.

Non Graceful node shutdown

FEATURE STATE: Kubernetes v1.24 [alpha]

A node shutdown action may not be detected by kubelet's Node Shutdown Manager, either because the command does not trigger the inhibitor locks mechanism used by kubelet or because of a user error, i.e., the ShutdownGracePeriod and ShutdownGracePeriodCriticalPods are not configured properly. Please refer to above section Graceful Node Shutdown for more details.

When a node is shutdown but not detected by kubelet's Node Shutdown Manager, the pods that are part of a StatefulSet will be stuck in terminating status on the shutdown node and cannot move to a new running node. This is because kubelet on the shutdown node is not available to delete the pods so the StatefulSet cannot create a new pod with the same name. If there are volumes used by the pods, the VolumeAttachments will not be deleted from the original shutdown node so the volumes used by these pods cannot be attached to a new running node. As a result, the application running on the StatefulSet cannot function properly. If the original shutdown node comes up, the pods will be deleted by kubelet and new pods will be created on a different running node. If the original shutdown node does not come up,
these pods will be stuck in terminating status on the shutdown node forever.

To mitigate the above situation, a user can manually add the taint node.kubernetes.io/out-of-service with either NoExecute or NoSchedule effect to a Node marking it out-of-service. If the NodeOutOfServiceVolumeDetachfeature gate is enabled on kube-controller-manager, and a Node is marked out-of-service with this taint, the pods on the node will be forcefully deleted if there are no matching tolerations on it and volume detach operations for the pods terminating on the node will happen immediately. This allows the Pods on the out-of-service node to recover quickly on a different node.

During a non-graceful shutdown, Pods are terminated in the two phases:

Force delete the Pods that do not have matching out-of-service tolerations.
Immediately perform detach volume operation for such pods.

Note:

Before adding the taint node.kubernetes.io/out-of-service , it should be verified that the node is already in shutdown or power off state (not in the middle of restarting).
The user is required to manually remove the out-of-service taint after the pods are moved to a new node and the user has checked that the shutdown node has been recovered since the user was the one who originally added the taint.

Pod Priority based graceful node shutdown

FEATURE STATE: Kubernetes v1.23 [alpha]

To provide more flexibility during graceful node shutdown around the ordering of pods during shutdown, graceful node shutdown honors the PriorityClass for Pods, provided that you enabled this feature in your cluster. The feature allows cluster administers to explicitly define the ordering of pods during graceful node shutdown based on priority classes.

The Graceful Node Shutdown feature, as described above, shuts down pods in two phases, non-critical pods, followed by critical pods. If additional flexibility is needed to explicitly define the ordering of pods during shutdown in a more granular way, pod priority based graceful shutdown can be used.

When graceful node shutdown honors pod priorities, this makes it possible to do graceful node shutdown in multiple phases, each phase shutting down a particular priority class of pods. The kubelet can be configured with the exact phases and shutdown time per phase.

Assuming the following custom pod priority classes in a cluster,

Pod priority class name	Pod priority class value
`custom-class-a`	100000
`custom-class-b`	10000
`custom-class-c`	1000
`regular/unset`	0

Within the kubelet configuration the settings for shutdownGracePeriodByPodPriority could look like:

Pod priority class value	Shutdown period
100000	10 seconds
10000	180 seconds
1000	120 seconds
0	60 seconds

The corresponding kubelet config YAML configuration would be:

shutdownGracePeriodByPodPriority:
  - priority: 100000
    shutdownGracePeriodSeconds: 10
  - priority: 10000
    shutdownGracePeriodSeconds: 180
  - priority: 1000
    shutdownGracePeriodSeconds: 120
  - priority: 0
    shutdownGracePeriodSeconds: 60

The above table implies that any pod with priority value >= 100000 will get just 10 seconds to stop, any pod with value >= 10000 and < 100000 will get 180 seconds to stop, any pod with value >= 1000 and < 10000 will get 120 seconds to stop. Finally, all other pods will get 60 seconds to stop.

One doesn't have to specify values corresponding to all of the classes. For example, you could instead use these settings:

Pod priority class value	Shutdown period
100000	300 seconds
1000	120 seconds
0	60 seconds

In the above case, the pods with custom-class-b will go into the same bucket as custom-class-c for shutdown.

If there are no pods in a particular range, then the kubelet does not wait for pods in that priority range. Instead, the kubelet immediately skips to the next priority class value range.

If this feature is enabled and no configuration is provided, then no ordering action will be taken.

Using this feature requires enabling the GracefulNodeShutdownBasedOnPodPriority feature gate , and setting ShutdownGracePeriodByPodPriority in the kubelet config to the desired configuration containing the pod priority class values and their respective shutdown periods.

Note: The ability to take Pod priority into account during graceful node shutdown was introduced as an Alpha feature in Kubernetes v1.23. In Kubernetes 1.25 the feature is Beta and is enabled by default.

Metrics graceful_shutdown_start_time_seconds and graceful_shutdown_end_time_seconds are emitted under the kubelet subsystem to monitor node shutdowns.

Swap memory management

FEATURE STATE: Kubernetes v1.22 [alpha]

Prior to Kubernetes 1.22, nodes did not support the use of swap memory, and a kubelet would by default fail to start if swap was detected on a node. In 1.22 onwards, swap memory support can be enabled on a per-node basis.

To enable swap on a node, the NodeSwap feature gate must be enabled on the kubelet, and the --fail-swap-on command line flag or failSwapOn configuration setting must be set to false.

Warning: When the memory swap feature is turned on, Kubernetes data such as the content of Secret objects that were written to tmpfs now could be swapped to disk.

A user can also optionally configure memorySwap.swapBehavior in order to specify how a node will use swap memory. For example,

memorySwap:
  swapBehavior: LimitedSwap

The available configuration options for swapBehavior are:

LimitedSwap: Kubernetes workloads are limited in how much swap they can use. Workloads on the node not managed by Kubernetes can still swap.
UnlimitedSwap: Kubernetes workloads can use as much swap memory as they request, up to the system limit.

If configuration for memorySwap is not specified and the feature gate is enabled, by default the kubelet will apply the same behaviour as the LimitedSwap setting.

The behaviour of the LimitedSwap setting depends if the node is running with v1 or v2 of control groups (also known as "cgroups"):

cgroupsv1: Kubernetes workloads can use any combination of memory and swap, up to the pod's memory limit, if set.
cgroupsv2: Kubernetes workloads cannot use swap memory.

For more information, and to assist with testing and provide feedback, please see KEP-2400 and its design proposal.

What's next

Learn about the components that make up a node.
Read the API definition for Node.
Read the Node section of the architecture design document.
Read about taints and tolerations.

2.2 - Communication between Nodes and the Control Plane

This document catalogs the communication paths between the API server and the Kubernetes cluster. The intent is to allow users to customize their installation to harden the network configuration such that the cluster can be run on an untrusted network (or on fully public IPs on a cloud provider).

Node to Control Plane

Kubernetes has a "hub-and-spoke" API pattern. All API usage from nodes (or the pods they run) terminates at the API server. None of the other control plane components are designed to expose remote services. The API server is configured to listen for remote connections on a secure HTTPS port (typically 443) with one or more forms of client authentication enabled. One or more forms of authorization should be enabled, especially if anonymous requests or service account tokens are allowed.

Nodes should be provisioned with the public root certificate for the cluster such that they can connect securely to the API server along with valid client credentials. A good approach is that the client credentials provided to the kubelet are in the form of a client certificate. See kubelet TLS bootstrapping for automated provisioning of kubelet client certificates.

Pods that wish to connect to the API server can do so securely by leveraging a service account so that Kubernetes will automatically inject the public root certificate and a valid bearer token into the pod when it is instantiated. The kubernetes service (in default namespace) is configured with a virtual IP address that is redirected (via kube-proxy) to the HTTPS endpoint on the API server.

The control plane components also communicate with the API server over the secure port.

As a result, the default operating mode for connections from the nodes and pods running on the nodes to the control plane is secured by default and can run over untrusted and/or public networks.

Control plane to node

There are two primary communication paths from the control plane (the API server) to the nodes. The first is from the API server to the kubelet process which runs on each node in the cluster. The second is from the API server to any node, pod, or service through the API server's proxy functionality.

API server to kubelet

The connections from the API server to the kubelet are used for:

Fetching logs for pods.
Attaching (usually through kubectl) to running pods.
Providing the kubelet's port-forwarding functionality.

These connections terminate at the kubelet's HTTPS endpoint. By default, the API server does not verify the kubelet's serving certificate, which makes the connection subject to man-in-the-middle attacks and unsafe to run over untrusted and/or public networks.

To verify this connection, use the --kubelet-certificate-authority flag to provide the API server with a root certificate bundle to use to verify the kubelet's serving certificate.

If that is not possible, use SSH tunneling between the API server and kubelet if required to avoid connecting over an untrusted or public network.

Finally, Kubelet authentication and/or authorization should be enabled to secure the kubelet API.

API server to nodes, pods, and services

The connections from the API server to a node, pod, or service default to plain HTTP connections and are therefore neither authenticated nor encrypted. They can be run over a secure HTTPS connection by prefixing https: to the node, pod, or service name in the API URL, but they will not validate the certificate provided by the HTTPS endpoint nor provide client credentials. So while the connection will be encrypted, it will not provide any guarantees of integrity. These connections are not currently safe to run over untrusted or public networks.

SSH tunnels

Kubernetes supports SSH tunnels to protect the control plane to nodes communication paths. In this configuration, the API server initiates an SSH tunnel to each node in the cluster (connecting to the SSH server listening on port 22) and passes all traffic destined for a kubelet, node, pod, or service through the tunnel. This tunnel ensures that the traffic is not exposed outside of the network in which the nodes are running.

Note: SSH tunnels are currently deprecated, so you shouldn't opt to use them unless you know what you are doing. The Konnectivity service is a replacement for this communication channel.

Konnectivity service

FEATURE STATE: Kubernetes v1.18 [beta]

As a replacement to the SSH tunnels, the Konnectivity service provides TCP level proxy for the control plane to cluster communication. The Konnectivity service consists of two parts: the Konnectivity server in the control plane network and the Konnectivity agents in the nodes network. The Konnectivity agents initiate connections to the Konnectivity server and maintain the network connections. After enabling the Konnectivity service, all control plane to nodes traffic goes through these connections.

Follow the Konnectivity service task to set up the Konnectivity service in your cluster.

2.3 - Controllers

In robotics and automation, a control loop is a non-terminating loop that regulates the state of a system.

Here is one example of a control loop: a thermostat in a room.

When you set the temperature, that's telling the thermostat about your desired state. The actual room temperature is the current state. The thermostat acts to bring the current state closer to the desired state, by turning equipment on or off.

In Kubernetes, controllers are control loops that watch the state of your , then make or request changes where needed. Each controller tries to move the current cluster state closer to the desired state.

Controller pattern

A controller tracks at least one Kubernetes resource type. These objects have a spec field that represents the desired state. The controller(s) for that resource are responsible for making the current state come closer to that desired state.

The controller might carry the action out itself; more commonly, in Kubernetes, a controller will send messages to the API server that have useful side effects. You'll see examples of this below.

Control via API server

The Job controller is an example of a Kubernetes built-in controller. Built-in controllers manage state by interacting with the cluster API server.

Job is a Kubernetes resource that runs a Pod, or perhaps several Pods, to carry out a task and then stop.

(Once scheduled, Pod objects become part of the desired state for a kubelet).

When the Job controller sees a new task it makes sure that, somewhere in your cluster, the kubelets on a set of Nodes are running the right number of Pods to get the work done. The Job controller does not run any Pods or containers itself. Instead, the Job controller tells the API server to create or remove Pods. Other components in the control plane act on the new information (there are new Pods to schedule and run), and eventually the work is done.

After you create a new Job, the desired state is for that Job to be completed. The Job controller makes the current state for that Job be nearer to your desired state: creating Pods that do the work you wanted for that Job, so that the Job is closer to completion.

Controllers also update the objects that configure them. For example: once the work is done for a Job, the Job controller updates that Job object to mark it Finished.

(This is a bit like how some thermostats turn a light off to indicate that your room is now at the temperature you set).

Direct control

By contrast with Job, some controllers need to make changes to things outside of your cluster.

For example, if you use a control loop to make sure there are enough Nodes in your cluster, then that controller needs something outside the current cluster to set up new Nodes when needed.

Controllers that interact with external state find their desired state from the API server, then communicate directly with an external system to bring the current state closer in line.

(There actually is a controller that horizontally scales the nodes in your cluster.)

The important point here is that the controller makes some change to bring about your desired state, and then reports current state back to your cluster's API server. Other control loops can observe that reported data and take their own actions.

In the thermostat example, if the room is very cold then a different controller might also turn on a frost protection heater. With Kubernetes clusters, the control plane indirectly works with IP address management tools, storage services, cloud provider APIs, and other services by extending Kubernetes to implement that.

Desired versus current state

Kubernetes takes a cloud-native view of systems, and is able to handle constant change.

Your cluster could be changing at any point as work happens and control loops automatically fix failures. This means that, potentially, your cluster never reaches a stable state.

As long as the controllers for your cluster are running and able to make useful changes, it doesn't matter if the overall state is stable or not.

Design

As a tenet of its design, Kubernetes uses lots of controllers that each manage a particular aspect of cluster state. Most commonly, a particular control loop (controller) uses one kind of resource as its desired state, and has a different kind of resource that it manages to make that desired state happen. For example, a controller for Jobs tracks Job objects (to discover new work) and Pod objects (to run the Jobs, and then to see when the work is finished). In this case something else creates the Jobs, whereas the Job controller creates Pods.

It's useful to have simple controllers rather than one, monolithic set of control loops that are interlinked. Controllers can fail, so Kubernetes is designed to allow for that.

Note:

There can be several controllers that create or update the same kind of object. Behind the scenes, Kubernetes controllers make sure that they only pay attention to the resources linked to their controlling resource.

For example, you can have Deployments and Jobs; these both create Pods. The Job controller does not delete the Pods that your Deployment created, because there is information (labels) the controllers can use to tell those Pods apart.

Ways of running controllers

Kubernetes comes with a set of built-in controllers that run inside the kube-controller-manager. These built-in controllers provide important core behaviors.

The Deployment controller and Job controller are examples of controllers that come as part of Kubernetes itself ("built-in" controllers). Kubernetes lets you run a resilient control plane, so that if any of the built-in controllers were to fail, another part of the control plane will take over the work.

You can find controllers that run outside the control plane, to extend Kubernetes. Or, if you want, you can write a new controller yourself. You can run your own controller as a set of Pods, or externally to Kubernetes. What fits best will depend on what that particular controller does.

What's next

Read about the Kubernetes control plane
Discover some of the basic Kubernetes objects
Learn more about the Kubernetes API
If you want to write your own controller, see Extension Patterns in Extending Kubernetes.

2.4 - Cloud Controller Manager

FEATURE STATE: Kubernetes v1.11 [beta]

Cloud infrastructure technologies let you run Kubernetes on public, private, and hybrid clouds. Kubernetes believes in automated, API-driven infrastructure without tight coupling between components.

The cloud-controller-manager is a Kubernetes control plane component that embeds cloud-specific control logic. The cloud controller manager lets you link your cluster into your cloud provider's API, and separates out the components that interact with that cloud platform from components that only interact with your cluster.

By decoupling the interoperability logic between Kubernetes and the underlying cloud infrastructure, the cloud-controller-manager component enables cloud providers to release features at a different pace compared to the main Kubernetes project.

The cloud-controller-manager is structured using a plugin mechanism that allows different cloud providers to integrate their platforms with Kubernetes.

Design

Kubernetes components

The cloud controller manager runs in the control plane as a replicated set of processes (usually, these are containers in Pods). Each cloud-controller-manager implements multiple controllers in a single process.

Note: You can also run the cloud controller manager as a Kubernetes rather than as part of the control plane.

Cloud controller manager functions

The controllers inside the cloud controller manager include:

Node controller

The node controller is responsible for updating Node objects when new servers are created in your cloud infrastructure. The node controller obtains information about the hosts running inside your tenancy with the cloud provider. The node controller performs the following functions:

Update a Node object with the corresponding server's unique identifier obtained from the cloud provider API.
Annotating and labelling the Node object with cloud-specific information, such as the region the node is deployed into and the resources (CPU, memory, etc) that it has available.
Obtain the node's hostname and network addresses.
Verifying the node's health. In case a node becomes unresponsive, this controller checks with your cloud provider's API to see if the server has been deactivated / deleted / terminated. If the node has been deleted from the cloud, the controller deletes the Node object from your Kubernetes cluster.

Some cloud provider implementations split this into a node controller and a separate node lifecycle controller.

Route controller

The route controller is responsible for configuring routes in the cloud appropriately so that containers on different nodes in your Kubernetes cluster can communicate with each other.

Depending on the cloud provider, the route controller might also allocate blocks of IP addresses for the Pod network.

Service controller

Services integrate with cloud infrastructure components such as managed load balancers, IP addresses, network packet filtering, and target health checking. The service controller interacts with your cloud provider's APIs to set up load balancers and other infrastructure components when you declare a Service resource that requires them.

Authorization

This section breaks down the access that the cloud controller manager requires on various API objects, in order to perform its operations.

Node controller

The Node controller only works with Node objects. It requires full access to read and modify Node objects.

v1/Node:

Get
List
Create
Update
Patch
Watch
Delete

Route controller

The route controller listens to Node object creation and configures routes appropriately. It requires Get access to Node objects.

v1/Node:

Service controller

The service controller listens to Service object Create, Update and Delete events and then configures Endpoints for those Services appropriately.

To access Services, it requires List, and Watch access. To update Services, it requires Patch and Update access.

To set up Endpoints resources for the Services, it requires access to Create, List, Get, Watch, and Update.

v1/Service:

List
Get
Watch
Patch
Update

Others

The implementation of the core of the cloud controller manager requires access to create Event objects, and to ensure secure operation, it requires access to create ServiceAccounts.

v1/Event:

Create
Patch
Update

v1/ServiceAccount:

Create

The RBAC ClusterRole for the cloud controller manager looks like:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cloud-controller-manager
rules:
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - nodes
  verbs:
  - '*'
- apiGroups:
  - ""
  resources:
  - nodes/status
  verbs:
  - patch
- apiGroups:
  - ""
  resources:
  - services
  verbs:
  - list
  - patch
  - update
  - watch
- apiGroups:
  - ""
  resources:
  - serviceaccounts
  verbs:
  - create
- apiGroups:
  - ""
  resources:
  - persistentvolumes
  verbs:
  - get
  - list
  - update
  - watch
- apiGroups:
  - ""
  resources:
  - endpoints
  verbs:
  - create
  - get
  - list
  - watch
  - update

What's next

Cloud Controller Manager Administration has instructions on running and managing the cloud controller manager.

To upgrade a HA control plane to use the cloud controller manager, see Migrate Replicated Control Plane To Use Cloud Controller Manager.

Want to know how to implement your own cloud controller manager, or extend an existing project?

The cloud controller manager uses Go interfaces to allow implementations from any cloud to be plugged in. Specifically, it uses the CloudProvider interface defined in cloud.go from kubernetes/cloud-provider.

The implementation of the shared controllers highlighted in this document (Node, Route, and Service), and some scaffolding along with the shared cloudprovider interface, is part of the Kubernetes core. Implementations specific to cloud providers are outside the core of Kubernetes and implement the CloudProvider interface.

For more information about developing plugins, see Developing Cloud Controller Manager.

2.5 - About cgroup v2

On Linux, control groups constrain resources that are allocated to processes.

The kubelet and the underlying container runtime need to interface with cgroups to enforce resource management for pods and containers which includes cpu/memory requests and limits for containerized workloads.

There are two versions of cgroups in Linux: cgroup v1 and cgroup v2. cgroup v2 is the new generation of the cgroup API.

What is cgroup v2?

FEATURE STATE: Kubernetes v1.25 [stable]

cgroup v2 is the next version of the Linux cgroup API. cgroup v2 provides a unified control system with enhanced resource management capabilities.

cgroup v2 offers several improvements over cgroup v1, such as the following:

Single unified hierarchy design in API
Safer sub-tree delegation to containers
Newer features like Pressure Stall Information
Enhanced resource allocation management and isolation across multiple resources
- Unified accounting for different types of memory allocations (network memory, kernel memory, etc)
- Accounting for non-immediate resource changes such as page cache write backs

Some Kubernetes features exclusively use cgroup v2 for enhanced resource management and isolation. For example, the MemoryQoS feature improves memory QoS and relies on cgroup v2 primitives.

Using cgroup v2

The recommended way to use cgroup v2 is to use a Linux distribution that enables and uses cgroup v2 by default.

To check if your distribution uses cgroup v2, refer to Identify cgroup version on Linux nodes.

Requirements

cgroup v2 has the following requirements:

OS distribution enables cgroup v2
Linux Kernel version is 5.8 or later
Container runtime supports cgroup v2. For example:
- containerd v1.4 and later
- cri-o v1.20 and later
The kubelet and the container runtime are configured to use the systemd cgroup driver

Linux Distribution cgroup v2 support

For a list of Linux distributions that use cgroup v2, refer to the cgroup v2 documentation

Container Optimized OS (since M97)
Ubuntu (since 21.10, 22.04+ recommended)
Debian GNU/Linux (since Debian 11 bullseye)
Fedora (since 31)
Arch Linux (since April 2021)
RHEL and RHEL-like distributions (since 9)

To check if your distribution is using cgroup v2, refer to your distribution's documentation or follow the instructions in Identify the cgroup version on Linux nodes.

You can also enable cgroup v2 manually on your Linux distribution by modifying the kernel cmdline boot arguments. If your distribution uses GRUB, systemd.unified_cgroup_hierarchy=1 should be added in GRUB_CMDLINE_LINUX under /etc/default/grub, followed by sudo update-grub. However, the recommended approach is to use a distribution that already enables cgroup v2 by default.

Migrating to cgroup v2

To migrate to cgroup v2, ensure that you meet the requirements, then upgrade to a kernel version that enables cgroup v2 by default.

The kubelet automatically detects that the OS is running on cgroup v2 and performs accordingly with no additional configuration required.

There should not be any noticeable difference in the user experience when switching to cgroup v2, unless users are accessing the cgroup file system directly, either on the node or from within the containers.

cgroup v2 uses a different API than cgroup v1, so if there are any applications that directly access the cgroup file system, they need to be updated to newer versions that support cgroup v2. For example:

Some third-party monitoring and security agents may depend on the cgroup filesystem. Update these agents to versions that support cgroup v2.
If you run cAdvisor as a stand-alone DaemonSet for monitoring pods and containers, update it to v0.43.0 or later.
If you use JDK, prefer to use JDK 11.0.16 and later or JDK 15 and later, which fully support cgroup v2.

Identify the cgroup version on Linux Nodes

The cgroup version depends on on the Linux distribution being used and the default cgroup version configured on the OS. To check which cgroup version your distribution uses, run the stat -fc %T /sys/fs/cgroup/ command on the node:

stat -fc %T /sys/fs/cgroup/

For cgroup v2, the output is cgroup2fs.

For cgroup v1, the output is tmpfs.

What's next

Learn more about cgroups
Learn more about container runtime
Learn more about cgroup drivers

2.6 - Container Runtime Interface (CRI)

The CRI is a plugin interface which enables the kubelet to use a wide variety of container runtimes, without having a need to recompile the cluster components.

You need a working container runtime on each Node in your cluster, so that the kubelet can launch Pods and their containers.

The Container Runtime Interface (CRI) is the main protocol for the communication between the kubelet and Container Runtime.

The Kubernetes Container Runtime Interface (CRI) defines the main gRPC protocol for the communication between the cluster components kubelet and container runtime.

The API

FEATURE STATE: Kubernetes v1.23 [stable]

The kubelet acts as a client when connecting to the container runtime via gRPC. The runtime and image service endpoints have to be available in the container runtime, which can be configured separately within the kubelet by using the --image-service-endpoint and --container-runtime-endpoint command line flags

For Kubernetes v1.25, the kubelet prefers to use CRI v1. If a container runtime does not support v1 of the CRI, then the kubelet tries to negotiate any older supported version. The v1.25 kubelet can also negotiate CRI v1alpha2, but this version is considered as deprecated. If the kubelet cannot negotiate a supported CRI version, the kubelet gives up and doesn't register as a node.

Upgrading

When upgrading Kubernetes, then the kubelet tries to automatically select the latest CRI version on restart of the component. If that fails, then the fallback will take place as mentioned above. If a gRPC re-dial was required because the container runtime has been upgraded, then the container runtime must also support the initially selected version or the redial is expected to fail. This requires a restart of the kubelet.

What's next

Learn more about the CRI protocol definition

2.7 - Garbage Collection

Garbage collection is a collective term for the various mechanisms Kubernetes uses to clean up cluster resources. This allows the clean up of resources like the following:

Terminated pods
Completed Jobs
Objects without owner references
Unused containers and container images
Dynamically provisioned PersistentVolumes with a StorageClass reclaim policy of Delete
Stale or expired CertificateSigningRequests (CSRs)
Nodes deleted in the following scenarios:
- On a cloud when the cluster uses a cloud controller manager
- On-premises when the cluster uses an addon similar to a cloud controller manager
Node Lease objects

Owners and dependents

Many objects in Kubernetes link to each other through owner references. Owner references tell the control plane which objects are dependent on others. Kubernetes uses owner references to give the control plane, and other API clients, the opportunity to clean up related resources before deleting an object. In most cases, Kubernetes manages owner references automatically.

Ownership is different from the labels and selectors mechanism that some resources also use. For example, consider a Service that creates EndpointSlice objects. The Service uses labels to allow the control plane to determine which EndpointSlice objects are used for that Service. In addition to the labels, each EndpointSlice that is managed on behalf of a Service has an owner reference. Owner references help different parts of Kubernetes avoid interfering with objects they don’t control.

Note:

Cascading deletion

Kubernetes checks for and deletes objects that no longer have owner references, like the pods left behind when you delete a ReplicaSet. When you delete an object, you can control whether Kubernetes deletes the object's dependents automatically, in a process called cascading deletion. There are two types of cascading deletion, as follows:

Foreground cascading deletion
Background cascading deletion

You can also control how and when garbage collection deletes resources that have owner references using Kubernetes finalizers.

Foreground cascading deletion

In foreground cascading deletion, the owner object you're deleting first enters a deletion in progress state. In this state, the following happens to the owner object:

The Kubernetes API server sets the object's metadata.deletionTimestamp field to the time the object was marked for deletion.
The Kubernetes API server also sets the metadata.finalizers field to foregroundDeletion.
The object remains visible through the Kubernetes API until the deletion process is complete.

After the owner object enters the deletion in progress state, the controller deletes the dependents. After deleting all the dependent objects, the controller deletes the owner object. At this point, the object is no longer visible in the Kubernetes API.

During foreground cascading deletion, the only dependents that block owner deletion are those that have the ownerReference.blockOwnerDeletion=true field. See Use foreground cascading deletion to learn more.

Background cascading deletion

In background cascading deletion, the Kubernetes API server deletes the owner object immediately and the controller cleans up the dependent objects in the background. By default, Kubernetes uses background cascading deletion unless you manually use foreground deletion or choose to orphan the dependent objects.

See Use background cascading deletion to learn more.

Orphaned dependents

When Kubernetes deletes an owner object, the dependents left behind are called orphan objects. By default, Kubernetes deletes dependent objects. To learn how to override this behaviour, see Delete owner objects and orphan dependents.

Garbage collection of unused containers and images

The kubelet performs garbage collection on unused images every five minutes and on unused containers every minute. You should avoid using external garbage collection tools, as these can break the kubelet behavior and remove containers that should exist.

To configure options for unused container and image garbage collection, tune the kubelet using a configuration file and change the parameters related to garbage collection using the KubeletConfiguration resource type.

Container image lifecycle

Kubernetes manages the lifecycle of all images through its image manager, which is part of the kubelet, with the cooperation of cadvisor. The kubelet considers the following disk usage limits when making garbage collection decisions:

HighThresholdPercent
LowThresholdPercent

Disk usage above the configured HighThresholdPercent value triggers garbage collection, which deletes images in order based on the last time they were used, starting with the oldest first. The kubelet deletes images until disk usage reaches the LowThresholdPercent value.

Container garbage collection

The kubelet garbage collects unused containers based on the following variables, which you can define:

MinAge: the minimum age at which the kubelet can garbage collect a container. Disable by setting to 0.
MaxPerPodContainer: the maximum number of dead containers each Pod pair can have. Disable by setting to less than 0.
MaxContainers: the maximum number of dead containers the cluster can have. Disable by setting to less than 0.

In addition to these variables, the kubelet garbage collects unidentified and deleted containers, typically starting with the oldest first.

MaxPerPodContainer and MaxContainers may potentially conflict with each other in situations where retaining the maximum number of containers per Pod (MaxPerPodContainer) would go outside the allowable total of global dead containers (MaxContainers). In this situation, the kubelet adjusts MaxPerPodContainer to address the conflict. A worst-case scenario would be to downgrade MaxPerPodContainer to 1 and evict the oldest containers. Additionally, containers owned by pods that have been deleted are removed once they are older than MinAge.

Note: The kubelet only garbage collects the containers it manages.

Configuring garbage collection

You can tune garbage collection of resources by configuring options specific to the controllers managing those resources. The following pages show you how to configure garbage collection:

What's next

Learn more about ownership of Kubernetes objects.
Learn more about Kubernetes finalizers.
Learn about the TTL controller (beta) that cleans up finished Jobs.

3 - Containers

Technology for packaging an application along with its runtime dependencies.

Each container that you run is repeatable; the standardization from having dependencies included means that you get the same behavior wherever you run it.

Containers decouple applications from underlying host infrastructure. This makes deployment easier in different cloud or OS environments.

Container images

A container image is a ready-to-run software package, containing everything needed to run an application: the code and any runtime it requires, application and system libraries, and default values for any essential settings.

By design, a container is immutable: you cannot change the code of a container that is already running. If you have a containerized application and want to make changes, you need to build a new image that includes the change, then recreate the container to start from the updated image.

Container runtimes

The container runtime is the software that is responsible for running containers.

Kubernetes supports container runtimes such as containerd, CRI-O, and any other implementation of the Kubernetes CRI (Container Runtime Interface).

What's next

Read about container images
Read about Pods

3.1 - Images

A container image represents binary data that encapsulates an application and all its software dependencies. Container images are executable software bundles that can run standalone and that make very well defined assumptions about their runtime environment.

You typically create a container image of your application and push it to a registry before referring to it in a Pod

This page provides an outline of the container image concept.

Image names

Container images are usually given a name such as pause, example/mycontainer, or kube-apiserver. Images can also include a registry hostname; for example: fictional.registry.example/imagename, and possibly a port number as well; for example: fictional.registry.example:10443/imagename.

If you don't specify a registry hostname, Kubernetes assumes that you mean the Docker public registry.

After the image name part you can add a tag (in the same way you would when using with commands like docker or podman). Tags let you identify different versions of the same series of images.

Image tags consist of lowercase and uppercase letters, digits, underscores (_), periods (.), and dashes (-).
There are additional rules about where you can place the separator characters (_, -, and .) inside an image tag.
If you don't specify a tag, Kubernetes assumes you mean the tag latest.

Updating images

When you first create a Deployment, StatefulSet, Pod, or other object that includes a Pod template, then by default the pull policy of all containers in that pod will be set to IfNotPresent if it is not explicitly specified. This policy causes the kubelet to skip pulling an image if it already exists.

Image pull policy

The imagePullPolicy for a container and the tag of the image affect when the kubelet attempts to pull (download) the specified image.

Here's a list of the values you can set for imagePullPolicy and the effects these values have:

IfNotPresent: the image is pulled only if it is not already present locally.
Always: every time the kubelet launches a container, the kubelet queries the container image registry to resolve the name to an image digest. If the kubelet has a container image with that exact digest cached locally, the kubelet uses its cached image; otherwise, the kubelet pulls the image with the resolved digest, and uses that image to launch the container.
Never: the kubelet does not try fetching the image. If the image is somehow already present locally, the kubelet attempts to start the container; otherwise, startup fails. See pre-pulled images for more details.

The caching semantics of the underlying image provider make even imagePullPolicy: Always efficient, as long as the registry is reliably accessible. Your container runtime can notice that the image layers already exist on the node so that they don't need to be downloaded again.

Note:

You should avoid using the :latest tag when deploying containers in production as it is harder to track which version of the image is running and more difficult to roll back properly.

Instead, specify a meaningful tag such as v1.42.0.

To make sure the Pod always uses the same version of a container image, you can specify the image's digest; replace <image-name>:<tag> with <image-name>@<digest> (for example, image@sha256:45b23dee08af5e43a7fea6c4cf9c25ccf269ee113168c19722f87876677c5cb2).

When using image tags, if the image registry were to change the code that the tag on that image represents, you might end up with a mix of Pods running the old and new code. An image digest uniquely identifies a specific version of the image, so Kubernetes runs the same code every time it starts a container with that image name and digest specified. Specifying an image by digest fixes the code that you run so that a change at the registry cannot lead to that mix of versions.

There are third-party admission controllers that mutate Pods (and pod templates) when they are created, so that the running workload is defined based on an image digest rather than a tag. That might be useful if you want to make sure that all your workload is running the same code no matter what tag changes happen at the registry.

Default image pull policy

When you (or a controller) submit a new Pod to the API server, your cluster sets the imagePullPolicy field when specific conditions are met:

if you omit the imagePullPolicy field, and the tag for the container image is :latest, imagePullPolicy is automatically set to Always;
if you omit the imagePullPolicy field, and you don't specify the tag for the container image, imagePullPolicy is automatically set to Always;
if you omit the imagePullPolicy field, and you specify the tag for the container image that isn't :latest, the imagePullPolicy is automatically set to IfNotPresent.

Note:

The value of imagePullPolicy of the container is always set when the object is first created, and is not updated if the image's tag later changes.

For example, if you create a Deployment with an image whose tag is not :latest, and later update that Deployment's image to a :latest tag, the imagePullPolicy field will not change to Always. You must manually change the pull policy of any object after its initial creation.

Required image pull

If you would like to always force a pull, you can do one of the following:

Set the imagePullPolicy of the container to Always.
Omit the imagePullPolicy and use :latest as the tag for the image to use; Kubernetes will set the policy to Always when you submit the Pod.
Omit the imagePullPolicy and the tag for the image to use; Kubernetes will set the policy to Always when you submit the Pod.
Enable the AlwaysPullImages admission controller.

ImagePullBackOff

When a kubelet starts creating containers for a Pod using a container runtime, it might be possible the container is in Waiting state because of ImagePullBackOff.

The status ImagePullBackOff means that a container could not start because Kubernetes could not pull a container image (for reasons such as invalid image name, or pulling from a private registry without imagePullSecret). The BackOff part indicates that Kubernetes will keep trying to pull the image, with an increasing back-off delay.

Kubernetes raises the delay between each attempt until it reaches a compiled-in limit, which is 300 seconds (5 minutes).

Multi-architecture images with image indexes

As well as providing binary images, a container registry can also serve a container image index. An image index can point to multiple image manifests for architecture-specific versions of a container. The idea is that you can have a name for an image (for example: pause, example/mycontainer, kube-apiserver) and allow different systems to fetch the right binary image for the machine architecture they are using.

Kubernetes itself typically names container images with a suffix -$(ARCH). For backward compatibility, please generate the older images with suffixes. The idea is to generate say pause image which has the manifest for all the arch(es) and say pause-amd64 which is backwards compatible for older configurations or YAML files which may have hard coded the images with suffixes.

Using a private registry

Private registries may require keys to read images from them.
Credentials can be provided in several ways:

Configuring Nodes to Authenticate to a Private Registry
- all pods can read any configured private registries
- requires node configuration by cluster administrator
Pre-pulled Images
- all pods can use any images cached on a node
- requires root access to all nodes to set up
Specifying ImagePullSecrets on a Pod
- only pods which provide own keys can access the private registry
Vendor-specific or local extensions
- if you're using a custom node configuration, you (or your cloud provider) can implement your mechanism for authenticating the node to the container registry.

These options are explained in more detail below.

Configuring nodes to authenticate to a private registry

Specific instructions for setting credentials depends on the container runtime and registry you chose to use. You should refer to your solution's documentation for the most accurate information.

For an example of configuring a private container image registry, see the Pull an Image from a Private Registry task. That example uses a private registry in Docker Hub.

Interpretation of config.json

The interpretation of config.json varies between the original Docker implementation and the Kubernetes interpretation. In Docker, the auths keys can only specify root URLs, whereas Kubernetes allows glob URLs as well as prefix-matched paths. This means that a config.json like this is valid:

{
    "auths": {
        "*my-registry.io/images": {
            "auth": "…"
        }
    }
}

The root URL (*my-registry.io) is matched by using the following syntax:

pattern:
    { term }

term:
    '*'         matches any sequence of non-Separator characters
    '?'         matches any single non-Separator character
    '[' [ '^' ] { character-range } ']'
                character class (must be non-empty)
    c           matches character c (c != '*', '?', '\\', '[')
    '\\' c      matches character c

character-range:
    c           matches character c (c != '\\', '-', ']')
    '\\' c      matches character c
    lo '-' hi   matches character c for lo <= c <= hi

Image pull operations would now pass the credentials to the CRI container runtime for every valid pattern. For example the following container image names would match successfully:

my-registry.io/images
my-registry.io/images/my-image
my-registry.io/images/another-image
sub.my-registry.io/images/my-image
a.sub.my-registry.io/images/my-image

The kubelet performs image pulls sequentially for every found credential. This means, that multiple entries in config.json are possible, too:

{
    "auths": {
        "my-registry.io/images": {
            "auth": "…"
        },
        "my-registry.io/images/subpath": {
            "auth": "…"
        }
    }
}

If now a container specifies an image my-registry.io/images/subpath/my-image to be pulled, then the kubelet will try to download them from both authentication sources if one of them fails.

Pre-pulled images

Note: This approach is suitable if you can control node configuration. It will not work reliably if your cloud provider manages nodes and replaces them automatically.

By default, the kubelet tries to pull each image from the specified registry. However, if the imagePullPolicy property of the container is set to IfNotPresent or Never, then a local image is used (preferentially or exclusively, respectively).

If you want to rely on pre-pulled images as a substitute for registry authentication, you must ensure all nodes in the cluster have the same pre-pulled images.

This can be used to preload certain images for speed or as an alternative to authenticating to a private registry.

All pods will have read access to any pre-pulled images.

Specifying imagePullSecrets on a Pod

Note: This is the recommended approach to run containers based on images in private registries.

Kubernetes supports specifying container image registry keys on a Pod. imagePullSecrets must all be in the same namespace as the Pod. The referenced Secrets must be of type kubernetes.io/dockercfg or kubernetes.io/dockerconfigjson.

Creating a Secret with a Docker config

You need to know the username, registry password and client email address for authenticating to the registry, as well as its hostname. Run the following command, substituting the appropriate uppercase values:

kubectl create secret docker-registry <name> --docker-server=DOCKER_REGISTRY_SERVER --docker-username=DOCKER_USER --docker-password=DOCKER_PASSWORD --docker-email=DOCKER_EMAIL

If you already have a Docker credentials file then, rather than using the above command, you can import the credentials file as a Kubernetes Secrets.
Create a Secret based on existing Docker credentials explains how to set this up.

This is particularly useful if you are using multiple private container registries, as kubectl create secret docker-registry creates a Secret that only works with a single private registry.

Note: Pods can only reference image pull secrets in their own namespace, so this process needs to be done one time per namespace.

Referring to an imagePullSecrets on a Pod

Now, you can create pods which reference that secret by adding an imagePullSecrets section to a Pod definition. Each item in the imagePullSecrets array can only reference a Secret in the same namespace.

For example:

cat <<EOF > pod.yaml
apiVersion: v1
kind: Pod
metadata:
  name: foo
  namespace: awesomeapps
spec:
  containers:
    - name: foo
      image: janedoe/awesomeapp:v1
  imagePullSecrets:
    - name: myregistrykey
EOF

cat <<EOF >> ./kustomization.yaml
resources:
- pod.yaml
EOF

This needs to be done for each pod that is using a private registry.

However, setting of this field can be automated by setting the imagePullSecrets in a ServiceAccount resource.

Check Add ImagePullSecrets to a Service Account for detailed instructions.

You can use this in conjunction with a per-node .docker/config.json. The credentials will be merged.

Use cases

There are a number of solutions for configuring private registries. Here are some common use cases and suggested solutions.

Cluster running only non-proprietary (e.g. open-source) images. No need to hide images.
- Use public images from a public registry
  - No configuration required.
  - Some cloud providers automatically cache or mirror public images, which improves availability and reduces the time to pull images.
Cluster running some proprietary images which should be hidden to those outside the company, but visible to all cluster users.
- Use a hosted private registry
  - Manual configuration may be required on the nodes that need to access to private registry
- Or, run an internal private registry behind your firewall with open read access.
  - No Kubernetes configuration is required.
- Use a hosted container image registry service that controls image access
  - It will work better with cluster autoscaling than manual node configuration.
- Or, on a cluster where changing the node configuration is inconvenient, use imagePullSecrets.
Cluster with proprietary images, a few of which require stricter access control.
- Ensure AlwaysPullImages admission controller is active. Otherwise, all Pods potentially have access to all images.
- Move sensitive data into a "Secret" resource, instead of packaging it in an image.
A multi-tenant cluster where each tenant needs own private registry.
- Ensure AlwaysPullImages admission controller is active. Otherwise, all Pods of all tenants potentially have access to all images.
- Run a private registry with authorization required.
- Generate registry credential for each tenant, put into secret, and populate secret to each tenant namespace.
- The tenant adds that secret to imagePullSecrets of each namespace.

If you need access to multiple registries, you can create one secret for each registry.

What's next

Read the OCI Image Manifest Specification.
Learn about container image garbage collection.
Learn more about pulling an Image from a Private Registry.

3.2 - Container Environment

This page describes the resources available to Containers in the Container environment.

Container environment

The Kubernetes Container environment provides several important resources to Containers:

A filesystem, which is a combination of an image and one or more volumes.
Information about the Container itself.
Information about other objects in the cluster.

Container information

The hostname of a Container is the name of the Pod in which the Container is running. It is available through the hostname command or the gethostname function call in libc.

The Pod name and namespace are available as environment variables through the downward API.

User defined environment variables from the Pod definition are also available to the Container, as are any environment variables specified statically in the container image.

Cluster information

A list of all services that were running when a Container was created is available to that Container as environment variables. This list is limited to services within the same namespace as the new Container's Pod and Kubernetes control plane services.

For a service named foo that maps to a Container named bar, the following variables are defined:

FOO_SERVICE_HOST=<the host the service is running on>
FOO_SERVICE_PORT=<the port the service is running on>

Services have dedicated IP addresses and are available to the Container via DNS, if DNS addon is enabled.

What's next

Learn more about Container lifecycle hooks.
Get hands-on experience attaching handlers to Container lifecycle events.

3.3 - Runtime Class

FEATURE STATE: Kubernetes v1.20 [stable]

This page describes the RuntimeClass resource and runtime selection mechanism.

RuntimeClass is a feature for selecting the container runtime configuration. The container runtime configuration is used to run a Pod's containers.

Motivation

You can set a different RuntimeClass between different Pods to provide a balance of performance versus security. For example, if part of your workload deserves a high level of information security assurance, you might choose to schedule those Pods so that they run in a container runtime that uses hardware virtualization. You'd then benefit from the extra isolation of the alternative runtime, at the expense of some additional overhead.

You can also use RuntimeClass to run different Pods with the same container runtime but with different settings.

Setup

Configure the CRI implementation on nodes (runtime dependent)
Create the corresponding RuntimeClass resources

1. Configure the CRI implementation on nodes

The configurations available through RuntimeClass are Container Runtime Interface (CRI) implementation dependent. See the corresponding documentation (below) for your CRI implementation for how to configure.

Note: RuntimeClass assumes a homogeneous node configuration across the cluster by default (which means that all nodes are configured the same way with respect to container runtimes). To support heterogeneous node configurations, see Scheduling below.

The configurations have a corresponding handler name, referenced by the RuntimeClass. The handler must be a valid DNS label name.

2. Create the corresponding RuntimeClass resources

The configurations setup in step 1 should each have an associated handler name, which identifies the configuration. For each handler, create a corresponding RuntimeClass object.

The RuntimeClass resource currently only has 2 significant fields: the RuntimeClass name (metadata.name) and the handler (handler). The object definition looks like this:

# RuntimeClass is defined in the node.k8s.io API group
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  # The name the RuntimeClass will be referenced by.
  # RuntimeClass is a non-namespaced resource.
  name: myclass 
# The name of the corresponding CRI configuration
handler: myconfiguration

The name of a RuntimeClass object must be a valid DNS subdomain name.

Note: It is recommended that RuntimeClass write operations (create/update/patch/delete) be restricted to the cluster administrator. This is typically the default. See Authorization Overview for more details.

Usage

Once RuntimeClasses are configured for the cluster, you can specify a runtimeClassName in the Pod spec to use it. For example:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  runtimeClassName: myclass
  # ...

This will instruct the kubelet to use the named RuntimeClass to run this pod. If the named RuntimeClass does not exist, or the CRI cannot run the corresponding handler, the pod will enter the Failed terminal phase. Look for a corresponding event for an error message.

If no runtimeClassName is specified, the default RuntimeHandler will be used, which is equivalent to the behavior when the RuntimeClass feature is disabled.

CRI Configuration

For more details on setting up CRI runtimes, see CRI installation.

containerd

Runtime handlers are configured through containerd's configuration at /etc/containerd/config.toml. Valid handlers are configured under the runtimes section:

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.${HANDLER_NAME}]

See containerd's config documentation for more details:

CRI-O

Runtime handlers are configured through CRI-O's configuration at /etc/crio/crio.conf. Valid handlers are configured under the crio.runtime table:

[crio.runtime.runtimes.${HANDLER_NAME}]
  runtime_path = "${PATH_TO_BINARY}"

See CRI-O's config documentation for more details.

Scheduling

FEATURE STATE: Kubernetes v1.16 [beta]

By specifying the scheduling field for a RuntimeClass, you can set constraints to ensure that Pods running with this RuntimeClass are scheduled to nodes that support it. If scheduling is not set, this RuntimeClass is assumed to be supported by all nodes.

To ensure pods land on nodes supporting a specific RuntimeClass, that set of nodes should have a common label which is then selected by the runtimeclass.scheduling.nodeSelector field. The RuntimeClass's nodeSelector is merged with the pod's nodeSelector in admission, effectively taking the intersection of the set of nodes selected by each. If there is a conflict, the pod will be rejected.

If the supported nodes are tainted to prevent other RuntimeClass pods from running on the node, you can add tolerations to the RuntimeClass. As with the nodeSelector, the tolerations are merged with the pod's tolerations in admission, effectively taking the union of the set of nodes tolerated by each.

To learn more about configuring the node selector and tolerations, see Assigning Pods to Nodes.

Pod Overhead

FEATURE STATE: Kubernetes v1.24 [stable]

You can specify overhead resources that are associated with running a Pod. Declaring overhead allows the cluster (including the scheduler) to account for it when making decisions about Pods and resources.

Pod overhead is defined in RuntimeClass through the overhead field. Through the use of this field, you can specify the overhead of running pods utilizing this RuntimeClass and ensure these overheads are accounted for in Kubernetes.

What's next

3.4 - Container Lifecycle Hooks

This page describes how kubelet managed Containers can use the Container lifecycle hook framework to run code triggered by events during their management lifecycle.

Overview

Analogous to many programming language frameworks that have component lifecycle hooks, such as Angular, Kubernetes provides Containers with lifecycle hooks. The hooks enable Containers to be aware of events in their management lifecycle and run code implemented in a handler when the corresponding lifecycle hook is executed.

Container hooks

There are two hooks that are exposed to Containers:

PostStart

This hook is executed immediately after a container is created. However, there is no guarantee that the hook will execute before the container ENTRYPOINT. No parameters are passed to the handler.

PreStop

This hook is called immediately before a container is terminated due to an API request or management event such as a liveness/startup probe failure, preemption, resource contention and others. A call to the PreStop hook fails if the container is already in a terminated or completed state and the hook must complete before the TERM signal to stop the container can be sent. The Pod's termination grace period countdown begins before the PreStop hook is executed, so regardless of the outcome of the handler, the container will eventually terminate within the Pod's termination grace period. No parameters are passed to the handler.

A more detailed description of the termination behavior can be found in Termination of Pods.

Hook handler implementations

Containers can access a hook by implementing and registering a handler for that hook. There are two types of hook handlers that can be implemented for Containers:

Exec - Executes a specific command, such as pre-stop.sh, inside the cgroups and namespaces of the Container. Resources consumed by the command are counted against the Container.
HTTP - Executes an HTTP request against a specific endpoint on the Container.

Hook handler execution

When a Container lifecycle management hook is called, the Kubernetes management system executes the handler according to the hook action, httpGet and tcpSocket are executed by the kubelet process, and exec is executed in the container.

Hook handler calls are synchronous within the context of the Pod containing the Container. This means that for a PostStart hook, the Container ENTRYPOINT and hook fire asynchronously. However, if the hook takes too long to run or hangs, the Container cannot reach a running state.

PreStop hooks are not executed asynchronously from the signal to stop the Container; the hook must complete its execution before the TERM signal can be sent. If a PreStop hook hangs during execution, the Pod's phase will be Terminating and remain there until the Pod is killed after its terminationGracePeriodSeconds expires. This grace period applies to the total time it takes for both the PreStop hook to execute and for the Container to stop normally. If, for example, terminationGracePeriodSeconds is 60, and the hook takes 55 seconds to complete, and the Container takes 10 seconds to stop normally after receiving the signal, then the Container will be killed before it can stop normally, since terminationGracePeriodSeconds is less than the total time (55+10) it takes for these two things to happen.

If either a PostStart or PreStop hook fails, it kills the Container.

Users should make their hook handlers as lightweight as possible. There are cases, however, when long running commands make sense, such as when saving state prior to stopping a Container.

Hook delivery guarantees

Hook delivery is intended to be at least once, which means that a hook may be called multiple times for any given event, such as for PostStart or PreStop. It is up to the hook implementation to handle this correctly.

Generally, only single deliveries are made. If, for example, an HTTP hook receiver is down and is unable to take traffic, there is no attempt to resend. In some rare cases, however, double delivery may occur. For instance, if a kubelet restarts in the middle of sending a hook, the hook might be resent after the kubelet comes back up.

Debugging Hook handlers

The logs for a Hook handler are not exposed in Pod events. If a handler fails for some reason, it broadcasts an event. For PostStart, this is the FailedPostStartHook event, and for PreStop, this is the FailedPreStopHook event. To generate a failed FailedPostStartHook event yourself, modify the lifecycle-events.yaml file to change the postStart command to "badcommand" and apply it. Here is some example output of the resulting events you see from running kubectl describe pod lifecycle-demo:

Events:
  Type     Reason               Age              From               Message
  ----     ------               ----             ----               -------
  Normal   Scheduled            7s               default-scheduler  Successfully assigned default/lifecycle-demo to ip-XXX-XXX-XX-XX.us-east-2...
  Normal   Pulled               6s               kubelet            Successfully pulled image "nginx" in 229.604315ms
  Normal   Pulling              4s (x2 over 6s)  kubelet            Pulling image "nginx"
  Normal   Created              4s (x2 over 5s)  kubelet            Created container lifecycle-demo-container
  Normal   Started              4s (x2 over 5s)  kubelet            Started container lifecycle-demo-container
  Warning  FailedPostStartHook  4s (x2 over 5s)  kubelet            Exec lifecycle hook ([badcommand]) for Container "lifecycle-demo-container" in Pod "lifecycle-demo_default(30229739-9651-4e5a-9a32-a8f1688862db)" failed - error: command 'badcommand' exited with 126: , message: "OCI runtime exec failed: exec failed: container_linux.go:380: starting container process caused: exec: \"badcommand\": executable file not found in $PATH: unknown\r\n"
  Normal   Killing              4s (x2 over 5s)  kubelet            FailedPostStartHook
  Normal   Pulled               4s               kubelet            Successfully pulled image "nginx" in 215.66395ms
  Warning  BackOff              2s (x2 over 3s)  kubelet            Back-off restarting failed container

What's next

Learn more about the Container environment.
Get hands-on experience attaching handlers to Container lifecycle events.

4 - Windows in Kubernetes

4.1 - Windows containers in Kubernetes

Windows applications constitute a large portion of the services and applications that run in many organizations. Windows containers provide a way to encapsulate processes and package dependencies, making it easier to use DevOps practices and follow cloud native patterns for Windows applications.

Organizations with investments in Windows-based applications and Linux-based applications don't have to look for separate orchestrators to manage their workloads, leading to increased operational efficiencies across their deployments, regardless of operating system.

Windows nodes in Kubernetes

To enable the orchestration of Windows containers in Kubernetes, include Windows nodes in your existing Linux cluster. Scheduling Windows containers in Pods on Kubernetes is similar to scheduling Linux-based containers.

In order to run Windows containers, your Kubernetes cluster must include multiple operating systems. While you can only run the control plane on Linux, you can deploy worker nodes running either Windows or Linux.

Windows nodes are supported provided that the operating system is Windows Server 2019.

This document uses the term Windows containers to mean Windows containers with process isolation. Kubernetes does not support running Windows containers with Hyper-V isolation.

Compatibility and limitations

Some node features are only available if you use a specific container runtime; others are not available on Windows nodes, including:

HugePages: not supported for Windows containers
Privileged containers: not supported for Windows containers. HostProcess Containers offer similar functionality.
TerminationGracePeriod: requires containerD

Not all features of shared namespaces are supported. See API compatibility for more details.

See Windows OS version compatibility for details on the Windows versions that Kubernetes is tested against.

From an API and kubectl perspective, Windows containers behave in much the same way as Linux-based containers. However, there are some notable differences in key functionality which are outlined in this section.

Comparison with Linux

Key Kubernetes elements work the same way in Windows as they do in Linux. This section refers to several key workload abstractions and how they map to Windows.

Pods

A Pod is the basic building block of Kubernetes–the smallest and simplest unit in the Kubernetes object model that you create or deploy. You may not deploy Windows and Linux containers in the same Pod. All containers in a Pod are scheduled onto a single Node where each Node represents a specific platform and architecture. The following Pod capabilities, properties and events are supported with Windows containers:
- Single or multiple containers per Pod with process isolation and volume sharing
- Pod status fields
- Readiness, liveness, and startup probes
- postStart & preStop container lifecycle hooks
- ConfigMap, Secrets: as environment variables or volumes
- emptyDir volumes
- Named pipe host mounts
- Resource limits
- OS field:
  
  The .spec.os.name field should be set to windows to indicate that the current Pod uses Windows containers.
  
  Note: Starting from 1.25, the IdentifyPodOS feature gate is in GA stage and defaults to be enabled.
  
  If you set the .spec.os.name field to windows, you must not set the following fields in the .spec of that Pod:
  - spec.hostPID
  - spec.hostIPC
  - spec.securityContext.seLinuxOptions
  - spec.securityContext.seccompProfile
  - spec.securityContext.fsGroup
  - spec.securityContext.fsGroupChangePolicy
  - spec.securityContext.sysctls
  - spec.shareProcessNamespace
  - spec.securityContext.runAsUser
  - spec.securityContext.runAsGroup
  - spec.securityContext.supplementalGroups
  - spec.containers[*].securityContext.seLinuxOptions
  - spec.containers[*].securityContext.seccompProfile
  - spec.containers[*].securityContext.capabilities
  - spec.containers[*].securityContext.readOnlyRootFilesystem
  - spec.containers[*].securityContext.privileged
  - spec.containers[*].securityContext.allowPrivilegeEscalation
  - spec.containers[*].securityContext.procMount
  - spec.containers[*].securityContext.runAsUser
  - spec.containers[*].securityContext.runAsGroup
  In the above list, wildcards (*) indicate all elements in a list. For example, spec.containers[*].securityContext refers to the SecurityContext object for all containers. If any of these fields is specified, the Pod will not be admitted by the API server.
Workload resources including:
- ReplicaSet
- Deployment
- StatefulSet
- DaemonSet
- Job
- CronJob
- ReplicationController
Services See Load balancing and Services for more details.

Pods, workload resources, and Services are critical elements to managing Windows workloads on Kubernetes. However, on their own they are not enough to enable the proper lifecycle management of Windows workloads in a dynamic cloud native environment.

kubectl exec
Pod and container metrics
Horizontal pod autoscaling
Resource quotas
Scheduler preemption

Command line options for the kubelet

Some kubelet command line options behave differently on Windows, as described below:

The --windows-priorityclass lets you set the scheduling priority of the kubelet process (see CPU resource management)
The --kube-reserved, --system-reserved , and --eviction-hard flags update NodeAllocatable
Eviction by using --enforce-node-allocable is not implemented
Eviction by using --eviction-hard and --eviction-soft are not implemented
When running on a Windows node the kubelet does not have memory or CPU restrictions. --kube-reserved and --system-reserved only subtract from NodeAllocatable and do not guarantee resource provided for workloads. See Resource Management for Windows nodes for more information.
The MemoryPressure Condition is not implemented
The kubelet does not take OOM eviction actions

API compatibility

There are subtle differences in the way the Kubernetes APIs work for Windows due to the OS and container runtime. Some workload properties were designed for Linux, and fail to run on Windows.

At a high level, these OS concepts are different:

Identity - Linux uses userID (UID) and groupID (GID) which are represented as integer types. User and group names are not canonical - they are just an alias in /etc/groups or /etc/passwd back to UID+GID. Windows uses a larger binary security identifier (SID) which is stored in the Windows Security Access Manager (SAM) database. This database is not shared between the host and containers, or between containers.
File permissions - Windows uses an access control list based on (SIDs), whereas POSIX systems such as Linux use a bitmask based on object permissions and UID+GID, plus optional access control lists.
File paths - the convention on Windows is to use \ instead of /. The Go IO libraries typically accept both and just make it work, but when you're setting a path or command line that's interpreted inside a container, \ may be needed.
Signals - Windows interactive apps handle termination differently, and can implement one or more of these:
- A UI thread handles well-defined messages including WM_CLOSE.
- Console apps handle Ctrl-C or Ctrl-break using a Control Handler.
- Services register a Service Control Handler function that can accept SERVICE_CONTROL_STOP control codes.

Container exit codes follow the same convention where 0 is success, and nonzero is failure. The specific error codes may differ across Windows and Linux. However, exit codes passed from the Kubernetes components (kubelet, kube-proxy) are unchanged.

Field compatibility for container specifications

The following list documents differences between how Pod container specifications work between Windows and Linux:

Huge pages are not implemented in the Windows container runtime, and are not available. They require asserting a user privilege that's not configurable for containers.
requests.cpu and requests.memory - requests are subtracted from node available resources, so they can be used to avoid overprovisioning a node. However, they cannot be used to guarantee resources in an overprovisioned node. They should be applied to all containers as a best practice if the operator wants to avoid overprovisioning entirely.
securityContext.allowPrivilegeEscalation - not possible on Windows; none of the capabilities are hooked up
securityContext.capabilities - POSIX capabilities are not implemented on Windows
securityContext.privileged - Windows doesn't support privileged containers, use HostProcess Containers instead
securityContext.procMount - Windows doesn't have a /proc filesystem
securityContext.readOnlyRootFilesystem - not possible on Windows; write access is required for registry & system processes to run inside the container
securityContext.runAsGroup - not possible on Windows as there is no GID support
securityContext.runAsNonRoot - this setting will prevent containers from running as ContainerAdministrator which is the closest equivalent to a root user on Windows.
securityContext.runAsUser - use runAsUserName instead
securityContext.seLinuxOptions - not possible on Windows as SELinux is Linux-specific
terminationMessagePath - this has some limitations in that Windows doesn't support mapping single files. The default value is /dev/termination-log, which does work because it does not exist on Windows by default.

Field compatibility for Pod specifications

The following list documents differences between how Pod specifications work between Windows and Linux:

hostIPC and hostpid - host namespace sharing is not possible on Windows
hostNetwork - There is no Windows OS support to share the host network
dnsPolicy - setting the Pod dnsPolicy to ClusterFirstWithHostNet is not supported on Windows because host networking is not provided. Pods always run with a container network.
podSecurityContext (see below)
shareProcessNamespace - this is a beta feature, and depends on Linux namespaces which are not implemented on Windows. Windows cannot share process namespaces or the container's root filesystem. Only the network can be shared.
terminationGracePeriodSeconds - this is not fully implemented in Docker on Windows, see the GitHub issue. The behavior today is that the ENTRYPOINT process is sent CTRL_SHUTDOWN_EVENT, then Windows waits 5 seconds by default, and finally shuts down all processes using the normal Windows shutdown behavior. The 5 second default is actually in the Windows registry inside the container, so it can be overridden when the container is built.
volumeDevices - this is a beta feature, and is not implemented on Windows. Windows cannot attach raw block devices to pods.
volumes
- If you define an emptyDir volume, you cannot set its volume source to memory.
You cannot enable mountPropagation for volume mounts as this is not supported on Windows.

Field compatibility for Pod security context

None of the Pod securityContext fields work on Windows.

Node problem detector

The node problem detector (see Monitor Node Health) has preliminary support for Windows. For more information, visit the project's GitHub page.

Pause container

In a Kubernetes Pod, an infrastructure or “pause” container is first created to host the container. In Linux, the cgroups and namespaces that make up a pod need a process to maintain their continued existence; the pause process provides this. Containers that belong to the same pod, including infrastructure and worker containers, share a common network endpoint (same IPv4 and / or IPv6 address, same network port spaces). Kubernetes uses pause containers to allow for worker containers crashing or restarting without losing any of the networking configuration.

Kubernetes maintains a multi-architecture image that includes support for Windows. For Kubernetes v1.25 the recommended pause image is registry.k8s.io/pause:3.6. The source code is available on GitHub.

Microsoft maintains a different multi-architecture image, with Linux and Windows amd64 support, that you can find as mcr.microsoft.com/oss/kubernetes/pause:3.6. This image is built from the same source as the Kubernetes maintained image but all of the Windows binaries are authenticode signed by Microsoft. The Kubernetes project recommends using the Microsoft maintained image if you are deploying to a production or production-like environment that requires signed binaries.

Container runtimes

You need to install a container runtime into each node in the cluster so that Pods can run there.

The following container runtimes work with Windows:

Note: This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.

ContainerD

FEATURE STATE: Kubernetes v1.20 [stable]

You can use ContainerD 1.4.0+ as the container runtime for Kubernetes nodes that run Windows.

Learn how to install ContainerD on a Windows node.

Note: There is a known limitation when using GMSA with containerd to access Windows network shares, which requires a kernel patch.

Mirantis Container Runtime

Mirantis Container Runtime (MCR) is available as a container runtime for all Windows Server 2019 and later versions.

See Install MCR on Windows Servers for more information.

Windows OS version compatibility

On Windows nodes, strict compatibility rules apply where the host OS version must match the container base image OS version. Only Windows containers with a container operating system of Windows Server 2019 are fully supported.

For Kubernetes v1.25, operating system compatibility for Windows nodes (and Pods) is as follows:

Windows Server LTSC release: Windows Server 2019; Windows Server 2022
Windows Server SAC release: Windows Server version 20H2

The Kubernetes version-skew policy also applies.

Getting help and troubleshooting

Your main source of help for troubleshooting your Kubernetes cluster should start with the Troubleshooting page.

Some additional, Windows-specific troubleshooting help is included in this section. Logs are an important element of troubleshooting issues in Kubernetes. Make sure to include them any time you seek troubleshooting assistance from other contributors. Follow the instructions in the SIG Windows contributing guide on gathering logs.

Reporting issues and feature requests

If you have what looks like a bug, or you would like to make a feature request, please follow the SIG Windows contributing guide to create a new issue. You should first search the list of issues in case it was reported previously and comment with your experience on the issue and add additional logs. SIG Windows channel on the Kubernetes Slack is also a great avenue to get some initial support and troubleshooting ideas prior to creating a ticket.

Deployment tools

The kubeadm tool helps you to deploy a Kubernetes cluster, providing the control plane to manage the cluster it, and nodes to run your workloads. Adding Windows nodes explains how to deploy Windows nodes to your cluster using kubeadm.

The Kubernetes cluster API project also provides means to automate deployment of Windows nodes.

Windows distribution channels

For a detailed explanation of Windows distribution channels see the Microsoft documentation.

Information on the different Windows Server servicing channels including their support models can be found at Windows Server servicing channels.

4.2 - Guide for scheduling Windows containers in Kubernetes

Windows applications constitute a large portion of the services and applications that run in many organizations. This guide walks you through the steps to configure and deploy Windows containers in Kubernetes.

Objectives

Configure an example deployment to run Windows containers on the Windows node
Highlight Windows specific functionality in Kubernetes

Before you begin

Create a Kubernetes cluster that includes a control plane and a worker node running Windows Server
It is important to note that creating and deploying services and workloads on Kubernetes behaves in much the same way for Linux and Windows containers. Kubectl commands to interface with the cluster are identical. The example in the section below is provided to jumpstart your experience with Windows containers.

Getting Started: Deploying a Windows container

The example YAML file below deploys a simple webserver application running inside a Windows container.

Create a service spec named win-webserver.yaml with the contents below:

apiVersion: v1
kind: Service
metadata:
  name: win-webserver
  labels:
    app: win-webserver
spec:
  ports:
    # the port that this service should serve on
    - port: 80
      targetPort: 80
  selector:
    app: win-webserver
  type: NodePort
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: win-webserver
  name: win-webserver
spec:
  replicas: 2
  selector:
    matchLabels:
      app: win-webserver
  template:
    metadata:
      labels:
        app: win-webserver
      name: win-webserver
    spec:
     containers:
      - name: windowswebserver
        image: mcr.microsoft.com/windows/servercore:ltsc2019
        command:
        - powershell.exe
        - -command
        - "<#code used from https://gist.github.com/19WAS85/5424431#> ; $$listener = New-Object System.Net.HttpListener ; $$listener.Prefixes.Add('http://*:80/') ; $$listener.Start() ; $$callerCounts = @{} ; Write-Host('Listening at http://*:80/') ; while ($$listener.IsListening) { ;$$context = $$listener.GetContext() ;$$requestUrl = $$context.Request.Url ;$$clientIP = $$context.Request.RemoteEndPoint.Address ;$$response = $$context.Response ;Write-Host '' ;Write-Host('> {0}' -f $$requestUrl) ;  ;$$count = 1 ;$$k=$$callerCounts.Get_Item($$clientIP) ;if ($$k -ne $$null) { $$count += $$k } ;$$callerCounts.Set_Item($$clientIP, $$count) ;$$ip=(Get-NetAdapter | Get-NetIpAddress); $$header='<html><body><H1>Windows Container Web Server</H1>' ;$$callerCountsString='' ;$$callerCounts.Keys | % { $$callerCountsString+='<p>IP {0} callerCount {1} ' -f $$ip[1].IPAddress,$$callerCounts.Item($$_) } ;$$footer='</body></html>' ;$$content='{0}{1}{2}' -f $$header,$$callerCountsString,$$footer ;Write-Output $$content ;$$buffer = [System.Text.Encoding]::UTF8.GetBytes($$content) ;$$response.ContentLength64 = $$buffer.Length ;$$response.OutputStream.Write($$buffer, 0, $$buffer.Length) ;$$response.Close() ;$$responseStatus = $$response.StatusCode ;Write-Host('< {0}' -f $$responseStatus)  } ; "
     nodeSelector:
      kubernetes.io/os: windows

Note: Port mapping is also supported, but for simplicity this example exposes port 80 of the container directly to the Service.

Check that all nodes are healthy:
```
kubectl get nodes
```
Deploy the service and watch for pod updates:
```
kubectl apply -f win-webserver.yaml
kubectl get pods -o wide -w
```
When the service is deployed correctly both Pods are marked as Ready. To exit the watch command, press Ctrl+C.
Check that the deployment succeeded. To verify:
- Two pods listed from the Linux control plane node, use kubectl get pods
- Node-to-pod communication across the network, curl port 80 of your pod IPs from the Linux control plane node to check for a web server response
- Pod-to-pod communication, ping between pods (and across hosts, if you have more than one Windows node) using docker exec or kubectl exec
- Service-to-pod communication, curl the virtual service IP (seen under kubectl get services) from the Linux control plane node and from individual pods
- Service discovery, curl the service name with the Kubernetes default DNS suffix
- Inbound connectivity, curl the NodePort from the Linux control plane node or machines outside of the cluster
- Outbound connectivity, curl external IPs from inside the pod using kubectl exec

Note: Windows container hosts are not able to access the IP of services scheduled on them due to current platform limitations of the Windows networking stack. Only Windows pods are able to access service IPs.

Observability

Capturing logs from workloads

Logs are an important element of observability; they enable users to gain insights into the operational aspect of workloads and are a key ingredient to troubleshooting issues. Because Windows containers and workloads inside Windows containers behave differently from Linux containers, users had a hard time collecting logs, limiting operational visibility. Windows workloads for example are usually configured to log to ETW (Event Tracing for Windows) or push entries to the application event log. LogMonitor, an open source tool by Microsoft, is the recommended way to monitor configured log sources inside a Windows container. LogMonitor supports monitoring event logs, ETW providers, and custom application logs, piping them to STDOUT for consumption by kubectl logs <pod>.

Follow the instructions in the LogMonitor GitHub page to copy its binaries and configuration files to all your containers and add the necessary entrypoints for LogMonitor to push your logs to STDOUT.

Configuring container user

Using configurable Container usernames

Windows containers can be configured to run their entrypoints and processes with different usernames than the image defaults. Learn more about it here.

Managing Workload Identity with Group Managed Service Accounts

Windows container workloads can be configured to use Group Managed Service Accounts (GMSA). Group Managed Service Accounts are a specific type of Active Directory account that provide automatic password management, simplified service principal name (SPN) management, and the ability to delegate the management to other administrators across multiple servers. Containers configured with a GMSA can access external Active Directory Domain resources while carrying the identity configured with the GMSA. Learn more about configuring and using GMSA for Windows containers here.

Taints and Tolerations

Users need to use some combination of taints and node selectors in order to schedule Linux and Windows workloads to their respective OS-specific nodes. The recommended approach is outlined below, with one of its main goals being that this approach should not break compatibility for existing Linux workloads.

Starting from 1.25, you can (and should) set .spec.os.name for each Pod, to indicate the operating system that the containers in that Pod are designed for. For Pods that run Linux containers, set .spec.os.name to linux. For Pods that run Windows containers, set .spec.os.name to windows.

Note: Starting from 1.25, the IdentifyPodOS feature is in GA stage and defaults to be enabled.

The scheduler does not use the value of .spec.os.name when assigning Pods to nodes. You should use normal Kubernetes mechanisms for assigning pods to nodes to ensure that the control plane for your cluster places pods onto nodes that are running the appropriate operating system.

The .spec.os.name value has no effect on the scheduling of the Windows pods, so taints and tolerations and node selectors are still required to ensure that the Windows pods land onto appropriate Windows nodes.

Ensuring OS-specific workloads land on the appropriate container host

Users can ensure Windows containers can be scheduled on the appropriate host using Taints and Tolerations. All Kubernetes nodes today have the following default labels:

kubernetes.io/os = [windows|linux]
kubernetes.io/arch = [amd64|arm64|...]

If a Pod specification does not specify a nodeSelector like "kubernetes.io/os": windows, it is possible the Pod can be scheduled on any host, Windows or Linux. This can be problematic since a Windows container can only run on Windows and a Linux container can only run on Linux. The best practice is to use a nodeSelector.

However, we understand that in many cases users have a pre-existing large number of deployments for Linux containers, as well as an ecosystem of off-the-shelf configurations, such as community Helm charts, and programmatic Pod generation cases, such as with Operators. In those situations, you may be hesitant to make the configuration change to add nodeSelectors. The alternative is to use Taints. Because the kubelet can set Taints during registration, it could easily be modified to automatically add a taint when running on Windows only.

For example: --register-with-taints='os=windows:NoSchedule'

By adding a taint to all Windows nodes, nothing will be scheduled on them (that includes existing Linux Pods). In order for a Windows Pod to be scheduled on a Windows node, it would need both the nodeSelector and the appropriate matching toleration to choose Windows.

nodeSelector:
    kubernetes.io/os: windows
    node.kubernetes.io/windows-build: '10.0.17763'
tolerations:
    - key: "os"
      operator: "Equal"
      value: "windows"
      effect: "NoSchedule"

Handling multiple Windows versions in the same cluster

The Windows Server version used by each pod must match that of the node. If you want to use multiple Windows Server versions in the same cluster, then you should set additional node labels and nodeSelectors.

Kubernetes 1.17 automatically adds a new label node.kubernetes.io/windows-build to simplify this. If you're running an older version, then it's recommended to add this label manually to Windows nodes.

This label reflects the Windows major, minor, and build number that need to match for compatibility. Here are values used today for each Windows Server version.

Product Name	Build Number(s)
Windows Server 2019	10.0.17763
Windows Server, Version 20H2	10.0.19042
Windows Server 2022	10.0.20348

Simplifying with RuntimeClass

RuntimeClass can be used to simplify the process of using taints and tolerations. A cluster administrator can create a RuntimeClass object which is used to encapsulate these taints and tolerations.

Save this file to runtimeClasses.yml. It includes the appropriate nodeSelector for the Windows OS, architecture, and version.

apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: windows-2019
handler: 'docker'
scheduling:
  nodeSelector:
    kubernetes.io/os: 'windows'
    kubernetes.io/arch: 'amd64'
    node.kubernetes.io/windows-build: '10.0.17763'
  tolerations:
  - effect: NoSchedule
    key: os
    operator: Equal
    value: "windows"

Run kubectl create -f runtimeClasses.yml using as a cluster administrator
Add runtimeClassName: windows-2019 as appropriate to Pod specs

For example:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: iis-2019
  labels:
    app: iis-2019
spec:
  replicas: 1
  template:
    metadata:
      name: iis-2019
      labels:
        app: iis-2019
    spec:
      runtimeClassName: windows-2019
      containers:
      - name: iis
        image: mcr.microsoft.com/windows/servercore/iis:windowsservercore-ltsc2019
        resources:
          limits:
            cpu: 1
            memory: 800Mi
          requests:
            cpu: .1
            memory: 300Mi
        ports:
          - containerPort: 80
 selector:
    matchLabels:
      app: iis-2019
---
apiVersion: v1
kind: Service
metadata:
  name: iis
spec:
  type: LoadBalancer
  ports:
  - protocol: TCP
    port: 80
  selector:
    app: iis-2019

5 - Workloads

Understand Pods, the smallest deployable compute object in Kubernetes, and the higher-level abstractions that help you to run them.

A workload is an application running on Kubernetes. Whether your workload is a single component or several that work together, on Kubernetes you run it inside a set of pods. In Kubernetes, a Pod represents a set of running containers on your cluster.

Kubernetes pods have a defined lifecycle. For example, once a pod is running in your cluster then a critical fault on the node where that pod is running means that all the pods on that node fail. Kubernetes treats that level of failure as final: you would need to create a new Pod to recover, even if the node later becomes healthy.

However, to make life considerably easier, you don't need to manage each Pod directly. Instead, you can use workload resources that manage a set of pods on your behalf. These resources configure controllers that make sure the right number of the right kind of pod are running, to match the state you specified.

Kubernetes provides several built-in workload resources:

Deployment and ReplicaSet (replacing the legacy resource ReplicationController). Deployment is a good fit for managing a stateless application workload on your cluster, where any Pod in the Deployment is interchangeable and can be replaced if needed.
StatefulSet lets you run one or more related Pods that do track state somehow. For example, if your workload records data persistently, you can run a StatefulSet that matches each Pod with a PersistentVolume. Your code, running in the Pods for that StatefulSet, can replicate data to other Pods in the same StatefulSet to improve overall resilience.
DaemonSet defines Pods that provide node-local facilities. These might be fundamental to the operation of your cluster, such as a networking helper tool, or be part of an add-on.
Every time you add a node to your cluster that matches the specification in a DaemonSet, the control plane schedules a Pod for that DaemonSet onto the new node.
Job and CronJob define tasks that run to completion and then stop. Jobs represent one-off tasks, whereas CronJobs recur according to a schedule.

In the wider Kubernetes ecosystem, you can find third-party workload resources that provide additional behaviors. Using a custom resource definition, you can add in a third-party workload resource if you want a specific behavior that's not part of Kubernetes' core. For example, if you wanted to run a group of Pods for your application but stop work unless all the Pods are available (perhaps for some high-throughput distributed task), then you can implement or install an extension that does provide that feature.

What's next

As well as reading about each resource, you can learn about specific tasks that relate to them:

Run a stateless application using a Deployment
Run a stateful application either as a single instance or as a replicated set
Run automated tasks with a CronJob

To learn about Kubernetes' mechanisms for separating code from configuration, visit Configuration.

There are two supporting concepts that provide backgrounds about how Kubernetes manages pods for applications:

Garbage collection tidies up objects from your cluster after their owning resource has been removed.
The time-to-live after finished controller removes Jobs once a defined time has passed since they completed.

Once your application is running, you might want to make it available on the internet as a Service or, for web application only, using an Ingress.

5.1 - Pods

Pods are the smallest deployable units of computing that you can create and manage in Kubernetes.

A Pod (as in a pod of whales or pea pod) is a group of one or more containers, with shared storage and network resources, and a specification for how to run the containers. A Pod's contents are always co-located and co-scheduled, and run in a shared context. A Pod models an application-specific "logical host": it contains one or more application containers which are relatively tightly coupled. In non-cloud contexts, applications executed on the same physical or virtual machine are analogous to cloud applications executed on the same logical host.

As well as application containers, a Pod can contain init containers that run during Pod startup. You can also inject ephemeral containers for debugging if your cluster offers this.

What is a Pod?

Note: While Kubernetes supports more than just Docker, Docker is the most commonly known runtime, and it helps to describe Pods using some terminology from Docker.

The shared context of a Pod is a set of Linux namespaces, cgroups, and potentially other facets of isolation - the same things that isolate a container. Within a Pod's context, the individual applications may have further sub-isolations applied.

A Pod is similar to a set of containers with shared namespaces and shared filesystem volumes.

Using Pods

The following is an example of a Pod which consists of a container running the image nginx:1.14.2.

pods/simple-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  containers:
  - name: nginx
    image: nginx:1.14.2
    ports:
    - containerPort: 80

To create the Pod shown above, run the following command:

kubectl apply -f https://k8s.io/examples/pods/simple-pod.yaml

Pods are generally not created directly and are created using workload resources. See Working with Pods for more information on how Pods are used with workload resources.

Workload resources for managing pods

Usually you don't need to create Pods directly, even singleton Pods. Instead, create them using workload resources such as Deployment or Job. If your Pods need to track state, consider the StatefulSet resource.

Pods in a Kubernetes cluster are used in two main ways:

Pods that run a single container. The "one-container-per-Pod" model is the most common Kubernetes use case; in this case, you can think of a Pod as a wrapper around a single container; Kubernetes manages Pods rather than managing the containers directly.
Pods that run multiple containers that need to work together. A Pod can encapsulate an application composed of multiple co-located containers that are tightly coupled and need to share resources. These co-located containers form a single cohesive unit of service—for example, one container serving data stored in a shared volume to the public, while a separate sidecar container refreshes or updates those files. The Pod wraps these containers, storage resources, and an ephemeral network identity together as a single unit.

Note: Grouping multiple co-located and co-managed containers in a single Pod is a relatively advanced use case. You should use this pattern only in specific instances in which your containers are tightly coupled.

Each Pod is meant to run a single instance of a given application. If you want to scale your application horizontally (to provide more overall resources by running more instances), you should use multiple Pods, one for each instance. In Kubernetes, this is typically referred to as replication. Replicated Pods are usually created and managed as a group by a workload resource and its controller.

See Pods and controllers for more information on how Kubernetes uses workload resources, and their controllers, to implement application scaling and auto-healing.

How Pods manage multiple containers

Pods are designed to support multiple cooperating processes (as containers) that form a cohesive unit of service. The containers in a Pod are automatically co-located and co-scheduled on the same physical or virtual machine in the cluster. The containers can share resources and dependencies, communicate with one another, and coordinate when and how they are terminated.

For example, you might have a container that acts as a web server for files in a shared volume, and a separate "sidecar" container that updates those files from a remote source, as in the following diagram:

Some Pods have init containers as well as app containers. Init containers run and complete before the app containers are started.

Pods natively provide two kinds of shared resources for their constituent containers: networking and storage.

Working with Pods

You'll rarely create individual Pods directly in Kubernetes—even singleton Pods. This is because Pods are designed as relatively ephemeral, disposable entities. When a Pod gets created (directly by you, or indirectly by a controller), the new Pod is scheduled to run on a Node in your cluster. The Pod remains on that node until the Pod finishes execution, the Pod object is deleted, the Pod is evicted for lack of resources, or the node fails.

Note: Restarting a container in a Pod should not be confused with restarting a Pod. A Pod is not a process, but an environment for running container(s). A Pod persists until it is deleted.

When you create the manifest for a Pod object, make sure the name specified is a valid DNS subdomain name.

Pod OS

FEATURE STATE: Kubernetes v1.25 [stable]

You should set the .spec.os.name field to either windows or linux to indicate the OS on which you want the pod to run. These two are the only operating systems supported for now by Kubernetes. In future, this list may be expanded.

In Kubernetes v1.25, the value you set for this field has no effect on scheduling of the pods. Setting the .spec.os.name helps to identify the pod OS authoratitively and is used for validation. The kubelet refuses to run a Pod where you have specified a Pod OS, if this isn't the same as the operating system for the node where that kubelet is running. The Pod security standards also use this field to avoid enforcing policies that aren't relevant to that operating system.

Pods and controllers

You can use workload resources to create and manage multiple Pods for you. A controller for the resource handles replication and rollout and automatic healing in case of Pod failure. For example, if a Node fails, a controller notices that Pods on that Node have stopped working and creates a replacement Pod. The scheduler places the replacement Pod onto a healthy Node.

Here are some examples of workload resources that manage one or more Pods:

Pod templates

Controllers for workload resources create Pods from a pod template and manage those Pods on your behalf.

PodTemplates are specifications for creating Pods, and are included in workload resources such as Deployments, Jobs, and DaemonSets.

Each controller for a workload resource uses the PodTemplate inside the workload object to make actual Pods. The PodTemplate is part of the desired state of whatever workload resource you used to run your app.

The sample below is a manifest for a simple Job with a template that starts one container. The container in that Pod prints a message then pauses.

apiVersion: batch/v1
kind: Job
metadata:
  name: hello
spec:
  template:
    # This is the pod template
    spec:
      containers:
      - name: hello
        image: busybox:1.28
        command: ['sh', '-c', 'echo "Hello, Kubernetes!" && sleep 3600']
      restartPolicy: OnFailure
    # The pod template ends here

Modifying the pod template or switching to a new pod template has no direct effect on the Pods that already exist. If you change the pod template for a workload resource, that resource needs to create replacement Pods that use the updated template.

For example, the StatefulSet controller ensures that the running Pods match the current pod template for each StatefulSet object. If you edit the StatefulSet to change its pod template, the StatefulSet starts to create new Pods based on the updated template. Eventually, all of the old Pods are replaced with new Pods, and the update is complete.

Each workload resource implements its own rules for handling changes to the Pod template. If you want to read more about StatefulSet specifically, read Update strategy in the StatefulSet Basics tutorial.

On Nodes, the kubelet does not directly observe or manage any of the details around pod templates and updates; those details are abstracted away. That abstraction and separation of concerns simplifies system semantics, and makes it feasible to extend the cluster's behavior without changing existing code.

Pod update and replacement

As mentioned in the previous section, when the Pod template for a workload resource is changed, the controller creates new Pods based on the updated template instead of updating or patching the existing Pods.

Kubernetes doesn't prevent you from managing Pods directly. It is possible to update some fields of a running Pod, in place. However, Pod update operations like patch, and replace have some limitations:

Most of the metadata about a Pod is immutable. For example, you cannot change the namespace, name, uid, or creationTimestamp fields; the generation field is unique. It only accepts updates that increment the field's current value.
If the metadata.deletionTimestamp is set, no new entry can be added to the metadata.finalizers list.
Pod updates may not change fields other than spec.containers[*].image, spec.initContainers[*].image, spec.activeDeadlineSeconds or spec.tolerations. For spec.tolerations, you can only add new entries.
When updating the spec.activeDeadlineSeconds field, two types of updates are allowed:
1. setting the unassigned field to a positive number;
2. updating the field from a positive number to a smaller, non-negative number.

Pods enable data sharing and communication among their constituent containers.

Storage in Pods

A Pod can specify a set of shared storage volumes. All containers in the Pod can access the shared volumes, allowing those containers to share data. Volumes also allow persistent data in a Pod to survive in case one of the containers within needs to be restarted. See Storage for more information on how Kubernetes implements shared storage and makes it available to Pods.

Pod networking

Each Pod is assigned a unique IP address for each address family. Every container in a Pod shares the network namespace, including the IP address and network ports. Inside a Pod (and only then), the containers that belong to the Pod can communicate with one another using localhost. When containers in a Pod communicate with entities outside the Pod, they must coordinate how they use the shared network resources (such as ports). Within a Pod, containers share an IP address and port space, and can find each other via localhost. The containers in a Pod can also communicate with each other using standard inter-process communications like SystemV semaphores or POSIX shared memory. Containers in different Pods have distinct IP addresses and can not communicate by OS-level IPC without special configuration. Containers that want to interact with a container running in a different Pod can use IP networking to communicate.

Containers within the Pod see the system hostname as being the same as the configured name for the Pod. There's more about this in the networking section.

Privileged mode for containers

In Linux, any container in a Pod can enable privileged mode using the privileged (Linux) flag on the security context of the container spec. This is useful for containers that want to use operating system administrative capabilities such as manipulating the network stack or accessing hardware devices.

If your cluster has the WindowsHostProcessContainers feature enabled, you can create a Windows HostProcess pod by setting the windowsOptions.hostProcess flag on the security context of the pod spec. All containers in these pods must run as Windows HostProcess containers. HostProcess pods run directly on the host and can also be used to perform administrative tasks as is done with Linux privileged containers.

Note: Your must support the concept of a privileged container for this setting to be relevant.

Static Pods

Static Pods are managed directly by the kubelet daemon on a specific node, without the API server observing them. Whereas most Pods are managed by the control plane (for example, a Deployment), for static Pods, the kubelet directly supervises each static Pod (and restarts it if it fails).

Static Pods are always bound to one Kubelet on a specific node. The main use for static Pods is to run a self-hosted control plane: in other words, using the kubelet to supervise the individual control plane components.

The kubelet automatically tries to create a mirror Pod on the Kubernetes API server for each static Pod. This means that the Pods running on a node are visible on the API server, but cannot be controlled from there.

Note: The spec of a static Pod cannot refer to other API objects (e.g., , , , etc).

Container probes

A probe is a diagnostic performed periodically by the kubelet on a container. To perform a diagnostic, the kubelet can invoke different actions:

ExecAction (performed with the help of the container runtime)
TCPSocketAction (checked directly by the kubelet)
HTTPGetAction (checked directly by the kubelet)

You can read more about probes in the Pod Lifecycle documentation.

What's next

Learn about the lifecycle of a Pod.
Learn about RuntimeClass and how you can use it to configure different Pods with different container runtime configurations.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.
Pod is a top-level resource in the Kubernetes REST API. The Pod object definition describes the object in detail.
The Distributed System Toolkit: Patterns for Composite Containers explains common layouts for Pods with more than one container.
Read about Pod topology spread constraints

To understand the context for why Kubernetes wraps a common Pod API in other resources (such as StatefulSets or Deployments), you can read about the prior art, including:

5.1.1 - Pod Lifecycle

This page describes the lifecycle of a Pod. Pods follow a defined lifecycle, starting in the Pending phase, moving through Running if at least one of its primary containers starts OK, and then through either the Succeeded or Failed phases depending on whether any container in the Pod terminated in failure.

Whilst a Pod is running, the kubelet is able to restart containers to handle some kind of faults. Within a Pod, Kubernetes tracks different container states and determines what action to take to make the Pod healthy again.

In the Kubernetes API, Pods have both a specification and an actual status. The status for a Pod object consists of a set of Pod conditions. You can also inject custom readiness information into the condition data for a Pod, if that is useful to your application.

Pods are only scheduled once in their lifetime. Once a Pod is scheduled (assigned) to a Node, the Pod runs on that Node until it stops or is terminated.

Pod lifetime

Like individual application containers, Pods are considered to be relatively ephemeral (rather than durable) entities. Pods are created, assigned a unique ID (UID), and scheduled to nodes where they remain until termination (according to restart policy) or deletion. If a Node dies, the Pods scheduled to that node are scheduled for deletion after a timeout period.

Pods do not, by themselves, self-heal. If a Pod is scheduled to a node that then fails, the Pod is deleted; likewise, a Pod won't survive an eviction due to a lack of resources or Node maintenance. Kubernetes uses a higher-level abstraction, called a controller, that handles the work of managing the relatively disposable Pod instances.

A given Pod (as defined by a UID) is never "rescheduled" to a different node; instead, that Pod can be replaced by a new, near-identical Pod, with even the same name if desired, but with a different UID.

When something is said to have the same lifetime as a Pod, such as a volume, that means that the thing exists as long as that specific Pod (with that exact UID) exists. If that Pod is deleted for any reason, and even if an identical replacement is created, the related thing (a volume, in this example) is also destroyed and created anew.

A multi-container Pod that contains a file puller and a web server that uses a persistent volume for shared storage between the containers.

Pod phase

A Pod's status field is a PodStatus object, which has a phase field.

The phase of a Pod is a simple, high-level summary of where the Pod is in its lifecycle. The phase is not intended to be a comprehensive rollup of observations of container or Pod state, nor is it intended to be a comprehensive state machine.

The number and meanings of Pod phase values are tightly guarded. Other than what is documented here, nothing should be assumed about Pods that have a given phase value.

Here are the possible values for phase:

Value	Description
`Pending`	The Pod has been accepted by the Kubernetes cluster, but one or more of the containers has not been set up and made ready to run. This includes time a Pod spends waiting to be scheduled as well as the time spent downloading container images over the network.
`Running`	The Pod has been bound to a node, and all of the containers have been created. At least one container is still running, or is in the process of starting or restarting.
`Succeeded`	All containers in the Pod have terminated in success, and will not be restarted.
`Failed`	All containers in the Pod have terminated, and at least one container has terminated in failure. That is, the container either exited with non-zero status or was terminated by the system.
`Unknown`	For some reason the state of the Pod could not be obtained. This phase typically occurs due to an error in communicating with the node where the Pod should be running.

Note: When a Pod is being deleted, it is shown as Terminating by some kubectl commands. This Terminating status is not one of the Pod phases. A Pod is granted a term to terminate gracefully, which defaults to 30 seconds. You can use the flag --force to terminate a Pod by force.

If a node dies or is disconnected from the rest of the cluster, Kubernetes applies a policy for setting the phase of all Pods on the lost node to Failed.

Container states

As well as the phase of the Pod overall, Kubernetes tracks the state of each container inside a Pod. You can use container lifecycle hooks to trigger events to run at certain points in a container's lifecycle.

Once the scheduler assigns a Pod to a Node, the kubelet starts creating containers for that Pod using a container runtime. There are three possible container states: Waiting, Running, and Terminated.

To check the state of a Pod's containers, you can use kubectl describe pod <name-of-pod>. The output shows the state for each container within that Pod.

Each state has a specific meaning:

`Waiting`

If a container is not in either the Running or Terminated state, it is Waiting. A container in the Waiting state is still running the operations it requires in order to complete start up: for example, pulling the container image from a container image registry, or applying Secret data. When you use kubectl to query a Pod with a container that is Waiting, you also see a Reason field to summarize why the container is in that state.

`Running`

The Running status indicates that a container is executing without issues. If there was a postStart hook configured, it has already executed and finished. When you use kubectl to query a Pod with a container that is Running, you also see information about when the container entered the Running state.

`Terminated`

A container in the Terminated state began execution and then either ran to completion or failed for some reason. When you use kubectl to query a Pod with a container that is Terminated, you see a reason, an exit code, and the start and finish time for that container's period of execution.

If a container has a preStop hook configured, this hook runs before the container enters the Terminated state.

Container restart policy

The spec of a Pod has a restartPolicy field with possible values Always, OnFailure, and Never. The default value is Always.

The restartPolicy applies to all containers in the Pod. restartPolicy only refers to restarts of the containers by the kubelet on the same node. After containers in a Pod exit, the kubelet restarts them with an exponential back-off delay (10s, 20s, 40s, …), that is capped at five minutes. Once a container has executed for 10 minutes without any problems, the kubelet resets the restart backoff timer for that container.

Pod conditions

A Pod has a PodStatus, which has an array of PodConditions through which the Pod has or has not passed. Kubelet manages the following PodConditions:

PodScheduled: the Pod has been scheduled to a node.
PodHasNetwork: (alpha feature; must be enabled explicitly) the Pod sandbox has been successfully created and networking configured.
ContainersReady: all containers in the Pod are ready.
Initialized: all init containers have completed successfully.
Ready: the Pod is able to serve requests and should be added to the load balancing pools of all matching Services.

Field name	Description
`type`	Name of this Pod condition.
`status`	Indicates whether that condition is applicable, with possible values "`True`", "`False`", or "`Unknown`".
`lastProbeTime`	Timestamp of when the Pod condition was last probed.
`lastTransitionTime`	Timestamp for when the Pod last transitioned from one status to another.
`reason`	Machine-readable, UpperCamelCase text indicating the reason for the condition's last transition.
`message`	Human-readable message indicating details about the last status transition.

Pod readiness

FEATURE STATE: Kubernetes v1.14 [stable]

Your application can inject extra feedback or signals into PodStatus: Pod readiness. To use this, set readinessGates in the Pod's spec to specify a list of additional conditions that the kubelet evaluates for Pod readiness.

Readiness gates are determined by the current state of status.condition fields for the Pod. If Kubernetes cannot find such a condition in the status.conditions field of a Pod, the status of the condition is defaulted to "False".

Here is an example:

kind: Pod
...
spec:
  readinessGates:
    - conditionType: "www.example.com/feature-1"
status:
  conditions:
    - type: Ready                              # a built in PodCondition
      status: "False"
      lastProbeTime: null
      lastTransitionTime: 2018-01-01T00:00:00Z
    - type: "www.example.com/feature-1"        # an extra PodCondition
      status: "False"
      lastProbeTime: null
      lastTransitionTime: 2018-01-01T00:00:00Z
  containerStatuses:
    - containerID: docker://abcd...
      ready: true
...

The Pod conditions you add must have names that meet the Kubernetes label key format.

Status for Pod readiness

The kubectl patch command does not support patching object status. To set these status.conditions for the pod, applications and operators should use the PATCH action. You can use a Kubernetes client library to write code that sets custom Pod conditions for Pod readiness.

For a Pod that uses custom conditions, that Pod is evaluated to be ready only when both the following statements apply:

All containers in the Pod are ready.
All conditions specified in readinessGates are True.

When a Pod's containers are Ready but at least one custom condition is missing or False, the kubelet sets the Pod's condition to ContainersReady.

Pod network readiness

FEATURE STATE: Kubernetes v1.25 [alpha]

After a Pod gets scheduled on a node, it needs to be admitted by the Kubelet and have any volumes mounted. Once these phases are complete, the Kubelet works with a container runtime (using Container runtime interface (CRI)) to set up a runtime sandbox and configure networking for the Pod. If the PodHasNetworkCondition feature gate is enabled, Kubelet reports whether a pod has reached this initialization milestone through the PodHasNetwork condition in the status.conditions field of a Pod.

The PodHasNetwork condition is set to False by the Kubelet when it detects a Pod does not have a runtime sandbox with networking configured. This occurs in the following scenarios:

Early in the lifecycle of the Pod, when the kubelet has not yet begun to set up a sandbox for the Pod using the container runtime.
Later in the lifecycle of the Pod, when the Pod sandbox has been destroyed due to either:
- the node rebooting, without the Pod getting evicted
- for container runtimes that use virtual machines for isolation, the Pod sandbox virtual machine rebooting, which then requires creating a new sandbox and fresh container network configuration.

The PodHasNetwork condition is set to True by the kubelet after the successful completion of sandbox creation and network configuration for the Pod by the runtime plugin. The kubelet can start pulling container images and create containers after PodHasNetwork condition has been set to True.

For a Pod with init containers, the kubelet sets the Initialized condition to True after the init containers have successfully completed (which happens after successful sandbox creation and network configuration by the runtime plugin). For a Pod without init containers, the kubelet sets the Initialized condition to True before sandbox creation and network configuration starts.

Container probes

A probe is a diagnostic performed periodically by the kubelet on a container. To perform a diagnostic, the kubelet either executes code within the container, or makes a network request.

Check mechanisms

There are four different ways to check a container using a probe. Each probe must define exactly one of these four mechanisms:

exec: Executes a specified command inside the container. The diagnostic is considered successful if the command exits with a status code of 0.
grpc: Performs a remote procedure call using gRPC. The target should implement gRPC health checks. The diagnostic is considered successful if the status of the response is SERVING.
gRPC probes are an alpha feature and are only available if you enable the GRPCContainerProbe feature gate.
httpGet: Performs an HTTP GET request against the Pod's IP address on a specified port and path. The diagnostic is considered successful if the response has a status code greater than or equal to 200 and less than 400.
tcpSocket: Performs a TCP check against the Pod's IP address on a specified port. The diagnostic is considered successful if the port is open. If the remote system (the container) closes the connection immediately after it opens, this counts as healthy.

Probe outcome

Each probe has one of three results:

Success: The container passed the diagnostic.
Failure: The container failed the diagnostic.
Unknown: The diagnostic failed (no action should be taken, and the kubelet will make further checks).

Types of probe

The kubelet can optionally perform and react to three kinds of probes on running containers:

livenessProbe: Indicates whether the container is running. If the liveness probe fails, the kubelet kills the container, and the container is subjected to its restart policy. If a container does not provide a liveness probe, the default state is Success.
readinessProbe: Indicates whether the container is ready to respond to requests. If the readiness probe fails, the endpoints controller removes the Pod's IP address from the endpoints of all Services that match the Pod. The default state of readiness before the initial delay is Failure. If a container does not provide a readiness probe, the default state is Success.
startupProbe: Indicates whether the application within the container is started. All other probes are disabled if a startup probe is provided, until it succeeds. If the startup probe fails, the kubelet kills the container, and the container is subjected to its restart policy. If a container does not provide a startup probe, the default state is Success.

For more information about how to set up a liveness, readiness, or startup probe, see Configure Liveness, Readiness and Startup Probes.

When should you use a liveness probe?

FEATURE STATE: Kubernetes v1.0 [stable]

If the process in your container is able to crash on its own whenever it encounters an issue or becomes unhealthy, you do not necessarily need a liveness probe; the kubelet will automatically perform the correct action in accordance with the Pod's restartPolicy.

If you'd like your container to be killed and restarted if a probe fails, then specify a liveness probe, and specify a restartPolicy of Always or OnFailure.

When should you use a readiness probe?

FEATURE STATE: Kubernetes v1.0 [stable]

If you'd like to start sending traffic to a Pod only when a probe succeeds, specify a readiness probe. In this case, the readiness probe might be the same as the liveness probe, but the existence of the readiness probe in the spec means that the Pod will start without receiving any traffic and only start receiving traffic after the probe starts succeeding.

If you want your container to be able to take itself down for maintenance, you can specify a readiness probe that checks an endpoint specific to readiness that is different from the liveness probe.

If your app has a strict dependency on back-end services, you can implement both a liveness and a readiness probe. The liveness probe passes when the app itself is healthy, but the readiness probe additionally checks that each required back-end service is available. This helps you avoid directing traffic to Pods that can only respond with error messages.

If your container needs to work on loading large data, configuration files, or migrations during startup, you can use a startup probe. However, if you want to detect the difference between an app that has failed and an app that is still processing its startup data, you might prefer a readiness probe.

Note: If you want to be able to drain requests when the Pod is deleted, you do not necessarily need a readiness probe; on deletion, the Pod automatically puts itself into an unready state regardless of whether the readiness probe exists. The Pod remains in the unready state while it waits for the containers in the Pod to stop.

When should you use a startup probe?

FEATURE STATE: Kubernetes v1.20 [stable]

Startup probes are useful for Pods that have containers that take a long time to come into service. Rather than set a long liveness interval, you can configure a separate configuration for probing the container as it starts up, allowing a time longer than the liveness interval would allow.

If your container usually starts in more than initialDelaySeconds + failureThreshold × periodSeconds, you should specify a startup probe that checks the same endpoint as the liveness probe. The default for periodSeconds is 10s. You should then set its failureThreshold high enough to allow the container to start, without changing the default values of the liveness probe. This helps to protect against deadlocks.

Termination of Pods

Because Pods represent processes running on nodes in the cluster, it is important to allow those processes to gracefully terminate when they are no longer needed (rather than being abruptly stopped with a KILL signal and having no chance to clean up).

The design aim is for you to be able to request deletion and know when processes terminate, but also be able to ensure that deletes eventually complete. When you request deletion of a Pod, the cluster records and tracks the intended grace period before the Pod is allowed to be forcefully killed. With that forceful shutdown tracking in place, the kubelet attempts graceful shutdown.

Typically, the container runtime sends a TERM signal to the main process in each container. Many container runtimes respect the STOPSIGNAL value defined in the container image and send this instead of TERM. Once the grace period has expired, the KILL signal is sent to any remaining processes, and the Pod is then deleted from the API Server. If the kubelet or the container runtime's management service is restarted while waiting for processes to terminate, the cluster retries from the start including the full original grace period.

An example flow:

You use the kubectl tool to manually delete a specific Pod, with the default grace period (30 seconds).
The Pod in the API server is updated with the time beyond which the Pod is considered "dead" along with the grace period. If you use kubectl describe to check on the Pod you're deleting, that Pod shows up as "Terminating". On the node where the Pod is running: as soon as the kubelet sees that a Pod has been marked as terminating (a graceful shutdown duration has been set), the kubelet begins the local Pod shutdown process.
1. If one of the Pod's containers has defined a preStop hook, the kubelet runs that hook inside of the container. If the preStop hook is still running after the grace period expires, the kubelet requests a small, one-off grace period extension of 2 seconds.
  Note: If the preStop hook needs longer to complete than the default grace period allows, you must modify terminationGracePeriodSeconds to suit this.
2. The kubelet triggers the container runtime to send a TERM signal to process 1 inside each container.
  Note: The containers in the Pod receive the TERM signal at different times and in an arbitrary order. If the order of shutdowns matters, consider using a preStop hook to synchronize.
At the same time as the kubelet is starting graceful shutdown, the control plane removes that shutting-down Pod from Endpoints (and, if enabled, EndpointSlice) objects where these represent a Service with a configured selector. ReplicaSets and other workload resources no longer treat the shutting-down Pod as a valid, in-service replica. Pods that shut down slowly cannot continue to serve traffic as load balancers (like the service proxy) remove the Pod from the list of endpoints as soon as the termination grace period begins.
When the grace period expires, the kubelet triggers forcible shutdown. The container runtime sends SIGKILL to any processes still running in any container in the Pod. The kubelet also cleans up a hidden pause container if that container runtime uses one.
The kubelet triggers forcible removal of Pod object from the API server, by setting grace period to 0 (immediate deletion).
The API server deletes the Pod's API object, which is then no longer visible from any client.

Forced Pod termination

Caution: Forced deletions can be potentially disruptive for some workloads and their Pods.

By default, all deletes are graceful within 30 seconds. The kubectl delete command supports the --grace-period=<seconds> option which allows you to override the default and specify your own value.

Setting the grace period to 0 forcibly and immediately deletes the Pod from the API server. If the pod was still running on a node, that forcible deletion triggers the kubelet to begin immediate cleanup.

Note: You must specify an additional flag --force along with --grace-period=0 in order to perform force deletions.

When a force deletion is performed, the API server does not wait for confirmation from the kubelet that the Pod has been terminated on the node it was running on. It removes the Pod in the API immediately so a new Pod can be created with the same name. On the node, Pods that are set to terminate immediately will still be given a small grace period before being force killed.

If you need to force-delete Pods that are part of a StatefulSet, refer to the task documentation for deleting Pods from a StatefulSet.

Garbage collection of terminated Pods

For failed Pods, the API objects remain in the cluster's API until a human or controller process explicitly removes them.

The control plane cleans up terminated Pods (with a phase of Succeeded or Failed), when the number of Pods exceeds the configured threshold (determined by terminated-pod-gc-threshold in the kube-controller-manager). This avoids a resource leak as Pods are created and terminated over time.

What's next

Get hands-on experience attaching handlers to container lifecycle events.
Get hands-on experience configuring Liveness, Readiness and Startup Probes.
Learn more about container lifecycle hooks.
For detailed information about Pod and container status in the API, see the API reference documentation covering .status for Pod.

5.1.2 - Init Containers

This page provides an overview of init containers: specialized containers that run before app containers in a Pod. Init containers can contain utilities or setup scripts not present in an app image.

You can specify init containers in the Pod specification alongside the containers array (which describes app containers).

Understanding init containers

A Pod can have multiple containers running apps within it, but it can also have one or more init containers, which are run before the app containers are started.

Init containers are exactly like regular containers, except:

Init containers always run to completion.
Each init container must complete successfully before the next one starts.

If a Pod's init container fails, the kubelet repeatedly restarts that init container until it succeeds. However, if the Pod has a restartPolicy of Never, and an init container fails during startup of that Pod, Kubernetes treats the overall Pod as failed.

To specify an init container for a Pod, add the initContainers field into the Pod specification, as an array of container items (similar to the app containers field and its contents). See Container in the API reference for more details.

The status of the init containers is returned in .status.initContainerStatuses field as an array of the container statuses (similar to the .status.containerStatuses field).

Differences from regular containers

Init containers support all the fields and features of app containers, including resource limits, volumes, and security settings. However, the resource requests and limits for an init container are handled differently, as documented in Resources.

Also, init containers do not support lifecycle, livenessProbe, readinessProbe, or startupProbe because they must run to completion before the Pod can be ready.

If you specify multiple init containers for a Pod, kubelet runs each init container sequentially. Each init container must succeed before the next can run. When all of the init containers have run to completion, kubelet initializes the application containers for the Pod and runs them as usual.

Using init containers

Because init containers have separate images from app containers, they have some advantages for start-up related code:

Init containers can contain utilities or custom code for setup that are not present in an app image. For example, there is no need to make an image FROM another image just to use a tool like sed, awk, python, or dig during setup.
The application image builder and deployer roles can work independently without the need to jointly build a single app image.
Init containers can run with a different view of the filesystem than app containers in the same Pod. Consequently, they can be given access to Secrets that app containers cannot access.
Because init containers run to completion before any app containers start, init containers offer a mechanism to block or delay app container startup until a set of preconditions are met. Once preconditions are met, all of the app containers in a Pod can start in parallel.
Init containers can securely run utilities or custom code that would otherwise make an app container image less secure. By keeping unnecessary tools separate you can limit the attack surface of your app container image.

Examples

Here are some ideas for how to use init containers:

Wait for a Service to be created, using a shell one-line command like:

for i in {1..100}; do sleep 1; if dig myservice; then exit 0; fi; done; exit 1

curl -X POST http://$MANAGEMENT_SERVICE_HOST:$MANAGEMENT_SERVICE_PORT/register -d 'instance=$(<POD_NAME>)&ip=$(<POD_IP>)'

Wait for some time before starting the app container with a command like
```
sleep 60
```
Clone a Git repository into a Volume
Place values into a configuration file and run a template tool to dynamically generate a configuration file for the main app container. For example, place the POD_IP value in a configuration and generate the main app configuration file using Jinja.

Init containers in use

This example defines a simple Pod that has two init containers. The first waits for myservice, and the second waits for mydb. Once both init containers complete, the Pod runs the app container from its spec section.

apiVersion: v1
kind: Pod
metadata:
  name: myapp-pod
  labels:
    app.kubernetes.io/name: MyApp
spec:
  containers:
  - name: myapp-container
    image: busybox:1.28
    command: ['sh', '-c', 'echo The app is running! && sleep 3600']
  initContainers:
  - name: init-myservice
    image: busybox:1.28
    command: ['sh', '-c', "until nslookup myservice.$(cat /var/run/secrets/kubernetes.io/serviceaccount/namespace).svc.cluster.local; do echo waiting for myservice; sleep 2; done"]
  - name: init-mydb
    image: busybox:1.28
    command: ['sh', '-c', "until nslookup mydb.$(cat /var/run/secrets/kubernetes.io/serviceaccount/namespace).svc.cluster.local; do echo waiting for mydb; sleep 2; done"]

You can start this Pod by running:

kubectl apply -f myapp.yaml

The output is similar to this:

pod/myapp-pod created

And check on its status with:

kubectl get -f myapp.yaml

The output is similar to this:

NAME        READY     STATUS     RESTARTS   AGE
myapp-pod   0/1       Init:0/2   0          6m

or for more details:

kubectl describe -f myapp.yaml

The output is similar to this:

Name:          myapp-pod
Namespace:     default
[...]
Labels:        app.kubernetes.io/name=MyApp
Status:        Pending
[...]
Init Containers:
  init-myservice:
[...]
    State:         Running
[...]
  init-mydb:
[...]
    State:         Waiting
      Reason:      PodInitializing
    Ready:         False
[...]
Containers:
  myapp-container:
[...]
    State:         Waiting
      Reason:      PodInitializing
    Ready:         False
[...]
Events:
  FirstSeen    LastSeen    Count    From                      SubObjectPath                           Type          Reason        Message
  ---------    --------    -----    ----                      -------------                           --------      ------        -------
  16s          16s         1        {default-scheduler }                                              Normal        Scheduled     Successfully assigned myapp-pod to 172.17.4.201
  16s          16s         1        {kubelet 172.17.4.201}    spec.initContainers{init-myservice}     Normal        Pulling       pulling image "busybox"
  13s          13s         1        {kubelet 172.17.4.201}    spec.initContainers{init-myservice}     Normal        Pulled        Successfully pulled image "busybox"
  13s          13s         1        {kubelet 172.17.4.201}    spec.initContainers{init-myservice}     Normal        Created       Created container init-myservice
  13s          13s         1        {kubelet 172.17.4.201}    spec.initContainers{init-myservice}     Normal        Started       Started container init-myservice

To see logs for the init containers in this Pod, run:

kubectl logs myapp-pod -c init-myservice # Inspect the first init container
kubectl logs myapp-pod -c init-mydb      # Inspect the second init container

At this point, those init containers will be waiting to discover Services named mydb and myservice.

Here's a configuration you can use to make those Services appear:

---
apiVersion: v1
kind: Service
metadata:
  name: myservice
spec:
  ports:
  - protocol: TCP
    port: 80
    targetPort: 9376
---
apiVersion: v1
kind: Service
metadata:
  name: mydb
spec:
  ports:
  - protocol: TCP
    port: 80
    targetPort: 9377

To create the mydb and myservice services:

kubectl apply -f services.yaml

The output is similar to this:

service/myservice created
service/mydb created

You'll then see that those init containers complete, and that the myapp-pod Pod moves into the Running state:

kubectl get -f myapp.yaml

The output is similar to this:

NAME        READY     STATUS    RESTARTS   AGE
myapp-pod   1/1       Running   0          9m

This simple example should provide some inspiration for you to create your own init containers. What's next contains a link to a more detailed example.

Detailed behavior

During Pod startup, the kubelet delays running init containers until the networking and storage are ready. Then the kubelet runs the Pod's init containers in the order they appear in the Pod's spec.

Each init container must exit successfully before the next container starts. If a container fails to start due to the runtime or exits with failure, it is retried according to the Pod restartPolicy. However, if the Pod restartPolicy is set to Always, the init containers use restartPolicy OnFailure.

A Pod cannot be Ready until all init containers have succeeded. The ports on an init container are not aggregated under a Service. A Pod that is initializing is in the Pending state but should have a condition Initialized set to false.

If the Pod restarts, or is restarted, all init containers must execute again.

Changes to the init container spec are limited to the container image field. Altering an init container image field is equivalent to restarting the Pod.

Because init containers can be restarted, retried, or re-executed, init container code should be idempotent. In particular, code that writes to files on EmptyDirs should be prepared for the possibility that an output file already exists.

Init containers have all of the fields of an app container. However, Kubernetes prohibits readinessProbe from being used because init containers cannot define readiness distinct from completion. This is enforced during validation.

Use activeDeadlineSeconds on the Pod to prevent init containers from failing forever. The active deadline includes init containers. However it is recommended to use activeDeadlineSeconds only if teams deploy their application as a Job, because activeDeadlineSeconds has an effect even after initContainer finished. The Pod which is already running correctly would be killed by activeDeadlineSeconds if you set.

The name of each app and init container in a Pod must be unique; a validation error is thrown for any container sharing a name with another.

Resources

Given the ordering and execution for init containers, the following rules for resource usage apply:

The highest of any particular resource request or limit defined on all init containers is the effective init request/limit. If any resource has no resource limit specified this is considered as the highest limit.
The Pod's effective request/limit for a resource is the higher of:
- the sum of all app containers request/limit for a resource
- the effective init request/limit for a resource
Scheduling is done based on effective requests/limits, which means init containers can reserve resources for initialization that are not used during the life of the Pod.
The QoS (quality of service) tier of the Pod's effective QoS tier is the QoS tier for init containers and app containers alike.

Quota and limits are applied based on the effective Pod request and limit.

Pod level control groups (cgroups) are based on the effective Pod request and limit, the same as the scheduler.

Pod restart reasons

A Pod can restart, causing re-execution of init containers, for the following reasons:

The Pod infrastructure container is restarted. This is uncommon and would have to be done by someone with root access to nodes.
All containers in a Pod are terminated while restartPolicy is set to Always, forcing a restart, and the init container completion record has been lost due to garbage collection.

The Pod will not be restarted when the init container image is changed, or the init container completion record has been lost due to garbage collection. This applies for Kubernetes v1.20 and later. If you are using an earlier version of Kubernetes, consult the documentation for the version you are using.

What's next

Read about creating a Pod that has an init container
Learn how to debug init containers

5.1.3 - Disruptions

This guide is for application owners who want to build highly available applications, and thus need to understand what types of disruptions can happen to Pods.

It is also for cluster administrators who want to perform automated cluster actions, like upgrading and autoscaling clusters.

Voluntary and involuntary disruptions

Pods do not disappear until someone (a person or a controller) destroys them, or there is an unavoidable hardware or system software error.

We call these unavoidable cases involuntary disruptions to an application. Examples are:

a hardware failure of the physical machine backing the node
cluster administrator deletes VM (instance) by mistake
cloud provider or hypervisor failure makes VM disappear
a kernel panic
the node disappears from the cluster due to cluster network partition
eviction of a pod due to the node being out-of-resources.

Except for the out-of-resources condition, all these conditions should be familiar to most users; they are not specific to Kubernetes.

We call other cases voluntary disruptions. These include both actions initiated by the application owner and those initiated by a Cluster Administrator. Typical application owner actions include:

deleting the deployment or other controller that manages the pod
updating a deployment's pod template causing a restart
directly deleting a pod (e.g. by accident)

Cluster administrator actions include:

Draining a node for repair or upgrade.
Draining a node from a cluster to scale the cluster down (learn about Cluster Autoscaling ).
Removing a pod from a node to permit something else to fit on that node.

These actions might be taken directly by the cluster administrator, or by automation run by the cluster administrator, or by your cluster hosting provider.

Ask your cluster administrator or consult your cloud provider or distribution documentation to determine if any sources of voluntary disruptions are enabled for your cluster. If none are enabled, you can skip creating Pod Disruption Budgets.

Caution: Not all voluntary disruptions are constrained by Pod Disruption Budgets. For example, deleting deployments or pods bypasses Pod Disruption Budgets.

Dealing with disruptions

Here are some ways to mitigate involuntary disruptions:

Ensure your pod requests the resources it needs.
Replicate your application if you need higher availability. (Learn about running replicated stateless and stateful applications.)
For even higher availability when running replicated applications, spread applications across racks (using anti-affinity) or across zones (if using a multi-zone cluster.)

The frequency of voluntary disruptions varies. On a basic Kubernetes cluster, there are no automated voluntary disruptions (only user-triggered ones). However, your cluster administrator or hosting provider may run some additional services which cause voluntary disruptions. For example, rolling out node software updates can cause voluntary disruptions. Also, some implementations of cluster (node) autoscaling may cause voluntary disruptions to defragment and compact nodes. Your cluster administrator or hosting provider should have documented what level of voluntary disruptions, if any, to expect. Certain configuration options, such as using PriorityClasses in your pod spec can also cause voluntary (and involuntary) disruptions.

Pod disruption budgets

FEATURE STATE: Kubernetes v1.21 [stable]

Kubernetes offers features to help you run highly available applications even when you introduce frequent voluntary disruptions.

As an application owner, you can create a PodDisruptionBudget (PDB) for each application. A PDB limits the number of Pods of a replicated application that are down simultaneously from voluntary disruptions. For example, a quorum-based application would like to ensure that the number of replicas running is never brought below the number needed for a quorum. A web front end might want to ensure that the number of replicas serving load never falls below a certain percentage of the total.

Cluster managers and hosting providers should use tools which respect PodDisruptionBudgets by calling the Eviction API instead of directly deleting pods or deployments.

For example, the kubectl drain subcommand lets you mark a node as going out of service. When you run kubectl drain, the tool tries to evict all of the Pods on the Node you're taking out of service. The eviction request that kubectl submits on your behalf may be temporarily rejected, so the tool periodically retries all failed requests until all Pods on the target node are terminated, or until a configurable timeout is reached.

A PDB specifies the number of replicas that an application can tolerate having, relative to how many it is intended to have. For example, a Deployment which has a .spec.replicas: 5 is supposed to have 5 pods at any given time. If its PDB allows for there to be 4 at a time, then the Eviction API will allow voluntary disruption of one (but not two) pods at a time.

The group of pods that comprise the application is specified using a label selector, the same as the one used by the application's controller (deployment, stateful-set, etc).

The "intended" number of pods is computed from the .spec.replicas of the workload resource that is managing those pods. The control plane discovers the owning workload resource by examining the .metadata.ownerReferences of the Pod.

Involuntary disruptions cannot be prevented by PDBs; however they do count against the budget.

Pods which are deleted or unavailable due to a rolling upgrade to an application do count against the disruption budget, but workload resources (such as Deployment and StatefulSet) are not limited by PDBs when doing rolling upgrades. Instead, the handling of failures during application updates is configured in the spec for the specific workload resource.

When a pod is evicted using the eviction API, it is gracefully terminated, honoring the terminationGracePeriodSeconds setting in its PodSpec.

PodDisruptionBudget example

Consider a cluster with 3 nodes, node-1 through node-3. The cluster is running several applications. One of them has 3 replicas initially called pod-a, pod-b, and pod-c. Another, unrelated pod without a PDB, called pod-x, is also shown. Initially, the pods are laid out as follows:

node-1	node-2	node-3
pod-a available	pod-b available	pod-c available
pod-x available

All 3 pods are part of a deployment, and they collectively have a PDB which requires there be at least 2 of the 3 pods to be available at all times.

For example, assume the cluster administrator wants to reboot into a new kernel version to fix a bug in the kernel. The cluster administrator first tries to drain node-1 using the kubectl drain command. That tool tries to evict pod-a and pod-x. This succeeds immediately. Both pods go into the terminating state at the same time. This puts the cluster in this state:

node-1 draining	node-2	node-3
pod-a terminating	pod-b available	pod-c available
pod-x terminating

The deployment notices that one of the pods is terminating, so it creates a replacement called pod-d. Since node-1 is cordoned, it lands on another node. Something has also created pod-y as a replacement for pod-x.

(Note: for a StatefulSet, pod-a, which would be called something like pod-0, would need to terminate completely before its replacement, which is also called pod-0 but has a different UID, could be created. Otherwise, the example applies to a StatefulSet as well.)

Now the cluster is in this state:

node-1 draining	node-2	node-3
pod-a terminating	pod-b available	pod-c available
pod-x terminating	pod-d starting	pod-y

At some point, the pods terminate, and the cluster looks like this:

node-1 drained	node-2	node-3
	pod-b available	pod-c available
	pod-d starting	pod-y

At this point, if an impatient cluster administrator tries to drain node-2 or node-3, the drain command will block, because there are only 2 available pods for the deployment, and its PDB requires at least 2. After some time passes, pod-d becomes available.

The cluster state now looks like this:

node-1 drained	node-2	node-3
	pod-b available	pod-c available
	pod-d available	pod-y

Now, the cluster administrator tries to drain node-2. The drain command will try to evict the two pods in some order, say pod-b first and then pod-d. It will succeed at evicting pod-b. But, when it tries to evict pod-d, it will be refused because that would leave only one pod available for the deployment.

The deployment creates a replacement for pod-b called pod-e. Because there are not enough resources in the cluster to schedule pod-e the drain will again block. The cluster may end up in this state:

node-1 drained	node-2	node-3	no node
	pod-b terminating	pod-c available	pod-e pending
	pod-d available	pod-y

At this point, the cluster administrator needs to add a node back to the cluster to proceed with the upgrade.

You can see how Kubernetes varies the rate at which disruptions can happen, according to:

how many replicas an application needs
how long it takes to gracefully shutdown an instance
how long it takes a new instance to start up
the type of controller
the cluster's resource capacity

Pod disruption conditions

FEATURE STATE: Kubernetes v1.25 [alpha]

Note: In order to use this behavior, you must enable the PodDisruptionConditions feature gate in your cluster.

When enabled, a dedicated Pod DisruptionTarget condition is added to indicate that the Pod is about to be deleted due to a disruption. The reason field of the condition additionally indicates one of the following reasons for the Pod termination:

PreemptionByKubeScheduler: Pod is due to be preempted by a scheduler in order to accommodate a new Pod with a higher priority. For more information, see Pod priority preemption.
DeletionByTaintManager: Pod is due to be deleted by Taint Manager (which is part of the node lifecycle controller within kube-controller-manager) due to a NoExecute taint that the Pod does not tolerate; see taint-based evictions.
EvictionByEvictionAPI: Pod has been marked for eviction using the Kubernetes API .
DeletionByPodGC: Pod, that is bound to a no longer existing Node, is due to be deleted by Pod garbage collection.

Note: A Pod disruption might be interrupted. The control plane might re-attempt to continue the disruption of the same Pod, but it is not guaranteed. As a result, the DisruptionTarget condition might be added to a Pod, but that Pod might then not actually be deleted. In such a situation, after some time, the Pod disruption condition will be cleared.

When using a Job (or CronJob), you may want to use these Pod disruption conditions as part of your Job's Pod failure policy.

Separating Cluster Owner and Application Owner Roles

Often, it is useful to think of the Cluster Manager and Application Owner as separate roles with limited knowledge of each other. This separation of responsibilities may make sense in these scenarios:

when there are many application teams sharing a Kubernetes cluster, and there is natural specialization of roles
when third-party tools or services are used to automate cluster management

Pod Disruption Budgets support this separation of roles by providing an interface between the roles.

If you do not have such a separation of responsibilities in your organization, you may not need to use Pod Disruption Budgets.

How to perform Disruptive Actions on your Cluster

If you are a Cluster Administrator, and you need to perform a disruptive action on all the nodes in your cluster, such as a node or system software upgrade, here are some options:

Accept downtime during the upgrade.
Failover to another complete replica cluster.
- No downtime, but may be costly both for the duplicated nodes and for human effort to orchestrate the switchover.
Write disruption tolerant applications and use PDBs.
- No downtime.
- Minimal resource duplication.
- Allows more automation of cluster administration.
- Writing disruption-tolerant applications is tricky, but the work to tolerate voluntary disruptions largely overlaps with work to support autoscaling and tolerating involuntary disruptions.

What's next

Follow steps to protect your application by configuring a Pod Disruption Budget.
Learn more about draining nodes
Learn about updating a deployment including steps to maintain its availability during the rollout.

5.1.4 - Ephemeral Containers

FEATURE STATE: Kubernetes v1.25 [stable]

This page provides an overview of ephemeral containers: a special type of container that runs temporarily in an existing Pod to accomplish user-initiated actions such as troubleshooting. You use ephemeral containers to inspect services rather than to build applications.

Understanding ephemeral containers

Pods are the fundamental building block of Kubernetes applications. Since Pods are intended to be disposable and replaceable, you cannot add a container to a Pod once it has been created. Instead, you usually delete and replace Pods in a controlled fashion using deployments.

Sometimes it's necessary to inspect the state of an existing Pod, however, for example to troubleshoot a hard-to-reproduce bug. In these cases you can run an ephemeral container in an existing Pod to inspect its state and run arbitrary commands.

What is an ephemeral container?

Ephemeral containers differ from other containers in that they lack guarantees for resources or execution, and they will never be automatically restarted, so they are not appropriate for building applications. Ephemeral containers are described using the same ContainerSpec as regular containers, but many fields are incompatible and disallowed for ephemeral containers.

Ephemeral containers may not have ports, so fields such as ports, livenessProbe, readinessProbe are disallowed.
Pod resource allocations are immutable, so setting resources is disallowed.
For a complete list of allowed fields, see the EphemeralContainer reference documentation.

Ephemeral containers are created using a special ephemeralcontainers handler in the API rather than by adding them directly to pod.spec, so it's not possible to add an ephemeral container using kubectl edit.

Like regular containers, you may not change or remove an ephemeral container after you have added it to a Pod.

Uses for ephemeral containers

Ephemeral containers are useful for interactive troubleshooting when kubectl exec is insufficient because a container has crashed or a container image doesn't include debugging utilities.

In particular, distroless images enable you to deploy minimal container images that reduce attack surface and exposure to bugs and vulnerabilities. Since distroless images do not include a shell or any debugging utilities, it's difficult to troubleshoot distroless images using kubectl exec alone.

When using ephemeral containers, it's helpful to enable process namespace sharing so you can view processes in other containers.

What's next

Learn how to debug pods using ephemeral containers.

5.1.5 - User Namespaces

FEATURE STATE: Kubernetes v1.25 [alpha]

This page explains how user namespaces are used in Kubernetes pods. A user namespace allows to isolate the user running inside the container from the one in the host.

A process running as root in a container can run as a different (non-root) user in the host; in other words, the process has full privileges for operations inside the user namespace, but is unprivileged for operations outside the namespace.

You can use this feature to reduce the damage a compromised container can do to the host or other pods in the same node. There are several security vulnerabilities rated either HIGH or CRITICAL that were not exploitable when user namespaces is active. It is expected user namespace will mitigate some future vulnerabilities too.

Before you begin

🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information

This is a Linux only feature. In addition, support is needed in the container runtime to use this feature with Kubernetes stateless pods:

CRI-O: v1.25 has support for user namespaces.
containerd: support is planned for the 1.7 release. See containerd issue #7063 for more details.

Support for this in cri-dockerd is not planned yet.

Introduction

User namespaces is a Linux feature that allows to map users in the container to different users in the host. Furthermore, the capabilities granted to a pod in a user namespace are valid only in the namespace and void outside of it.

A pod can opt-in to use user namespaces by setting the pod.spec.hostUsers field to false.

The kubelet will pick host UIDs/GIDs a pod is mapped to, and will do so in a way to guarantee that no two stateless pods on the same node use the same mapping.

The runAsUser, runAsGroup, fsGroup, etc. fields in the pod.spec always refer to the user inside the container.

The valid UIDs/GIDs when this feature is enabled is the range 0-65535. This applies to files and processes (runAsUser, runAsGroup, etc.).

Files using a UID/GID outside this range will be seen as belonging to the overflow ID, usually 65534 (configured in /proc/sys/kernel/overflowuid and /proc/sys/kernel/overflowgid). However, it is not possible to modify those files, even by running as the 65534 user/group.

Most applications that need to run as root but don't access other host namespaces or resources, should continue to run fine without any changes needed if user namespaces is activated.

Understanding user namespaces for stateless pods

Several container runtimes with their default configuration (like Docker Engine, containerd, CRI-O) use Linux namespaces for isolation. Other technologies exist and can be used with those runtimes too (e.g. Kata Containers uses VMs instead of Linux namespaces). This page is applicable for container runtimes using Linux namespaces for isolation.

When creating a pod, by default, several new namespaces are used for isolation: a network namespace to isolate the network of the container, a PID namespace to isolate the view of processes, etc. If a user namespace is used, this will isolate the users in the container from the users in the node.

This means containers can run as root and be mapped to a non-root user on the host. Inside the container the process will think it is running as root (and therefore tools like apt, yum, etc. work fine), while in reality the process doesn't have privileges on the host. You can verify this, for example, if you check the user the container process is running ps from the host. The user ps shows is not the same as the user you see if you execute inside the container the command id.

This abstraction limits what can happen, for example, if the container manages to escape to the host. Given that the container is running as a non-privileged user on the host, it is limited what it can do to the host.

Furthermore, as users on each pod will be mapped to different non-overlapping users in the host, it is limited what they can do to other pods too.

Capabilities granted to a pod are also limited to the pod user namespace and mostly invalid out of it, some are even completely void. Here are two examples:

CAP_SYS_MODULE does not have any effect if granted to a pod using user namespaces, the pod isn't able to load kernel modules.
CAP_SYS_ADMIN is limited to the pod's user namespace and invalid outside of it.

Without using a user namespace a container running as root, in the case of a container breakout, has root privileges on the node. And if some capability were granted to the container, the capabilities are valid on the host too. None of this is true when we use user namespaces.

If you want to know more details about what changes when user namespaces are in use, see man 7 user_namespaces.

Set up a node to support user namespaces

It is recommended that the host's files and host's processes use UIDs/GIDs in the range of 0-65535.

The kubelet will assign UIDs/GIDs higher than that to pods. Therefore, to guarantee as much isolation as possible, the UIDs/GIDs used by the host's files and host's processes should be in the range 0-65535.

Note that this recommendation is important to mitigate the impact of CVEs like CVE-2021-25741, where a pod can potentially read arbitrary files in the hosts. If the UIDs/GIDs of the pod and the host don't overlap, it is limited what a pod would be able to do: the pod UID/GID won't match the host's file owner/group.

Limitations

When using a user namespace for the pod, it is disallowed to use other host namespaces. In particular, if you set hostUsers: false then you are not allowed to set any of:

hostNetwork: true
hostIPC: true
hostPID: true

The pod is allowed to use no volumes at all or, if using volumes, only these volume types are allowed:

configmap
secret
projected
downwardAPI
emptyDir

To guarantee that the pod can read the files of such volumes, volumes are created as if you specified .spec.securityContext.fsGroup as 0 for the Pod. If it is specified to a different value, this other value will of course be honored instead.

As a by-product of this, folders and files for these volumes will have permissions for the group, even if defaultMode or mode to specific items of the volumes were specified without permissions to groups. For example, it is not possible to mount these volumes in a way that its files have permissions only for the owner.

5.1.6 - Downward API

There are two ways to expose Pod and container fields to a running container: environment variables, and as files that are populated by a special volume type. Together, these two ways of exposing Pod and container fields are called the downward API.

It is sometimes useful for a container to have information about itself, without being overly coupled to Kubernetes. The downward API allows containers to consume information about themselves or the cluster without using the Kubernetes client or API server.

An example is an existing application that assumes a particular well-known environment variable holds a unique identifier. One possibility is to wrap the application, but that is tedious and error-prone, and it violates the goal of low coupling. A better option would be to use the Pod's name as an identifier, and inject the Pod's name into the well-known environment variable.

In Kubernetes, there are two ways to expose Pod and container fields to a running container:

Together, these two ways of exposing Pod and container fields are called the downward API.

Available fields

Only some Kubernetes API fields are available through the downward API. This section lists which fields you can make available.

You can pass information from available Pod-level fields using fieldRef. At the API level, the spec for a Pod always defines at least one Container. You can pass information from available Container-level fields using resourceFieldRef.

Information available via `fieldRef`

For most Pod-level fields, you can provide them to a container either as an environment variable or using a downwardAPI volume. The fields available via either mechanism are:

metadata.name: the pod's name
metadata.namespace: the pod's namespace
metadata.uid: the pod's unique ID
metadata.annotations['<KEY>']: the value of the pod's annotation named <KEY> (for example, metadata.annotations['myannotation'])
metadata.labels['<KEY>']: the text value of the pod's label named <KEY> (for example, metadata.labels['mylabel'])
spec.serviceAccountName: the name of the pod's service account
spec.nodeName: the name of the node where the Pod is executing
status.hostIP: the primary IP address of the node to which the Pod is assigned
status.podIP: the pod's primary IP address (usually, its IPv4 address)

In addition, the following information is available through a downwardAPI volume fieldRef, but not as environment variables:

metadata.labels: all of the pod's labels, formatted as label-key="escaped-label-value" with one label per line
metadata.annotations: all of the pod's annotations, formatted as annotation-key="escaped-annotation-value" with one annotation per line

Information available via `resourceFieldRef`

These container-level fields allow you to provide information about requests and limits for resources such as CPU and memory.

resource: limits.cpu: A container's CPU limit
resource: requests.cpu: A container's CPU request
resource: limits.memory: A container's memory limit
resource: requests.memory: A container's memory request
resource: limits.hugepages-*: A container's hugepages limit (provided that the DownwardAPIHugePages feature gate is enabled)
resource: requests.hugepages-*: A container's hugepages request (provided that the DownwardAPIHugePages feature gate is enabled)
resource: limits.ephemeral-storage: A container's ephemeral-storage limit
resource: requests.ephemeral-storage: A container's ephemeral-storage request

Fallback information for resource limits

If CPU and memory limits are not specified for a container, and you use the downward API to try to expose that information, then the kubelet defaults to exposing the maximum allocatable value for CPU and memory based on the node allocatable calculation.

What's next

You can read about downwardAPI volumes.

You can try using the downward API to expose container- or Pod-level information:

5.2 - Workload Resources

5.2.1 - Deployments

A Deployment provides declarative updates for Pods and ReplicaSets.

You describe a desired state in a Deployment, and the Deployment Controller changes the actual state to the desired state at a controlled rate. You can define Deployments to create new ReplicaSets, or to remove existing Deployments and adopt all their resources with new Deployments.

Note: Do not manage ReplicaSets owned by a Deployment. Consider opening an issue in the main Kubernetes repository if your use case is not covered below.

Use Case

The following are typical use cases for Deployments:

Create a Deployment to rollout a ReplicaSet. The ReplicaSet creates Pods in the background. Check the status of the rollout to see if it succeeds or not.
Declare the new state of the Pods by updating the PodTemplateSpec of the Deployment. A new ReplicaSet is created and the Deployment manages moving the Pods from the old ReplicaSet to the new one at a controlled rate. Each new ReplicaSet updates the revision of the Deployment.
Rollback to an earlier Deployment revision if the current state of the Deployment is not stable. Each rollback updates the revision of the Deployment.
Scale up the Deployment to facilitate more load.
Pause the rollout of a Deployment to apply multiple fixes to its PodTemplateSpec and then resume it to start a new rollout.
Use the status of the Deployment as an indicator that a rollout has stuck.
Clean up older ReplicaSets that you don't need anymore.

Creating a Deployment

The following is an example of a Deployment. It creates a ReplicaSet to bring up three nginx Pods:

controllers/nginx-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

In this example:

A Deployment named nginx-deployment is created, indicated by the .metadata.name field.
The Deployment creates three replicated Pods, indicated by the .spec.replicas field.
The .spec.selector field defines how the Deployment finds which Pods to manage. In this case, you select a label that is defined in the Pod template (app: nginx). However, more sophisticated selection rules are possible, as long as the Pod template itself satisfies the rule.

Note: The .spec.selector.matchLabels field is a map of {key,value} pairs. A single {key,value} in the matchLabels map is equivalent to an element of matchExpressions, whose key field is "key", the operator is "In", and the values array contains only "value". All of the requirements, from both matchLabels and matchExpressions, must be satisfied in order to match.
The template field contains the following sub-fields:
- The Pods are labeled app: nginxusing the .metadata.labels field.
- The Pod template's specification, or .template.spec field, indicates that the Pods run one container, nginx, which runs the nginx Docker Hub image at version 1.14.2.
- Create one container and name it nginx using the .spec.template.spec.containers[0].name field.

Before you begin, make sure your Kubernetes cluster is up and running. Follow the steps given below to create the above Deployment:

Create the Deployment by running the following command:

kubectl apply -f https://k8s.io/examples/controllers/nginx-deployment.yaml

Run kubectl get deployments to check if the Deployment was created.

If the Deployment is still being created, the output is similar to the following:
```
NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   0/3     0            0           1s
```
When you inspect the Deployments in your cluster, the following fields are displayed:
- NAME lists the names of the Deployments in the namespace.
- READY displays how many replicas of the application are available to your users. It follows the pattern ready/desired.
- UP-TO-DATE displays the number of replicas that have been updated to achieve the desired state.
- AVAILABLE displays how many replicas of the application are available to your users.
- AGE displays the amount of time that the application has been running.
Notice how the number of desired replicas is 3 according to .spec.replicas field.

To see the Deployment rollout status, run kubectl rollout status deployment/nginx-deployment.

The output is similar to:

Waiting for rollout to finish: 2 out of 3 new replicas have been updated...
deployment "nginx-deployment" successfully rolled out

Run the kubectl get deployments again a few seconds later. The output is similar to this:
```
NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   3/3     3            3           18s
```
Notice that the Deployment has created all three replicas, and all replicas are up-to-date (they contain the latest Pod template) and available.
To see the ReplicaSet (rs) created by the Deployment, run kubectl get rs. The output is similar to this:
```
NAME                          DESIRED   CURRENT   READY   AGE
nginx-deployment-75675f5897   3         3         3       18s
```
ReplicaSet output shows the following fields:
- NAME lists the names of the ReplicaSets in the namespace.
- DESIRED displays the desired number of replicas of the application, which you define when you create the Deployment. This is the desired state.
- CURRENT displays how many replicas are currently running.
- READY displays how many replicas of the application are available to your users.
- AGE displays the amount of time that the application has been running.
Notice that the name of the ReplicaSet is always formatted as [DEPLOYMENT-NAME]-[HASH]. The HASH string is the same as the pod-template-hash label on the ReplicaSet.

To see the labels automatically generated for each Pod, run kubectl get pods --show-labels. The output is similar to:

NAME                                READY     STATUS    RESTARTS   AGE       LABELS
nginx-deployment-75675f5897-7ci7o   1/1       Running   0          18s       app=nginx,pod-template-hash=3123191453
nginx-deployment-75675f5897-kzszj   1/1       Running   0          18s       app=nginx,pod-template-hash=3123191453
nginx-deployment-75675f5897-qqcnn   1/1       Running   0          18s       app=nginx,pod-template-hash=3123191453

The created ReplicaSet ensures that there are three nginx Pods.

Note:

You must specify an appropriate selector and Pod template labels in a Deployment (in this case, app: nginx).

Do not overlap labels or selectors with other controllers (including other Deployments and StatefulSets). Kubernetes doesn't stop you from overlapping, and if multiple controllers have overlapping selectors those controllers might conflict and behave unexpectedly.

Pod-template-hash label

Caution: Do not change this label.

The pod-template-hash label is added by the Deployment controller to every ReplicaSet that a Deployment creates or adopts.

This label ensures that child ReplicaSets of a Deployment do not overlap. It is generated by hashing the PodTemplate of the ReplicaSet and using the resulting hash as the label value that is added to the ReplicaSet selector, Pod template labels, and in any existing Pods that the ReplicaSet might have.

Updating a Deployment

Note: A Deployment's rollout is triggered if and only if the Deployment's Pod template (that is, .spec.template) is changed, for example if the labels or container images of the template are updated. Other updates, such as scaling the Deployment, do not trigger a rollout.

Follow the steps given below to update your Deployment:

Let's update the nginx Pods to use the nginx:1.16.1 image instead of the nginx:1.14.2 image.
```
kubectl set image deployment.v1.apps/nginx-deployment nginx=nginx:1.16.1
```
or use the following command:
```
kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
```
The output is similar to:
```
deployment.apps/nginx-deployment image updated
```
Alternatively, you can edit the Deployment and change .spec.template.spec.containers[0].image from nginx:1.14.2 to nginx:1.16.1:
```
kubectl edit deployment/nginx-deployment
```
The output is similar to:
```
deployment.apps/nginx-deployment edited
```

To see the rollout status, run:

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 2 out of 3 new replicas have been updated...

deployment "nginx-deployment" successfully rolled out

Get more details on your updated Deployment:

After the rollout succeeds, you can view the Deployment by running kubectl get deployments. The output is similar to this:
```
NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   3/3     3            3           36s
```
Run kubectl get rs to see that the Deployment updated the Pods by creating a new ReplicaSet and scaling it up to 3 replicas, as well as scaling down the old ReplicaSet to 0 replicas.
```
kubectl get rs
```
The output is similar to this:
```
NAME                          DESIRED   CURRENT   READY   AGE
nginx-deployment-1564180365   3         3         3       6s
nginx-deployment-2035384211   0         0         0       36s
```
Running get pods should now show only the new Pods:
```
kubectl get pods
```
The output is similar to this:
```
NAME                                READY     STATUS    RESTARTS   AGE
nginx-deployment-1564180365-khku8   1/1       Running   0          14s
nginx-deployment-1564180365-nacti   1/1       Running   0          14s
nginx-deployment-1564180365-z9gth   1/1       Running   0          14s
```
Next time you want to update these Pods, you only need to update the Deployment's Pod template again.

Deployment ensures that only a certain number of Pods are down while they are being updated. By default, it ensures that at least 75% of the desired number of Pods are up (25% max unavailable).

Deployment also ensures that only a certain number of Pods are created above the desired number of Pods. By default, it ensures that at most 125% of the desired number of Pods are up (25% max surge).

For example, if you look at the above Deployment closely, you will see that it first creates a new Pod, then deletes an old Pod, and creates another new one. It does not kill old Pods until a sufficient number of new Pods have come up, and does not create new Pods until a sufficient number of old Pods have been killed. It makes sure that at least 3 Pods are available and that at max 4 Pods in total are available. In case of a Deployment with 4 replicas, the number of Pods would be between 3 and 5.

Get details of your Deployment:

kubectl describe deployments

The output is similar to this:

Name:                   nginx-deployment
Namespace:              default
CreationTimestamp:      Thu, 30 Nov 2017 10:56:25 +0000
Labels:                 app=nginx
Annotations:            deployment.kubernetes.io/revision=2
Selector:               app=nginx
Replicas:               3 desired | 3 updated | 3 total | 3 available | 0 unavailable
StrategyType:           RollingUpdate
MinReadySeconds:        0
RollingUpdateStrategy:  25% max unavailable, 25% max surge
Pod Template:
  Labels:  app=nginx
   Containers:
    nginx:
      Image:        nginx:1.16.1
      Port:         80/TCP
      Environment:  <none>
      Mounts:       <none>
    Volumes:        <none>
  Conditions:
    Type           Status  Reason
    ----           ------  ------
    Available      True    MinimumReplicasAvailable
    Progressing    True    NewReplicaSetAvailable
  OldReplicaSets:  <none>
  NewReplicaSet:   nginx-deployment-1564180365 (3/3 replicas created)
  Events:
    Type    Reason             Age   From                   Message
    ----    ------             ----  ----                   -------
    Normal  ScalingReplicaSet  2m    deployment-controller  Scaled up replica set nginx-deployment-2035384211 to 3
    Normal  ScalingReplicaSet  24s   deployment-controller  Scaled up replica set nginx-deployment-1564180365 to 1
    Normal  ScalingReplicaSet  22s   deployment-controller  Scaled down replica set nginx-deployment-2035384211 to 2
    Normal  ScalingReplicaSet  22s   deployment-controller  Scaled up replica set nginx-deployment-1564180365 to 2
    Normal  ScalingReplicaSet  19s   deployment-controller  Scaled down replica set nginx-deployment-2035384211 to 1
    Normal  ScalingReplicaSet  19s   deployment-controller  Scaled up replica set nginx-deployment-1564180365 to 3
    Normal  ScalingReplicaSet  14s   deployment-controller  Scaled down replica set nginx-deployment-2035384211 to 0

Here you see that when you first created the Deployment, it created a ReplicaSet (nginx-deployment-2035384211) and scaled it up to 3 replicas directly. When you updated the Deployment, it created a new ReplicaSet (nginx-deployment-1564180365) and scaled it up to 1 and waited for it to come up. Then it scaled down the old ReplicaSet to 2 and scaled up the new ReplicaSet to 2 so that at least 3 Pods were available and at most 4 Pods were created at all times. It then continued scaling up and down the new and the old ReplicaSet, with the same rolling update strategy. Finally, you'll have 3 available replicas in the new ReplicaSet, and the old ReplicaSet is scaled down to 0.

Note: Kubernetes doesn't count terminating Pods when calculating the number of availableReplicas, which must be between replicas - maxUnavailable and replicas + maxSurge. As a result, you might notice that there are more Pods than expected during a rollout, and that the total resources consumed by the Deployment is more than replicas + maxSurge until the terminationGracePeriodSeconds of the terminating Pods expires.

Rollover (aka multiple updates in-flight)

Each time a new Deployment is observed by the Deployment controller, a ReplicaSet is created to bring up the desired Pods. If the Deployment is updated, the existing ReplicaSet that controls Pods whose labels match .spec.selector but whose template does not match .spec.template are scaled down. Eventually, the new ReplicaSet is scaled to .spec.replicas and all old ReplicaSets is scaled to 0.

If you update a Deployment while an existing rollout is in progress, the Deployment creates a new ReplicaSet as per the update and start scaling that up, and rolls over the ReplicaSet that it was scaling up previously -- it will add it to its list of old ReplicaSets and start scaling it down.

For example, suppose you create a Deployment to create 5 replicas of nginx:1.14.2, but then update the Deployment to create 5 replicas of nginx:1.16.1, when only 3 replicas of nginx:1.14.2 had been created. In that case, the Deployment immediately starts killing the 3 nginx:1.14.2 Pods that it had created, and starts creating nginx:1.16.1 Pods. It does not wait for the 5 replicas of nginx:1.14.2 to be created before changing course.

Label selector updates

It is generally discouraged to make label selector updates and it is suggested to plan your selectors up front. In any case, if you need to perform a label selector update, exercise great caution and make sure you have grasped all of the implications.

Note: In API version apps/v1, a Deployment's label selector is immutable after it gets created.

Selector additions require the Pod template labels in the Deployment spec to be updated with the new label too, otherwise a validation error is returned. This change is a non-overlapping one, meaning that the new selector does not select ReplicaSets and Pods created with the old selector, resulting in orphaning all old ReplicaSets and creating a new ReplicaSet.
Selector updates changes the existing value in a selector key -- result in the same behavior as additions.
Selector removals removes an existing key from the Deployment selector -- do not require any changes in the Pod template labels. Existing ReplicaSets are not orphaned, and a new ReplicaSet is not created, but note that the removed label still exists in any existing Pods and ReplicaSets.

Rolling Back a Deployment

Sometimes, you may want to rollback a Deployment; for example, when the Deployment is not stable, such as crash looping. By default, all of the Deployment's rollout history is kept in the system so that you can rollback anytime you want (you can change that by modifying revision history limit).

Note: A Deployment's revision is created when a Deployment's rollout is triggered. This means that the new revision is created if and only if the Deployment's Pod template (.spec.template) is changed, for example if you update the labels or container images of the template. Other updates, such as scaling the Deployment, do not create a Deployment revision, so that you can facilitate simultaneous manual- or auto-scaling. This means that when you roll back to an earlier revision, only the Deployment's Pod template part is rolled back.

Suppose that you made a typo while updating the Deployment, by putting the image name as nginx:1.161 instead of nginx:1.16.1:
```
kubectl set image deployment/nginx-deployment nginx=nginx:1.161 
```
The output is similar to this:
```
deployment.apps/nginx-deployment image updated
```

The rollout gets stuck. You can verify it by checking the rollout status:

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 1 out of 3 new replicas have been updated...

Press Ctrl-C to stop the above rollout status watch. For more information on stuck rollouts, read more here.

You see that the number of old replicas (nginx-deployment-1564180365 and nginx-deployment-2035384211) is 2, and new replicas (nginx-deployment-3066724191) is 1.

kubectl get rs

The output is similar to this:

NAME                          DESIRED   CURRENT   READY   AGE
nginx-deployment-1564180365   3         3         3       25s
nginx-deployment-2035384211   0         0         0       36s
nginx-deployment-3066724191   1         1         0       6s

Looking at the Pods created, you see that 1 Pod created by new ReplicaSet is stuck in an image pull loop.

kubectl get pods

The output is similar to this:

NAME                                READY     STATUS             RESTARTS   AGE
nginx-deployment-1564180365-70iae   1/1       Running            0          25s
nginx-deployment-1564180365-jbqqo   1/1       Running            0          25s
nginx-deployment-1564180365-hysrc   1/1       Running            0          25s
nginx-deployment-3066724191-08mng   0/1       ImagePullBackOff   0          6s

Note: The Deployment controller stops the bad rollout automatically, and stops scaling up the new ReplicaSet. This depends on the rollingUpdate parameters (maxUnavailable specifically) that you have specified. Kubernetes by default sets the value to 25%.

Get the description of the Deployment:

kubectl describe deployment

The output is similar to this:

Name:           nginx-deployment
Namespace:      default
CreationTimestamp:  Tue, 15 Mar 2016 14:48:04 -0700
Labels:         app=nginx
Selector:       app=nginx
Replicas:       3 desired | 1 updated | 4 total | 3 available | 1 unavailable
StrategyType:       RollingUpdate
MinReadySeconds:    0
RollingUpdateStrategy:  25% max unavailable, 25% max surge
Pod Template:
  Labels:  app=nginx
  Containers:
   nginx:
    Image:        nginx:1.161
    Port:         80/TCP
    Host Port:    0/TCP
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Conditions:
  Type           Status  Reason
  ----           ------  ------
  Available      True    MinimumReplicasAvailable
  Progressing    True    ReplicaSetUpdated
OldReplicaSets:     nginx-deployment-1564180365 (3/3 replicas created)
NewReplicaSet:      nginx-deployment-3066724191 (1/1 replicas created)
Events:
  FirstSeen LastSeen    Count   From                    SubObjectPath   Type        Reason              Message
  --------- --------    -----   ----                    -------------   --------    ------              -------
  1m        1m          1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-2035384211 to 3
  22s       22s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-1564180365 to 1
  22s       22s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled down replica set nginx-deployment-2035384211 to 2
  22s       22s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-1564180365 to 2
  21s       21s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled down replica set nginx-deployment-2035384211 to 1
  21s       21s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-1564180365 to 3
  13s       13s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled down replica set nginx-deployment-2035384211 to 0
  13s       13s         1       {deployment-controller }                Normal      ScalingReplicaSet   Scaled up replica set nginx-deployment-3066724191 to 1

To fix this, you need to rollback to a previous revision of Deployment that is stable.

Checking Rollout History of a Deployment

Follow the steps given below to check the rollout history:

First, check the revisions of this Deployment:
```
kubectl rollout history deployment/nginx-deployment
```
The output is similar to this:
```
deployments "nginx-deployment"
REVISION    CHANGE-CAUSE
1           kubectl apply --filename=https://k8s.io/examples/controllers/nginx-deployment.yaml
2           kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
3           kubectl set image deployment/nginx-deployment nginx=nginx:1.161
```
CHANGE-CAUSE is copied from the Deployment annotation kubernetes.io/change-cause to its revisions upon creation. You can specify theCHANGE-CAUSE message by:
- Annotating the Deployment with kubectl annotate deployment/nginx-deployment kubernetes.io/change-cause="image updated to 1.16.1"
- Manually editing the manifest of the resource.

To see the details of each revision, run:

kubectl rollout history deployment/nginx-deployment --revision=2

The output is similar to this:

deployments "nginx-deployment" revision 2
  Labels:       app=nginx
          pod-template-hash=1159050644
  Annotations:  kubernetes.io/change-cause=kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
  Containers:
   nginx:
    Image:      nginx:1.16.1
    Port:       80/TCP
     QoS Tier:
        cpu:      BestEffort
        memory:   BestEffort
    Environment Variables:      <none>
  No volumes.

Rolling Back to a Previous Revision

Follow the steps given below to rollback the Deployment from the current version to the previous version, which is version 2.

Now you've decided to undo the current rollout and rollback to the previous revision:
```
kubectl rollout undo deployment/nginx-deployment
```
The output is similar to this:
```
deployment.apps/nginx-deployment rolled back
```
Alternatively, you can rollback to a specific revision by specifying it with --to-revision:
```
kubectl rollout undo deployment/nginx-deployment --to-revision=2
```
The output is similar to this:
```
deployment.apps/nginx-deployment rolled back
```
For more details about rollout related commands, read kubectl rollout.

The Deployment is now rolled back to a previous stable revision. As you can see, a DeploymentRollback event for rolling back to revision 2 is generated from Deployment controller.

Check if the rollback was successful and the Deployment is running as expected, run:

kubectl get deployment nginx-deployment

The output is similar to this:

NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   3/3     3            3           30m

Get the description of the Deployment:

kubectl describe deployment nginx-deployment

The output is similar to this:

Name:                   nginx-deployment
Namespace:              default
CreationTimestamp:      Sun, 02 Sep 2018 18:17:55 -0500
Labels:                 app=nginx
Annotations:            deployment.kubernetes.io/revision=4
                        kubernetes.io/change-cause=kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1
Selector:               app=nginx
Replicas:               3 desired | 3 updated | 3 total | 3 available | 0 unavailable
StrategyType:           RollingUpdate
MinReadySeconds:        0
RollingUpdateStrategy:  25% max unavailable, 25% max surge
Pod Template:
  Labels:  app=nginx
  Containers:
   nginx:
    Image:        nginx:1.16.1
    Port:         80/TCP
    Host Port:    0/TCP
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Conditions:
  Type           Status  Reason
  ----           ------  ------
  Available      True    MinimumReplicasAvailable
  Progressing    True    NewReplicaSetAvailable
OldReplicaSets:  <none>
NewReplicaSet:   nginx-deployment-c4747d96c (3/3 replicas created)
Events:
  Type    Reason              Age   From                   Message
  ----    ------              ----  ----                   -------
  Normal  ScalingReplicaSet   12m   deployment-controller  Scaled up replica set nginx-deployment-75675f5897 to 3
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-c4747d96c to 1
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled down replica set nginx-deployment-75675f5897 to 2
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-c4747d96c to 2
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled down replica set nginx-deployment-75675f5897 to 1
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-c4747d96c to 3
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled down replica set nginx-deployment-75675f5897 to 0
  Normal  ScalingReplicaSet   11m   deployment-controller  Scaled up replica set nginx-deployment-595696685f to 1
  Normal  DeploymentRollback  15s   deployment-controller  Rolled back deployment "nginx-deployment" to revision 2
  Normal  ScalingReplicaSet   15s   deployment-controller  Scaled down replica set nginx-deployment-595696685f to 0

Scaling a Deployment

You can scale a Deployment by using the following command:

kubectl scale deployment/nginx-deployment --replicas=10

The output is similar to this:

deployment.apps/nginx-deployment scaled

Assuming horizontal Pod autoscaling is enabled in your cluster, you can set up an autoscaler for your Deployment and choose the minimum and maximum number of Pods you want to run based on the CPU utilization of your existing Pods.

kubectl autoscale deployment/nginx-deployment --min=10 --max=15 --cpu-percent=80

The output is similar to this:

deployment.apps/nginx-deployment scaled

Proportional scaling

RollingUpdate Deployments support running multiple versions of an application at the same time. When you or an autoscaler scales a RollingUpdate Deployment that is in the middle of a rollout (either in progress or paused), the Deployment controller balances the additional replicas in the existing active ReplicaSets (ReplicaSets with Pods) in order to mitigate risk. This is called proportional scaling.

For example, you are running a Deployment with 10 replicas, maxSurge=3, and maxUnavailable=2.

Ensure that the 10 replicas in your Deployment are running.

kubectl get deploy

The output is similar to this:

NAME                 DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment     10        10        10           10          50s

You update to a new image which happens to be unresolvable from inside the cluster.

kubectl set image deployment/nginx-deployment nginx=nginx:sometag

The output is similar to this:

deployment.apps/nginx-deployment image updated

The image update starts a new rollout with ReplicaSet nginx-deployment-1989198191, but it's blocked due to the maxUnavailable requirement that you mentioned above. Check out the rollout status:

kubectl get rs

The output is similar to this:

NAME                          DESIRED   CURRENT   READY     AGE
nginx-deployment-1989198191   5         5         0         9s
nginx-deployment-618515232    8         8         8         1m

Then a new scaling request for the Deployment comes along. The autoscaler increments the Deployment replicas to 15. The Deployment controller needs to decide where to add these new 5 replicas. If you weren't using proportional scaling, all 5 of them would be added in the new ReplicaSet. With proportional scaling, you spread the additional replicas across all ReplicaSets. Bigger proportions go to the ReplicaSets with the most replicas and lower proportions go to ReplicaSets with less replicas. Any leftovers are added to the ReplicaSet with the most replicas. ReplicaSets with zero replicas are not scaled up.

In our example above, 3 replicas are added to the old ReplicaSet and 2 replicas are added to the new ReplicaSet. The rollout process should eventually move all replicas to the new ReplicaSet, assuming the new replicas become healthy. To confirm this, run:

kubectl get deploy

The output is similar to this:

NAME                 DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment     15        18        7            8           7m

The rollout status confirms how the replicas were added to each ReplicaSet.

kubectl get rs

The output is similar to this:

NAME                          DESIRED   CURRENT   READY     AGE
nginx-deployment-1989198191   7         7         0         7m
nginx-deployment-618515232    11        11        11        7m

Pausing and Resuming a rollout of a Deployment

When you update a Deployment, or plan to, you can pause rollouts for that Deployment before you trigger one or more updates. When you're ready to apply those changes, you resume rollouts for the Deployment. This approach allows you to apply multiple fixes in between pausing and resuming without triggering unnecessary rollouts.

For example, with a Deployment that was created:

Get the Deployment details:

kubectl get deploy

The output is similar to this:

NAME      DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
nginx     3         3         3            3           1m

Get the rollout status:

kubectl get rs

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   3         3         3         1m

Pause by running the following command:

kubectl rollout pause deployment/nginx-deployment

The output is similar to this:

deployment.apps/nginx-deployment paused

Then update the image of the Deployment:

kubectl set image deployment/nginx-deployment nginx=nginx:1.16.1

The output is similar to this:

deployment.apps/nginx-deployment image updated

Notice that no new rollout started:

kubectl rollout history deployment/nginx-deployment

The output is similar to this:

deployments "nginx"
REVISION  CHANGE-CAUSE
1   <none>

Get the rollout status to verify that the existing ReplicaSet has not changed:

kubectl get rs

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   3         3         3         2m

You can make as many updates as you wish, for example, update the resources that will be used:
```
kubectl set resources deployment/nginx-deployment -c=nginx --limits=cpu=200m,memory=512Mi
```
The output is similar to this:
```
deployment.apps/nginx-deployment resource requirements updated
```
The initial state of the Deployment prior to pausing its rollout will continue its function, but new updates to the Deployment will not have any effect as long as the Deployment rollout is paused.
Eventually, resume the Deployment rollout and observe a new ReplicaSet coming up with all the new updates:
```
kubectl rollout resume deployment/nginx-deployment
```
The output is similar to this:
```
deployment.apps/nginx-deployment resumed
```

Watch the status of the rollout until it's done.

kubectl get rs -w

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   2         2         2         2m
nginx-3926361531   2         2         0         6s
nginx-3926361531   2         2         1         18s
nginx-2142116321   1         2         2         2m
nginx-2142116321   1         2         2         2m
nginx-3926361531   3         2         1         18s
nginx-3926361531   3         2         1         18s
nginx-2142116321   1         1         1         2m
nginx-3926361531   3         3         1         18s
nginx-3926361531   3         3         2         19s
nginx-2142116321   0         1         1         2m
nginx-2142116321   0         1         1         2m
nginx-2142116321   0         0         0         2m
nginx-3926361531   3         3         3         20s

Get the status of the latest rollout:

kubectl get rs

The output is similar to this:

NAME               DESIRED   CURRENT   READY     AGE
nginx-2142116321   0         0         0         2m
nginx-3926361531   3         3         3         28s

Note: You cannot rollback a paused Deployment until you resume it.

Deployment status

A Deployment enters various states during its lifecycle. It can be progressing while rolling out a new ReplicaSet, it can be complete, or it can fail to progress.

Progressing Deployment

Kubernetes marks a Deployment as progressing when one of the following tasks is performed:

The Deployment creates a new ReplicaSet.
The Deployment is scaling up its newest ReplicaSet.
The Deployment is scaling down its older ReplicaSet(s).
New Pods become ready or available (ready for at least MinReadySeconds).

When the rollout becomes “progressing”, the Deployment controller adds a condition with the following attributes to the Deployment's .status.conditions:

type: Progressing
status: "True"
reason: NewReplicaSetCreated | reason: FoundNewReplicaSet | reason: ReplicaSetUpdated

You can monitor the progress for a Deployment by using kubectl rollout status.

Complete Deployment

Kubernetes marks a Deployment as complete when it has the following characteristics:

All of the replicas associated with the Deployment have been updated to the latest version you've specified, meaning any updates you've requested have been completed.
All of the replicas associated with the Deployment are available.
No old replicas for the Deployment are running.

When the rollout becomes “complete”, the Deployment controller sets a condition with the following attributes to the Deployment's .status.conditions:

type: Progressing
status: "True"
reason: NewReplicaSetAvailable

This Progressing condition will retain a status value of "True" until a new rollout is initiated. The condition holds even when availability of replicas changes (which does instead affect the Available condition).

You can check if a Deployment has completed by using kubectl rollout status. If the rollout completed successfully, kubectl rollout status returns a zero exit code.

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 2 of 3 updated replicas are available...
deployment "nginx-deployment" successfully rolled out

and the exit status from kubectl rollout is 0 (success):

echo $?

Failed Deployment

Your Deployment may get stuck trying to deploy its newest ReplicaSet without ever completing. This can occur due to some of the following factors:

Insufficient quota
Readiness probe failures
Image pull errors
Insufficient permissions
Limit ranges
Application runtime misconfiguration

One way you can detect this condition is to specify a deadline parameter in your Deployment spec: (.spec.progressDeadlineSeconds). .spec.progressDeadlineSeconds denotes the number of seconds the Deployment controller waits before indicating (in the Deployment status) that the Deployment progress has stalled.

The following kubectl command sets the spec with progressDeadlineSeconds to make the controller report lack of progress of a rollout for a Deployment after 10 minutes:

kubectl patch deployment/nginx-deployment -p '{"spec":{"progressDeadlineSeconds":600}}'

The output is similar to this:

deployment.apps/nginx-deployment patched

Once the deadline has been exceeded, the Deployment controller adds a DeploymentCondition with the following attributes to the Deployment's .status.conditions:

type: Progressing
status: "False"
reason: ProgressDeadlineExceeded

This condition can also fail early and is then set to status value of "False" due to reasons as ReplicaSetCreateError. Also, the deadline is not taken into account anymore once the Deployment rollout completes.

See the Kubernetes API conventions for more information on status conditions.

Note: Kubernetes takes no action on a stalled Deployment other than to report a status condition with reason: ProgressDeadlineExceeded. Higher level orchestrators can take advantage of it and act accordingly, for example, rollback the Deployment to its previous version.

Note: If you pause a Deployment rollout, Kubernetes does not check progress against your specified deadline. You can safely pause a Deployment rollout in the middle of a rollout and resume without triggering the condition for exceeding the deadline.

You may experience transient errors with your Deployments, either due to a low timeout that you have set or due to any other kind of error that can be treated as transient. For example, let's suppose you have insufficient quota. If you describe the Deployment you will notice the following section:

kubectl describe deployment nginx-deployment

The output is similar to this:

<...>
Conditions:
  Type            Status  Reason
  ----            ------  ------
  Available       True    MinimumReplicasAvailable
  Progressing     True    ReplicaSetUpdated
  ReplicaFailure  True    FailedCreate
<...>

If you run kubectl get deployment nginx-deployment -o yaml, the Deployment status is similar to this:

status:
  availableReplicas: 2
  conditions:
  - lastTransitionTime: 2016-10-04T12:25:39Z
    lastUpdateTime: 2016-10-04T12:25:39Z
    message: Replica set "nginx-deployment-4262182780" is progressing.
    reason: ReplicaSetUpdated
    status: "True"
    type: Progressing
  - lastTransitionTime: 2016-10-04T12:25:42Z
    lastUpdateTime: 2016-10-04T12:25:42Z
    message: Deployment has minimum availability.
    reason: MinimumReplicasAvailable
    status: "True"
    type: Available
  - lastTransitionTime: 2016-10-04T12:25:39Z
    lastUpdateTime: 2016-10-04T12:25:39Z
    message: 'Error creating: pods "nginx-deployment-4262182780-" is forbidden: exceeded quota:
      object-counts, requested: pods=1, used: pods=3, limited: pods=2'
    reason: FailedCreate
    status: "True"
    type: ReplicaFailure
  observedGeneration: 3
  replicas: 2
  unavailableReplicas: 2

Eventually, once the Deployment progress deadline is exceeded, Kubernetes updates the status and the reason for the Progressing condition:

Conditions:
  Type            Status  Reason
  ----            ------  ------
  Available       True    MinimumReplicasAvailable
  Progressing     False   ProgressDeadlineExceeded
  ReplicaFailure  True    FailedCreate

You can address an issue of insufficient quota by scaling down your Deployment, by scaling down other controllers you may be running, or by increasing quota in your namespace. If you satisfy the quota conditions and the Deployment controller then completes the Deployment rollout, you'll see the Deployment's status update with a successful condition (status: "True" and reason: NewReplicaSetAvailable).

Conditions:
  Type          Status  Reason
  ----          ------  ------
  Available     True    MinimumReplicasAvailable
  Progressing   True    NewReplicaSetAvailable

type: Available with status: "True" means that your Deployment has minimum availability. Minimum availability is dictated by the parameters specified in the deployment strategy. type: Progressing with status: "True" means that your Deployment is either in the middle of a rollout and it is progressing or that it has successfully completed its progress and the minimum required new replicas are available (see the Reason of the condition for the particulars - in our case reason: NewReplicaSetAvailable means that the Deployment is complete).

You can check if a Deployment has failed to progress by using kubectl rollout status. kubectl rollout status returns a non-zero exit code if the Deployment has exceeded the progression deadline.

kubectl rollout status deployment/nginx-deployment

The output is similar to this:

Waiting for rollout to finish: 2 out of 3 new replicas have been updated...
error: deployment "nginx" exceeded its progress deadline

and the exit status from kubectl rollout is 1 (indicating an error):

echo $?

Operating on a failed deployment

All actions that apply to a complete Deployment also apply to a failed Deployment. You can scale it up/down, roll back to a previous revision, or even pause it if you need to apply multiple tweaks in the Deployment Pod template.

Clean up Policy

You can set .spec.revisionHistoryLimit field in a Deployment to specify how many old ReplicaSets for this Deployment you want to retain. The rest will be garbage-collected in the background. By default, it is 10.

Note: Explicitly setting this field to 0, will result in cleaning up all the history of your Deployment thus that Deployment will not be able to roll back.

Canary Deployment

If you want to roll out releases to a subset of users or servers using the Deployment, you can create multiple Deployments, one for each release, following the canary pattern described in managing resources.

Writing a Deployment Spec

As with all other Kubernetes configs, a Deployment needs .apiVersion, .kind, and .metadata fields. For general information about working with config files, see deploying applications, configuring containers, and using kubectl to manage resources documents. The name of a Deployment object must be a valid DNS subdomain name.

A Deployment also needs a .spec section.

Pod Template

The .spec.template and .spec.selector are the only required fields of the .spec.

The .spec.template is a Pod template. It has exactly the same schema as a Pod, except it is nested and does not have an apiVersion or kind.

In addition to required fields for a Pod, a Pod template in a Deployment must specify appropriate labels and an appropriate restart policy. For labels, make sure not to overlap with other controllers. See selector.

Only a .spec.template.spec.restartPolicy equal to Always is allowed, which is the default if not specified.

Replicas

.spec.replicas is an optional field that specifies the number of desired Pods. It defaults to 1.

Should you manually scale a Deployment, example via kubectl scale deployment deployment --replicas=X, and then you update that Deployment based on a manifest (for example: by running kubectl apply -f deployment.yaml), then applying that manifest overwrites the manual scaling that you previously did.

If a HorizontalPodAutoscaler (or any similar API for horizontal scaling) is managing scaling for a Deployment, don't set .spec.replicas.

Instead, allow the Kubernetes control plane to manage the .spec.replicas field automatically.

Selector

.spec.selector is a required field that specifies a label selector for the Pods targeted by this Deployment.

.spec.selector must match .spec.template.metadata.labels, or it will be rejected by the API.

In API version apps/v1, .spec.selector and .metadata.labels do not default to .spec.template.metadata.labels if not set. So they must be set explicitly. Also note that .spec.selector is immutable after creation of the Deployment in apps/v1.

A Deployment may terminate Pods whose labels match the selector if their template is different from .spec.template or if the total number of such Pods exceeds .spec.replicas. It brings up new Pods with .spec.template if the number of Pods is less than the desired number.

Note: You should not create other Pods whose labels match this selector, either directly, by creating another Deployment, or by creating another controller such as a ReplicaSet or a ReplicationController. If you do so, the first Deployment thinks that it created these other Pods. Kubernetes does not stop you from doing this.

If you have multiple controllers that have overlapping selectors, the controllers will fight with each other and won't behave correctly.

Strategy

.spec.strategy specifies the strategy used to replace old Pods by new ones. .spec.strategy.type can be "Recreate" or "RollingUpdate". "RollingUpdate" is the default value.

Recreate Deployment

All existing Pods are killed before new ones are created when .spec.strategy.type==Recreate.

Note: This will only guarantee Pod termination previous to creation for upgrades. If you upgrade a Deployment, all Pods of the old revision will be terminated immediately. Successful removal is awaited before any Pod of the new revision is created. If you manually delete a Pod, the lifecycle is controlled by the ReplicaSet and the replacement will be created immediately (even if the old Pod is still in a Terminating state). If you need an "at most" guarantee for your Pods, you should consider using a StatefulSet.

Rolling Update Deployment

The Deployment updates Pods in a rolling update fashion when .spec.strategy.type==RollingUpdate. You can specify maxUnavailable and maxSurge to control the rolling update process.

Max Unavailable

.spec.strategy.rollingUpdate.maxUnavailable is an optional field that specifies the maximum number of Pods that can be unavailable during the update process. The value can be an absolute number (for example, 5) or a percentage of desired Pods (for example, 10%). The absolute number is calculated from percentage by rounding down. The value cannot be 0 if .spec.strategy.rollingUpdate.maxSurge is 0. The default value is 25%.

For example, when this value is set to 30%, the old ReplicaSet can be scaled down to 70% of desired Pods immediately when the rolling update starts. Once new Pods are ready, old ReplicaSet can be scaled down further, followed by scaling up the new ReplicaSet, ensuring that the total number of Pods available at all times during the update is at least 70% of the desired Pods.

Max Surge

.spec.strategy.rollingUpdate.maxSurge is an optional field that specifies the maximum number of Pods that can be created over the desired number of Pods. The value can be an absolute number (for example, 5) or a percentage of desired Pods (for example, 10%). The value cannot be 0 if MaxUnavailable is 0. The absolute number is calculated from the percentage by rounding up. The default value is 25%.

For example, when this value is set to 30%, the new ReplicaSet can be scaled up immediately when the rolling update starts, such that the total number of old and new Pods does not exceed 130% of desired Pods. Once old Pods have been killed, the new ReplicaSet can be scaled up further, ensuring that the total number of Pods running at any time during the update is at most 130% of desired Pods.

Progress Deadline Seconds

.spec.progressDeadlineSeconds is an optional field that specifies the number of seconds you want to wait for your Deployment to progress before the system reports back that the Deployment has failed progressing - surfaced as a condition with type: Progressing, status: "False". and reason: ProgressDeadlineExceeded in the status of the resource. The Deployment controller will keep retrying the Deployment. This defaults to 600. In the future, once automatic rollback will be implemented, the Deployment controller will roll back a Deployment as soon as it observes such a condition.

If specified, this field needs to be greater than .spec.minReadySeconds.

Min Ready Seconds

.spec.minReadySeconds is an optional field that specifies the minimum number of seconds for which a newly created Pod should be ready without any of its containers crashing, for it to be considered available. This defaults to 0 (the Pod will be considered available as soon as it is ready). To learn more about when a Pod is considered ready, see Container Probes.

Revision History Limit

A Deployment's revision history is stored in the ReplicaSets it controls.

.spec.revisionHistoryLimit is an optional field that specifies the number of old ReplicaSets to retain to allow rollback. These old ReplicaSets consume resources in etcd and crowd the output of kubectl get rs. The configuration of each Deployment revision is stored in its ReplicaSets; therefore, once an old ReplicaSet is deleted, you lose the ability to rollback to that revision of Deployment. By default, 10 old ReplicaSets will be kept, however its ideal value depends on the frequency and stability of new Deployments.

More specifically, setting this field to zero means that all old ReplicaSets with 0 replicas will be cleaned up. In this case, a new Deployment rollout cannot be undone, since its revision history is cleaned up.

Paused

.spec.paused is an optional boolean field for pausing and resuming a Deployment. The only difference between a paused Deployment and one that is not paused, is that any changes into the PodTemplateSpec of the paused Deployment will not trigger new rollouts as long as it is paused. A Deployment is not paused by default when it is created.

What's next

Learn about Pods.
Run a Stateless Application Using a Deployment.
Deployment is a top-level resource in the Kubernetes REST API. Read the Deployment object definition to understand the API for deployments.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.

5.2.2 - ReplicaSet

A ReplicaSet's purpose is to maintain a stable set of replica Pods running at any given time. As such, it is often used to guarantee the availability of a specified number of identical Pods.

How a ReplicaSet works

A ReplicaSet is defined with fields, including a selector that specifies how to identify Pods it can acquire, a number of replicas indicating how many Pods it should be maintaining, and a pod template specifying the data of new Pods it should create to meet the number of replicas criteria. A ReplicaSet then fulfills its purpose by creating and deleting Pods as needed to reach the desired number. When a ReplicaSet needs to create new Pods, it uses its Pod template.

A ReplicaSet is linked to its Pods via the Pods' metadata.ownerReferences field, which specifies what resource the current object is owned by. All Pods acquired by a ReplicaSet have their owning ReplicaSet's identifying information within their ownerReferences field. It's through this link that the ReplicaSet knows of the state of the Pods it is maintaining and plans accordingly.

A ReplicaSet identifies new Pods to acquire by using its selector. If there is a Pod that has no OwnerReference or the OwnerReference is not a Controller and it matches a ReplicaSet's selector, it will be immediately acquired by said ReplicaSet.

When to use a ReplicaSet

A ReplicaSet ensures that a specified number of pod replicas are running at any given time. However, a Deployment is a higher-level concept that manages ReplicaSets and provides declarative updates to Pods along with a lot of other useful features. Therefore, we recommend using Deployments instead of directly using ReplicaSets, unless you require custom update orchestration or don't require updates at all.

This actually means that you may never need to manipulate ReplicaSet objects: use a Deployment instead, and define your application in the spec section.

Example

controllers/frontend.yaml

apiVersion: apps/v1
kind: ReplicaSet
metadata:
  name: frontend
  labels:
    app: guestbook
    tier: frontend
spec:
  # modify replicas according to your case
  replicas: 3
  selector:
    matchLabels:
      tier: frontend
  template:
    metadata:
      labels:
        tier: frontend
    spec:
      containers:
      - name: php-redis
        image: gcr.io/google_samples/gb-frontend:v3

Saving this manifest into frontend.yaml and submitting it to a Kubernetes cluster will create the defined ReplicaSet and the Pods that it manages.

kubectl apply -f https://kubernetes.io/examples/controllers/frontend.yaml

You can then get the current ReplicaSets deployed:

kubectl get rs

And see the frontend one you created:

NAME       DESIRED   CURRENT   READY   AGE
frontend   3         3         3       6s

You can also check on the state of the ReplicaSet:

kubectl describe rs/frontend

And you will see output similar to:

Name:         frontend
Namespace:    default
Selector:     tier=frontend
Labels:       app=guestbook
              tier=frontend
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"apps/v1","kind":"ReplicaSet","metadata":{"annotations":{},"labels":{"app":"guestbook","tier":"frontend"},"name":"frontend",...
Replicas:     3 current / 3 desired
Pods Status:  3 Running / 0 Waiting / 0 Succeeded / 0 Failed
Pod Template:
  Labels:  tier=frontend
  Containers:
   php-redis:
    Image:        gcr.io/google_samples/gb-frontend:v3
    Port:         <none>
    Host Port:    <none>
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Events:
  Type    Reason            Age   From                   Message
  ----    ------            ----  ----                   -------
  Normal  SuccessfulCreate  117s  replicaset-controller  Created pod: frontend-wtsmm
  Normal  SuccessfulCreate  116s  replicaset-controller  Created pod: frontend-b2zdv
  Normal  SuccessfulCreate  116s  replicaset-controller  Created pod: frontend-vcmts

And lastly you can check for the Pods brought up:

kubectl get pods

You should see Pod information similar to:

NAME             READY   STATUS    RESTARTS   AGE
frontend-b2zdv   1/1     Running   0          6m36s
frontend-vcmts   1/1     Running   0          6m36s
frontend-wtsmm   1/1     Running   0          6m36s

You can also verify that the owner reference of these pods is set to the frontend ReplicaSet. To do this, get the yaml of one of the Pods running:

kubectl get pods frontend-b2zdv -o yaml

The output will look similar to this, with the frontend ReplicaSet's info set in the metadata's ownerReferences field:

apiVersion: v1
kind: Pod
metadata:
  creationTimestamp: "2020-02-12T07:06:16Z"
  generateName: frontend-
  labels:
    tier: frontend
  name: frontend-b2zdv
  namespace: default
  ownerReferences:
  - apiVersion: apps/v1
    blockOwnerDeletion: true
    controller: true
    kind: ReplicaSet
    name: frontend
    uid: f391f6db-bb9b-4c09-ae74-6a1f77f3d5cf
...

Non-Template Pod acquisitions

While you can create bare Pods with no problems, it is strongly recommended to make sure that the bare Pods do not have labels which match the selector of one of your ReplicaSets. The reason for this is because a ReplicaSet is not limited to owning Pods specified by its template-- it can acquire other Pods in the manner specified in the previous sections.

Take the previous frontend ReplicaSet example, and the Pods specified in the following manifest:

pods/pod-rs.yaml

apiVersion: v1
kind: Pod
metadata:
  name: pod1
  labels:
    tier: frontend
spec:
  containers:
  - name: hello1
    image: gcr.io/google-samples/hello-app:2.0

---

apiVersion: v1
kind: Pod
metadata:
  name: pod2
  labels:
    tier: frontend
spec:
  containers:
  - name: hello2
    image: gcr.io/google-samples/hello-app:1.0

As those Pods do not have a Controller (or any object) as their owner reference and match the selector of the frontend ReplicaSet, they will immediately be acquired by it.

Suppose you create the Pods after the frontend ReplicaSet has been deployed and has set up its initial Pod replicas to fulfill its replica count requirement:

kubectl apply -f https://kubernetes.io/examples/pods/pod-rs.yaml

The new Pods will be acquired by the ReplicaSet, and then immediately terminated as the ReplicaSet would be over its desired count.

Fetching the Pods:

kubectl get pods

The output shows that the new Pods are either already terminated, or in the process of being terminated:

NAME             READY   STATUS        RESTARTS   AGE
frontend-b2zdv   1/1     Running       0          10m
frontend-vcmts   1/1     Running       0          10m
frontend-wtsmm   1/1     Running       0          10m
pod1             0/1     Terminating   0          1s
pod2             0/1     Terminating   0          1s

If you create the Pods first:

kubectl apply -f https://kubernetes.io/examples/pods/pod-rs.yaml

And then create the ReplicaSet however:

kubectl apply -f https://kubernetes.io/examples/controllers/frontend.yaml

You shall see that the ReplicaSet has acquired the Pods and has only created new ones according to its spec until the number of its new Pods and the original matches its desired count. As fetching the Pods:

kubectl get pods

Will reveal in its output:

NAME             READY   STATUS    RESTARTS   AGE
frontend-hmmj2   1/1     Running   0          9s
pod1             1/1     Running   0          36s
pod2             1/1     Running   0          36s

In this manner, a ReplicaSet can own a non-homogenous set of Pods

Writing a ReplicaSet manifest

As with all other Kubernetes API objects, a ReplicaSet needs the apiVersion, kind, and metadata fields. For ReplicaSets, the kind is always a ReplicaSet.

The name of a ReplicaSet object must be a valid DNS subdomain name.

A ReplicaSet also needs a .spec section.

Pod Template

The .spec.template is a pod template which is also required to have labels in place. In our frontend.yaml example we had one label: tier: frontend. Be careful not to overlap with the selectors of other controllers, lest they try to adopt this Pod.

For the template's restart policy field, .spec.template.spec.restartPolicy, the only allowed value is Always, which is the default.

Pod Selector

The .spec.selector field is a label selector. As discussed earlier these are the labels used to identify potential Pods to acquire. In our frontend.yaml example, the selector was:

matchLabels:
  tier: frontend

In the ReplicaSet, .spec.template.metadata.labels must match spec.selector, or it will be rejected by the API.

Note: For 2 ReplicaSets specifying the same .spec.selector but different .spec.template.metadata.labels and .spec.template.spec fields, each ReplicaSet ignores the Pods created by the other ReplicaSet.

Replicas

You can specify how many Pods should run concurrently by setting .spec.replicas. The ReplicaSet will create/delete its Pods to match this number.

If you do not specify .spec.replicas, then it defaults to 1.

Working with ReplicaSets

Deleting a ReplicaSet and its Pods

To delete a ReplicaSet and all of its Pods, use kubectl delete. The Garbage collector automatically deletes all of the dependent Pods by default.

When using the REST API or the client-go library, you must set propagationPolicy to Background or Foreground in the -d option. For example:

kubectl proxy --port=8080
curl -X DELETE  'localhost:8080/apis/apps/v1/namespaces/default/replicasets/frontend' \
> -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Foreground"}' \
> -H "Content-Type: application/json"

Deleting just a ReplicaSet

You can delete a ReplicaSet without affecting any of its Pods using kubectl delete with the --cascade=orphan option. When using the REST API or the client-go library, you must set propagationPolicy to Orphan. For example:

kubectl proxy --port=8080
curl -X DELETE  'localhost:8080/apis/apps/v1/namespaces/default/replicasets/frontend' \
> -d '{"kind":"DeleteOptions","apiVersion":"v1","propagationPolicy":"Orphan"}' \
> -H "Content-Type: application/json"

Once the original is deleted, you can create a new ReplicaSet to replace it. As long as the old and new .spec.selector are the same, then the new one will adopt the old Pods. However, it will not make any effort to make existing Pods match a new, different pod template. To update Pods to a new spec in a controlled way, use a Deployment, as ReplicaSets do not support a rolling update directly.

Isolating Pods from a ReplicaSet

You can remove Pods from a ReplicaSet by changing their labels. This technique may be used to remove Pods from service for debugging, data recovery, etc. Pods that are removed in this way will be replaced automatically ( assuming that the number of replicas is not also changed).

Scaling a ReplicaSet

A ReplicaSet can be easily scaled up or down by simply updating the .spec.replicas field. The ReplicaSet controller ensures that a desired number of Pods with a matching label selector are available and operational.

When scaling down, the ReplicaSet controller chooses which pods to delete by sorting the available pods to prioritize scaling down pods based on the following general algorithm:

Pending (and unschedulable) pods are scaled down first
If controller.kubernetes.io/pod-deletion-cost annotation is set, then the pod with the lower value will come first.
Pods on nodes with more replicas come before pods on nodes with fewer replicas.
If the pods' creation times differ, the pod that was created more recently comes before the older pod (the creation times are bucketed on an integer log scale when the LogarithmicScaleDown feature gate is enabled)

If all of the above match, then selection is random.

Pod deletion cost

FEATURE STATE: Kubernetes v1.22 [beta]

Using the controller.kubernetes.io/pod-deletion-cost annotation, users can set a preference regarding which pods to remove first when downscaling a ReplicaSet.

The annotation should be set on the pod, the range is [-2147483647, 2147483647]. It represents the cost of deleting a pod compared to other pods belonging to the same ReplicaSet. Pods with lower deletion cost are preferred to be deleted before pods with higher deletion cost.

The implicit value for this annotation for pods that don't set it is 0; negative values are permitted. Invalid values will be rejected by the API server.

This feature is beta and enabled by default. You can disable it using the feature gate PodDeletionCost in both kube-apiserver and kube-controller-manager.

Note:

This is honored on a best-effort basis, so it does not offer any guarantees on pod deletion order.
Users should avoid updating the annotation frequently, such as updating it based on a metric value, because doing so will generate a significant number of pod updates on the apiserver.

Example Use Case

The different pods of an application could have different utilization levels. On scale down, the application may prefer to remove the pods with lower utilization. To avoid frequently updating the pods, the application should update controller.kubernetes.io/pod-deletion-cost once before issuing a scale down (setting the annotation to a value proportional to pod utilization level). This works if the application itself controls the down scaling; for example, the driver pod of a Spark deployment.

ReplicaSet as a Horizontal Pod Autoscaler Target

A ReplicaSet can also be a target for Horizontal Pod Autoscalers (HPA). That is, a ReplicaSet can be auto-scaled by an HPA. Here is an example HPA targeting the ReplicaSet we created in the previous example.

controllers/hpa-rs.yaml

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: frontend-scaler
spec:
  scaleTargetRef:
    kind: ReplicaSet
    name: frontend
  minReplicas: 3
  maxReplicas: 10
  targetCPUUtilizationPercentage: 50

Saving this manifest into hpa-rs.yaml and submitting it to a Kubernetes cluster should create the defined HPA that autoscales the target ReplicaSet depending on the CPU usage of the replicated Pods.

kubectl apply -f https://k8s.io/examples/controllers/hpa-rs.yaml

Alternatively, you can use the kubectl autoscale command to accomplish the same (and it's easier!)

kubectl autoscale rs frontend --max=10 --min=3 --cpu-percent=50

Alternatives to ReplicaSet

Deployment (recommended)

Deployment is an object which can own ReplicaSets and update them and their Pods via declarative, server-side rolling updates. While ReplicaSets can be used independently, today they're mainly used by Deployments as a mechanism to orchestrate Pod creation, deletion and updates. When you use Deployments you don't have to worry about managing the ReplicaSets that they create. Deployments own and manage their ReplicaSets. As such, it is recommended to use Deployments when you want ReplicaSets.

Bare Pods

Unlike the case where a user directly created Pods, a ReplicaSet replaces Pods that are deleted or terminated for any reason, such as in the case of node failure or disruptive node maintenance, such as a kernel upgrade. For this reason, we recommend that you use a ReplicaSet even if your application requires only a single Pod. Think of it similarly to a process supervisor, only it supervises multiple Pods across multiple nodes instead of individual processes on a single node. A ReplicaSet delegates local container restarts to some agent on the node such as Kubelet.

Job

Use a Job instead of a ReplicaSet for Pods that are expected to terminate on their own (that is, batch jobs).

DaemonSet

Use a DaemonSet instead of a ReplicaSet for Pods that provide a machine-level function, such as machine monitoring or machine logging. These Pods have a lifetime that is tied to a machine lifetime: the Pod needs to be running on the machine before other Pods start, and are safe to terminate when the machine is otherwise ready to be rebooted/shutdown.

ReplicationController

ReplicaSets are the successors to ReplicationControllers. The two serve the same purpose, and behave similarly, except that a ReplicationController does not support set-based selector requirements as described in the labels user guide. As such, ReplicaSets are preferred over ReplicationControllers

What's next

Learn about Pods.
Learn about Deployments.
Run a Stateless Application Using a Deployment, which relies on ReplicaSets to work.
ReplicaSet is a top-level resource in the Kubernetes REST API. Read the ReplicaSet object definition to understand the API for replica sets.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.

5.2.3 - StatefulSets

StatefulSet is the workload API object used to manage stateful applications.

Manages the deployment and scaling of a set of Pods, and provides guarantees about the ordering and uniqueness of these Pods.

Like a Deployment, a StatefulSet manages Pods that are based on an identical container spec. Unlike a Deployment, a StatefulSet maintains a sticky identity for each of their Pods. These pods are created from the same spec, but are not interchangeable: each has a persistent identifier that it maintains across any rescheduling.

If you want to use storage volumes to provide persistence for your workload, you can use a StatefulSet as part of the solution. Although individual Pods in a StatefulSet are susceptible to failure, the persistent Pod identifiers make it easier to match existing volumes to the new Pods that replace any that have failed.

Using StatefulSets

StatefulSets are valuable for applications that require one or more of the following.

Stable, unique network identifiers.
Stable, persistent storage.
Ordered, graceful deployment and scaling.
Ordered, automated rolling updates.

In the above, stable is synonymous with persistence across Pod (re)scheduling. If an application doesn't require any stable identifiers or ordered deployment, deletion, or scaling, you should deploy your application using a workload object that provides a set of stateless replicas. Deployment or ReplicaSet may be better suited to your stateless needs.

Limitations

The storage for a given Pod must either be provisioned by a PersistentVolume Provisioner based on the requested storage class, or pre-provisioned by an admin.
Deleting and/or scaling a StatefulSet down will not delete the volumes associated with the StatefulSet. This is done to ensure data safety, which is generally more valuable than an automatic purge of all related StatefulSet resources.
StatefulSets currently require a Headless Service to be responsible for the network identity of the Pods. You are responsible for creating this Service.
StatefulSets do not provide any guarantees on the termination of pods when a StatefulSet is deleted. To achieve ordered and graceful termination of the pods in the StatefulSet, it is possible to scale the StatefulSet down to 0 prior to deletion.
When using Rolling Updates with the default Pod Management Policy (OrderedReady), it's possible to get into a broken state that requires manual intervention to repair.

Components

The example below demonstrates the components of a StatefulSet.

apiVersion: v1
kind: Service
metadata:
  name: nginx
  labels:
    app: nginx
spec:
  ports:
  - port: 80
    name: web
  clusterIP: None
  selector:
    app: nginx
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: web
spec:
  selector:
    matchLabels:
      app: nginx # has to match .spec.template.metadata.labels
  serviceName: "nginx"
  replicas: 3 # by default is 1
  minReadySeconds: 10 # by default is 0
  template:
    metadata:
      labels:
        app: nginx # has to match .spec.selector.matchLabels
    spec:
      terminationGracePeriodSeconds: 10
      containers:
      - name: nginx
        image: registry.k8s.io/nginx-slim:0.8
        ports:
        - containerPort: 80
          name: web
        volumeMounts:
        - name: www
          mountPath: /usr/share/nginx/html
  volumeClaimTemplates:
  - metadata:
      name: www
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: "my-storage-class"
      resources:
        requests:
          storage: 1Gi

In the above example:

A Headless Service, named nginx, is used to control the network domain.
The StatefulSet, named web, has a Spec that indicates that 3 replicas of the nginx container will be launched in unique Pods.
The volumeClaimTemplates will provide stable storage using PersistentVolumes provisioned by a PersistentVolume Provisioner.

The name of a StatefulSet object must be a valid DNS subdomain name.

Pod Selector

You must set the .spec.selector field of a StatefulSet to match the labels of its .spec.template.metadata.labels. Failing to specify a matching Pod Selector will result in a validation error during StatefulSet creation.

Volume Claim Templates

You can set the .spec.volumeClaimTemplates which can provide stable storage using PersistentVolumes provisioned by a PersistentVolume Provisioner.

Minimum ready seconds

FEATURE STATE: Kubernetes v1.25 [stable]

.spec.minReadySeconds is an optional field that specifies the minimum number of seconds for which a newly created Pod should be running and ready without any of its containers crashing, for it to be considered available. This is used to check progression of a rollout when using a Rolling Update strategy. This field defaults to 0 (the Pod will be considered available as soon as it is ready). To learn more about when a Pod is considered ready, see Container Probes.

Pod Identity

StatefulSet Pods have a unique identity that is comprised of an ordinal, a stable network identity, and stable storage. The identity sticks to the Pod, regardless of which node it's (re)scheduled on.

Ordinal Index

For a StatefulSet with N replicas, each Pod in the StatefulSet will be assigned an integer ordinal, from 0 up through N-1, that is unique over the Set.

Stable Network ID

Each Pod in a StatefulSet derives its hostname from the name of the StatefulSet and the ordinal of the Pod. The pattern for the constructed hostname is $(statefulset name)-$(ordinal). The example above will create three Pods named web-0,web-1,web-2. A StatefulSet can use a Headless Service to control the domain of its Pods. The domain managed by this Service takes the form: $(service name).$(namespace).svc.cluster.local, where "cluster.local" is the cluster domain. As each Pod is created, it gets a matching DNS subdomain, taking the form: $(podname).$(governing service domain), where the governing service is defined by the serviceName field on the StatefulSet.

Depending on how DNS is configured in your cluster, you may not be able to look up the DNS name for a newly-run Pod immediately. This behavior can occur when other clients in the cluster have already sent queries for the hostname of the Pod before it was created. Negative caching (normal in DNS) means that the results of previous failed lookups are remembered and reused, even after the Pod is running, for at least a few seconds.

If you need to discover Pods promptly after they are created, you have a few options:

Query the Kubernetes API directly (for example, using a watch) rather than relying on DNS lookups.
Decrease the time of caching in your Kubernetes DNS provider (typically this means editing the config map for CoreDNS, which currently caches for 30 seconds).

As mentioned in the limitations section, you are responsible for creating the Headless Service responsible for the network identity of the pods.

Here are some examples of choices for Cluster Domain, Service name, StatefulSet name, and how that affects the DNS names for the StatefulSet's Pods.

Cluster Domain	Service (ns/name)	StatefulSet (ns/name)	StatefulSet Domain	Pod DNS	Pod Hostname
cluster.local	default/nginx	default/web	nginx.default.svc.cluster.local	web-{0..N-1}.nginx.default.svc.cluster.local	web-{0..N-1}
cluster.local	foo/nginx	foo/web	nginx.foo.svc.cluster.local	web-{0..N-1}.nginx.foo.svc.cluster.local	web-{0..N-1}
kube.local	foo/nginx	foo/web	nginx.foo.svc.kube.local	web-{0..N-1}.nginx.foo.svc.kube.local	web-{0..N-1}

Note: Cluster Domain will be set to cluster.local unless otherwise configured.

Stable Storage

For each VolumeClaimTemplate entry defined in a StatefulSet, each Pod receives one PersistentVolumeClaim. In the nginx example above, each Pod receives a single PersistentVolume with a StorageClass of my-storage-class and 1 Gib of provisioned storage. If no StorageClass is specified, then the default StorageClass will be used. When a Pod is (re)scheduled onto a node, its volumeMounts mount the PersistentVolumes associated with its PersistentVolume Claims. Note that, the PersistentVolumes associated with the Pods' PersistentVolume Claims are not deleted when the Pods, or StatefulSet are deleted. This must be done manually.

Pod Name Label

When the StatefulSet Controller creates a Pod, it adds a label, statefulset.kubernetes.io/pod-name, that is set to the name of the Pod. This label allows you to attach a Service to a specific Pod in the StatefulSet.

Deployment and Scaling Guarantees

For a StatefulSet with N replicas, when Pods are being deployed, they are created sequentially, in order from {0..N-1}.
When Pods are being deleted, they are terminated in reverse order, from {N-1..0}.
Before a scaling operation is applied to a Pod, all of its predecessors must be Running and Ready.
Before a Pod is terminated, all of its successors must be completely shutdown.

The StatefulSet should not specify a pod.Spec.TerminationGracePeriodSeconds of 0. This practice is unsafe and strongly discouraged. For further explanation, please refer to force deleting StatefulSet Pods.

When the nginx example above is created, three Pods will be deployed in the order web-0, web-1, web-2. web-1 will not be deployed before web-0 is Running and Ready, and web-2 will not be deployed until web-1 is Running and Ready. If web-0 should fail, after web-1 is Running and Ready, but before web-2 is launched, web-2 will not be launched until web-0 is successfully relaunched and becomes Running and Ready.

If a user were to scale the deployed example by patching the StatefulSet such that replicas=1, web-2 would be terminated first. web-1 would not be terminated until web-2 is fully shutdown and deleted. If web-0 were to fail after web-2 has been terminated and is completely shutdown, but prior to web-1's termination, web-1 would not be terminated until web-0 is Running and Ready.

Pod Management Policies

StatefulSet allows you to relax its ordering guarantees while preserving its uniqueness and identity guarantees via its .spec.podManagementPolicy field.

OrderedReady Pod Management

OrderedReady pod management is the default for StatefulSets. It implements the behavior described above.

Parallel Pod Management

Parallel pod management tells the StatefulSet controller to launch or terminate all Pods in parallel, and to not wait for Pods to become Running and Ready or completely terminated prior to launching or terminating another Pod. This option only affects the behavior for scaling operations. Updates are not affected.

Update strategies

A StatefulSet's .spec.updateStrategy field allows you to configure and disable automated rolling updates for containers, labels, resource request/limits, and annotations for the Pods in a StatefulSet. There are two possible values:

OnDelete: When a StatefulSet's .spec.updateStrategy.type is set to OnDelete, the StatefulSet controller will not automatically update the Pods in a StatefulSet. Users must manually delete Pods to cause the controller to create new Pods that reflect modifications made to a StatefulSet's .spec.template.
RollingUpdate: The RollingUpdate update strategy implements automated, rolling update for the Pods in a StatefulSet. This is the default update strategy.

Rolling Updates

When a StatefulSet's .spec.updateStrategy.type is set to RollingUpdate, the StatefulSet controller will delete and recreate each Pod in the StatefulSet. It will proceed in the same order as Pod termination (from the largest ordinal to the smallest), updating each Pod one at a time.

The Kubernetes control plane waits until an updated Pod is Running and Ready prior to updating its predecessor. If you have set .spec.minReadySeconds (see Minimum Ready Seconds), the control plane additionally waits that amount of time after the Pod turns ready, before moving on.

Partitioned rolling updates

The RollingUpdate update strategy can be partitioned, by specifying a .spec.updateStrategy.rollingUpdate.partition. If a partition is specified, all Pods with an ordinal that is greater than or equal to the partition will be updated when the StatefulSet's .spec.template is updated. All Pods with an ordinal that is less than the partition will not be updated, and, even if they are deleted, they will be recreated at the previous version. If a StatefulSet's .spec.updateStrategy.rollingUpdate.partition is greater than its .spec.replicas, updates to its .spec.template will not be propagated to its Pods. In most cases you will not need to use a partition, but they are useful if you want to stage an update, roll out a canary, or perform a phased roll out.

Maximum unavailable Pods

FEATURE STATE: Kubernetes v1.24 [alpha]

You can control the maximum number of Pods that can be unavailable during an update by specifying the .spec.updateStrategy.rollingUpdate.maxUnavailable field. The value can be an absolute number (for example, 5) or a percentage of desired Pods (for example, 10%). Absolute number is calculated from the percentage value by rounding it up. This field cannot be 0. The default setting is 1.

This field applies to all Pods in the range 0 to replicas - 1. If there is any unavailable Pod in the range 0 to replicas - 1, it will be counted towards maxUnavailable.

Note: The maxUnavailable field is in Alpha stage and it is honored only by API servers that are running with the MaxUnavailableStatefulSet feature gate enabled.

Forced rollback

When using Rolling Updates with the default Pod Management Policy (OrderedReady), it's possible to get into a broken state that requires manual intervention to repair.

If you update the Pod template to a configuration that never becomes Running and Ready (for example, due to a bad binary or application-level configuration error), StatefulSet will stop the rollout and wait.

In this state, it's not enough to revert the Pod template to a good configuration. Due to a known issue, StatefulSet will continue to wait for the broken Pod to become Ready (which never happens) before it will attempt to revert it back to the working configuration.

After reverting the template, you must also delete any Pods that StatefulSet had already attempted to run with the bad configuration. StatefulSet will then begin to recreate the Pods using the reverted template.

PersistentVolumeClaim retention

FEATURE STATE: Kubernetes v1.23 [alpha]

The optional .spec.persistentVolumeClaimRetentionPolicy field controls if and how PVCs are deleted during the lifecycle of a StatefulSet. You must enable the StatefulSetAutoDeletePVC feature gate to use this field. Once enabled, there are two policies you can configure for each StatefulSet:

whenDeleted: configures the volume retention behavior that applies when the StatefulSet is deleted
whenScaled: configures the volume retention behavior that applies when the replica count of the StatefulSet is reduced; for example, when scaling down the set.

For each policy that you can configure, you can set the value to either Delete or Retain.

Delete: The PVCs created from the StatefulSet volumeClaimTemplate are deleted for each Pod affected by the policy. With the whenDeleted policy all PVCs from the volumeClaimTemplate are deleted after their Pods have been deleted. With the whenScaled policy, only PVCs corresponding to Pod replicas being scaled down are deleted, after their Pods have been deleted.
Retain (default): PVCs from the volumeClaimTemplate are not affected when their Pod is deleted. This is the behavior before this new feature.

Bear in mind that these policies only apply when Pods are being removed due to the StatefulSet being deleted or scaled down. For example, if a Pod associated with a StatefulSet fails due to node failure, and the control plane creates a replacement Pod, the StatefulSet retains the existing PVC. The existing volume is unaffected, and the cluster will attach it to the node where the new Pod is about to launch.

The default for policies is Retain, matching the StatefulSet behavior before this new feature.

Here is an example policy.

apiVersion: apps/v1
kind: StatefulSet
...
spec:
  persistentVolumeClaimRetentionPolicy:
    whenDeleted: Retain
    whenScaled: Delete
...

The StatefulSet controller adds owner references to its PVCs, which are then deleted by the garbage collector after the Pod is terminated. This enables the Pod to cleanly unmount all volumes before the PVCs are deleted (and before the backing PV and volume are deleted, depending on the retain policy). When you set the whenDeleted policy to Delete, an owner reference to the StatefulSet instance is placed on all PVCs associated with that StatefulSet.

The whenScaled policy must delete PVCs only when a Pod is scaled down, and not when a Pod is deleted for another reason. When reconciling, the StatefulSet controller compares its desired replica count to the actual Pods present on the cluster. Any StatefulSet Pod whose id greater than the replica count is condemned and marked for deletion. If the whenScaled policy is Delete, the condemned Pods are first set as owners to the associated StatefulSet template PVCs, before the Pod is deleted. This causes the PVCs to be garbage collected after only the condemned Pods have terminated.

This means that if the controller crashes and restarts, no Pod will be deleted before its owner reference has been updated appropriate to the policy. If a condemned Pod is force-deleted while the controller is down, the owner reference may or may not have been set up, depending on when the controller crashed. It may take several reconcile loops to update the owner references, so some condemned Pods may have set up owner references and other may not. For this reason we recommend waiting for the controller to come back up, which will verify owner references before terminating Pods. If that is not possible, the operator should verify the owner references on PVCs to ensure the expected objects are deleted when Pods are force-deleted.

Replicas

.spec.replicas is an optional field that specifies the number of desired Pods. It defaults to 1.

Should you manually scale a deployment, example via kubectl scale statefulset statefulset --replicas=X, and then you update that StatefulSet based on a manifest (for example: by running kubectl apply -f statefulset.yaml), then applying that manifest overwrites the manual scaling that you previously did.

If a HorizontalPodAutoscaler (or any similar API for horizontal scaling) is managing scaling for a Statefulset, don't set .spec.replicas. Instead, allow the Kubernetes control plane to manage the .spec.replicas field automatically.

What's next

Learn about Pods.
Find out how to use StatefulSets
- Follow an example of deploying a stateful application.
- Follow an example of deploying Cassandra with Stateful Sets.
- Follow an example of running a replicated stateful application.
- Learn how to scale a StatefulSet.
- Learn what's involved when you delete a StatefulSet.
- Learn how to configure a Pod to use a volume for storage.
- Learn how to configure a Pod to use a PersistentVolume for storage.
StatefulSet is a top-level resource in the Kubernetes REST API. Read the StatefulSet object definition to understand the API for stateful sets.
Read about PodDisruptionBudget and how you can use it to manage application availability during disruptions.

5.2.4 - DaemonSet

A DaemonSet ensures that all (or some) Nodes run a copy of a Pod. As nodes are added to the cluster, Pods are added to them. As nodes are removed from the cluster, those Pods are garbage collected. Deleting a DaemonSet will clean up the Pods it created.

Some typical uses of a DaemonSet are:

running a cluster storage daemon on every node
running a logs collection daemon on every node
running a node monitoring daemon on every node

In a simple case, one DaemonSet, covering all nodes, would be used for each type of daemon. A more complex setup might use multiple DaemonSets for a single type of daemon, but with different flags and/or different memory and cpu requests for different hardware types.

Writing a DaemonSet Spec

Create a DaemonSet

You can describe a DaemonSet in a YAML file. For example, the daemonset.yaml file below describes a DaemonSet that runs the fluentd-elasticsearch Docker image:

controllers/daemonset.yaml

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-elasticsearch
  namespace: kube-system
  labels:
    k8s-app: fluentd-logging
spec:
  selector:
    matchLabels:
      name: fluentd-elasticsearch
  template:
    metadata:
      labels:
        name: fluentd-elasticsearch
    spec:
      tolerations:
      # these tolerations are to have the daemonset runnable on control plane nodes
      # remove them if your control plane nodes should not run pods
      - key: node-role.kubernetes.io/control-plane
        operator: Exists
        effect: NoSchedule
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      containers:
      - name: fluentd-elasticsearch
        image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2
        resources:
          limits:
            memory: 200Mi
          requests:
            cpu: 100m
            memory: 200Mi
        volumeMounts:
        - name: varlog
          mountPath: /var/log
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log

Create a DaemonSet based on the YAML file:

kubectl apply -f https://k8s.io/examples/controllers/daemonset.yaml

Required Fields

As with all other Kubernetes config, a DaemonSet needs apiVersion, kind, and metadata fields. For general information about working with config files, see running stateless applications and object management using kubectl.

The name of a DaemonSet object must be a valid DNS subdomain name.

A DaemonSet also needs a .spec section.

Pod Template

The .spec.template is one of the required fields in .spec.

The .spec.template is a pod template. It has exactly the same schema as a Pod, except it is nested and does not have an apiVersion or kind.

In addition to required fields for a Pod, a Pod template in a DaemonSet has to specify appropriate labels (see pod selector).

A Pod Template in a DaemonSet must have a RestartPolicy equal to Always, or be unspecified, which defaults to Always.

Pod Selector

The .spec.selector field is a pod selector. It works the same as the .spec.selector of a Job.

You must specify a pod selector that matches the labels of the .spec.template. Also, once a DaemonSet is created, its .spec.selector can not be mutated. Mutating the pod selector can lead to the unintentional orphaning of Pods, and it was found to be confusing to users.

The .spec.selector is an object consisting of two fields:

matchLabels - works the same as the .spec.selector of a ReplicationController.
matchExpressions - allows to build more sophisticated selectors by specifying key, list of values and an operator that relates the key and values.

When the two are specified the result is ANDed.

The .spec.selector must match the .spec.template.metadata.labels. Config with these two not matching will be rejected by the API.

Running Pods on select Nodes

If you specify a .spec.template.spec.nodeSelector, then the DaemonSet controller will create Pods on nodes which match that node selector. Likewise if you specify a .spec.template.spec.affinity, then DaemonSet controller will create Pods on nodes which match that node affinity. If you do not specify either, then the DaemonSet controller will create Pods on all nodes.

How Daemon Pods are scheduled

Scheduled by default scheduler

FEATURE STATE: Kubernetes 1.17 [stable]

A DaemonSet ensures that all eligible nodes run a copy of a Pod. Normally, the node that a Pod runs on is selected by the Kubernetes scheduler. However, DaemonSet pods are created and scheduled by the DaemonSet controller instead. That introduces the following issues:

Inconsistent Pod behavior: Normal Pods waiting to be scheduled are created and in Pending state, but DaemonSet pods are not created in Pending state. This is confusing to the user.
Pod preemption is handled by default scheduler. When preemption is enabled, the DaemonSet controller will make scheduling decisions without considering pod priority and preemption.

ScheduleDaemonSetPods allows you to schedule DaemonSets using the default scheduler instead of the DaemonSet controller, by adding the NodeAffinity term to the DaemonSet pods, instead of the .spec.nodeName term. The default scheduler is then used to bind the pod to the target host. If node affinity of the DaemonSet pod already exists, it is replaced (the original node affinity was taken into account before selecting the target host). The DaemonSet controller only performs these operations when creating or modifying DaemonSet pods, and no changes are made to the spec.template of the DaemonSet.

nodeAffinity:
  requiredDuringSchedulingIgnoredDuringExecution:
    nodeSelectorTerms:
    - matchFields:
      - key: metadata.name
        operator: In
        values:
        - target-host-name

In addition, node.kubernetes.io/unschedulable:NoSchedule toleration is added automatically to DaemonSet Pods. The default scheduler ignores unschedulable Nodes when scheduling DaemonSet Pods.

Taints and Tolerations

Although Daemon Pods respect taints and tolerations, the following tolerations are added to DaemonSet Pods automatically according to the related features.

Toleration Key	Effect	Version	Description
`node.kubernetes.io/not-ready`	NoExecute	1.13+	DaemonSet pods will not be evicted when there are node problems such as a network partition.
`node.kubernetes.io/unreachable`	NoExecute	1.13+	DaemonSet pods will not be evicted when there are node problems such as a network partition.
`node.kubernetes.io/disk-pressure`	NoSchedule	1.8+	DaemonSet pods tolerate disk-pressure attributes by default scheduler.
`node.kubernetes.io/memory-pressure`	NoSchedule	1.8+	DaemonSet pods tolerate memory-pressure attributes by default scheduler.
`node.kubernetes.io/unschedulable`	NoSchedule	1.12+	DaemonSet pods tolerate unschedulable attributes by default scheduler.
`node.kubernetes.io/network-unavailable`	NoSchedule	1.12+	DaemonSet pods, who uses host network, tolerate network-unavailable attributes by default scheduler.

Communicating with Daemon Pods

Some possible patterns for communicating with Pods in a DaemonSet are:

Push: Pods in the DaemonSet are configured to send updates to another service, such as a stats database. They do not have clients.
NodeIP and Known Port: Pods in the DaemonSet can use a hostPort, so that the pods are reachable via the node IPs. Clients know the list of node IPs somehow, and know the port by convention.
DNS: Create a headless service with the same pod selector, and then discover DaemonSets using the endpoints resource or retrieve multiple A records from DNS.
Service: Create a service with the same Pod selector, and use the service to reach a daemon on a random node. (No way to reach specific node.)

Updating a DaemonSet

If node labels are changed, the DaemonSet will promptly add Pods to newly matching nodes and delete Pods from newly not-matching nodes.

You can modify the Pods that a DaemonSet creates. However, Pods do not allow all fields to be updated. Also, the DaemonSet controller will use the original template the next time a node (even with the same name) is created.

You can delete a DaemonSet. If you specify --cascade=orphan with kubectl, then the Pods will be left on the nodes. If you subsequently create a new DaemonSet with the same selector, the new DaemonSet adopts the existing Pods. If any Pods need replacing the DaemonSet replaces them according to its updateStrategy.

You can perform a rolling update on a DaemonSet.

Alternatives to DaemonSet

Init scripts

It is certainly possible to run daemon processes by directly starting them on a node (e.g. using init, upstartd, or systemd). This is perfectly fine. However, there are several advantages to running such processes via a DaemonSet:

Ability to monitor and manage logs for daemons in the same way as applications.
Same config language and tools (e.g. Pod templates, kubectl) for daemons and applications.
Running daemons in containers with resource limits increases isolation between daemons from app containers. However, this can also be accomplished by running the daemons in a container but not in a Pod.

Bare Pods

It is possible to create Pods directly which specify a particular node to run on. However, a DaemonSet replaces Pods that are deleted or terminated for any reason, such as in the case of node failure or disruptive node maintenance, such as a kernel upgrade. For this reason, you should use a DaemonSet rather than creating individual Pods.

Static Pods

It is possible to create Pods by writing a file to a certain directory watched by Kubelet. These are called static pods. Unlike DaemonSet, static Pods cannot be managed with kubectl or other Kubernetes API clients. Static Pods do not depend on the apiserver, making them useful in cluster bootstrapping cases. Also, static Pods may be deprecated in the future.

Deployments

DaemonSets are similar to Deployments in that they both create Pods, and those Pods have processes which are not expected to terminate (e.g. web servers, storage servers).

Use a Deployment for stateless services, like frontends, where scaling up and down the number of replicas and rolling out updates are more important than controlling exactly which host the Pod runs on. Use a DaemonSet when it is important that a copy of a Pod always run on all or certain hosts, if the DaemonSet provides node-level functionality that allows other Pods to run correctly on that particular node.

For example, network plugins often include a component that runs as a DaemonSet. The DaemonSet component makes sure that the node where it's running has working cluster networking.

What's next

Learn about Pods.
- Learn about static Pods, which are useful for running Kubernetes control plane components.
Find out how to use DaemonSets
- Perform a rolling update on a DaemonSet
- Perform a rollback on a DaemonSet (for example, if a roll out didn't work how you expected).
Understand how Kubernetes assigns Pods to Nodes.
Learn about device plugins and add ons, which often run as DaemonSets.
DaemonSet is a top-level resource in the Kubernetes REST API. Read the DaemonSet object definition to understand the API for daemon sets.

5.2.5 - Jobs

A Job creates one or more Pods and will continue to retry execution of the Pods until a specified number of them successfully terminate. As pods successfully complete, the Job tracks the successful completions. When a specified number of successful completions is reached, the task (ie, Job) is complete. Deleting a Job will clean up the Pods it created. Suspending a Job will delete its active Pods until the Job is resumed again.

A simple case is to create one Job object in order to reliably run one Pod to completion. The Job object will start a new Pod if the first Pod fails or is deleted (for example due to a node hardware failure or a node reboot).

You can also use a Job to run multiple Pods in parallel.

If you want to run a Job (either a single task, or several in parallel) on a schedule, see CronJob.

Running an example Job

Here is an example Job config. It computes π to 2000 places and prints it out. It takes around 10s to complete.

controllers/job.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: pi
spec:
  template:
    spec:
      containers:
      - name: pi
        image: perl:5.34.0
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never
  backoffLimit: 4

You can run the example with this command:

kubectl apply -f https://kubernetes.io/examples/controllers/job.yaml

The output is similar to this:

job.batch/pi created

Check on the status of the Job with kubectl:

kubectl describe job pi
kubectl get job pi -o yaml


Name:           pi
Namespace:      default
Selector:       controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
Labels:         controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
                job-name=pi
Annotations:    kubectl.kubernetes.io/last-applied-configuration:
                  {"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"pi","namespace":"default"},"spec":{"backoffLimit":4,"template":...
Parallelism:    1
Completions:    1
Start Time:     Mon, 02 Dec 2019 15:20:11 +0200
Completed At:   Mon, 02 Dec 2019 15:21:16 +0200
Duration:       65s
Pods Statuses:  0 Running / 1 Succeeded / 0 Failed
Pod Template:
  Labels:  controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
           job-name=pi
  Containers:
   pi:
    Image:      perl:5.34.0
    Port:       <none>
    Host Port:  <none>
    Command:
      perl
      -Mbignum=bpi
      -wle
      print bpi(2000)
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Events:
  Type    Reason            Age   From            Message
  ----    ------            ----  ----            -------
  Normal  SuccessfulCreate  14m   job-controller  Created pod: pi-5rwd7


apiVersion: batch/v1
kind: Job
metadata:
  annotations:
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"pi","namespace":"default"},"spec":{"backoffLimit":4,"template":{"spec":{"containers":[{"command":["perl","-Mbignum=bpi","-wle","print bpi(2000)"],"image":"perl","name":"pi"}],"restartPolicy":"Never"}}}}
  creationTimestamp: "2022-06-15T08:40:15Z"
  generation: 1
  labels:
    controller-uid: 863452e6-270d-420e-9b94-53a54146c223
    job-name: pi
  name: pi
  namespace: default
  resourceVersion: "987"
  uid: 863452e6-270d-420e-9b94-53a54146c223
spec:
  backoffLimit: 4
  completionMode: NonIndexed
  completions: 1
  parallelism: 1
  selector:
    matchLabels:
      controller-uid: 863452e6-270d-420e-9b94-53a54146c223
  suspend: false
  template:
    metadata:
      creationTimestamp: null
      labels:
        controller-uid: 863452e6-270d-420e-9b94-53a54146c223
        job-name: pi
    spec:
      containers:
      - command:
        - perl
        - -Mbignum=bpi
        - -wle
        - print bpi(2000)
        image: perl:5.34.0
        imagePullPolicy: Always
        name: pi
        resources: {}
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
      dnsPolicy: ClusterFirst
      restartPolicy: Never
      schedulerName: default-scheduler
      securityContext: {}
      terminationGracePeriodSeconds: 30
status:
  active: 1
  ready: 1
  startTime: "2022-06-15T08:40:15Z"

To view completed Pods of a Job, use kubectl get pods.

To list all the Pods that belong to a Job in a machine readable form, you can use a command like this:

pods=$(kubectl get pods --selector=job-name=pi --output=jsonpath='{.items[*].metadata.name}')
echo $pods

The output is similar to this:

pi-5rwd7

Here, the selector is the same as the selector for the Job. The --output=jsonpath option specifies an expression with the name from each Pod in the returned list.

View the standard output of one of the pods:

kubectl logs $pods

The output is similar to this:

3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031378387528865875332083814206171776691473035982534904287554687311595628638823537875937519577818577805321712268066130019278766111959092164201989380952572010654858632788659361533818279682303019520353018529689957736225994138912497217752834791315155748572424541506959508295331168617278558890750983817546374649393192550604009277016711390098488240128583616035637076601047101819429555961989467678374494482553797747268471040475346462080466842590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211653449872027559602364806654991198818347977535663698074265425278625518184175746728909777727938000816470600161452491921732172147723501414419735685481613611573525521334757418494684385233239073941433345477624168625189835694855620992192221842725502542568876717904946016534668049886272327917860857843838279679766814541009538837863609506800642251252051173929848960841284886269456042419652850222106611863067442786220391949450471237137869609563643719172874677646575739624138908658326459958133904780275901

Writing a Job spec

As with all other Kubernetes config, a Job needs apiVersion, kind, and metadata fields. Its name must be a valid DNS subdomain name.

A Job also needs a .spec section.

Pod Template

The .spec.template is the only required field of the .spec.

The .spec.template is a pod template. It has exactly the same schema as a Pod, except it is nested and does not have an apiVersion or kind.

In addition to required fields for a Pod, a pod template in a Job must specify appropriate labels (see pod selector) and an appropriate restart policy.

Only a RestartPolicy equal to Never or OnFailure is allowed.

Pod selector

The .spec.selector field is optional. In almost all cases you should not specify it. See section specifying your own pod selector.

Parallel execution for Jobs

There are three main types of task suitable to run as a Job:

Non-parallel Jobs
- normally, only one Pod is started, unless the Pod fails.
- the Job is complete as soon as its Pod terminates successfully.
Parallel Jobs with a fixed completion count:
- specify a non-zero positive value for .spec.completions.
- the Job represents the overall task, and is complete when there are .spec.completions successful Pods.
- when using .spec.completionMode="Indexed", each Pod gets a different index in the range 0 to .spec.completions-1.
Parallel Jobs with a work queue:
- do not specify .spec.completions, default to .spec.parallelism.
- the Pods must coordinate amongst themselves or an external service to determine what each should work on. For example, a Pod might fetch a batch of up to N items from the work queue.
- each Pod is independently capable of determining whether or not all its peers are done, and thus that the entire Job is done.
- when any Pod from the Job terminates with success, no new Pods are created.
- once at least one Pod has terminated with success and all Pods are terminated, then the Job is completed with success.
- once any Pod has exited with success, no other Pod should still be doing any work for this task or writing any output. They should all be in the process of exiting.

For a non-parallel Job, you can leave both .spec.completions and .spec.parallelism unset. When both are unset, both are defaulted to 1.

For a fixed completion count Job, you should set .spec.completions to the number of completions needed. You can set .spec.parallelism, or leave it unset and it will default to 1.

For a work queue Job, you must leave .spec.completions unset, and set .spec.parallelism to a non-negative integer.

For more information about how to make use of the different types of job, see the job patterns section.

Controlling parallelism

The requested parallelism (.spec.parallelism) can be set to any non-negative value. If it is unspecified, it defaults to 1. If it is specified as 0, then the Job is effectively paused until it is increased.

Actual parallelism (number of pods running at any instant) may be more or less than requested parallelism, for a variety of reasons:

For fixed completion count Jobs, the actual number of pods running in parallel will not exceed the number of remaining completions. Higher values of .spec.parallelism are effectively ignored.
For work queue Jobs, no new Pods are started after any Pod has succeeded -- remaining Pods are allowed to complete, however.
If the Job Controller has not had time to react.
If the Job controller failed to create Pods for any reason (lack of ResourceQuota, lack of permission, etc.), then there may be fewer pods than requested.
The Job controller may throttle new Pod creation due to excessive previous pod failures in the same Job.
When a Pod is gracefully shut down, it takes time to stop.

Completion mode

FEATURE STATE: Kubernetes v1.24 [stable]

Jobs with fixed completion count - that is, jobs that have non null .spec.completions - can have a completion mode that is specified in .spec.completionMode:

NonIndexed (default): the Job is considered complete when there have been .spec.completions successfully completed Pods. In other words, each Pod completion is homologous to each other. Note that Jobs that have null .spec.completions are implicitly NonIndexed.
Indexed: the Pods of a Job get an associated completion index from 0 to .spec.completions-1. The index is available through three mechanisms:
- The Pod annotation batch.kubernetes.io/job-completion-index.
- As part of the Pod hostname, following the pattern $(job-name)-$(index). When you use an Indexed Job in combination with a Service, Pods within the Job can use the deterministic hostnames to address each other via DNS.
- From the containerized task, in the environment variable JOB_COMPLETION_INDEX.
The Job is considered complete when there is one successfully completed Pod for each index. For more information about how to use this mode, see Indexed Job for Parallel Processing with Static Work Assignment. Note that, although rare, more than one Pod could be started for the same index, but only one of them will count towards the completion count.

Handling Pod and container failures

A container in a Pod may fail for a number of reasons, such as because the process in it exited with a non-zero exit code, or the container was killed for exceeding a memory limit, etc. If this happens, and the .spec.template.spec.restartPolicy = "OnFailure", then the Pod stays on the node, but the container is re-run. Therefore, your program needs to handle the case when it is restarted locally, or else specify .spec.template.spec.restartPolicy = "Never". See pod lifecycle for more information on restartPolicy.

An entire Pod can also fail, for a number of reasons, such as when the pod is kicked off the node (node is upgraded, rebooted, deleted, etc.), or if a container of the Pod fails and the .spec.template.spec.restartPolicy = "Never". When a Pod fails, then the Job controller starts a new Pod. This means that your application needs to handle the case when it is restarted in a new pod. In particular, it needs to handle temporary files, locks, incomplete output and the like caused by previous runs.

Note that even if you specify .spec.parallelism = 1 and .spec.completions = 1 and .spec.template.spec.restartPolicy = "Never", the same program may sometimes be started twice.

If you do specify .spec.parallelism and .spec.completions both greater than 1, then there may be multiple pods running at once. Therefore, your pods must also be tolerant of concurrency.

Pod backoff failure policy

There are situations where you want to fail a Job after some amount of retries due to a logical error in configuration etc. To do so, set .spec.backoffLimit to specify the number of retries before considering a Job as failed. The back-off limit is set by default to 6. Failed Pods associated with the Job are recreated by the Job controller with an exponential back-off delay (10s, 20s, 40s ...) capped at six minutes.

The number of retries is calculated in two ways:

The number of Pods with .status.phase = "Failed".
When using restartPolicy = "OnFailure", the number of retries in all the containers of Pods with .status.phase equal to Pending or Running.

If either of the calculations reaches the .spec.backoffLimit, the Job is considered failed.

When the JobTrackingWithFinalizers feature is disabled, the number of failed Pods is only based on Pods that are still present in the API.

Note: If your job has restartPolicy = "OnFailure", keep in mind that your Pod running the Job will be terminated once the job backoff limit has been reached. This can make debugging the Job's executable more difficult. We suggest setting restartPolicy = "Never" when debugging the Job or using a logging system to ensure output from failed Jobs is not lost inadvertently.

Job termination and cleanup

When a Job completes, no more Pods are created, but the Pods are usually not deleted either. Keeping them around allows you to still view the logs of completed pods to check for errors, warnings, or other diagnostic output. The job object also remains after it is completed so that you can view its status. It is up to the user to delete old jobs after noting their status. Delete the job with kubectl (e.g. kubectl delete jobs/pi or kubectl delete -f ./job.yaml). When you delete the job using kubectl, all the pods it created are deleted too.

By default, a Job will run uninterrupted unless a Pod fails (restartPolicy=Never) or a Container exits in error (restartPolicy=OnFailure), at which point the Job defers to the .spec.backoffLimit described above. Once .spec.backoffLimit has been reached the Job will be marked as failed and any running Pods will be terminated.

Another way to terminate a Job is by setting an active deadline. Do this by setting the .spec.activeDeadlineSeconds field of the Job to a number of seconds. The activeDeadlineSeconds applies to the duration of the job, no matter how many Pods are created. Once a Job reaches activeDeadlineSeconds, all of its running Pods are terminated and the Job status will become type: Failed with reason: DeadlineExceeded.

Note that a Job's .spec.activeDeadlineSeconds takes precedence over its .spec.backoffLimit. Therefore, a Job that is retrying one or more failed Pods will not deploy additional Pods once it reaches the time limit specified by activeDeadlineSeconds, even if the backoffLimit is not yet reached.

Example:

apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-timeout
spec:
  backoffLimit: 5
  activeDeadlineSeconds: 100
  template:
    spec:
      containers:
      - name: pi
        image: perl:5.34.0
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never

Note that both the Job spec and the Pod template spec within the Job have an activeDeadlineSeconds field. Ensure that you set this field at the proper level.

Keep in mind that the restartPolicy applies to the Pod, and not to the Job itself: there is no automatic Job restart once the Job status is type: Failed. That is, the Job termination mechanisms activated with .spec.activeDeadlineSeconds and .spec.backoffLimit result in a permanent Job failure that requires manual intervention to resolve.

Clean up finished jobs automatically

Finished Jobs are usually no longer needed in the system. Keeping them around in the system will put pressure on the API server. If the Jobs are managed directly by a higher level controller, such as CronJobs, the Jobs can be cleaned up by CronJobs based on the specified capacity-based cleanup policy.

TTL mechanism for finished Jobs

FEATURE STATE: Kubernetes v1.23 [stable]

Another way to clean up finished Jobs (either Complete or Failed) automatically is to use a TTL mechanism provided by a TTL controller for finished resources, by specifying the .spec.ttlSecondsAfterFinished field of the Job.

When the TTL controller cleans up the Job, it will delete the Job cascadingly, i.e. delete its dependent objects, such as Pods, together with the Job. Note that when the Job is deleted, its lifecycle guarantees, such as finalizers, will be honored.

For example:

apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-ttl
spec:
  ttlSecondsAfterFinished: 100
  template:
    spec:
      containers:
      - name: pi
        image: perl:5.34.0
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never

The Job pi-with-ttl will be eligible to be automatically deleted, 100 seconds after it finishes.

If the field is set to 0, the Job will be eligible to be automatically deleted immediately after it finishes. If the field is unset, this Job won't be cleaned up by the TTL controller after it finishes.

Note:

It is recommended to set ttlSecondsAfterFinished field because unmanaged jobs (Jobs that you created directly, and not indirectly through other workload APIs such as CronJob) have a default deletion policy of orphanDependents causing Pods created by an unmanaged Job to be left around after that Job is fully deleted. Even though the control plane eventually garbage collects the Pods from a deleted Job after they either fail or complete, sometimes those lingering pods may cause cluster performance degradation or in worst case cause the cluster to go offline due to this degradation.

You can use LimitRanges and ResourceQuotas to place a cap on the amount of resources that a particular namespace can consume.

Job patterns

The Job object can be used to support reliable parallel execution of Pods. The Job object is not designed to support closely-communicating parallel processes, as commonly found in scientific computing. It does support parallel processing of a set of independent but related work items. These might be emails to be sent, frames to be rendered, files to be transcoded, ranges of keys in a NoSQL database to scan, and so on.

In a complex system, there may be multiple different sets of work items. Here we are just considering one set of work items that the user wants to manage together — a batch job.

There are several different patterns for parallel computation, each with strengths and weaknesses. The tradeoffs are:

One Job object for each work item, vs. a single Job object for all work items. The latter is better for large numbers of work items. The former creates some overhead for the user and for the system to manage large numbers of Job objects.
Number of pods created equals number of work items, vs. each Pod can process multiple work items. The former typically requires less modification to existing code and containers. The latter is better for large numbers of work items, for similar reasons to the previous bullet.
Several approaches use a work queue. This requires running a queue service, and modifications to the existing program or container to make it use the work queue. Other approaches are easier to adapt to an existing containerised application.

The tradeoffs are summarized here, with columns 2 to 4 corresponding to the above tradeoffs. The pattern names are also links to examples and more detailed description.

Pattern	Single Job object	Fewer pods than work items?	Use app unmodified?
Queue with Pod Per Work Item	✓		sometimes
Queue with Variable Pod Count	✓	✓
Indexed Job with Static Work Assignment	✓		✓
Job Template Expansion			✓

When you specify completions with .spec.completions, each Pod created by the Job controller has an identical spec. This means that all pods for a task will have the same command line and the same image, the same volumes, and (almost) the same environment variables. These patterns are different ways to arrange for pods to work on different things.

This table shows the required settings for .spec.parallelism and .spec.completions for each of the patterns. Here, W is the number of work items.

Pattern	`.spec.completions`	`.spec.parallelism`
Queue with Pod Per Work Item	W	any
Queue with Variable Pod Count	null	any
Indexed Job with Static Work Assignment	W	any
Job Template Expansion	1	should be 1

Advanced usage

Suspending a Job

FEATURE STATE: Kubernetes v1.24 [stable]

When a Job is created, the Job controller will immediately begin creating Pods to satisfy the Job's requirements and will continue to do so until the Job is complete. However, you may want to temporarily suspend a Job's execution and resume it later, or start Jobs in suspended state and have a custom controller decide later when to start them.

To suspend a Job, you can update the .spec.suspend field of the Job to true; later, when you want to resume it again, update it to false. Creating a Job with .spec.suspend set to true will create it in the suspended state.

When a Job is resumed from suspension, its .status.startTime field will be reset to the current time. This means that the .spec.activeDeadlineSeconds timer will be stopped and reset when a Job is suspended and resumed.

When you suspend a Job, any running Pods that don't have a status of Completed will be terminated. with a SIGTERM signal. The Pod's graceful termination period will be honored and your Pod must handle this signal in this period. This may involve saving progress for later or undoing changes. Pods terminated this way will not count towards the Job's completions count.

An example Job definition in the suspended state can be like so:

kubectl get job myjob -o yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: myjob
spec:
  suspend: true
  parallelism: 1
  completions: 5
  template:
    spec:
      ...

You can also toggle Job suspension by patching the Job using the command line.

Suspend an active Job:

kubectl patch job/myjob --type=strategic --patch '{"spec":{"suspend":true}}'

Resume a suspended Job:

kubectl patch job/myjob --type=strategic --patch '{"spec":{"suspend":false}}'

The Job's status can be used to determine if a Job is suspended or has been suspended in the past:

kubectl get jobs/myjob -o yaml

apiVersion: batch/v1
kind: Job
# .metadata and .spec omitted
status:
  conditions:
  - lastProbeTime: "2021-02-05T13:14:33Z"
    lastTransitionTime: "2021-02-05T13:14:33Z"
    status: "True"
    type: Suspended
  startTime: "2021-02-05T13:13:48Z"

The Job condition of type "Suspended" with status "True" means the Job is suspended; the lastTransitionTime field can be used to determine how long the Job has been suspended for. If the status of that condition is "False", then the Job was previously suspended and is now running. If such a condition does not exist in the Job's status, the Job has never been stopped.

Events are also created when the Job is suspended and resumed:

kubectl describe jobs/myjob

Name:           myjob
...
Events:
  Type    Reason            Age   From            Message
  ----    ------            ----  ----            -------
  Normal  SuccessfulCreate  12m   job-controller  Created pod: myjob-hlrpl
  Normal  SuccessfulDelete  11m   job-controller  Deleted pod: myjob-hlrpl
  Normal  Suspended         11m   job-controller  Job suspended
  Normal  SuccessfulCreate  3s    job-controller  Created pod: myjob-jvb44
  Normal  Resumed           3s    job-controller  Job resumed

The last four events, particularly the "Suspended" and "Resumed" events, are directly a result of toggling the .spec.suspend field. In the time between these two events, we see that no Pods were created, but Pod creation restarted as soon as the Job was resumed.

Mutable Scheduling Directives

FEATURE STATE: Kubernetes v1.23 [beta]

Note: In order to use this behavior, you must enable the JobMutableNodeSchedulingDirectives feature gate on the API server. It is enabled by default.

In most cases a parallel job will want the pods to run with constraints, like all in the same zone, or all either on GPU model x or y but not a mix of both.

The suspend field is the first step towards achieving those semantics. Suspend allows a custom queue controller to decide when a job should start; However, once a job is unsuspended, a custom queue controller has no influence on where the pods of a job will actually land.

This feature allows updating a Job's scheduling directives before it starts, which gives custom queue controllers the ability to influence pod placement while at the same time offloading actual pod-to-node assignment to kube-scheduler. This is allowed only for suspended Jobs that have never been unsuspended before.

The fields in a Job's pod template that can be updated are node affinity, node selector, tolerations, labels and annotations.

Specifying your own Pod selector

Normally, when you create a Job object, you do not specify .spec.selector. The system defaulting logic adds this field when the Job is created. It picks a selector value that will not overlap with any other jobs.

However, in some cases, you might need to override this automatically set selector. To do this, you can specify the .spec.selector of the Job.

Be very careful when doing this. If you specify a label selector which is not unique to the pods of that Job, and which matches unrelated Pods, then pods of the unrelated job may be deleted, or this Job may count other Pods as completing it, or one or both Jobs may refuse to create Pods or run to completion. If a non-unique selector is chosen, then other controllers (e.g. ReplicationController) and their Pods may behave in unpredictable ways too. Kubernetes will not stop you from making a mistake when specifying .spec.selector.

Here is an example of a case when you might want to use this feature.

Say Job old is already running. You want existing Pods to keep running, but you want the rest of the Pods it creates to use a different pod template and for the Job to have a new name. You cannot update the Job because these fields are not updatable. Therefore, you delete Job old but leave its pods running, using kubectl delete jobs/old --cascade=orphan. Before deleting it, you make a note of what selector it uses:

kubectl get job old -o yaml

The output is similar to this:

kind: Job
metadata:
  name: old
  ...
spec:
  selector:
    matchLabels:
      controller-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
  ...

Then you create a new Job with name new and you explicitly specify the same selector. Since the existing Pods have label controller-uid=a8f3d00d-c6d2-11e5-9f87-42010af00002, they are controlled by Job new as well.

You need to specify manualSelector: true in the new Job since you are not using the selector that the system normally generates for you automatically.

kind: Job
metadata:
  name: new
  ...
spec:
  manualSelector: true
  selector:
    matchLabels:
      controller-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
  ...

The new Job itself will have a different uid from a8f3d00d-c6d2-11e5-9f87-42010af00002. Setting manualSelector: true tells the system that you know what you are doing and to allow this mismatch.

Pod failure policy

FEATURE STATE: Kubernetes v1.25 [alpha]

Note: You can only configure a Pod failure policy for a Job if you have the JobPodFailurePolicy feature gate enabled in your cluster. Additionally, it is recommended to enable the PodDisruptionConditions feature gate in order to be able to detect and handle Pod disruption conditions in the Pod failure policy (see also: Pod disruption conditions). Both feature gates are available in Kubernetes v1.25.

A Pod failure policy, defined with the .spec.podFailurePolicy field, enables your cluster to handle Pod failures based on the container exit codes and the Pod conditions.

In some situations, you may want to have a better control when handling Pod failures than the control provided by the Pod backoff failure policy, which is based on the Job's .spec.backoffLimit. These are some examples of use cases:

To optimize costs of running workloads by avoiding unnecessary Pod restarts, you can terminate a Job as soon as one of its Pods fails with an exit code indicating a software bug.
To guarantee that your Job finishes even if there are disruptions, you can ignore Pod failures caused by disruptions (such preemption, API-initiated eviction or taint-based eviction) so that they don't count towards the .spec.backoffLimit limit of retries.

You can configure a Pod failure policy, in the .spec.podFailurePolicy field, to meet the above use cases. This policy can handle Pod failures based on the container exit codes and the Pod conditions.

Here is a manifest for a Job that defines a podFailurePolicy:

/controllers/job-pod-failure-policy-example.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: job-pod-failure-policy-example
spec:
  completions: 12
  parallelism: 3
  template:
    spec:
      restartPolicy: Never
      containers:
      - name: main
        image: docker.io/library/bash:5
        command: ["bash"]        # example command simulating a bug which triggers the FailJob action
        args:
        - -c
        - echo "Hello world!" && sleep 5 && exit 42
  backoffLimit: 6
  podFailurePolicy:
    rules:
    - action: FailJob
      onExitCodes:
        containerName: main      # optional
        operator: In             # one of: In, NotIn
        values: [42]
    - action: Ignore             # one of: Ignore, FailJob, Count
      onPodConditions:
      - type: DisruptionTarget   # indicates Pod disruption

In the example above, the first rule of the Pod failure policy specifies that the Job should be marked failed if the main container fails with the 42 exit code. The following are the rules for the main container specifically:

an exit code of 0 means that the container succeeded
an exit code of 42 means that the entire Job failed
any other exit code represents that the container failed, and hence the entire Pod. The Pod will be re-created if the total number of restarts is below backoffLimit. If the backoffLimit is reached the entire Job failed.

Note: Because the Pod template specifies a restartPolicy: Never, the kubelet does not restart the main container in that particular Pod.

The second rule of the Pod failure policy, specifying the Ignore action for failed Pods with condition DisruptionTarget excludes Pod disruptions from being counted towards the .spec.backoffLimit limit of retries.

Note: If the Job failed, either by the Pod failure policy or Pod backoff failure policy, and the Job is running multiple Pods, Kubernetes terminates all the Pods in that Job that are still Pending or Running.

These are some requirements and semantics of the API:

if you want to use a .spec.podFailurePolicy field for a Job, you must also define that Job's pod template with .spec.restartPolicy set to Never.
the Pod failure policy rules you specify under spec.podFailurePolicy.rules are evaluated in order. Once a rule matches a Pod failure, the remaining rules are ignored. When no rule matches the Pod failure, the default handling applies.
you may want to restrict a rule to a specific container by specifing its name inspec.podFailurePolicy.rules[*].containerName. When not specified the rule applies to all containers. When specified, it should match one the container or initContainer names in the Pod template.
you may specify the action taken when a Pod failure policy is matched by spec.podFailurePolicy.rules[*].action. Possible values are:
- FailJob: use to indicate that the Pod's job should be marked as Failed and all running Pods should be terminated.
- Ignore: use to indicate that the counter towards the .spec.backoffLimit should not be incremented and a replacement Pod should be created.
- Count: use to indicate that the Pod should be handled in the default way. The counter towards the .spec.backoffLimit should be incremented.

Job tracking with finalizers

FEATURE STATE: Kubernetes v1.23 [beta]

Note:

In order to use this behavior, you must enable the JobTrackingWithFinalizers feature gate on the API server and the controller manager.

When enabled, the control plane tracks new Jobs using the behavior described below. Jobs created before the feature was enabled are unaffected. As a user, the only difference you would see is that the control plane tracking of Job completion is more accurate.

When this feature isn't enabled, the Job Controller relies on counting the Pods that exist in the cluster to track the Job status, that is, to keep the counters for succeeded and failed Pods. However, Pods can be removed for a number of reasons, including:

The garbage collector that removes orphan Pods when a Node goes down.
The garbage collector that removes finished Pods (in Succeeded or Failed phase) after a threshold.
Human intervention to delete Pods belonging to a Job.
An external controller (not provided as part of Kubernetes) that removes or replaces Pods.

If you enable the JobTrackingWithFinalizers feature for your cluster, the control plane keeps track of the Pods that belong to any Job and notices if any such Pod is removed from the API server. To do that, the Job controller creates Pods with the finalizer batch.kubernetes.io/job-tracking. The controller removes the finalizer only after the Pod has been accounted for in the Job status, allowing the Pod to be removed by other controllers or users.

The Job controller uses the new algorithm for new Jobs only. Jobs created before the feature is enabled are unaffected. You can determine if the Job controller is tracking a Job using Pod finalizers by checking if the Job has the annotation batch.kubernetes.io/job-tracking. You should not manually add or remove this annotation from Jobs.

Alternatives

Bare Pods

When the node that a Pod is running on reboots or fails, the pod is terminated and will not be restarted. However, a Job will create new Pods to replace terminated ones. For this reason, we recommend that you use a Job rather than a bare Pod, even if your application requires only a single Pod.

Replication Controller

Jobs are complementary to Replication Controllers. A Replication Controller manages Pods which are not expected to terminate (e.g. web servers), and a Job manages Pods that are expected to terminate (e.g. batch tasks).

As discussed in Pod Lifecycle, Job is only appropriate for pods with RestartPolicy equal to OnFailure or Never. (Note: If RestartPolicy is not set, the default value is Always.)

Single Job starts controller Pod

Another pattern is for a single Job to create a Pod which then creates other Pods, acting as a sort of custom controller for those Pods. This allows the most flexibility, but may be somewhat complicated to get started with and offers less integration with Kubernetes.

One example of this pattern would be a Job which starts a Pod which runs a script that in turn starts a Spark master controller (see spark example), runs a spark driver, and then cleans up.

An advantage of this approach is that the overall process gets the completion guarantee of a Job object, but maintains complete control over what Pods are created and how work is assigned to them.

What's next

Learn about Pods.
Read about different ways of running Jobs:
- Coarse Parallel Processing Using a Work Queue
- Fine Parallel Processing Using a Work Queue
- Use an indexed Job for parallel processing with static work assignment
- Create multiple Jobs based on a template: Parallel Processing using Expansions
Follow the links within Clean up finished jobs automatically to learn more about how your cluster can clean up completed and / or failed tasks.
Job is part of the Kubernetes REST API. Read the Job object definition to understand the API for jobs.
Read about CronJob, which you can use to define a series of Jobs that will run based on a schedule, similar to the UNIX tool cron.
Practice how to configure handling of retriable and non-retriable pod failures using podFailurePolicy, based on the step-by-step examples.

5.2.6 - Automatic Clean-up for Finished Jobs

FEATURE STATE: Kubernetes v1.23 [stable]

TTL-after-finished controller provides a TTL (time to live) mechanism to limit the lifetime of resource objects that have finished execution. TTL controller only handles Jobs.

TTL-after-finished Controller

The TTL-after-finished controller is only supported for Jobs. A cluster operator can use this feature to clean up finished Jobs (either Complete or Failed) automatically by specifying the .spec.ttlSecondsAfterFinished field of a Job, as in this example. The TTL-after-finished controller will assume that a job is eligible to be cleaned up TTL seconds after the job has finished, in other words, when the TTL has expired. When the TTL-after-finished controller cleans up a job, it will delete it cascadingly, that is to say it will delete its dependent objects together with it. Note that when the job is deleted, its lifecycle guarantees, such as finalizers, will be honored.

The TTL seconds can be set at any time. Here are some examples for setting the .spec.ttlSecondsAfterFinished field of a Job:

Specify this field in the job manifest, so that a Job can be cleaned up automatically some time after it finishes.
Set this field of existing, already finished jobs, to adopt this new feature.
Use a mutating admission webhook to set this field dynamically at job creation time. Cluster administrators can use this to enforce a TTL policy for finished jobs.
Use a mutating admission webhook to set this field dynamically after the job has finished, and choose different TTL values based on job status, labels, etc.

Caveat

Updating TTL Seconds

Note that the TTL period, e.g. .spec.ttlSecondsAfterFinished field of Jobs, can be modified after the job is created or has finished. However, once the Job becomes eligible to be deleted (when the TTL has expired), the system won't guarantee that the Jobs will be kept, even if an update to extend the TTL returns a successful API response.

Time Skew

Because TTL-after-finished controller uses timestamps stored in the Kubernetes jobs to determine whether the TTL has expired or not, this feature is sensitive to time skew in the cluster, which may cause TTL-after-finish controller to clean up job objects at the wrong time.

Clocks aren't always correct, but the difference should be very small. Please be aware of this risk when setting a non-zero TTL.

What's next

5.2.7 - CronJob

FEATURE STATE: Kubernetes v1.21 [stable]

A CronJob creates Jobs on a repeating schedule.

One CronJob object is like one line of a crontab (cron table) file. It runs a job periodically on a given schedule, written in Cron format.

Caution:

All CronJob schedule: times are based on the timezone of the kube-controller-manager.

If your control plane runs the kube-controller-manager in Pods or bare containers, the timezone set for the kube-controller-manager container determines the timezone that the cron job controller uses.

Caution:

The v1 CronJob API does not officially support setting timezone as explained above.

Setting variables such as CRON_TZ or TZ is not officially supported by the Kubernetes project. CRON_TZ or TZ is an implementation detail of the internal library being used for parsing and calculating the next Job creation time. Any usage of it is not recommended in a production cluster.

When creating the manifest for a CronJob resource, make sure the name you provide is a valid DNS subdomain name. The name must be no longer than 52 characters. This is because the CronJob controller will automatically append 11 characters to the job name provided and there is a constraint that the maximum length of a Job name is no more than 63 characters.

CronJob

CronJobs are meant for performing regular scheduled actions such as backups, report generation, and so on. Each of those tasks should be configured to recur indefinitely (for example: once a day / week / month); you can define the point in time within that interval when the job should start.

Example

This example CronJob manifest prints the current time and a hello message every minute:

application/job/cronjob.yaml

apiVersion: batch/v1
kind: CronJob
metadata:
  name: hello
spec:
  schedule: "* * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: hello
            image: busybox:1.28
            imagePullPolicy: IfNotPresent
            command:
            - /bin/sh
            - -c
            - date; echo Hello from the Kubernetes cluster
          restartPolicy: OnFailure

(Running Automated Tasks with a CronJob takes you through this example in more detail).

Cron schedule syntax

# ┌───────────── minute (0 - 59)
# │ ┌───────────── hour (0 - 23)
# │ │ ┌───────────── day of the month (1 - 31)
# │ │ │ ┌───────────── month (1 - 12)
# │ │ │ │ ┌───────────── day of the week (0 - 6) (Sunday to Saturday;
# │ │ │ │ │                                   7 is also Sunday on some systems)
# │ │ │ │ │                                   OR sun, mon, tue, wed, thu, fri, sat
# │ │ │ │ │
# * * * * *

Entry	Description	Equivalent to
@yearly (or @annually)	Run once a year at midnight of 1 January	0 0 1 1 *
@monthly	Run once a month at midnight of the first day of the month	0 0 1 * *
@weekly	Run once a week at midnight on Sunday morning	0 0 * * 0
@daily (or @midnight)	Run once a day at midnight	0 0 * * *
@hourly	Run once an hour at the beginning of the hour	0 * * * *

For example, the line below states that the task must be started every Friday at midnight, as well as on the 13th of each month at midnight:

0 0 13 * 5

To generate CronJob schedule expressions, you can also use web tools like crontab.guru.

Time zones

For CronJobs with no time zone specified, the kube-controller-manager interprets schedules relative to its local time zone.

FEATURE STATE: Kubernetes v1.25 [beta]

If you enable the CronJobTimeZone feature gate, you can specify a time zone for a CronJob (if you don't enable that feature gate, or if you are using a version of Kubernetes that does not have experimental time zone support, all CronJobs in your cluster have an unspecified timezone).

When you have the feature enabled, you can set spec.timeZone to the name of a valid time zone name. For example, setting spec.timeZone: "Etc/UTC" instructs Kubernetes to interpret the schedule relative to Coordinated Universal Time.

A time zone database from the Go standard library is included in the binaries and used as a fallback in case an external database is not available on the system.

CronJob limitations

A cron job creates a job object about once per execution time of its schedule. We say "about" because there are certain circumstances where two jobs might be created, or no job might be created. We attempt to make these rare, but do not completely prevent them. Therefore, jobs should be idempotent.

If startingDeadlineSeconds is set to a large value or left unset (the default) and if concurrencyPolicy is set to Allow, the jobs will always run at least once.

Caution: If startingDeadlineSeconds is set to a value less than 10 seconds, the CronJob may not be scheduled. This is because the CronJob controller checks things every 10 seconds.

For every CronJob, the CronJob Controller checks how many schedules it missed in the duration from its last scheduled time until now. If there are more than 100 missed schedules, then it does not start the job and logs the error

Cannot determine if job needs to be started. Too many missed start time (> 100). Set or decrease .spec.startingDeadlineSeconds or check clock skew.

It is important to note that if the startingDeadlineSeconds field is set (not nil), the controller counts how many missed jobs occurred from the value of startingDeadlineSeconds until now rather than from the last scheduled time until now. For example, if startingDeadlineSeconds is 200, the controller counts how many missed jobs occurred in the last 200 seconds.

A CronJob is counted as missed if it has failed to be created at its scheduled time. For example, If concurrencyPolicy is set to Forbid and a CronJob was attempted to be scheduled when there was a previous schedule still running, then it would count as missed.

For example, suppose a CronJob is set to schedule a new Job every one minute beginning at 08:30:00, and its startingDeadlineSeconds field is not set. If the CronJob controller happens to be down from 08:29:00 to 10:21:00, the job will not start as the number of missed jobs which missed their schedule is greater than 100.

To illustrate this concept further, suppose a CronJob is set to schedule a new Job every one minute beginning at 08:30:00, and its startingDeadlineSeconds is set to 200 seconds. If the CronJob controller happens to be down for the same period as the previous example (08:29:00 to 10:21:00,) the Job will still start at 10:22:00. This happens as the controller now checks how many missed schedules happened in the last 200 seconds (ie, 3 missed schedules), rather than from the last scheduled time until now.

The CronJob is only responsible for creating Jobs that match its schedule, and the Job in turn is responsible for the management of the Pods it represents.

Controller version

Starting with Kubernetes v1.21 the second version of the CronJob controller is the default implementation. To disable the default CronJob controller and use the original CronJob controller instead, one pass the CronJobControllerV2 feature gate flag to the kube-controller-manager, and set this flag to false. For example:

--feature-gates="CronJobControllerV2=false"

What's next

Learn about Pods and Jobs, two concepts that CronJobs rely upon.
Read about the format of CronJob .spec.schedule fields.
For instructions on creating and working with CronJobs, and for an example of a CronJob manifest, see Running automated tasks with CronJobs.
For instructions to clean up failed or completed jobs automatically, see Clean up Jobs automatically
CronJob is part of the Kubernetes REST API. Read the CronJob object definition to understand the API for Kubernetes cron jobs.

5.2.8 - ReplicationController

Note: A Deployment that configures a ReplicaSet is now the recommended way to set up replication.

A ReplicationController ensures that a specified number of pod replicas are running at any one time. In other words, a ReplicationController makes sure that a pod or a homogeneous set of pods is always up and available.

How a ReplicationController Works

If there are too many pods, the ReplicationController terminates the extra pods. If there are too few, the ReplicationController starts more pods. Unlike manually created pods, the pods maintained by a ReplicationController are automatically replaced if they fail, are deleted, or are terminated. For example, your pods are re-created on a node after disruptive maintenance such as a kernel upgrade. For this reason, you should use a ReplicationController even if your application requires only a single pod. A ReplicationController is similar to a process supervisor, but instead of supervising individual processes on a single node, the ReplicationController supervises multiple pods across multiple nodes.

ReplicationController is often abbreviated to "rc" in discussion, and as a shortcut in kubectl commands.

A simple case is to create one ReplicationController object to reliably run one instance of a Pod indefinitely. A more complex use case is to run several identical replicas of a replicated service, such as web servers.

Running an example ReplicationController

This example ReplicationController config runs three copies of the nginx web server.

controllers/replication.yaml

apiVersion: v1
kind: ReplicationController
metadata:
  name: nginx
spec:
  replicas: 3
  selector:
    app: nginx
  template:
    metadata:
      name: nginx
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx
        ports:
        - containerPort: 80

Run the example job by downloading the example file and then running this command:

kubectl apply -f https://k8s.io/examples/controllers/replication.yaml

The output is similar to this:

replicationcontroller/nginx created

Check on the status of the ReplicationController using this command:

kubectl describe replicationcontrollers/nginx

The output is similar to this:

Name:        nginx
Namespace:   default
Selector:    app=nginx
Labels:      app=nginx
Annotations:    <none>
Replicas:    3 current / 3 desired
Pods Status: 0 Running / 3 Waiting / 0 Succeeded / 0 Failed
Pod Template:
  Labels:       app=nginx
  Containers:
   nginx:
    Image:              nginx
    Port:               80/TCP
    Environment:        <none>
    Mounts:             <none>
  Volumes:              <none>
Events:
  FirstSeen       LastSeen     Count    From                        SubobjectPath    Type      Reason              Message
  ---------       --------     -----    ----                        -------------    ----      ------              -------
  20s             20s          1        {replication-controller }                    Normal    SuccessfulCreate    Created pod: nginx-qrm3m
  20s             20s          1        {replication-controller }                    Normal    SuccessfulCreate    Created pod: nginx-3ntk0
  20s             20s          1        {replication-controller }                    Normal    SuccessfulCreate    Created pod: nginx-4ok8v

Here, three pods are created, but none is running yet, perhaps because the image is being pulled. A little later, the same command may show:

Pods Status:    3 Running / 0 Waiting / 0 Succeeded / 0 Failed

To list all the pods that belong to the ReplicationController in a machine readable form, you can use a command like this:

pods=$(kubectl get pods --selector=app=nginx --output=jsonpath={.items..metadata.name})
echo $pods

The output is similar to this:

nginx-3ntk0 nginx-4ok8v nginx-qrm3m

Here, the selector is the same as the selector for the ReplicationController (seen in the kubectl describe output), and in a different form in replication.yaml. The --output=jsonpath option specifies an expression with the name from each pod in the returned list.

Writing a ReplicationController Spec

As with all other Kubernetes config, a ReplicationController needs apiVersion, kind, and metadata fields. The name of a ReplicationController object must be a valid DNS subdomain name. For general information about working with configuration files, see object management.

A ReplicationController also needs a .spec section.

Pod Template

The .spec.template is the only required field of the .spec.

The .spec.template is a pod template. It has exactly the same schema as a Pod, except it is nested and does not have an apiVersion or kind.

In addition to required fields for a Pod, a pod template in a ReplicationController must specify appropriate labels and an appropriate restart policy. For labels, make sure not to overlap with other controllers. See pod selector.

Only a .spec.template.spec.restartPolicy equal to Always is allowed, which is the default if not specified.

For local container restarts, ReplicationControllers delegate to an agent on the node, for example the Kubelet.

Labels on the ReplicationController

The ReplicationController can itself have labels (.metadata.labels). Typically, you would set these the same as the .spec.template.metadata.labels; if .metadata.labels is not specified then it defaults to .spec.template.metadata.labels. However, they are allowed to be different, and the .metadata.labels do not affect the behavior of the ReplicationController.

Pod Selector

The .spec.selector field is a label selector. A ReplicationController manages all the pods with labels that match the selector. It does not distinguish between pods that it created or deleted and pods that another person or process created or deleted. This allows the ReplicationController to be replaced without affecting the running pods.

If specified, the .spec.template.metadata.labels must be equal to the .spec.selector, or it will be rejected by the API. If .spec.selector is unspecified, it will be defaulted to .spec.template.metadata.labels.

Also you should not normally create any pods whose labels match this selector, either directly, with another ReplicationController, or with another controller such as Job. If you do so, the ReplicationController thinks that it created the other pods. Kubernetes does not stop you from doing this.

If you do end up with multiple controllers that have overlapping selectors, you will have to manage the deletion yourself (see below).

Multiple Replicas

You can specify how many pods should run concurrently by setting .spec.replicas to the number of pods you would like to have running concurrently. The number running at any time may be higher or lower, such as if the replicas were just increased or decreased, or if a pod is gracefully shutdown, and a replacement starts early.

If you do not specify .spec.replicas, then it defaults to 1.

Working with ReplicationControllers

Deleting a ReplicationController and its Pods

To delete a ReplicationController and all its pods, use kubectl delete. Kubectl will scale the ReplicationController to zero and wait for it to delete each pod before deleting the ReplicationController itself. If this kubectl command is interrupted, it can be restarted.

When using the REST API or client library, you need to do the steps explicitly (scale replicas to 0, wait for pod deletions, then delete the ReplicationController).

Deleting only a ReplicationController

You can delete a ReplicationController without affecting any of its pods.

Using kubectl, specify the --cascade=orphan option to kubectl delete.

When using the REST API or client library, you can delete the ReplicationController object.

Once the original is deleted, you can create a new ReplicationController to replace it. As long as the old and new .spec.selector are the same, then the new one will adopt the old pods. However, it will not make any effort to make existing pods match a new, different pod template. To update pods to a new spec in a controlled way, use a rolling update.

Isolating pods from a ReplicationController

Pods may be removed from a ReplicationController's target set by changing their labels. This technique may be used to remove pods from service for debugging and data recovery. Pods that are removed in this way will be replaced automatically (assuming that the number of replicas is not also changed).

Common usage patterns

Rescheduling

As mentioned above, whether you have 1 pod you want to keep running, or 1000, a ReplicationController will ensure that the specified number of pods exists, even in the event of node failure or pod termination (for example, due to an action by another control agent).

Scaling

The ReplicationController enables scaling the number of replicas up or down, either manually or by an auto-scaling control agent, by updating the replicas field.

Rolling updates

The ReplicationController is designed to facilitate rolling updates to a service by replacing pods one-by-one.

As explained in #1353, the recommended approach is to create a new ReplicationController with 1 replica, scale the new (+1) and old (-1) controllers one by one, and then delete the old controller after it reaches 0 replicas. This predictably updates the set of pods regardless of unexpected failures.

Ideally, the rolling update controller would take application readiness into account, and would ensure that a sufficient number of pods were productively serving at any given time.

The two ReplicationControllers would need to create pods with at least one differentiating label, such as the image tag of the primary container of the pod, since it is typically image updates that motivate rolling updates.

Multiple release tracks

In addition to running multiple releases of an application while a rolling update is in progress, it's common to run multiple releases for an extended period of time, or even continuously, using multiple release tracks. The tracks would be differentiated by labels.

For instance, a service might target all pods with tier in (frontend), environment in (prod). Now say you have 10 replicated pods that make up this tier. But you want to be able to 'canary' a new version of this component. You could set up a ReplicationController with replicas set to 9 for the bulk of the replicas, with labels tier=frontend, environment=prod, track=stable, and another ReplicationController with replicas set to 1 for the canary, with labels tier=frontend, environment=prod, track=canary. Now the service is covering both the canary and non-canary pods. But you can mess with the ReplicationControllers separately to test things out, monitor the results, etc.

Using ReplicationControllers with Services

Multiple ReplicationControllers can sit behind a single service, so that, for example, some traffic goes to the old version, and some goes to the new version.

A ReplicationController will never terminate on its own, but it isn't expected to be as long-lived as services. Services may be composed of pods controlled by multiple ReplicationControllers, and it is expected that many ReplicationControllers may be created and destroyed over the lifetime of a service (for instance, to perform an update of pods that run the service). Both services themselves and their clients should remain oblivious to the ReplicationControllers that maintain the pods of the services.

Writing programs for Replication

Pods created by a ReplicationController are intended to be fungible and semantically identical, though their configurations may become heterogeneous over time. This is an obvious fit for replicated stateless servers, but ReplicationControllers can also be used to maintain availability of master-elected, sharded, and worker-pool applications. Such applications should use dynamic work assignment mechanisms, such as the RabbitMQ work queues, as opposed to static/one-time customization of the configuration of each pod, which is considered an anti-pattern. Any pod customization performed, such as vertical auto-sizing of resources (for example, cpu or memory), should be performed by another online controller process, not unlike the ReplicationController itself.

Responsibilities of the ReplicationController

The ReplicationController ensures that the desired number of pods matches its label selector and are operational. Currently, only terminated pods are excluded from its count. In the future, readiness and other information available from the system may be taken into account, we may add more controls over the replacement policy, and we plan to emit events that could be used by external clients to implement arbitrarily sophisticated replacement and/or scale-down policies.

The ReplicationController is forever constrained to this narrow responsibility. It itself will not perform readiness nor liveness probes. Rather than performing auto-scaling, it is intended to be controlled by an external auto-scaler (as discussed in #492), which would change its replicas field. We will not add scheduling policies (for example, spreading) to the ReplicationController. Nor should it verify that the pods controlled match the currently specified template, as that would obstruct auto-sizing and other automated processes. Similarly, completion deadlines, ordering dependencies, configuration expansion, and other features belong elsewhere. We even plan to factor out the mechanism for bulk pod creation (#170).

The ReplicationController is intended to be a composable building-block primitive. We expect higher-level APIs and/or tools to be built on top of it and other complementary primitives for user convenience in the future. The "macro" operations currently supported by kubectl (run, scale) are proof-of-concept examples of this. For instance, we could imagine something like Asgard managing ReplicationControllers, auto-scalers, services, scheduling policies, canaries, etc.

API Object

Replication controller is a top-level resource in the Kubernetes REST API. More details about the API object can be found at: ReplicationController API object.

Alternatives to ReplicationController

ReplicaSet

ReplicaSet is the next-generation ReplicationController that supports the new set-based label selector. It's mainly used by Deployment as a mechanism to orchestrate pod creation, deletion and updates. Note that we recommend using Deployments instead of directly using Replica Sets, unless you require custom update orchestration or don't require updates at all.

Deployment (Recommended)

Deployment is a higher-level API object that updates its underlying Replica Sets and their Pods. Deployments are recommended if you want the rolling update functionality, because they are declarative, server-side, and have additional features.

Bare Pods

Unlike in the case where a user directly created pods, a ReplicationController replaces pods that are deleted or terminated for any reason, such as in the case of node failure or disruptive node maintenance, such as a kernel upgrade. For this reason, we recommend that you use a ReplicationController even if your application requires only a single pod. Think of it similarly to a process supervisor, only it supervises multiple pods across multiple nodes instead of individual processes on a single node. A ReplicationController delegates local container restarts to some agent on the node, such as the kubelet.

Job

Use a Job instead of a ReplicationController for pods that are expected to terminate on their own (that is, batch jobs).

DaemonSet

Use a DaemonSet instead of a ReplicationController for pods that provide a machine-level function, such as machine monitoring or machine logging. These pods have a lifetime that is tied to a machine lifetime: the pod needs to be running on the machine before other pods start, and are safe to terminate when the machine is otherwise ready to be rebooted/shutdown.

What's next

Learn about Pods.
Learn about Deployment, the replacement for ReplicationController.
ReplicationController is part of the Kubernetes REST API. Read the ReplicationController object definition to understand the API for replication controllers.

6 - Services, Load Balancing, and Networking

Concepts and resources behind networking in Kubernetes.

The Kubernetes network model

Every Pod in a cluster gets its own unique cluster-wide IP address. This means you do not need to explicitly create links between Pods and you almost never need to deal with mapping container ports to host ports.
This creates a clean, backwards-compatible model where Pods can be treated much like VMs or physical hosts from the perspectives of port allocation, naming, service discovery, load balancing, application configuration, and migration.

Kubernetes imposes the following fundamental requirements on any networking implementation (barring any intentional network segmentation policies):

pods can communicate with all other pods on any other node without NAT
agents on a node (e.g. system daemons, kubelet) can communicate with all pods on that node

Note: For those platforms that support Pods running in the host network (e.g. Linux), when pods are attached to the host network of a node they can still communicate with all pods on all nodes without NAT.

This model is not only less complex overall, but it is principally compatible with the desire for Kubernetes to enable low-friction porting of apps from VMs to containers. If your job previously ran in a VM, your VM had an IP and could talk to other VMs in your project. This is the same basic model.

Kubernetes IP addresses exist at the Pod scope - containers within a Pod share their network namespaces - including their IP address and MAC address. This means that containers within a Pod can all reach each other's ports on localhost. This also means that containers within a Pod must coordinate port usage, but this is no different from processes in a VM. This is called the "IP-per-pod" model.

How this is implemented is a detail of the particular container runtime in use.

It is possible to request ports on the Node itself which forward to your Pod (called host ports), but this is a very niche operation. How that forwarding is implemented is also a detail of the container runtime. The Pod itself is blind to the existence or non-existence of host ports.

Kubernetes networking addresses four concerns:

Containers within a Pod use networking to communicate via loopback.
Cluster networking provides communication between different Pods.
The Service resource lets you expose an application running in Pods to be reachable from outside your cluster.
- Ingress provides extra functionality specifically for exposing HTTP applications, websites and APIs.
You can also use Services to publish services only for consumption inside your cluster.

Cluster Networking explains how to set up networking for your cluster, and also provides an overview of the technologies involved.

6.1 - Service

Expose an application running in your cluster behind a single outward-facing endpoint, even when the workload is split across multiple backends.

An abstract way to expose an application running on a set of as a network service.

With Kubernetes you don't need to modify your application to use an unfamiliar service discovery mechanism. Kubernetes gives Pods their own IP addresses and a single DNS name for a set of Pods, and can load-balance across them.

Motivation

Kubernetes Pods are created and destroyed to match the desired state of your cluster. Pods are nonpermanent resources. If you use a Deployment to run your app, it can create and destroy Pods dynamically.

Each Pod gets its own IP address, however in a Deployment, the set of Pods running in one moment in time could be different from the set of Pods running that application a moment later.

This leads to a problem: if some set of Pods (call them "backends") provides functionality to other Pods (call them "frontends") inside your cluster, how do the frontends find out and keep track of which IP address to connect to, so that the frontend can use the backend part of the workload?

Enter Services.

Service resources

In Kubernetes, a Service is an abstraction which defines a logical set of Pods and a policy by which to access them (sometimes this pattern is called a micro-service). The set of Pods targeted by a Service is usually determined by a selector. To learn about other ways to define Service endpoints, see Services without selectors.

For example, consider a stateless image-processing backend which is running with 3 replicas. Those replicas are fungible—frontends do not care which backend they use. While the actual Pods that compose the backend set may change, the frontend clients should not need to be aware of that, nor should they need to keep track of the set of backends themselves.

The Service abstraction enables this decoupling.

Cloud-native service discovery

If you're able to use Kubernetes APIs for service discovery in your application, you can query the API server for Endpoints, that get updated whenever the set of Pods in a Service changes.

For non-native applications, Kubernetes offers ways to place a network port or load balancer in between your application and the backend Pods.

Defining a Service

A Service in Kubernetes is a REST object, similar to a Pod. Like all of the REST objects, you can POST a Service definition to the API server to create a new instance. The name of a Service object must be a valid RFC 1035 label name.

For example, suppose you have a set of Pods where each listens on TCP port 9376 and contains a label app.kubernetes.io/name=MyApp:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376

This specification creates a new Service object named "my-service", which targets TCP port 9376 on any Pod with the app.kubernetes.io/name=MyApp label.

Kubernetes assigns this Service an IP address (sometimes called the "cluster IP"), which is used by the Service proxies (see Virtual IPs and service proxies below).

The controller for the Service selector continuously scans for Pods that match its selector, and then POSTs any updates to an Endpoint object also named "my-service".

Note: A Service can map any incoming port to a targetPort. By default and for convenience, the targetPort is set to the same value as the port field.

Port definitions in Pods have names, and you can reference these names in the targetPort attribute of a Service. For example, we can bind the targetPort of the Service to the Pod port in the following way:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    app.kubernetes.io/name: proxy
spec:
  containers:
  - name: nginx
    image: nginx:stable
    ports:
      - containerPort: 80
        name: http-web-svc

---
apiVersion: v1
kind: Service
metadata:
  name: nginx-service
spec:
  selector:
    app.kubernetes.io/name: proxy
  ports:
  - name: name-of-service-port
    protocol: TCP
    port: 80
    targetPort: http-web-svc

This works even if there is a mixture of Pods in the Service using a single configured name, with the same network protocol available via different port numbers. This offers a lot of flexibility for deploying and evolving your Services. For example, you can change the port numbers that Pods expose in the next version of your backend software, without breaking clients.

The default protocol for Services is TCP; you can also use any other supported protocol.

As many Services need to expose more than one port, Kubernetes supports multiple port definitions on a Service object. Each port definition can have the same protocol, or a different one.

Services without selectors

Services most commonly abstract access to Kubernetes Pods thanks to the selector, but when used with a corresponding Endpoints object and without a selector, the Service can abstract other kinds of backends, including ones that run outside the cluster. For example:

You want to have an external database cluster in production, but in your test environment you use your own databases.
You want to point your Service to a Service in a different Namespace or on another cluster.
You are migrating a workload to Kubernetes. While evaluating the approach, you run only a portion of your backends in Kubernetes.

In any of these scenarios you can define a Service without a Pod selector. For example:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376

Because this Service has no selector, the corresponding Endpoints object is not created automatically. You can manually map the Service to the network address and port where it's running, by adding an Endpoints object manually:

apiVersion: v1
kind: Endpoints
metadata:
  # the name here should match the name of the Service
  name: my-service
subsets:
  - addresses:
      - ip: 192.0.2.42
    ports:
      - port: 9376

The name of the Endpoints object must be a valid DNS subdomain name.

When you create an Endpoints object for a Service, you set the name of the new object to be the same as that of the Service.

Note:

The endpoint IPs must not be: loopback (127.0.0.0/8 for IPv4, ::1/128 for IPv6), or link-local (169.254.0.0/16 and 224.0.0.0/24 for IPv4, fe80::/64 for IPv6).

Endpoint IP addresses cannot be the cluster IPs of other Kubernetes Services, because kube-proxy doesn't support virtual IPs as a destination.

Accessing a Service without a selector works the same as if it had a selector. In the example above, traffic is routed to the single endpoint defined in the YAML: 192.0.2.42:9376 (TCP).

Note: The Kubernetes API server does not allow proxying to endpoints that are not mapped to pods. Actions such as kubectl proxy <service-name> where the service has no selector will fail due to this constraint. This prevents the Kubernetes API server from being used as a proxy to endpoints the caller may not be authorized to access.

An ExternalName Service is a special case of Service that does not have selectors and uses DNS names instead. For more information, see the ExternalName section later in this document.

Over Capacity Endpoints

If an Endpoints resource has more than 1000 endpoints then a Kubernetes v1.22 (or later) cluster annotates that Endpoints with endpoints.kubernetes.io/over-capacity: truncated. This annotation indicates that the affected Endpoints object is over capacity and that the endpoints controller has truncated the number of endpoints to 1000.

EndpointSlices

FEATURE STATE: Kubernetes v1.21 [stable]

EndpointSlices are an API resource that can provide a more scalable alternative to Endpoints. Although conceptually quite similar to Endpoints, EndpointSlices allow for distributing network endpoints across multiple resources. By default, an EndpointSlice is considered "full" once it reaches 100 endpoints, at which point additional EndpointSlices will be created to store any additional endpoints.

EndpointSlices provide additional attributes and functionality which is described in detail in EndpointSlices.

Application protocol

FEATURE STATE: Kubernetes v1.20 [stable]

The appProtocol field provides a way to specify an application protocol for each Service port. The value of this field is mirrored by the corresponding Endpoints and EndpointSlice objects.

This field follows standard Kubernetes label syntax. Values should either be IANA standard service names or domain prefixed names such as mycompany.com/my-custom-protocol.

Virtual IPs and service proxies

Every node in a Kubernetes cluster runs a kube-proxy. kube-proxy is responsible for implementing a form of virtual IP for Services of type other than ExternalName.

Why not use round-robin DNS?

A question that pops up every now and then is why Kubernetes relies on proxying to forward inbound traffic to backends. What about other approaches? For example, would it be possible to configure DNS records that have multiple A values (or AAAA for IPv6), and rely on round-robin name resolution?

There are a few reasons for using proxying for Services:

There is a long history of DNS implementations not respecting record TTLs, and caching the results of name lookups after they should have expired.
Some apps do DNS lookups only once and cache the results indefinitely.
Even if apps and libraries did proper re-resolution, the low or zero TTLs on the DNS records could impose a high load on DNS that then becomes difficult to manage.

Later in this page you can read about how various kube-proxy implementations work. Overall, you should note that, when running kube-proxy, kernel level rules may be modified (for example, iptables rules might get created), which won't get cleaned up, in some cases until you reboot. Thus, running kube-proxy is something that should only be done by an administrator which understands the consequences of having a low level, privileged network proxying service on a computer. Although the kube-proxy executable supports a cleanup function, this function is not an official feature and thus is only available to use as-is.

Configuration

Note that the kube-proxy starts up in different modes, which are determined by its configuration.

The kube-proxy's configuration is done via a ConfigMap, and the ConfigMap for kube-proxy effectively deprecates the behaviour for almost all of the flags for the kube-proxy.
The ConfigMap for the kube-proxy does not support live reloading of configuration.
The ConfigMap parameters for the kube-proxy cannot all be validated and verified on startup. For example, if your operating system doesn't allow you to run iptables commands, the standard kernel kube-proxy implementation will not work. Likewise, if you have an operating system which doesn't support netsh, it will not run in Windows userspace mode.

User space proxy mode

In this (legacy) mode, kube-proxy watches the Kubernetes control plane for the addition and removal of Service and Endpoint objects. For each Service it opens a port (randomly chosen) on the local node. Any connections to this "proxy port" are proxied to one of the Service's backend Pods (as reported via Endpoints). kube-proxy takes the SessionAffinity setting of the Service into account when deciding which backend Pod to use.

Lastly, the user-space proxy installs iptables rules which capture traffic to the Service's clusterIP (which is virtual) and port. The rules redirect that traffic to the proxy port which proxies the backend Pod.

By default, kube-proxy in userspace mode chooses a backend via a round-robin algorithm.

Services overview diagram for userspace proxy

`iptables` proxy mode

In this mode, kube-proxy watches the Kubernetes control plane for the addition and removal of Service and Endpoint objects. For each Service, it installs iptables rules, which capture traffic to the Service's clusterIP and port, and redirect that traffic to one of the Service's backend sets. For each Endpoint object, it installs iptables rules which select a backend Pod.

By default, kube-proxy in iptables mode chooses a backend at random.

Using iptables to handle traffic has a lower system overhead, because traffic is handled by Linux netfilter without the need to switch between userspace and the kernel space. This approach is also likely to be more reliable.

If kube-proxy is running in iptables mode and the first Pod that's selected does not respond, the connection fails. This is different from userspace mode: in that scenario, kube-proxy would detect that the connection to the first Pod had failed and would automatically retry with a different backend Pod.

You can use Pod readiness probes to verify that backend Pods are working OK, so that kube-proxy in iptables mode only sees backends that test out as healthy. Doing this means you avoid having traffic sent via kube-proxy to a Pod that's known to have failed.

Services overview diagram for iptables proxy

IPVS proxy mode

FEATURE STATE: Kubernetes v1.11 [stable]

In ipvs mode, kube-proxy watches Kubernetes Services and Endpoints, calls netlink interface to create IPVS rules accordingly and synchronizes IPVS rules with Kubernetes Services and Endpoints periodically. This control loop ensures that IPVS status matches the desired state. When accessing a Service, IPVS directs traffic to one of the backend Pods.

The IPVS proxy mode is based on netfilter hook function that is similar to iptables mode, but uses a hash table as the underlying data structure and works in the kernel space. That means kube-proxy in IPVS mode redirects traffic with lower latency than kube-proxy in iptables mode, with much better performance when synchronising proxy rules. Compared to the other proxy modes, IPVS mode also supports a higher throughput of network traffic.

IPVS provides more options for balancing traffic to backend Pods; these are:

rr: round-robin
lc: least connection (smallest number of open connections)
dh: destination hashing
sh: source hashing
sed: shortest expected delay
nq: never queue

Note:

To run kube-proxy in IPVS mode, you must make IPVS available on the node before starting kube-proxy.

When kube-proxy starts in IPVS proxy mode, it verifies whether IPVS kernel modules are available. If the IPVS kernel modules are not detected, then kube-proxy falls back to running in iptables proxy mode.

Services overview diagram for IPVS proxy

In these proxy models, the traffic bound for the Service's IP:Port is proxied to an appropriate backend without the clients knowing anything about Kubernetes or Services or Pods.

If you want to make sure that connections from a particular client are passed to the same Pod each time, you can select the session affinity based on the client's IP addresses by setting service.spec.sessionAffinity to "ClientIP" (the default is "None"). You can also set the maximum session sticky time by setting service.spec.sessionAffinityConfig.clientIP.timeoutSeconds appropriately. (the default value is 10800, which works out to be 3 hours).

Note: On Windows, setting the maximum session sticky time for Services is not supported.

Multi-Port Services

For some Services, you need to expose more than one port. Kubernetes lets you configure multiple port definitions on a Service object. When using multiple ports for a Service, you must give all of your ports names so that these are unambiguous. For example:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - name: http
      protocol: TCP
      port: 80
      targetPort: 9376
    - name: https
      protocol: TCP
      port: 443
      targetPort: 9377

Note:

As with Kubernetes names in general, names for ports must only contain lowercase alphanumeric characters and -. Port names must also start and end with an alphanumeric character.

For example, the names 123-abc and web are valid, but 123_abc and -web are not.

Choosing your own IP address

You can specify your own cluster IP address as part of a Service creation request. To do this, set the .spec.clusterIP field. For example, if you already have an existing DNS entry that you wish to reuse, or legacy systems that are configured for a specific IP address and difficult to re-configure.

The IP address that you choose must be a valid IPv4 or IPv6 address from within the service-cluster-ip-range CIDR range that is configured for the API server. If you try to create a Service with an invalid clusterIP address value, the API server will return a 422 HTTP status code to indicate that there's a problem.

Traffic policies

External traffic policy

You can set the spec.externalTrafficPolicy field to control how traffic from external sources is routed. Valid values are Cluster and Local. Set the field to Cluster to route external traffic to all ready endpoints and Local to only route to ready node-local endpoints. If the traffic policy is Local and there are no node-local endpoints, the kube-proxy does not forward any traffic for the relevant Service.

Note:

FEATURE STATE: Kubernetes v1.22 [alpha]

If you enable the ProxyTerminatingEndpoints feature gate for the kube-proxy, the kube-proxy checks if the node has local endpoints and whether or not all the local endpoints are marked as terminating. If there are local endpoints and all of those are terminating, then the kube-proxy ignores any external traffic policy of Local. Instead, whilst the node-local endpoints remain as all terminating, the kube-proxy forwards traffic for that Service to healthy endpoints elsewhere, as if the external traffic policy were set to Cluster. This forwarding behavior for terminating endpoints exists to allow external load balancers to gracefully drain connections that are backed by NodePort Services, even when the health check node port starts to fail. Otherwise, traffic can be lost between the time a node is still in the node pool of a load balancer and traffic is being dropped during the termination period of a pod.

Internal traffic policy

FEATURE STATE: Kubernetes v1.22 [beta]

You can set the spec.internalTrafficPolicy field to control how traffic from internal sources is routed. Valid values are Cluster and Local. Set the field to Cluster to route internal traffic to all ready endpoints and Local to only route to ready node-local endpoints. If the traffic policy is Local and there are no node-local endpoints, traffic is dropped by kube-proxy.

Discovering services

Kubernetes supports 2 primary modes of finding a Service - environment variables and DNS.

Environment variables

When a Pod is run on a Node, the kubelet adds a set of environment variables for each active Service. It adds {SVCNAME}_SERVICE_HOST and {SVCNAME}_SERVICE_PORT variables, where the Service name is upper-cased and dashes are converted to underscores. It also supports variables (see makeLinkVariables) that are compatible with Docker Engine's "legacy container links" feature.

For example, the Service redis-primary which exposes TCP port 6379 and has been allocated cluster IP address 10.0.0.11, produces the following environment variables:

REDIS_PRIMARY_SERVICE_HOST=10.0.0.11
REDIS_PRIMARY_SERVICE_PORT=6379
REDIS_PRIMARY_PORT=tcp://10.0.0.11:6379
REDIS_PRIMARY_PORT_6379_TCP=tcp://10.0.0.11:6379
REDIS_PRIMARY_PORT_6379_TCP_PROTO=tcp
REDIS_PRIMARY_PORT_6379_TCP_PORT=6379
REDIS_PRIMARY_PORT_6379_TCP_ADDR=10.0.0.11

Note:

When you have a Pod that needs to access a Service, and you are using the environment variable method to publish the port and cluster IP to the client Pods, you must create the Service before the client Pods come into existence. Otherwise, those client Pods won't have their environment variables populated.

If you only use DNS to discover the cluster IP for a Service, you don't need to worry about this ordering issue.

DNS

You can (and almost always should) set up a DNS service for your Kubernetes cluster using an add-on.

A cluster-aware DNS server, such as CoreDNS, watches the Kubernetes API for new Services and creates a set of DNS records for each one. If DNS has been enabled throughout your cluster then all Pods should automatically be able to resolve Services by their DNS name.

For example, if you have a Service called my-service in a Kubernetes namespace my-ns, the control plane and the DNS Service acting together create a DNS record for my-service.my-ns. Pods in the my-ns namespace should be able to find the service by doing a name lookup for my-service (my-service.my-ns would also work).

Pods in other namespaces must qualify the name as my-service.my-ns. These names will resolve to the cluster IP assigned for the Service.

Kubernetes also supports DNS SRV (Service) records for named ports. If the my-service.my-ns Service has a port named http with the protocol set to TCP, you can do a DNS SRV query for _http._tcp.my-service.my-ns to discover the port number for http, as well as the IP address.

The Kubernetes DNS server is the only way to access ExternalName Services. You can find more information about ExternalName resolution in DNS Pods and Services.

Headless Services

Sometimes you don't need load-balancing and a single Service IP. In this case, you can create what are termed "headless" Services, by explicitly specifying "None" for the cluster IP (.spec.clusterIP).

You can use a headless Service to interface with other service discovery mechanisms, without being tied to Kubernetes' implementation.

For headless Services, a cluster IP is not allocated, kube-proxy does not handle these Services, and there is no load balancing or proxying done by the platform for them. How DNS is automatically configured depends on whether the Service has selectors defined:

With selectors

For headless Services that define selectors, the endpoints controller creates Endpoints records in the API, and modifies the DNS configuration to return A records (IP addresses) that point directly to the Pods backing the Service.

Without selectors

For headless Services that do not define selectors, the endpoints controller does not create Endpoints records. However, the DNS system looks for and configures either:

CNAME records for ExternalName-type Services.
A records for any Endpoints that share a name with the Service, for all other types.

Publishing Services (ServiceTypes)

For some parts of your application (for example, frontends) you may want to expose a Service onto an external IP address, that's outside of your cluster.

Kubernetes ServiceTypes allow you to specify what kind of Service you want. The default is ClusterIP.

Type values and their behaviors are:

ClusterIP: Exposes the Service on a cluster-internal IP. Choosing this value makes the Service only reachable from within the cluster. This is the default ServiceType.
NodePort: Exposes the Service on each Node's IP at a static port (the NodePort). A ClusterIP Service, to which the NodePort Service routes, is automatically created. You'll be able to contact the NodePort Service, from outside the cluster, by requesting <NodeIP>:<NodePort>.
LoadBalancer: Exposes the Service externally using a cloud provider's load balancer. NodePort and ClusterIP Services, to which the external load balancer routes, are automatically created.
ExternalName: Maps the Service to the contents of the externalName field (e.g. foo.bar.example.com), by returning a CNAME record with its value. No proxying of any kind is set up.
Note: You need either kube-dns version 1.7 or CoreDNS version 0.0.8 or higher to use the ExternalName type.

You can also use Ingress to expose your Service. Ingress is not a Service type, but it acts as the entry point for your cluster. It lets you consolidate your routing rules into a single resource as it can expose multiple services under the same IP address.

Type NodePort

If you set the type field to NodePort, the Kubernetes control plane allocates a port from a range specified by --service-node-port-range flag (default: 30000-32767). Each node proxies that port (the same port number on every Node) into your Service. Your Service reports the allocated port in its .spec.ports[*].nodePort field.

If you want to specify particular IP(s) to proxy the port, you can set the --nodeport-addresses flag for kube-proxy or the equivalent nodePortAddresses field of the kube-proxy configuration file to particular IP block(s).

This flag takes a comma-delimited list of IP blocks (e.g. 10.0.0.0/8, 192.0.2.0/25) to specify IP address ranges that kube-proxy should consider as local to this node.

For example, if you start kube-proxy with the --nodeport-addresses=127.0.0.0/8 flag, kube-proxy only selects the loopback interface for NodePort Services. The default for --nodeport-addresses is an empty list. This means that kube-proxy should consider all available network interfaces for NodePort. (That's also compatible with earlier Kubernetes releases).

If you want a specific port number, you can specify a value in the nodePort field. The control plane will either allocate you that port or report that the API transaction failed. This means that you need to take care of possible port collisions yourself. You also have to use a valid port number, one that's inside the range configured for NodePort use.

Using a NodePort gives you the freedom to set up your own load balancing solution, to configure environments that are not fully supported by Kubernetes, or even to expose one or more nodes' IPs directly.

Note that this Service is visible as <NodeIP>:spec.ports[*].nodePort and .spec.clusterIP:spec.ports[*].port. If the --nodeport-addresses flag for kube-proxy or the equivalent field in the kube-proxy configuration file is set, <NodeIP> would be filtered node IP(s).

For example:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  type: NodePort
  selector:
    app.kubernetes.io/name: MyApp
  ports:
      # By default and for convenience, the `targetPort` is set to the same value as the `port` field.
    - port: 80
      targetPort: 80
      # Optional field
      # By default and for convenience, the Kubernetes control plane will allocate a port from a range (default: 30000-32767)
      nodePort: 30007

Type LoadBalancer

On cloud providers which support external load balancers, setting the type field to LoadBalancer provisions a load balancer for your Service. The actual creation of the load balancer happens asynchronously, and information about the provisioned balancer is published in the Service's .status.loadBalancer field. For example:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376
  clusterIP: 10.0.171.239
  type: LoadBalancer
status:
  loadBalancer:
    ingress:
    - ip: 192.0.2.127

Traffic from the external load balancer is directed at the backend Pods. The cloud provider decides how it is load balanced.

Some cloud providers allow you to specify the loadBalancerIP. In those cases, the load-balancer is created with the user-specified loadBalancerIP. If the loadBalancerIP field is not specified, the loadBalancer is set up with an ephemeral IP address. If you specify a loadBalancerIP but your cloud provider does not support the feature, the loadbalancerIP field that you set is ignored.

Note:

On Azure, if you want to use a user-specified public type loadBalancerIP, you first need to create a static type public IP address resource. This public IP address resource should be in the same resource group of the other automatically created resources of the cluster. For example, MC_myResourceGroup_myAKSCluster_eastus.

Specify the assigned IP address as loadBalancerIP. Ensure that you have updated the securityGroupName in the cloud provider configuration file. For information about troubleshooting CreatingLoadBalancerFailed permission issues see, Use a static IP address with the Azure Kubernetes Service (AKS) load balancer or CreatingLoadBalancerFailed on AKS cluster with advanced networking.

Load balancers with mixed protocol types

FEATURE STATE: Kubernetes v1.24 [beta]

By default, for LoadBalancer type of Services, when there is more than one port defined, all ports must have the same protocol, and the protocol must be one which is supported by the cloud provider.

The feature gate MixedProtocolLBService (enabled by default for the kube-apiserver as of v1.24) allows the use of different protocols for LoadBalancer type of Services, when there is more than one port defined.

Note: The set of protocols that can be used for LoadBalancer type of Services is still defined by the cloud provider. If a cloud provider does not support mixed protocols they will provide only a single protocol.

Disabling load balancer NodePort allocation

FEATURE STATE: Kubernetes v1.24 [stable]

You can optionally disable node port allocation for a Service of type=LoadBalancer, by setting the field spec.allocateLoadBalancerNodePorts to false. This should only be used for load balancer implementations that route traffic directly to pods as opposed to using node ports. By default, spec.allocateLoadBalancerNodePorts is true and type LoadBalancer Services will continue to allocate node ports. If spec.allocateLoadBalancerNodePorts is set to false on an existing Service with allocated node ports, those node ports will not be de-allocated automatically. You must explicitly remove the nodePorts entry in every Service port to de-allocate those node ports.

Specifying class of load balancer implementation

FEATURE STATE: Kubernetes v1.24 [stable]

spec.loadBalancerClass enables you to use a load balancer implementation other than the cloud provider default. By default, spec.loadBalancerClass is nil and a LoadBalancer type of Service uses the cloud provider's default load balancer implementation if the cluster is configured with a cloud provider using the --cloud-provider component flag. If spec.loadBalancerClass is specified, it is assumed that a load balancer implementation that matches the specified class is watching for Services. Any default load balancer implementation (for example, the one provided by the cloud provider) will ignore Services that have this field set. spec.loadBalancerClass can be set on a Service of type LoadBalancer only. Once set, it cannot be changed. The value of spec.loadBalancerClass must be a label-style identifier, with an optional prefix such as "internal-vip" or "example.com/internal-vip". Unprefixed names are reserved for end-users.

Internal load balancer

In a mixed environment it is sometimes necessary to route traffic from Services inside the same (virtual) network address block.

In a split-horizon DNS environment you would need two Services to be able to route both external and internal traffic to your endpoints.

To set an internal load balancer, add one of the following annotations to your Service depending on the cloud Service provider you're using.

Select one of the tabs.

[...]
metadata:
    name: my-service
    annotations:
        cloud.google.com/load-balancer-type: "Internal"
[...]

[...]
metadata:
    name: my-service
    annotations:
        service.beta.kubernetes.io/aws-load-balancer-internal: "true"
[...]

[...]
metadata:
    name: my-service
    annotations:
        service.beta.kubernetes.io/azure-load-balancer-internal: "true"
[...]

[...]
metadata:
    name: my-service
    annotations:
        service.kubernetes.io/ibm-load-balancer-cloud-provider-ip-type: "private"
[...]

[...]
metadata:
    name: my-service
    annotations:
        service.beta.kubernetes.io/openstack-internal-load-balancer: "true"
[...]

[...]
metadata:
    name: my-service
    annotations:
        service.beta.kubernetes.io/cce-load-balancer-internal-vpc: "true"
[...]

[...]
metadata:
  annotations:
    service.kubernetes.io/qcloud-loadbalancer-internal-subnetid: subnet-xxxxx
[...]

[...]
metadata:
  annotations:
    service.beta.kubernetes.io/alibaba-cloud-loadbalancer-address-type: "intranet"
[...]

[...]
metadata:
    name: my-service
    annotations:
        service.beta.kubernetes.io/oci-load-balancer-internal: true
[...]

TLS support on AWS

For partial TLS / SSL support on clusters running on AWS, you can add three annotations to a LoadBalancer service:

metadata:
  name: my-service
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-ssl-cert: arn:aws:acm:us-east-1:123456789012:certificate/12345678-1234-1234-1234-123456789012

The first specifies the ARN of the certificate to use. It can be either a certificate from a third party issuer that was uploaded to IAM or one created within AWS Certificate Manager.

metadata:
  name: my-service
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-backend-protocol: (https|http|ssl|tcp)

The second annotation specifies which protocol a Pod speaks. For HTTPS and SSL, the ELB expects the Pod to authenticate itself over the encrypted connection, using a certificate.

HTTP and HTTPS selects layer 7 proxying: the ELB terminates the connection with the user, parses headers, and injects the X-Forwarded-For header with the user's IP address (Pods only see the IP address of the ELB at the other end of its connection) when forwarding requests.

TCP and SSL selects layer 4 proxying: the ELB forwards traffic without modifying the headers.

In a mixed-use environment where some ports are secured and others are left unencrypted, you can use the following annotations:

    metadata:
      name: my-service
      annotations:
        service.beta.kubernetes.io/aws-load-balancer-backend-protocol: http
        service.beta.kubernetes.io/aws-load-balancer-ssl-ports: "443,8443"

In the above example, if the Service contained three ports, 80, 443, and 8443, then 443 and 8443 would use the SSL certificate, but 80 would be proxied HTTP.

From Kubernetes v1.9 onwards you can use predefined AWS SSL policies with HTTPS or SSL listeners for your Services. To see which policies are available for use, you can use the aws command line tool:

aws elb describe-load-balancer-policies --query 'PolicyDescriptions[].PolicyName'

You can then specify any one of those policies using the "service.beta.kubernetes.io/aws-load-balancer-ssl-negotiation-policy" annotation; for example:

    metadata:
      name: my-service
      annotations:
        service.beta.kubernetes.io/aws-load-balancer-ssl-negotiation-policy: "ELBSecurityPolicy-TLS-1-2-2017-01"

PROXY protocol support on AWS

To enable PROXY protocol support for clusters running on AWS, you can use the following service annotation:

    metadata:
      name: my-service
      annotations:
        service.beta.kubernetes.io/aws-load-balancer-proxy-protocol: "*"

Since version 1.3.0, the use of this annotation applies to all ports proxied by the ELB and cannot be configured otherwise.

ELB Access Logs on AWS

There are several annotations to manage access logs for ELB Services on AWS.

The annotation service.beta.kubernetes.io/aws-load-balancer-access-log-enabled controls whether access logs are enabled.

The annotation service.beta.kubernetes.io/aws-load-balancer-access-log-emit-interval controls the interval in minutes for publishing the access logs. You can specify an interval of either 5 or 60 minutes.

The annotation service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-name controls the name of the Amazon S3 bucket where load balancer access logs are stored.

The annotation service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-prefix specifies the logical hierarchy you created for your Amazon S3 bucket.

    metadata:
      name: my-service
      annotations:
        # Specifies whether access logs are enabled for the load balancer
        service.beta.kubernetes.io/aws-load-balancer-access-log-enabled: "true"

        # The interval for publishing the access logs. You can specify an interval of either 5 or 60 (minutes).
        service.beta.kubernetes.io/aws-load-balancer-access-log-emit-interval: "60"

        # The name of the Amazon S3 bucket where the access logs are stored
        service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-name: "my-bucket"

        # The logical hierarchy you created for your Amazon S3 bucket, for example `my-bucket-prefix/prod`
        service.beta.kubernetes.io/aws-load-balancer-access-log-s3-bucket-prefix: "my-bucket-prefix/prod"

Connection Draining on AWS

Connection draining for Classic ELBs can be managed with the annotation service.beta.kubernetes.io/aws-load-balancer-connection-draining-enabled set to the value of "true". The annotation service.beta.kubernetes.io/aws-load-balancer-connection-draining-timeout can also be used to set maximum time, in seconds, to keep the existing connections open before deregistering the instances.

    metadata:
      name: my-service
      annotations:
        service.beta.kubernetes.io/aws-load-balancer-connection-draining-enabled: "true"
        service.beta.kubernetes.io/aws-load-balancer-connection-draining-timeout: "60"

Other ELB annotations

There are other annotations to manage Classic Elastic Load Balancers that are described below.

    metadata:
      name: my-service
      annotations:
        # The time, in seconds, that the connection is allowed to be idle (no data has been sent
        # over the connection) before it is closed by the load balancer
        service.beta.kubernetes.io/aws-load-balancer-connection-idle-timeout: "60"

        # Specifies whether cross-zone load balancing is enabled for the load balancer
        service.beta.kubernetes.io/aws-load-balancer-cross-zone-load-balancing-enabled: "true"

        # A comma-separated list of key-value pairs which will be recorded as
        # additional tags in the ELB.
        service.beta.kubernetes.io/aws-load-balancer-additional-resource-tags: "environment=prod,owner=devops"

        # The number of successive successful health checks required for a backend to
        # be considered healthy for traffic. Defaults to 2, must be between 2 and 10
        service.beta.kubernetes.io/aws-load-balancer-healthcheck-healthy-threshold: ""

        # The number of unsuccessful health checks required for a backend to be
        # considered unhealthy for traffic. Defaults to 6, must be between 2 and 10
        service.beta.kubernetes.io/aws-load-balancer-healthcheck-unhealthy-threshold: "3"

        # The approximate interval, in seconds, between health checks of an
        # individual instance. Defaults to 10, must be between 5 and 300
        service.beta.kubernetes.io/aws-load-balancer-healthcheck-interval: "20"

        # The amount of time, in seconds, during which no response means a failed
        # health check. This value must be less than the service.beta.kubernetes.io/aws-load-balancer-healthcheck-interval
        # value. Defaults to 5, must be between 2 and 60
        service.beta.kubernetes.io/aws-load-balancer-healthcheck-timeout: "5"

        # A list of existing security groups to be configured on the ELB created. Unlike the annotation
        # service.beta.kubernetes.io/aws-load-balancer-extra-security-groups, this replaces all other
        # security groups previously assigned to the ELB and also overrides the creation
        # of a uniquely generated security group for this ELB.
        # The first security group ID on this list is used as a source to permit incoming traffic to
        # target worker nodes (service traffic and health checks).
        # If multiple ELBs are configured with the same security group ID, only a single permit line
        # will be added to the worker node security groups, that means if you delete any
        # of those ELBs it will remove the single permit line and block access for all ELBs that shared the same security group ID.
        # This can cause a cross-service outage if not used properly
        service.beta.kubernetes.io/aws-load-balancer-security-groups: "sg-53fae93f"

        # A list of additional security groups to be added to the created ELB, this leaves the uniquely
        # generated security group in place, this ensures that every ELB
        # has a unique security group ID and a matching permit line to allow traffic to the target worker nodes
        # (service traffic and health checks).
        # Security groups defined here can be shared between services.
        service.beta.kubernetes.io/aws-load-balancer-extra-security-groups: "sg-53fae93f,sg-42efd82e"

        # A comma separated list of key-value pairs which are used
        # to select the target nodes for the load balancer
        service.beta.kubernetes.io/aws-load-balancer-target-node-labels: "ingress-gw,gw-name=public-api"

Network Load Balancer support on AWS

FEATURE STATE: Kubernetes v1.15 [beta]

To use a Network Load Balancer on AWS, use the annotation service.beta.kubernetes.io/aws-load-balancer-type with the value set to nlb.

    metadata:
      name: my-service
      annotations:
        service.beta.kubernetes.io/aws-load-balancer-type: "nlb"

Note: NLB only works with certain instance classes; see the AWS documentation on Elastic Load Balancing for a list of supported instance types.

Unlike Classic Elastic Load Balancers, Network Load Balancers (NLBs) forward the client's IP address through to the node. If a Service's .spec.externalTrafficPolicy is set to Cluster, the client's IP address is not propagated to the end Pods.

By setting .spec.externalTrafficPolicy to Local, the client IP addresses is propagated to the end Pods, but this could result in uneven distribution of traffic. Nodes without any Pods for a particular LoadBalancer Service will fail the NLB Target Group's health check on the auto-assigned .spec.healthCheckNodePort and not receive any traffic.

In order to achieve even traffic, either use a DaemonSet or specify a pod anti-affinity to not locate on the same node.

You can also use NLB Services with the internal load balancer annotation.

In order for client traffic to reach instances behind an NLB, the Node security groups are modified with the following IP rules:

Rule	Protocol	Port(s)	IpRange(s)	IpRange Description
Health Check	TCP	NodePort(s) (`.spec.healthCheckNodePort` for `.spec.externalTrafficPolicy = Local`)	Subnet CIDR	kubernetes.io/rule/nlb/health=<loadBalancerName>
Client Traffic	TCP	NodePort(s)	`.spec.loadBalancerSourceRanges` (defaults to `0.0.0.0/0`)	kubernetes.io/rule/nlb/client=<loadBalancerName>
MTU Discovery	ICMP	3,4	`.spec.loadBalancerSourceRanges` (defaults to `0.0.0.0/0`)	kubernetes.io/rule/nlb/mtu=<loadBalancerName>

In order to limit which client IP's can access the Network Load Balancer, specify loadBalancerSourceRanges.

spec:
  loadBalancerSourceRanges:
    - "143.231.0.0/16"

Note: If .spec.loadBalancerSourceRanges is not set, Kubernetes allows traffic from 0.0.0.0/0 to the Node Security Group(s). If nodes have public IP addresses, be aware that non-NLB traffic can also reach all instances in those modified security groups.

Further documentation on annotations for Elastic IPs and other common use-cases may be found in the AWS Load Balancer Controller documentation.

Other CLB annotations on Tencent Kubernetes Engine (TKE)

There are other annotations for managing Cloud Load Balancers on TKE as shown below.

    metadata:
      name: my-service
      annotations:
        # Bind Loadbalancers with specified nodes
        service.kubernetes.io/qcloud-loadbalancer-backends-label: key in (value1, value2)

        # ID of an existing load balancer
        service.kubernetes.io/tke-existed-lbid：lb-6swtxxxx

        # Custom parameters for the load balancer (LB), does not support modification of LB type yet
        service.kubernetes.io/service.extensiveParameters: ""

        # Custom parameters for the LB listener
        service.kubernetes.io/service.listenerParameters: ""

        # Specifies the type of Load balancer;
        # valid values: classic (Classic Cloud Load Balancer) or application (Application Cloud Load Balancer)
        service.kubernetes.io/loadbalance-type: xxxxx

        # Specifies the public network bandwidth billing method;
        # valid values: TRAFFIC_POSTPAID_BY_HOUR(bill-by-traffic) and BANDWIDTH_POSTPAID_BY_HOUR (bill-by-bandwidth).
        service.kubernetes.io/qcloud-loadbalancer-internet-charge-type: xxxxxx

        # Specifies the bandwidth value (value range: [1,2000] Mbps).
        service.kubernetes.io/qcloud-loadbalancer-internet-max-bandwidth-out: "10"

        # When this annotation is set，the loadbalancers will only register nodes
        # with pod running on it, otherwise all nodes will be registered.
        service.kubernetes.io/local-svc-only-bind-node-with-pod: true

Type ExternalName

Services of type ExternalName map a Service to a DNS name, not to a typical selector such as my-service or cassandra. You specify these Services with the spec.externalName parameter.

This Service definition, for example, maps the my-service Service in the prod namespace to my.database.example.com:

apiVersion: v1
kind: Service
metadata:
  name: my-service
  namespace: prod
spec:
  type: ExternalName
  externalName: my.database.example.com

Note: ExternalName accepts an IPv4 address string, but as a DNS name comprised of digits, not as an IP address. ExternalNames that resemble IPv4 addresses are not resolved by CoreDNS or ingress-nginx because ExternalName is intended to specify a canonical DNS name. To hardcode an IP address, consider using headless Services.

When looking up the host my-service.prod.svc.cluster.local, the cluster DNS Service returns a CNAME record with the value my.database.example.com. Accessing my-service works in the same way as other Services but with the crucial difference that redirection happens at the DNS level rather than via proxying or forwarding. Should you later decide to move your database into your cluster, you can start its Pods, add appropriate selectors or endpoints, and change the Service's type.

Warning:

You may have trouble using ExternalName for some common protocols, including HTTP and HTTPS. If you use ExternalName then the hostname used by clients inside your cluster is different from the name that the ExternalName references.

For protocols that use hostnames this difference may lead to errors or unexpected responses. HTTP requests will have a Host: header that the origin server does not recognize; TLS servers will not be able to provide a certificate matching the hostname that the client connected to.

Note: This section is indebted to the Kubernetes Tips - Part 1 blog post from Alen Komljen.

External IPs

If there are external IPs that route to one or more cluster nodes, Kubernetes Services can be exposed on those externalIPs. Traffic that ingresses into the cluster with the external IP (as destination IP), on the Service port, will be routed to one of the Service endpoints. externalIPs are not managed by Kubernetes and are the responsibility of the cluster administrator.

In the Service spec, externalIPs can be specified along with any of the ServiceTypes. In the example below, "my-service" can be accessed by clients on "80.11.12.10:80" (externalIP:port)

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - name: http
      protocol: TCP
      port: 80
      targetPort: 9376
  externalIPs:
    - 80.11.12.10

Shortcomings

Using the userspace proxy for VIPs works at small to medium scale, but will not scale to very large clusters with thousands of Services. The original design proposal for portals has more details on this.

Using the userspace proxy obscures the source IP address of a packet accessing a Service. This makes some kinds of network filtering (firewalling) impossible. The iptables proxy mode does not obscure in-cluster source IPs, but it does still impact clients coming through a load balancer or node-port.

The Type field is designed as nested functionality - each level adds to the previous. This is not strictly required on all cloud providers (e.g. Google Compute Engine does not need to allocate a NodePort to make LoadBalancer work, but AWS does) but the current API requires it.

Virtual IP implementation

The previous information should be sufficient for many people who want to use Services. However, there is a lot going on behind the scenes that may be worth understanding.

Avoiding collisions

One of the primary philosophies of Kubernetes is that you should not be exposed to situations that could cause your actions to fail through no fault of your own. For the design of the Service resource, this means not making you choose your own port number if that choice might collide with someone else's choice. That is an isolation failure.

In order to allow you to choose a port number for your Services, we must ensure that no two Services can collide. Kubernetes does that by allocating each Service its own IP address from within the service-cluster-ip-range CIDR range that is configured for the API server.

To ensure each Service receives a unique IP, an internal allocator atomically updates a global allocation map in etcd prior to creating each Service. The map object must exist in the registry for Services to get IP address assignments, otherwise creations will fail with a message indicating an IP address could not be allocated.

In the control plane, a background controller is responsible for creating that map (needed to support migrating from older versions of Kubernetes that used in-memory locking). Kubernetes also uses controllers to check for invalid assignments (eg due to administrator intervention) and for cleaning up allocated IP addresses that are no longer used by any Services.

IP address ranges for `type: ClusterIP` Services

FEATURE STATE: Kubernetes v1.25 [beta]

However, there is a problem with this ClusterIP allocation strategy, because a user can also choose their own address for the service. This could result in a conflict if the internal allocator selects the same IP address for another Service.

The ServiceIPStaticSubrange feature gate is enabled by default in v1.25 and later, using an allocation strategy that divides the ClusterIP range into two bands, based on the size of the configured service-cluster-ip-range by using the following formula min(max(16, cidrSize / 16), 256), described as never less than 16 or more than 256, with a graduated step function between them. Dynamic IP allocations will be preferentially chosen from the upper band, reducing risks of conflicts with the IPs assigned from the lower band. This allows users to use the lower band of the service-cluster-ip-range for their Services with static IPs assigned with a very low risk of running into conflicts.

Service IP addresses

Unlike Pod IP addresses, which actually route to a fixed destination, Service IPs are not actually answered by a single host. Instead, kube-proxy uses iptables (packet processing logic in Linux) to define virtual IP addresses which are transparently redirected as needed. When clients connect to the VIP, their traffic is automatically transported to an appropriate endpoint. The environment variables and DNS for Services are actually populated in terms of the Service's virtual IP address (and port).

kube-proxy supports three proxy modes—userspace, iptables and IPVS—which each operate slightly differently.

Userspace

As an example, consider the image processing application described above. When the backend Service is created, the Kubernetes master assigns a virtual IP address, for example 10.0.0.1. Assuming the Service port is 1234, the Service is observed by all of the kube-proxy instances in the cluster. When a proxy sees a new Service, it opens a new random port, establishes an iptables redirect from the virtual IP address to this new port, and starts accepting connections on it.

When a client connects to the Service's virtual IP address, the iptables rule kicks in, and redirects the packets to the proxy's own port. The "Service proxy" chooses a backend, and starts proxying traffic from the client to the backend.

This means that Service owners can choose any port they want without risk of collision. Clients can connect to an IP and port, without being aware of which Pods they are actually accessing.

iptables

Again, consider the image processing application described above. When the backend Service is created, the Kubernetes control plane assigns a virtual IP address, for example 10.0.0.1. Assuming the Service port is 1234, the Service is observed by all of the kube-proxy instances in the cluster. When a proxy sees a new Service, it installs a series of iptables rules which redirect from the virtual IP address to per-Service rules. The per-Service rules link to per-Endpoint rules which redirect traffic (using destination NAT) to the backends.

When a client connects to the Service's virtual IP address the iptables rule kicks in. A backend is chosen (either based on session affinity or randomly) and packets are redirected to the backend. Unlike the userspace proxy, packets are never copied to userspace, the kube-proxy does not have to be running for the virtual IP address to work, and Nodes see traffic arriving from the unaltered client IP address.

This same basic flow executes when traffic comes in through a node-port or through a load-balancer, though in those cases the client IP does get altered.

IPVS

iptables operations slow down dramatically in large scale cluster e.g 10,000 Services. IPVS is designed for load balancing and based on in-kernel hash tables. So you can achieve performance consistency in large number of Services from IPVS-based kube-proxy. Meanwhile, IPVS-based kube-proxy has more sophisticated load balancing algorithms (least conns, locality, weighted, persistence).

API Object

Service is a top-level resource in the Kubernetes REST API. You can find more details about the Service API object.

Supported protocols

TCP

You can use TCP for any kind of Service, and it's the default network protocol.

UDP

You can use UDP for most Services. For type=LoadBalancer Services, UDP support depends on the cloud provider offering this facility.

SCTP

FEATURE STATE: Kubernetes v1.20 [stable]

When using a network plugin that supports SCTP traffic, you can use SCTP for most Services. For type=LoadBalancer Services, SCTP support depends on the cloud provider offering this facility. (Most do not).

Warnings

Support for multihomed SCTP associations

Warning:

The support of multihomed SCTP associations requires that the CNI plugin can support the assignment of multiple interfaces and IP addresses to a Pod.

NAT for multihomed SCTP associations requires special logic in the corresponding kernel modules.

Windows

Note: SCTP is not supported on Windows based nodes.

Userspace kube-proxy

Warning: The kube-proxy does not support the management of SCTP associations when it is in userspace mode.

HTTP

If your cloud provider supports it, you can use a Service in LoadBalancer mode to set up external HTTP / HTTPS reverse proxying, forwarded to the Endpoints of the Service.

Note: You can also use in place of Service to expose HTTP/HTTPS Services.

PROXY protocol

If your cloud provider supports it, you can use a Service in LoadBalancer mode to configure a load balancer outside of Kubernetes itself, that will forward connections prefixed with PROXY protocol.

The load balancer will send an initial series of octets describing the incoming connection, similar to this example

PROXY TCP4 192.0.2.202 10.0.42.7 12345 7\r\n

followed by the data from the client.

What's next

Read Connecting Applications with Services
Read about Ingress
Read about EndpointSlices

6.2 - Topology-aware traffic routing with topology keys

FEATURE STATE: Kubernetes v1.21 [deprecated]

Note: This feature, specifically the alpha topologyKeys API, is deprecated since Kubernetes v1.21. Topology Aware Hints, introduced in Kubernetes v1.21, provide similar functionality.

Service Topology enables a service to route traffic based upon the Node topology of the cluster. For example, a service can specify that traffic be preferentially routed to endpoints that are on the same Node as the client, or in the same availability zone.

Topology-aware traffic routing

By default, traffic sent to a ClusterIP or NodePort Service may be routed to any backend address for the Service. Kubernetes 1.7 made it possible to route "external" traffic to the Pods running on the same Node that received the traffic. For ClusterIP Services, the equivalent same-node preference for routing wasn't possible; nor could you configure your cluster to favor routing to endpoints within the same zone. By setting topologyKeys on a Service, you're able to define a policy for routing traffic based upon the Node labels for the originating and destination Nodes.

The label matching between the source and destination lets you, as a cluster operator, designate sets of Nodes that are "closer" and "farther" from one another. You can define labels to represent whatever metric makes sense for your own requirements. In public clouds, for example, you might prefer to keep network traffic within the same zone, because interzonal traffic has a cost associated with it (and intrazonal traffic typically does not). Other common needs include being able to route traffic to a local Pod managed by a DaemonSet, or directing traffic to Nodes connected to the same top-of-rack switch for the lowest latency.

Using Service Topology

If your cluster has the ServiceTopology feature gate enabled, you can control Service traffic routing by specifying the topologyKeys field on the Service spec. This field is a preference-order list of Node labels which will be used to sort endpoints when accessing this Service. Traffic will be directed to a Node whose value for the first label matches the originating Node's value for that label. If there is no backend for the Service on a matching Node, then the second label will be considered, and so forth, until no labels remain.

If no match is found, the traffic will be rejected, as if there were no backends for the Service at all. That is, endpoints are chosen based on the first topology key with available backends. If this field is specified and all entries have no backends that match the topology of the client, the service has no backends for that client and connections should fail. The special value "*" may be used to mean "any topology". This catch-all value, if used, only makes sense as the last value in the list.

If topologyKeys is not specified or empty, no topology constraints will be applied.

Consider a cluster with Nodes that are labeled with their hostname, zone name, and region name. Then you can set the topologyKeys values of a service to direct traffic as follows.

Only to endpoints on the same node, failing if no endpoint exists on the node: ["kubernetes.io/hostname"].
Preferentially to endpoints on the same node, falling back to endpoints in the same zone, followed by the same region, and failing otherwise: ["kubernetes.io/hostname", "topology.kubernetes.io/zone", "topology.kubernetes.io/region"]. This may be useful, for example, in cases where data locality is critical.
Preferentially to the same zone, but fallback on any available endpoint if none are available within this zone: ["topology.kubernetes.io/zone", "*"].

Constraints

Service topology is not compatible with externalTrafficPolicy=Local, and therefore a Service cannot use both of these features. It is possible to use both features in the same cluster on different Services, only not on the same Service.
Valid topology keys are currently limited to kubernetes.io/hostname, topology.kubernetes.io/zone, and topology.kubernetes.io/region, but will be generalized to other node labels in the future.
Topology keys must be valid label keys and at most 16 keys may be specified.
The catch-all value, "*", must be the last value in the topology keys, if it is used.

Examples

The following are common examples of using the Service Topology feature.

Only Node Local Endpoints

A Service that only routes to node local endpoints. If no endpoints exist on the node, traffic is dropped:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376
  topologyKeys:
    - "kubernetes.io/hostname"

Prefer Node Local Endpoints

A Service that prefers node local Endpoints but falls back to cluster wide endpoints if node local endpoints do not exist:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376
  topologyKeys:
    - "kubernetes.io/hostname"
    - "*"

Only Zonal or Regional Endpoints

A Service that prefers zonal then regional endpoints. If no endpoints exist in either, traffic is dropped.

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376
  topologyKeys:
    - "topology.kubernetes.io/zone"
    - "topology.kubernetes.io/region"

Prefer Node Local, Zonal, then Regional Endpoints

A Service that prefers node local, zonal, then regional endpoints but falls back to cluster wide endpoints.

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376
  topologyKeys:
    - "kubernetes.io/hostname"
    - "topology.kubernetes.io/zone"
    - "topology.kubernetes.io/region"
    - "*"

What's next

Read about enabling Service Topology
Read Connecting Applications with Services

6.3 - Ingress

Make your HTTP (or HTTPS) network service available using a protocol-aware configuration mechanism, that understands web concepts like URIs, hostnames, paths, and more. The Ingress concept lets you map traffic to different backends based on rules you define via the Kubernetes API.

FEATURE STATE: Kubernetes v1.19 [stable]

An API object that manages external access to the services in a cluster, typically HTTP.

Ingress may provide load balancing, SSL termination and name-based virtual hosting.

Terminology

For clarity, this guide defines the following terms:

Node: A worker machine in Kubernetes, part of a cluster.
Cluster: A set of Nodes that run containerized applications managed by Kubernetes. For this example, and in most common Kubernetes deployments, nodes in the cluster are not part of the public internet.
Edge router: A router that enforces the firewall policy for your cluster. This could be a gateway managed by a cloud provider or a physical piece of hardware.
Cluster network: A set of links, logical or physical, that facilitate communication within a cluster according to the Kubernetes networking model.
Service: A Kubernetes Service that identifies a set of Pods using label selectors. Unless mentioned otherwise, Services are assumed to have virtual IPs only routable within the cluster network.

What is Ingress?

Ingress exposes HTTP and HTTPS routes from outside the cluster to services within the cluster. Traffic routing is controlled by rules defined on the Ingress resource.

Here is a simple example where an Ingress sends all its traffic to one Service:

An Ingress may be configured to give Services externally-reachable URLs, load balance traffic, terminate SSL / TLS, and offer name-based virtual hosting. An Ingress controller is responsible for fulfilling the Ingress, usually with a load balancer, though it may also configure your edge router or additional frontends to help handle the traffic.

An Ingress does not expose arbitrary ports or protocols. Exposing services other than HTTP and HTTPS to the internet typically uses a service of type Service.Type=NodePort or Service.Type=LoadBalancer.

Prerequisites

You must have an Ingress controller to satisfy an Ingress. Only creating an Ingress resource has no effect.

You may need to deploy an Ingress controller such as ingress-nginx. You can choose from a number of Ingress controllers.

Ideally, all Ingress controllers should fit the reference specification. In reality, the various Ingress controllers operate slightly differently.

Note: Make sure you review your Ingress controller's documentation to understand the caveats of choosing it.

The Ingress resource

A minimal Ingress resource example:

service/networking/minimal-ingress.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: minimal-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  ingressClassName: nginx-example
  rules:
  - http:
      paths:
      - path: /testpath
        pathType: Prefix
        backend:
          service:
            name: test
            port:
              number: 80

An Ingress needs apiVersion, kind, metadata and spec fields. The name of an Ingress object must be a valid DNS subdomain name. For general information about working with config files, see deploying applications, configuring containers, managing resources. Ingress frequently uses annotations to configure some options depending on the Ingress controller, an example of which is the rewrite-target annotation. Different Ingress controllers support different annotations. Review the documentation for your choice of Ingress controller to learn which annotations are supported.

The Ingress spec has all the information needed to configure a load balancer or proxy server. Most importantly, it contains a list of rules matched against all incoming requests. Ingress resource only supports rules for directing HTTP(S) traffic.

If the ingressClassName is omitted, a default Ingress class should be defined.

There are some ingress controllers, that work without the definition of a default IngressClass. For example, the Ingress-NGINX controller can be configured with a flag --watch-ingress-without-class. It is recommended though, to specify the default IngressClass as shown below.

Ingress rules

Each HTTP rule contains the following information:

An optional host. In this example, no host is specified, so the rule applies to all inbound HTTP traffic through the IP address specified. If a host is provided (for example, foo.bar.com), the rules apply to that host.
A list of paths (for example, /testpath), each of which has an associated backend defined with a service.name and a service.port.name or service.port.number. Both the host and path must match the content of an incoming request before the load balancer directs traffic to the referenced Service.
A backend is a combination of Service and port names as described in the Service doc or a custom resource backend by way of a CRD. HTTP (and HTTPS) requests to the Ingress that matches the host and path of the rule are sent to the listed backend.

A defaultBackend is often configured in an Ingress controller to service any requests that do not match a path in the spec.

DefaultBackend

An Ingress with no rules sends all traffic to a single default backend and .spec.defaultBackend is the backend that should handle requests in that case. The defaultBackend is conventionally a configuration option of the Ingress controller and is not specified in your Ingress resources. If no .spec.rules are specified, .spec.defaultBackend must be specified. If defaultBackend is not set, the handling of requests that do not match any of the rules will be up to the ingress controller (consult the documentation for your ingress controller to find out how it handles this case).

If none of the hosts or paths match the HTTP request in the Ingress objects, the traffic is routed to your default backend.

Resource backends

A Resource backend is an ObjectRef to another Kubernetes resource within the same namespace as the Ingress object. A Resource is a mutually exclusive setting with Service, and will fail validation if both are specified. A common usage for a Resource backend is to ingress data to an object storage backend with static assets.

service/networking/ingress-resource-backend.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: ingress-resource-backend
spec:
  defaultBackend:
    resource:
      apiGroup: k8s.example.com
      kind: StorageBucket
      name: static-assets
  rules:
    - http:
        paths:
          - path: /icons
            pathType: ImplementationSpecific
            backend:
              resource:
                apiGroup: k8s.example.com
                kind: StorageBucket
                name: icon-assets

After creating the Ingress above, you can view it with the following command:

kubectl describe ingress ingress-resource-backend

Name:             ingress-resource-backend
Namespace:        default
Address:
Default backend:  APIGroup: k8s.example.com, Kind: StorageBucket, Name: static-assets
Rules:
  Host        Path  Backends
  ----        ----  --------
  *
              /icons   APIGroup: k8s.example.com, Kind: StorageBucket, Name: icon-assets
Annotations:  <none>
Events:       <none>

Path types

Each path in an Ingress is required to have a corresponding path type. Paths that do not include an explicit pathType will fail validation. There are three supported path types:

ImplementationSpecific: With this path type, matching is up to the IngressClass. Implementations can treat this as a separate pathType or treat it identically to Prefix or Exact path types.
Exact: Matches the URL path exactly and with case sensitivity.
Prefix: Matches based on a URL path prefix split by /. Matching is case sensitive and done on a path element by element basis. A path element refers to the list of labels in the path split by the / separator. A request is a match for path p if every p is an element-wise prefix of p of the request path.

Note: If the last element of the path is a substring of the last element in request path, it is not a match (for example: /foo/bar matches/foo/bar/baz, but does not match /foo/barbaz).

Examples

Kind	Path(s)	Request path(s)	Matches?
Prefix	`/`	(all paths)	Yes
Exact	`/foo`	`/foo`	Yes
Exact	`/foo`	`/bar`	No
Exact	`/foo`	`/foo/`	No
Exact	`/foo/`	`/foo`	No
Prefix	`/foo`	`/foo`, `/foo/`	Yes
Prefix	`/foo/`	`/foo`, `/foo/`	Yes
Prefix	`/aaa/bb`	`/aaa/bbb`	No
Prefix	`/aaa/bbb`	`/aaa/bbb`	Yes
Prefix	`/aaa/bbb/`	`/aaa/bbb`	Yes, ignores trailing slash
Prefix	`/aaa/bbb`	`/aaa/bbb/`	Yes, matches trailing slash
Prefix	`/aaa/bbb`	`/aaa/bbb/ccc`	Yes, matches subpath
Prefix	`/aaa/bbb`	`/aaa/bbbxyz`	No, does not match string prefix
Prefix	`/`, `/aaa`	`/aaa/ccc`	Yes, matches `/aaa` prefix
Prefix	`/`, `/aaa`, `/aaa/bbb`	`/aaa/bbb`	Yes, matches `/aaa/bbb` prefix
Prefix	`/`, `/aaa`, `/aaa/bbb`	`/ccc`	Yes, matches `/` prefix
Prefix	`/aaa`	`/ccc`	No, uses default backend
Mixed	`/foo` (Prefix), `/foo` (Exact)	`/foo`	Yes, prefers Exact

Multiple matches

In some cases, multiple paths within an Ingress will match a request. In those cases precedence will be given first to the longest matching path. If two paths are still equally matched, precedence will be given to paths with an exact path type over prefix path type.

Hostname wildcards

Hosts can be precise matches (for example “foo.bar.com”) or a wildcard (for example “*.foo.com”). Precise matches require that the HTTP host header matches the host field. Wildcard matches require the HTTP host header is equal to the suffix of the wildcard rule.

Host	Host header	Match?
`*.foo.com`	`bar.foo.com`	Matches based on shared suffix
`*.foo.com`	`baz.bar.foo.com`	No match, wildcard only covers a single DNS label
`*.foo.com`	`foo.com`	No match, wildcard only covers a single DNS label

service/networking/ingress-wildcard-host.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: ingress-wildcard-host
spec:
  rules:
  - host: "foo.bar.com"
    http:
      paths:
      - pathType: Prefix
        path: "/bar"
        backend:
          service:
            name: service1
            port:
              number: 80
  - host: "*.foo.com"
    http:
      paths:
      - pathType: Prefix
        path: "/foo"
        backend:
          service:
            name: service2
            port:
              number: 80

Ingress class

Ingresses can be implemented by different controllers, often with different configuration. Each Ingress should specify a class, a reference to an IngressClass resource that contains additional configuration including the name of the controller that should implement the class.

service/networking/external-lb.yaml

apiVersion: networking.k8s.io/v1
kind: IngressClass
metadata:
  name: external-lb
spec:
  controller: example.com/ingress-controller
  parameters:
    apiGroup: k8s.example.com
    kind: IngressParameters
    name: external-lb

The .spec.parameters field of an IngressClass lets you reference another resource that provides configuration related to that IngressClass.

The specific type of parameters to use depends on the ingress controller that you specify in the .spec.controller field of the IngressClass.

IngressClass scope

Depending on your ingress controller, you may be able to use parameters that you set cluster-wide, or just for one namespace.

Cluster
Namespaced

The default scope for IngressClass parameters is cluster-wide.

If you set the .spec.parameters field and don't set .spec.parameters.scope, or if you set .spec.parameters.scope to Cluster, then the IngressClass refers to a cluster-scoped resource. The kind (in combination the apiGroup) of the parameters refers to a cluster-scoped API (possibly a custom resource), and the name of the parameters identifies a specific cluster scoped resource for that API.

For example:

---
apiVersion: networking.k8s.io/v1
kind: IngressClass
metadata:
  name: external-lb-1
spec:
  controller: example.com/ingress-controller
  parameters:
    # The parameters for this IngressClass are specified in a
    # ClusterIngressParameter (API group k8s.example.net) named
    # "external-config-1". This definition tells Kubernetes to
    # look for a cluster-scoped parameter resource.
    scope: Cluster
    apiGroup: k8s.example.net
    kind: ClusterIngressParameter
    name: external-config-1

FEATURE STATE: Kubernetes v1.23 [stable]

If you set the .spec.parameters field and set .spec.parameters.scope to Namespace, then the IngressClass refers to a namespaced-scoped resource. You must also set the namespace field within .spec.parameters to the namespace that contains the parameters you want to use.

The kind (in combination the apiGroup) of the parameters refers to a namespaced API (for example: ConfigMap), and the name of the parameters identifies a specific resource in the namespace you specified in namespace.

Namespace-scoped parameters help the cluster operator delegate control over the configuration (for example: load balancer settings, API gateway definition) that is used for a workload. If you used a cluster-scoped parameter then either:

the cluster operator team needs to approve a different team's changes every time there's a new configuration change being applied.
the cluster operator must define specific access controls, such as RBAC roles and bindings, that let the application team make changes to the cluster-scoped parameters resource.

The IngressClass API itself is always cluster-scoped.

Here is an example of an IngressClass that refers to parameters that are namespaced:

---
apiVersion: networking.k8s.io/v1
kind: IngressClass
metadata:
  name: external-lb-2
spec:
  controller: example.com/ingress-controller
  parameters:
    # The parameters for this IngressClass are specified in an
    # IngressParameter (API group k8s.example.com) named "external-config",
    # that's in the "external-configuration" namespace.
    scope: Namespace
    apiGroup: k8s.example.com
    kind: IngressParameter
    namespace: external-configuration
    name: external-config

Deprecated annotation

Before the IngressClass resource and ingressClassName field were added in Kubernetes 1.18, Ingress classes were specified with a kubernetes.io/ingress.class annotation on the Ingress. This annotation was never formally defined, but was widely supported by Ingress controllers.

The newer ingressClassName field on Ingresses is a replacement for that annotation, but is not a direct equivalent. While the annotation was generally used to reference the name of the Ingress controller that should implement the Ingress, the field is a reference to an IngressClass resource that contains additional Ingress configuration, including the name of the Ingress controller.

Default IngressClass

You can mark a particular IngressClass as default for your cluster. Setting the ingressclass.kubernetes.io/is-default-class annotation to true on an IngressClass resource will ensure that new Ingresses without an ingressClassName field specified will be assigned this default IngressClass.

Caution: If you have more than one IngressClass marked as the default for your cluster, the admission controller prevents creating new Ingress objects that don't have an ingressClassName specified. You can resolve this by ensuring that at most 1 IngressClass is marked as default in your cluster.

service/networking/default-ingressclass.yaml

apiVersion: networking.k8s.io/v1
kind: IngressClass
metadata:
  labels:
    app.kubernetes.io/component: controller
  name: nginx-example
  annotations:
    ingressclass.kubernetes.io/is-default-class: "true"
spec:
  controller: k8s.io/ingress-nginx

Types of Ingress

Ingress backed by a single Service

There are existing Kubernetes concepts that allow you to expose a single Service (see alternatives). You can also do this with an Ingress by specifying a default backend with no rules.

service/networking/test-ingress.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: test-ingress
spec:
  defaultBackend:
    service:
      name: test
      port:
        number: 80

If you create it using kubectl apply -f you should be able to view the state of the Ingress you added:

kubectl get ingress test-ingress

NAME           CLASS         HOSTS   ADDRESS         PORTS   AGE
test-ingress   external-lb   *       203.0.113.123   80      59s

Where 203.0.113.123 is the IP allocated by the Ingress controller to satisfy this Ingress.

Note: Ingress controllers and load balancers may take a minute or two to allocate an IP address. Until that time, you often see the address listed as <pending>.

Simple fanout

A fanout configuration routes traffic from a single IP address to more than one Service, based on the HTTP URI being requested. An Ingress allows you to keep the number of load balancers down to a minimum. For example, a setup like:

ingress-fanout-diagram — Figure. Ingress Fan Out

would require an Ingress such as:

service/networking/simple-fanout-example.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: simple-fanout-example
spec:
  rules:
  - host: foo.bar.com
    http:
      paths:
      - path: /foo
        pathType: Prefix
        backend:
          service:
            name: service1
            port:
              number: 4200
      - path: /bar
        pathType: Prefix
        backend:
          service:
            name: service2
            port:
              number: 8080

When you create the Ingress with kubectl apply -f:

kubectl describe ingress simple-fanout-example

Name:             simple-fanout-example
Namespace:        default
Address:          178.91.123.132
Default backend:  default-http-backend:80 (10.8.2.3:8080)
Rules:
  Host         Path  Backends
  ----         ----  --------
  foo.bar.com
               /foo   service1:4200 (10.8.0.90:4200)
               /bar   service2:8080 (10.8.0.91:8080)
Events:
  Type     Reason  Age                From                     Message
  ----     ------  ----               ----                     -------
  Normal   ADD     22s                loadbalancer-controller  default/test

The Ingress controller provisions an implementation-specific load balancer that satisfies the Ingress, as long as the Services (service1, service2) exist. When it has done so, you can see the address of the load balancer at the Address field.

Note: Depending on the Ingress controller you are using, you may need to create a default-http-backend Service.

Name based virtual hosting

Name-based virtual hosts support routing HTTP traffic to multiple host names at the same IP address.

ingress-namebase-diagram — Figure. Ingress Name Based Virtual hosting

The following Ingress tells the backing load balancer to route requests based on the Host header.

service/networking/name-virtual-host-ingress.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: name-virtual-host-ingress
spec:
  rules:
  - host: foo.bar.com
    http:
      paths:
      - pathType: Prefix
        path: "/"
        backend:
          service:
            name: service1
            port:
              number: 80
  - host: bar.foo.com
    http:
      paths:
      - pathType: Prefix
        path: "/"
        backend:
          service:
            name: service2
            port:
              number: 80

If you create an Ingress resource without any hosts defined in the rules, then any web traffic to the IP address of your Ingress controller can be matched without a name based virtual host being required.

For example, the following Ingress routes traffic requested for first.bar.com to service1, second.bar.com to service2, and any traffic whose request host header doesn't match first.bar.com and second.bar.com to service3.

service/networking/name-virtual-host-ingress-no-third-host.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: name-virtual-host-ingress-no-third-host
spec:
  rules:
  - host: first.bar.com
    http:
      paths:
      - pathType: Prefix
        path: "/"
        backend:
          service:
            name: service1
            port:
              number: 80
  - host: second.bar.com
    http:
      paths:
      - pathType: Prefix
        path: "/"
        backend:
          service:
            name: service2
            port:
              number: 80
  - http:
      paths:
      - pathType: Prefix
        path: "/"
        backend:
          service:
            name: service3
            port:
              number: 80

TLS

You can secure an Ingress by specifying a Secret that contains a TLS private key and certificate. The Ingress resource only supports a single TLS port, 443, and assumes TLS termination at the ingress point (traffic to the Service and its Pods is in plaintext). If the TLS configuration section in an Ingress specifies different hosts, they are multiplexed on the same port according to the hostname specified through the SNI TLS extension (provided the Ingress controller supports SNI). The TLS secret must contain keys named tls.crt and tls.key that contain the certificate and private key to use for TLS. For example:

apiVersion: v1
kind: Secret
metadata:
  name: testsecret-tls
  namespace: default
data:
  tls.crt: base64 encoded cert
  tls.key: base64 encoded key
type: kubernetes.io/tls

Referencing this secret in an Ingress tells the Ingress controller to secure the channel from the client to the load balancer using TLS. You need to make sure the TLS secret you created came from a certificate that contains a Common Name (CN), also known as a Fully Qualified Domain Name (FQDN) for https-example.foo.com.

Note: Keep in mind that TLS will not work on the default rule because the certificates would have to be issued for all the possible sub-domains. Therefore, hosts in the tls section need to explicitly match the host in the rules section.

service/networking/tls-example-ingress.yaml

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: tls-example-ingress
spec:
  tls:
  - hosts:
      - https-example.foo.com
    secretName: testsecret-tls
  rules:
  - host: https-example.foo.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: service1
            port:
              number: 80

Note: There is a gap between TLS features supported by various Ingress controllers. Please refer to documentation on nginx, GCE, or any other platform specific Ingress controller to understand how TLS works in your environment.

Load balancing

An Ingress controller is bootstrapped with some load balancing policy settings that it applies to all Ingress, such as the load balancing algorithm, backend weight scheme, and others. More advanced load balancing concepts (e.g. persistent sessions, dynamic weights) are not yet exposed through the Ingress. You can instead get these features through the load balancer used for a Service.

It's also worth noting that even though health checks are not exposed directly through the Ingress, there exist parallel concepts in Kubernetes such as readiness probes that allow you to achieve the same end result. Please review the controller specific documentation to see how they handle health checks (for example: nginx, or GCE).

Updating an Ingress

To update an existing Ingress to add a new Host, you can update it by editing the resource:

kubectl describe ingress test

Name:             test
Namespace:        default
Address:          178.91.123.132
Default backend:  default-http-backend:80 (10.8.2.3:8080)
Rules:
  Host         Path  Backends
  ----         ----  --------
  foo.bar.com
               /foo   service1:80 (10.8.0.90:80)
Annotations:
  nginx.ingress.kubernetes.io/rewrite-target:  /
Events:
  Type     Reason  Age                From                     Message
  ----     ------  ----               ----                     -------
  Normal   ADD     35s                loadbalancer-controller  default/test

kubectl edit ingress test

This pops up an editor with the existing configuration in YAML format. Modify it to include the new Host:

spec:
  rules:
  - host: foo.bar.com
    http:
      paths:
      - backend:
          service:
            name: service1
            port:
              number: 80
        path: /foo
        pathType: Prefix
  - host: bar.baz.com
    http:
      paths:
      - backend:
          service:
            name: service2
            port:
              number: 80
        path: /foo
        pathType: Prefix
..

After you save your changes, kubectl updates the resource in the API server, which tells the Ingress controller to reconfigure the load balancer.

Verify this:

kubectl describe ingress test

Name:             test
Namespace:        default
Address:          178.91.123.132
Default backend:  default-http-backend:80 (10.8.2.3:8080)
Rules:
  Host         Path  Backends
  ----         ----  --------
  foo.bar.com
               /foo   service1:80 (10.8.0.90:80)
  bar.baz.com
               /foo   service2:80 (10.8.0.91:80)
Annotations:
  nginx.ingress.kubernetes.io/rewrite-target:  /
Events:
  Type     Reason  Age                From                     Message
  ----     ------  ----               ----                     -------
  Normal   ADD     45s                loadbalancer-controller  default/test

You can achieve the same outcome by invoking kubectl replace -f on a modified Ingress YAML file.

Failing across availability zones

Techniques for spreading traffic across failure domains differ between cloud providers. Please check the documentation of the relevant Ingress controller for details.

Alternatives

You can expose a Service in multiple ways that don't directly involve the Ingress resource:

Use Service.Type=LoadBalancer
Use Service.Type=NodePort

What's next

Learn about the Ingress API
Learn about Ingress controllers
Set up Ingress on Minikube with the NGINX Controller

6.4 - Connecting Applications with Services

The Kubernetes model for connecting containers

Now that you have a continuously running, replicated application you can expose it on a network.

Kubernetes assumes that pods can communicate with other pods, regardless of which host they land on. Kubernetes gives every pod its own cluster-private IP address, so you do not need to explicitly create links between pods or map container ports to host ports. This means that containers within a Pod can all reach each other's ports on localhost, and all pods in a cluster can see each other without NAT. The rest of this document elaborates on how you can run reliable services on such a networking model.

This guide uses a simple nginx server to demonstrate proof of concept.

Exposing pods to the cluster

We did this in a previous example, but let's do it once again and focus on the networking perspective. Create an nginx Pod, and note that it has a container port specification:

service/networking/run-my-nginx.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80

This makes it accessible from any node in your cluster. Check the nodes the Pod is running on:

kubectl apply -f ./run-my-nginx.yaml
kubectl get pods -l run=my-nginx -o wide

NAME                        READY     STATUS    RESTARTS   AGE       IP            NODE
my-nginx-3800858182-jr4a2   1/1       Running   0          13s       10.244.3.4    kubernetes-minion-905m
my-nginx-3800858182-kna2y   1/1       Running   0          13s       10.244.2.5    kubernetes-minion-ljyd

Check your pods' IPs:

kubectl get pods -l run=my-nginx -o custom-columns=POD_IP:.status.podIPs
    POD_IP
    [map[ip:10.244.3.4]]
    [map[ip:10.244.2.5]]

You should be able to ssh into any node in your cluster and use a tool such as curl to make queries against both IPs. Note that the containers are not using port 80 on the node, nor are there any special NAT rules to route traffic to the pod. This means you can run multiple nginx pods on the same node all using the same containerPort, and access them from any other pod or node in your cluster using the assigned IP address for the Service. If you want to arrange for a specific port on the host Node to be forwarded to backing Pods, you can - but the networking model should mean that you do not need to do so.

You can read more about the Kubernetes Networking Model if you're curious.

Creating a Service

So we have pods running nginx in a flat, cluster wide, address space. In theory, you could talk to these pods directly, but what happens when a node dies? The pods die with it, and the Deployment will create new ones, with different IPs. This is the problem a Service solves.

A Kubernetes Service is an abstraction which defines a logical set of Pods running somewhere in your cluster, that all provide the same functionality. When created, each Service is assigned a unique IP address (also called clusterIP). This address is tied to the lifespan of the Service, and will not change while the Service is alive. Pods can be configured to talk to the Service, and know that communication to the Service will be automatically load-balanced out to some pod that is a member of the Service.

You can create a Service for your 2 nginx replicas with kubectl expose:

kubectl expose deployment/my-nginx

service/my-nginx exposed

This is equivalent to kubectl apply -f the following yaml:

service/networking/nginx-svc.yaml

apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  ports:
  - port: 80
    protocol: TCP
  selector:
    run: my-nginx

This specification will create a Service which targets TCP port 80 on any Pod with the run: my-nginx label, and expose it on an abstracted Service port (targetPort: is the port the container accepts traffic on, port: is the abstracted Service port, which can be any port other pods use to access the Service). View Service API object to see the list of supported fields in service definition. Check your Service:

kubectl get svc my-nginx

NAME       TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)   AGE
my-nginx   ClusterIP   10.0.162.149   <none>        80/TCP    21s

As mentioned previously, a Service is backed by a group of Pods. These Pods are exposed through endpoints. The Service's selector will be evaluated continuously and the results will be POSTed to an Endpoints object also named my-nginx. When a Pod dies, it is automatically removed from the endpoints, and new Pods matching the Service's selector will automatically get added to the endpoints. Check the endpoints, and note that the IPs are the same as the Pods created in the first step:

kubectl describe svc my-nginx

Name:                my-nginx
Namespace:           default
Labels:              run=my-nginx
Annotations:         <none>
Selector:            run=my-nginx
Type:                ClusterIP
IP:                  10.0.162.149
Port:                <unset> 80/TCP
Endpoints:           10.244.2.5:80,10.244.3.4:80
Session Affinity:    None
Events:              <none>

kubectl get ep my-nginx

NAME       ENDPOINTS                     AGE
my-nginx   10.244.2.5:80,10.244.3.4:80   1m

You should now be able to curl the nginx Service on <CLUSTER-IP>:<PORT> from any node in your cluster. Note that the Service IP is completely virtual, it never hits the wire. If you're curious about how this works you can read more about the service proxy.

Accessing the Service

Kubernetes supports 2 primary modes of finding a Service - environment variables and DNS. The former works out of the box while the latter requires the CoreDNS cluster addon.

Note: If the service environment variables are not desired (because possible clashing with expected program ones, too many variables to process, only using DNS, etc) you can disable this mode by setting the enableServiceLinks flag to false on the pod spec.

Environment Variables

When a Pod runs on a Node, the kubelet adds a set of environment variables for each active Service. This introduces an ordering problem. To see why, inspect the environment of your running nginx Pods (your Pod name will be different):

kubectl exec my-nginx-3800858182-jr4a2 -- printenv | grep SERVICE

KUBERNETES_SERVICE_HOST=10.0.0.1
KUBERNETES_SERVICE_PORT=443
KUBERNETES_SERVICE_PORT_HTTPS=443

Note there's no mention of your Service. This is because you created the replicas before the Service. Another disadvantage of doing this is that the scheduler might put both Pods on the same machine, which will take your entire Service down if it dies. We can do this the right way by killing the 2 Pods and waiting for the Deployment to recreate them. This time around the Service exists before the replicas. This will give you scheduler-level Service spreading of your Pods (provided all your nodes have equal capacity), as well as the right environment variables:

kubectl scale deployment my-nginx --replicas=0; kubectl scale deployment my-nginx --replicas=2;

kubectl get pods -l run=my-nginx -o wide

NAME                        READY     STATUS    RESTARTS   AGE     IP            NODE
my-nginx-3800858182-e9ihh   1/1       Running   0          5s      10.244.2.7    kubernetes-minion-ljyd
my-nginx-3800858182-j4rm4   1/1       Running   0          5s      10.244.3.8    kubernetes-minion-905m

You may notice that the pods have different names, since they are killed and recreated.

kubectl exec my-nginx-3800858182-e9ihh -- printenv | grep SERVICE

KUBERNETES_SERVICE_PORT=443
MY_NGINX_SERVICE_HOST=10.0.162.149
KUBERNETES_SERVICE_HOST=10.0.0.1
MY_NGINX_SERVICE_PORT=80
KUBERNETES_SERVICE_PORT_HTTPS=443

DNS

Kubernetes offers a DNS cluster addon Service that automatically assigns dns names to other Services. You can check if it's running on your cluster:

kubectl get services kube-dns --namespace=kube-system

NAME       TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)         AGE
kube-dns   ClusterIP   10.0.0.10    <none>        53/UDP,53/TCP   8m

The rest of this section will assume you have a Service with a long lived IP (my-nginx), and a DNS server that has assigned a name to that IP. Here we use the CoreDNS cluster addon (application name kube-dns), so you can talk to the Service from any pod in your cluster using standard methods (e.g. gethostbyname()). If CoreDNS isn't running, you can enable it referring to the CoreDNS README or Installing CoreDNS. Let's run another curl application to test this:

kubectl run curl --image=radial/busyboxplus:curl -i --tty

Waiting for pod default/curl-131556218-9fnch to be running, status is Pending, pod ready: false
Hit enter for command prompt

Then, hit enter and run nslookup my-nginx:

[ root@curl-131556218-9fnch:/ ]$ nslookup my-nginx
Server:    10.0.0.10
Address 1: 10.0.0.10

Name:      my-nginx
Address 1: 10.0.162.149

Securing the Service

Till now we have only accessed the nginx server from within the cluster. Before exposing the Service to the internet, you want to make sure the communication channel is secure. For this, you will need:

Self signed certificates for https (unless you already have an identity certificate)
An nginx server configured to use the certificates
A secret that makes the certificates accessible to pods

You can acquire all these from the nginx https example. This requires having go and make tools installed. If you don't want to install those, then follow the manual steps later. In short:

make keys KEY=/tmp/nginx.key CERT=/tmp/nginx.crt
kubectl create secret tls nginxsecret --key /tmp/nginx.key --cert /tmp/nginx.crt

secret/nginxsecret created

kubectl get secrets

NAME                  TYPE                                  DATA      AGE
nginxsecret           kubernetes.io/tls                     2         1m

And also the configmap:

kubectl create configmap nginxconfigmap --from-file=default.conf

configmap/nginxconfigmap created

kubectl get configmaps

NAME             DATA   AGE
nginxconfigmap   1      114s

Following are the manual steps to follow in case you run into problems running make (on windows for example):

# Create a public private key pair
openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /d/tmp/nginx.key -out /d/tmp/nginx.crt -subj "/CN=my-nginx/O=my-nginx"
# Convert the keys to base64 encoding
cat /d/tmp/nginx.crt | base64
cat /d/tmp/nginx.key | base64

Use the output from the previous commands to create a yaml file as follows. The base64 encoded value should all be on a single line.

apiVersion: "v1"
kind: "Secret"
metadata:
  name: "nginxsecret"
  namespace: "default"
type: kubernetes.io/tls
data:
  tls.crt: "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"
  tls.key: "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"

Now create the secrets using the file:

kubectl apply -f nginxsecrets.yaml
kubectl get secrets

NAME                  TYPE                                  DATA      AGE
nginxsecret           kubernetes.io/tls                     2         1m

Now modify your nginx replicas to start an https server using the certificate in the secret, and the Service, to expose both ports (80 and 443):

service/networking/nginx-secure-app.yaml

apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  type: NodePort
  ports:
  - port: 8080
    targetPort: 80
    protocol: TCP
    name: http
  - port: 443
    protocol: TCP
    name: https
  selector:
    run: my-nginx
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 1
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      volumes:
      - name: secret-volume
        secret:
          secretName: nginxsecret
      - name: configmap-volume
        configMap:
          name: nginxconfigmap
      containers:
      - name: nginxhttps
        image: bprashanth/nginxhttps:1.0
        ports:
        - containerPort: 443
        - containerPort: 80
        volumeMounts:
        - mountPath: /etc/nginx/ssl
          name: secret-volume
        - mountPath: /etc/nginx/conf.d
          name: configmap-volume

Noteworthy points about the nginx-secure-app manifest:

It contains both Deployment and Service specification in the same file.
The nginx server serves HTTP traffic on port 80 and HTTPS traffic on 443, and nginx Service exposes both ports.
Each container has access to the keys through a volume mounted at /etc/nginx/ssl. This is set up before the nginx server is started.

kubectl delete deployments,svc my-nginx; kubectl create -f ./nginx-secure-app.yaml

At this point you can reach the nginx server from any node.

kubectl get pods -l run=my-nginx -o custom-columns=POD_IP:.status.podIPs
    POD_IP
    [map[ip:10.244.3.5]]

node $ curl -k https://10.244.3.5
...
<h1>Welcome to nginx!</h1>

Note how we supplied the -k parameter to curl in the last step, this is because we don't know anything about the pods running nginx at certificate generation time, so we have to tell curl to ignore the CName mismatch. By creating a Service we linked the CName used in the certificate with the actual DNS name used by pods during Service lookup. Let's test this from a pod (the same secret is being reused for simplicity, the pod only needs nginx.crt to access the Service):

service/networking/curlpod.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: curl-deployment
spec:
  selector:
    matchLabels:
      app: curlpod
  replicas: 1
  template:
    metadata:
      labels:
        app: curlpod
    spec:
      volumes:
      - name: secret-volume
        secret:
          secretName: nginxsecret
      containers:
      - name: curlpod
        command:
        - sh
        - -c
        - while true; do sleep 1; done
        image: radial/busyboxplus:curl
        volumeMounts:
        - mountPath: /etc/nginx/ssl
          name: secret-volume

kubectl apply -f ./curlpod.yaml
kubectl get pods -l app=curlpod

NAME                               READY     STATUS    RESTARTS   AGE
curl-deployment-1515033274-1410r   1/1       Running   0          1m

kubectl exec curl-deployment-1515033274-1410r -- curl https://my-nginx --cacert /etc/nginx/ssl/tls.crt
...
<title>Welcome to nginx!</title>
...

Exposing the Service

For some parts of your applications you may want to expose a Service onto an external IP address. Kubernetes supports two ways of doing this: NodePorts and LoadBalancers. The Service created in the last section already used NodePort, so your nginx HTTPS replica is ready to serve traffic on the internet if your node has a public IP.

kubectl get svc my-nginx -o yaml | grep nodePort -C 5
  uid: 07191fb3-f61a-11e5-8ae5-42010af00002
spec:
  clusterIP: 10.0.162.149
  ports:
  - name: http
    nodePort: 31704
    port: 8080
    protocol: TCP
    targetPort: 80
  - name: https
    nodePort: 32453
    port: 443
    protocol: TCP
    targetPort: 443
  selector:
    run: my-nginx

kubectl get nodes -o yaml | grep ExternalIP -C 1
    - address: 104.197.41.11
      type: ExternalIP
    allocatable:
--
    - address: 23.251.152.56
      type: ExternalIP
    allocatable:
...

$ curl https://<EXTERNAL-IP>:<NODE-PORT> -k
...
<h1>Welcome to nginx!</h1>

Let's now recreate the Service to use a cloud load balancer. Change the Type of my-nginx Service from NodePort to LoadBalancer:

kubectl edit svc my-nginx
kubectl get svc my-nginx

NAME       TYPE           CLUSTER-IP     EXTERNAL-IP        PORT(S)               AGE
my-nginx   LoadBalancer   10.0.162.149     xx.xxx.xxx.xxx     8080:30163/TCP        21s

curl https://<EXTERNAL-IP> -k
...
<title>Welcome to nginx!</title>

The IP address in the EXTERNAL-IP column is the one that is available on the public internet. The CLUSTER-IP is only available inside your cluster/private cloud network.

Note that on AWS, type LoadBalancer creates an ELB, which uses a (long) hostname, not an IP. It's too long to fit in the standard kubectl get svc output, in fact, so you'll need to do kubectl describe service my-nginx to see it. You'll see something like this:

kubectl describe service my-nginx
...
LoadBalancer Ingress:   a320587ffd19711e5a37606cf4a74574-1142138393.us-east-1.elb.amazonaws.com
...

What's next

Learn more about Using a Service to Access an Application in a Cluster
Learn more about Connecting a Front End to a Back End Using a Service
Learn more about Creating an External Load Balancer

6.5 - Ingress Controllers

In order for an Ingress to work in your cluster, there must be an ingress controller running. You need to select at least one ingress controller and make sure it is set up in your cluster. This page lists common ingress controllers that you can deploy.

In order for the Ingress resource to work, the cluster must have an ingress controller running.

Unlike other types of controllers which run as part of the kube-controller-manager binary, Ingress controllers are not started automatically with a cluster. Use this page to choose the ingress controller implementation that best fits your cluster.

Kubernetes as a project supports and maintains AWS, GCE, and nginx ingress controllers.

Additional controllers

AKS Application Gateway Ingress Controller is an ingress controller that configures the Azure Application Gateway.
Ambassador API Gateway is an Envoy-based ingress controller.
Apache APISIX ingress controller is an Apache APISIX-based ingress controller.
Avi Kubernetes Operator provides L4-L7 load-balancing using VMware NSX Advanced Load Balancer.
BFE Ingress Controller is a BFE-based ingress controller.
The Citrix ingress controller works with Citrix Application Delivery Controller.
Contour is an Envoy based ingress controller.
EnRoute is an Envoy based API gateway that can run as an ingress controller.
Easegress IngressController is an Easegress based API gateway that can run as an ingress controller.
F5 BIG-IP Container Ingress Services for Kubernetes lets you use an Ingress to configure F5 BIG-IP virtual servers.
Gloo is an open-source ingress controller based on Envoy, which offers API gateway functionality.
HAProxy Ingress is an ingress controller for HAProxy.
The HAProxy Ingress Controller for Kubernetes is also an ingress controller for HAProxy.
Istio Ingress is an Istio based ingress controller.
The Kong Ingress Controller for Kubernetes is an ingress controller driving Kong Gateway.
Kusk Gateway is an OpenAPI-driven ingress controller based on Envoy.
The NGINX Ingress Controller for Kubernetes works with the NGINX webserver (as a proxy).
The Pomerium Ingress Controller is based on Pomerium, which offers context-aware access policy.
Skipper HTTP router and reverse proxy for service composition, including use cases like Kubernetes Ingress, designed as a library to build your custom proxy.
The Traefik Kubernetes Ingress provider is an ingress controller for the Traefik proxy.
Tyk Operator extends Ingress with Custom Resources to bring API Management capabilities to Ingress. Tyk Operator works with the Open Source Tyk Gateway & Tyk Cloud control plane.
Voyager is an ingress controller for HAProxy.

Using multiple Ingress controllers

You may deploy any number of ingress controllers using ingress class within a cluster. Note the .metadata.name of your ingress class resource. When you create an ingress you would need that name to specify the ingressClassName field on your Ingress object (refer to IngressSpec v1 reference. ingressClassName is a replacement of the older annotation method.

If you do not specify an IngressClass for an Ingress, and your cluster has exactly one IngressClass marked as default, then Kubernetes applies the cluster's default IngressClass to the Ingress. You mark an IngressClass as default by setting the ingressclass.kubernetes.io/is-default-class annotation on that IngressClass, with the string value "true".

Ideally, all ingress controllers should fulfill this specification, but the various ingress controllers operate slightly differently.

Note: Make sure you review your ingress controller's documentation to understand the caveats of choosing it.

What's next

Learn more about Ingress.
Set up Ingress on Minikube with the NGINX Controller.

6.6 - EndpointSlices

The EndpointSlice API is the mechanism that Kubernetes uses to let your Service scale to handle large numbers of backends, and allows the cluster to update its list of healthy backends efficiently.

FEATURE STATE: Kubernetes v1.21 [stable]

EndpointSlices provide a simple way to track network endpoints within a Kubernetes cluster. They offer a more scalable and extensible alternative to Endpoints.

Motivation

The Endpoints API has provided a simple and straightforward way of tracking network endpoints in Kubernetes. Unfortunately as Kubernetes clusters and Services have grown to handle and send more traffic to more backend Pods, limitations of that original API became more visible. Most notably, those included challenges with scaling to larger numbers of network endpoints.

Since all network endpoints for a Service were stored in a single Endpoints resource, those resources could get quite large. That affected the performance of Kubernetes components (notably the master control plane) and resulted in significant amounts of network traffic and processing when Endpoints changed. EndpointSlices help you mitigate those issues as well as provide an extensible platform for additional features such as topological routing.

EndpointSlice resources

In Kubernetes, an EndpointSlice contains references to a set of network endpoints. The control plane automatically creates EndpointSlices for any Kubernetes Service that has a selector specified. These EndpointSlices include references to all the Pods that match the Service selector. EndpointSlices group network endpoints together by unique combinations of protocol, port number, and Service name. The name of a EndpointSlice object must be a valid DNS subdomain name.

As an example, here's a sample EndpointSlice resource for the example Kubernetes Service.

apiVersion: discovery.k8s.io/v1
kind: EndpointSlice
metadata:
  name: example-abc
  labels:
    kubernetes.io/service-name: example
addressType: IPv4
ports:
  - name: http
    protocol: TCP
    port: 80
endpoints:
  - addresses:
      - "10.1.2.3"
    conditions:
      ready: true
    hostname: pod-1
    nodeName: node-1
    zone: us-west2-a

By default, the control plane creates and manages EndpointSlices to have no more than 100 endpoints each. You can configure this with the --max-endpoints-per-slice kube-controller-manager flag, up to a maximum of 1000.

EndpointSlices can act as the source of truth for kube-proxy when it comes to how to route internal traffic. When enabled, they should provide a performance improvement for services with large numbers of endpoints.

Address types

EndpointSlices support three address types:

IPv4
IPv6
FQDN (Fully Qualified Domain Name)

Conditions

The EndpointSlice API stores conditions about endpoints that may be useful for consumers. The three conditions are ready, serving, and terminating.

Ready

ready is a condition that maps to a Pod's Ready condition. A running Pod with the Ready condition set to True should have this EndpointSlice condition also set to true. For compatibility reasons, ready is NEVER true when a Pod is terminating. Consumers should refer to the serving condition to inspect the readiness of terminating Pods. The only exception to this rule is for Services with spec.publishNotReadyAddresses set to true. Endpoints for these Services will always have the ready condition set to true.

Serving

FEATURE STATE: Kubernetes v1.22 [beta]

serving is identical to the ready condition, except it does not account for terminating states. Consumers of the EndpointSlice API should check this condition if they care about pod readiness while the pod is also terminating.

Note: Although serving is almost identical to ready, it was added to prevent break the existing meaning of ready. It may be unexpected for existing clients if ready could be true for terminating endpoints, since historically terminating endpoints were never included in the Endpoints or EndpointSlice API to begin with. For this reason, ready is always false for terminating endpoints, and a new condition serving was added in v1.20 so that clients can track readiness for terminating pods independent of the existing semantics for ready.

Terminating

FEATURE STATE: Kubernetes v1.22 [beta]

Terminating is a condition that indicates whether an endpoint is terminating. For pods, this is any pod that has a deletion timestamp set.

Topology information

Each endpoint within an EndpointSlice can contain relevant topology information. The topology information includes the location of the endpoint and information about the corresponding Node and zone. These are available in the following per endpoint fields on EndpointSlices:

nodeName - The name of the Node this endpoint is on.
zone - The zone this endpoint is in.

Note:

In the v1 API, the per endpoint topology was effectively removed in favor of the dedicated fields nodeName and zone.

Setting arbitrary topology fields on the endpoint field of an EndpointSlice resource has been deprecated and is not supported in the v1 API. Instead, the v1 API supports setting individual nodeName and zone fields. These fields are automatically translated between API versions. For example, the value of the "topology.kubernetes.io/zone" key in the topology field in the v1beta1 API is accessible as the zone field in the v1 API.

Management

Most often, the control plane (specifically, the endpoint slice controller) creates and manages EndpointSlice objects. There are a variety of other use cases for EndpointSlices, such as service mesh implementations, that could result in other entities or controllers managing additional sets of EndpointSlices.

To ensure that multiple entities can manage EndpointSlices without interfering with each other, Kubernetes defines the label endpointslice.kubernetes.io/managed-by, which indicates the entity managing an EndpointSlice. The endpoint slice controller sets endpointslice-controller.k8s.io as the value for this label on all EndpointSlices it manages. Other entities managing EndpointSlices should also set a unique value for this label.

Ownership

In most use cases, EndpointSlices are owned by the Service that the endpoint slice object tracks endpoints for. This ownership is indicated by an owner reference on each EndpointSlice as well as a kubernetes.io/service-name label that enables simple lookups of all EndpointSlices belonging to a Service.

EndpointSlice mirroring

In some cases, applications create custom Endpoints resources. To ensure that these applications do not need to concurrently write to both Endpoints and EndpointSlice resources, the cluster's control plane mirrors most Endpoints resources to corresponding EndpointSlices.

The control plane mirrors Endpoints resources unless:

the Endpoints resource has a endpointslice.kubernetes.io/skip-mirror label set to true.
the Endpoints resource has a control-plane.alpha.kubernetes.io/leader annotation.
the corresponding Service resource does not exist.
the corresponding Service resource has a non-nil selector.

Individual Endpoints resources may translate into multiple EndpointSlices. This will occur if an Endpoints resource has multiple subsets or includes endpoints with multiple IP families (IPv4 and IPv6). A maximum of 1000 addresses per subset will be mirrored to EndpointSlices.

Distribution of EndpointSlices

Each EndpointSlice has a set of ports that applies to all endpoints within the resource. When named ports are used for a Service, Pods may end up with different target port numbers for the same named port, requiring different EndpointSlices. This is similar to the logic behind how subsets are grouped with Endpoints.

The control plane tries to fill EndpointSlices as full as possible, but does not actively rebalance them. The logic is fairly straightforward:

Iterate through existing EndpointSlices, remove endpoints that are no longer desired and update matching endpoints that have changed.
Iterate through EndpointSlices that have been modified in the first step and fill them up with any new endpoints needed.
If there's still new endpoints left to add, try to fit them into a previously unchanged slice and/or create new ones.

Importantly, the third step prioritizes limiting EndpointSlice updates over a perfectly full distribution of EndpointSlices. As an example, if there are 10 new endpoints to add and 2 EndpointSlices with room for 5 more endpoints each, this approach will create a new EndpointSlice instead of filling up the 2 existing EndpointSlices. In other words, a single EndpointSlice creation is preferrable to multiple EndpointSlice updates.

With kube-proxy running on each Node and watching EndpointSlices, every change to an EndpointSlice becomes relatively expensive since it will be transmitted to every Node in the cluster. This approach is intended to limit the number of changes that need to be sent to every Node, even if it may result with multiple EndpointSlices that are not full.

In practice, this less than ideal distribution should be rare. Most changes processed by the EndpointSlice controller will be small enough to fit in an existing EndpointSlice, and if not, a new EndpointSlice is likely going to be necessary soon anyway. Rolling updates of Deployments also provide a natural repacking of EndpointSlices with all Pods and their corresponding endpoints getting replaced.

Duplicate endpoints

Due to the nature of EndpointSlice changes, endpoints may be represented in more than one EndpointSlice at the same time. This naturally occurs as changes to different EndpointSlice objects can arrive at the Kubernetes client watch/cache at different times. Implementations using EndpointSlice must be able to have the endpoint appear in more than one slice. A reference implementation of how to perform endpoint deduplication can be found in the EndpointSliceCache implementation in kube-proxy.

What's next

Read Connecting Applications with Services

6.7 - Network Policies

If you want to control traffic flow at the IP address or port level (OSI layer 3 or 4), NetworkPolicies allow you to specify rules for traffic flow within your cluster, and also between Pods and the outside world. Your cluster must use a network plugin that supports NetworkPolicy enforcement.

If you want to control traffic flow at the IP address or port level (OSI layer 3 or 4), then you might consider using Kubernetes NetworkPolicies for particular applications in your cluster. NetworkPolicies are an application-centric construct which allow you to specify how a pod is allowed to communicate with various network "entities" (we use the word "entity" here to avoid overloading the more common terms such as "endpoints" and "services", which have specific Kubernetes connotations) over the network. NetworkPolicies apply to a connection with a pod on one or both ends, and are not relevant to other connections.

The entities that a Pod can communicate with are identified through a combination of the following 3 identifiers:

Other pods that are allowed (exception: a pod cannot block access to itself)
Namespaces that are allowed
IP blocks (exception: traffic to and from the node where a Pod is running is always allowed, regardless of the IP address of the Pod or the node)

When defining a pod- or namespace- based NetworkPolicy, you use a selector to specify what traffic is allowed to and from the Pod(s) that match the selector.

Meanwhile, when IP based NetworkPolicies are created, we define policies based on IP blocks (CIDR ranges).

Prerequisites

Network policies are implemented by the network plugin. To use network policies, you must be using a networking solution which supports NetworkPolicy. Creating a NetworkPolicy resource without a controller that implements it will have no effect.

The Two Sorts of Pod Isolation

There are two sorts of isolation for a pod: isolation for egress, and isolation for ingress. They concern what connections may be established. "Isolation" here is not absolute, rather it means "some restrictions apply". The alternative, "non-isolated for $direction", means that no restrictions apply in the stated direction. The two sorts of isolation (or not) are declared independently, and are both relevant for a connection from one pod to another.

By default, a pod is non-isolated for egress; all outbound connections are allowed. A pod is isolated for egress if there is any NetworkPolicy that both selects the pod and has "Egress" in its policyTypes; we say that such a policy applies to the pod for egress. When a pod is isolated for egress, the only allowed connections from the pod are those allowed by the egress list of some NetworkPolicy that applies to the pod for egress. The effects of those egress lists combine additively.

By default, a pod is non-isolated for ingress; all inbound connections are allowed. A pod is isolated for ingress if there is any NetworkPolicy that both selects the pod and has "Ingress" in its policyTypes; we say that such a policy applies to the pod for ingress. When a pod is isolated for ingress, the only allowed connections into the pod are those from the pod's node and those allowed by the ingress list of some NetworkPolicy that applies to the pod for ingress. The effects of those ingress lists combine additively.

Network policies do not conflict; they are additive. If any policy or policies apply to a given pod for a given direction, the connections allowed in that direction from that pod is the union of what the applicable policies allow. Thus, order of evaluation does not affect the policy result.

For a connection from a source pod to a destination pod to be allowed, both the egress policy on the source pod and the ingress policy on the destination pod need to allow the connection. If either side does not allow the connection, it will not happen.

The NetworkPolicy resource

See the NetworkPolicy reference for a full definition of the resource.

An example NetworkPolicy might look like this:

service/networking/networkpolicy.yaml

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: test-network-policy
  namespace: default
spec:
  podSelector:
    matchLabels:
      role: db
  policyTypes:
    - Ingress
    - Egress
  ingress:
    - from:
        - ipBlock:
            cidr: 172.17.0.0/16
            except:
              - 172.17.1.0/24
        - namespaceSelector:
            matchLabels:
              project: myproject
        - podSelector:
            matchLabels:
              role: frontend
      ports:
        - protocol: TCP
          port: 6379
  egress:
    - to:
        - ipBlock:
            cidr: 10.0.0.0/24
      ports:
        - protocol: TCP
          port: 5978

Note: POSTing this to the API server for your cluster will have no effect unless your chosen networking solution supports network policy.

Mandatory Fields: As with all other Kubernetes config, a NetworkPolicy needs apiVersion, kind, and metadata fields. For general information about working with config files, see Configure a Pod to Use a ConfigMap, and Object Management.

spec: NetworkPolicy spec has all the information needed to define a particular network policy in the given namespace.

podSelector: Each NetworkPolicy includes a podSelector which selects the grouping of pods to which the policy applies. The example policy selects pods with the label "role=db". An empty podSelector selects all pods in the namespace.

policyTypes: Each NetworkPolicy includes a policyTypes list which may include either Ingress, Egress, or both. The policyTypes field indicates whether or not the given policy applies to ingress traffic to selected pod, egress traffic from selected pods, or both. If no policyTypes are specified on a NetworkPolicy then by default Ingress will always be set and Egress will be set if the NetworkPolicy has any egress rules.

ingress: Each NetworkPolicy may include a list of allowed ingress rules. Each rule allows traffic which matches both the from and ports sections. The example policy contains a single rule, which matches traffic on a single port, from one of three sources, the first specified via an ipBlock, the second via a namespaceSelector and the third via a podSelector.

egress: Each NetworkPolicy may include a list of allowed egress rules. Each rule allows traffic which matches both the to and ports sections. The example policy contains a single rule, which matches traffic on a single port to any destination in 10.0.0.0/24.

So, the example NetworkPolicy:

isolates "role=db" pods in the "default" namespace for both ingress and egress traffic (if they weren't already isolated)
(Ingress rules) allows connections to all pods in the "default" namespace with the label "role=db" on TCP port 6379 from:
- any pod in the "default" namespace with the label "role=frontend"
- any pod in a namespace with the label "project=myproject"
- IP addresses in the ranges 172.17.0.0–172.17.0.255 and 172.17.2.0–172.17.255.255 (ie, all of 172.17.0.0/16 except 172.17.1.0/24)
(Egress rules) allows connections from any pod in the "default" namespace with the label "role=db" to CIDR 10.0.0.0/24 on TCP port 5978

See the Declare Network Policy walkthrough for further examples.

Behavior of `to` and `from` selectors

There are four kinds of selectors that can be specified in an ingress from section or egress to section:

podSelector: This selects particular Pods in the same namespace as the NetworkPolicy which should be allowed as ingress sources or egress destinations.

namespaceSelector: This selects particular namespaces for which all Pods should be allowed as ingress sources or egress destinations.

namespaceSelector and podSelector: A single to/from entry that specifies both namespaceSelector and podSelector selects particular Pods within particular namespaces. Be careful to use correct YAML syntax; this policy:

  ...
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          user: alice
      podSelector:
        matchLabels:
          role: client
  ...

contains a single from element allowing connections from Pods with the label role=client in namespaces with the label user=alice. But this policy:

  ...
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          user: alice
    - podSelector:
        matchLabels:
          role: client
  ...

contains two elements in the from array, and allows connections from Pods in the local Namespace with the label role=client, or from any Pod in any namespace with the label user=alice.

When in doubt, use kubectl describe to see how Kubernetes has interpreted the policy.

ipBlock: This selects particular IP CIDR ranges to allow as ingress sources or egress destinations. These should be cluster-external IPs, since Pod IPs are ephemeral and unpredictable.

Cluster ingress and egress mechanisms often require rewriting the source or destination IP of packets. In cases where this happens, it is not defined whether this happens before or after NetworkPolicy processing, and the behavior may be different for different combinations of network plugin, cloud provider, Service implementation, etc.

In the case of ingress, this means that in some cases you may be able to filter incoming packets based on the actual original source IP, while in other cases, the "source IP" that the NetworkPolicy acts on may be the IP of a LoadBalancer or of the Pod's node, etc.

For egress, this means that connections from pods to Service IPs that get rewritten to cluster-external IPs may or may not be subject to ipBlock-based policies.

Default policies

By default, if no policies exist in a namespace, then all ingress and egress traffic is allowed to and from pods in that namespace. The following examples let you change the default behavior in that namespace.

Default deny all ingress traffic

You can create a "default" ingress isolation policy for a namespace by creating a NetworkPolicy that selects all pods but does not allow any ingress traffic to those pods.

service/networking/network-policy-default-deny-ingress.yaml

---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-ingress
spec:
  podSelector: {}
  policyTypes:
  - Ingress

This ensures that even pods that aren't selected by any other NetworkPolicy will still be isolated for ingress. This policy does not affect isolation for egress from any pod.

Allow all ingress traffic

If you want to allow all incoming connections to all pods in a namespace, you can create a policy that explicitly allows that.

service/networking/network-policy-allow-all-ingress.yaml

---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-all-ingress
spec:
  podSelector: {}
  ingress:
  - {}
  policyTypes:
  - Ingress

With this policy in place, no additional policy or policies can cause any incoming connection to those pods to be denied. This policy has no effect on isolation for egress from any pod.

Default deny all egress traffic

You can create a "default" egress isolation policy for a namespace by creating a NetworkPolicy that selects all pods but does not allow any egress traffic from those pods.

service/networking/network-policy-default-deny-egress.yaml

---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-egress
spec:
  podSelector: {}
  policyTypes:
  - Egress

This ensures that even pods that aren't selected by any other NetworkPolicy will not be allowed egress traffic. This policy does not change the ingress isolation behavior of any pod.

Allow all egress traffic

If you want to allow all connections from all pods in a namespace, you can create a policy that explicitly allows all outgoing connections from pods in that namespace.

service/networking/network-policy-allow-all-egress.yaml

---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-all-egress
spec:
  podSelector: {}
  egress:
  - {}
  policyTypes:
  - Egress

With this policy in place, no additional policy or policies can cause any outgoing connection from those pods to be denied. This policy has no effect on isolation for ingress to any pod.

Default deny all ingress and all egress traffic

You can create a "default" policy for a namespace which prevents all ingress AND egress traffic by creating the following NetworkPolicy in that namespace.

service/networking/network-policy-default-deny-all.yaml

---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-all
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress

This ensures that even pods that aren't selected by any other NetworkPolicy will not be allowed ingress or egress traffic.

SCTP support

FEATURE STATE: Kubernetes v1.20 [stable]

As a stable feature, this is enabled by default. To disable SCTP at a cluster level, you (or your cluster administrator) will need to disable the SCTPSupport feature gate for the API server with --feature-gates=SCTPSupport=false,…. When the feature gate is enabled, you can set the protocol field of a NetworkPolicy to SCTP.

Note: You must be using a plugin that supports SCTP protocol NetworkPolicies.

Targeting a range of ports

FEATURE STATE: Kubernetes v1.25 [stable]

When writing a NetworkPolicy, you can target a range of ports instead of a single port.

This is achievable with the usage of the endPort field, as the following example:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: multi-port-egress
  namespace: default
spec:
  podSelector:
    matchLabels:
      role: db
  policyTypes:
  - Egress
  egress:
  - to:
    - ipBlock:
        cidr: 10.0.0.0/24
    ports:
    - protocol: TCP
      port: 32000
      endPort: 32768

The above rule allows any Pod with label role=db on the namespace default to communicate with any IP within the range 10.0.0.0/24 over TCP, provided that the target port is between the range 32000 and 32768.

The following restrictions apply when using this field:

The endPort field must be equal to or greater than the port field.
endPort can only be defined if port is also defined.
Both ports must be numeric.

Note: Your cluster must be using a plugin that supports the endPort field in NetworkPolicy specifications. If your network plugin does not support the endPort field and you specify a NetworkPolicy with that, the policy will be applied only for the single port field.

Targeting a Namespace by its name

FEATURE STATE: Kubernetes 1.22 [stable]

While NetworkPolicy cannot target a namespace by its name with some object field, you can use the standardized label to target a specific namespace.

What you can't do with network policies (at least, not yet)

As of Kubernetes 1.25, the following functionality does not exist in the NetworkPolicy API, but you might be able to implement workarounds using Operating System components (such as SELinux, OpenVSwitch, IPTables, and so on) or Layer 7 technologies (Ingress controllers, Service Mesh implementations) or admission controllers. In case you are new to network security in Kubernetes, its worth noting that the following User Stories cannot (yet) be implemented using the NetworkPolicy API.

Forcing internal cluster traffic to go through a common gateway (this might be best served with a service mesh or other proxy).
Anything TLS related (use a service mesh or ingress controller for this).
Node specific policies (you can use CIDR notation for these, but you cannot target nodes by their Kubernetes identities specifically).
Targeting of services by name (you can, however, target pods or namespaces by their labels, which is often a viable workaround).
Creation or management of "Policy requests" that are fulfilled by a third party.
Default policies which are applied to all namespaces or pods (there are some third party Kubernetes distributions and projects which can do this).
Advanced policy querying and reachability tooling.
The ability to log network security events (for example connections that are blocked or accepted).
The ability to explicitly deny policies (currently the model for NetworkPolicies are deny by default, with only the ability to add allow rules).
The ability to prevent loopback or incoming host traffic (Pods cannot currently block localhost access, nor do they have the ability to block access from their resident node).

What's next

See the Declare Network Policy walkthrough for further examples.
See more recipes for common scenarios enabled by the NetworkPolicy resource.

6.8 - DNS for Services and Pods

Your workload can discover Services within your cluster using DNS; this page explains how that works.

Kubernetes creates DNS records for Services and Pods. You can contact Services with consistent DNS names instead of IP addresses.

Introduction

Kubernetes DNS schedules a DNS Pod and Service on the cluster, and configures the kubelets to tell individual containers to use the DNS Service's IP to resolve DNS names.

Every Service defined in the cluster (including the DNS server itself) is assigned a DNS name. By default, a client Pod's DNS search list includes the Pod's own namespace and the cluster's default domain.

Namespaces of Services

A DNS query may return different results based on the namespace of the Pod making it. DNS queries that don't specify a namespace are limited to the Pod's namespace. Access Services in other namespaces by specifying it in the DNS query.

For example, consider a Pod in a test namespace. A data Service is in the prod namespace.

A query for data returns no results, because it uses the Pod's test namespace.

A query for data.prod returns the intended result, because it specifies the namespace.

DNS queries may be expanded using the Pod's /etc/resolv.conf. Kubelet sets this file for each Pod. For example, a query for just data may be expanded to data.test.svc.cluster.local. The values of the search option are used to expand queries. To learn more about DNS queries, see the resolv.conf manual page.

nameserver 10.32.0.10
search <namespace>.svc.cluster.local svc.cluster.local cluster.local
options ndots:5

In summary, a Pod in the test namespace can successfully resolve either data.prod or data.prod.svc.cluster.local.

DNS Records

What objects get DNS records?

Services
Pods

The following sections detail the supported DNS record types and layout that is supported. Any other layout or names or queries that happen to work are considered implementation details and are subject to change without warning. For more up-to-date specification, see Kubernetes DNS-Based Service Discovery.

Services

A/AAAA records

"Normal" (not headless) Services are assigned a DNS A or AAAA record, depending on the IP family of the Service, for a name of the form my-svc.my-namespace.svc.cluster-domain.example. This resolves to the cluster IP of the Service.

"Headless" (without a cluster IP) Services are also assigned a DNS A or AAAA record, depending on the IP family of the Service, for a name of the form my-svc.my-namespace.svc.cluster-domain.example. Unlike normal Services, this resolves to the set of IPs of the Pods selected by the Service. Clients are expected to consume the set or else use standard round-robin selection from the set.

SRV records

SRV Records are created for named ports that are part of normal or Headless Services. For each named port, the SRV record would have the form _my-port-name._my-port-protocol.my-svc.my-namespace.svc.cluster-domain.example. For a regular Service, this resolves to the port number and the domain name: my-svc.my-namespace.svc.cluster-domain.example. For a headless Service, this resolves to multiple answers, one for each Pod that is backing the Service, and contains the port number and the domain name of the Pod of the form auto-generated-name.my-svc.my-namespace.svc.cluster-domain.example.

Pods

A/AAAA records

In general a Pod has the following DNS resolution:

pod-ip-address.my-namespace.pod.cluster-domain.example.

For example, if a Pod in the default namespace has the IP address 172.17.0.3, and the domain name for your cluster is cluster.local, then the Pod has a DNS name:

172-17-0-3.default.pod.cluster.local.

Any Pods exposed by a Service have the following DNS resolution available:

pod-ip-address.service-name.my-namespace.svc.cluster-domain.example.

Pod's hostname and subdomain fields

Currently when a Pod is created, its hostname is the Pod's metadata.name value.

The Pod spec has an optional hostname field, which can be used to specify the Pod's hostname. When specified, it takes precedence over the Pod's name to be the hostname of the Pod. For example, given a Pod with hostname set to "my-host", the Pod will have its hostname set to "my-host".

The Pod spec also has an optional subdomain field which can be used to specify its subdomain. For example, a Pod with hostname set to "foo", and subdomain set to "bar", in namespace "my-namespace", will have the fully qualified domain name (FQDN) "foo.bar.my-namespace.svc.cluster-domain.example".

Example:

apiVersion: v1
kind: Service
metadata:
  name: default-subdomain
spec:
  selector:
    name: busybox
  clusterIP: None
  ports:
  - name: foo # Actually, no port is needed.
    port: 1234
    targetPort: 1234
---
apiVersion: v1
kind: Pod
metadata:
  name: busybox1
  labels:
    name: busybox
spec:
  hostname: busybox-1
  subdomain: default-subdomain
  containers:
  - image: busybox:1.28
    command:
      - sleep
      - "3600"
    name: busybox
---
apiVersion: v1
kind: Pod
metadata:
  name: busybox2
  labels:
    name: busybox
spec:
  hostname: busybox-2
  subdomain: default-subdomain
  containers:
  - image: busybox:1.28
    command:
      - sleep
      - "3600"
    name: busybox

If there exists a headless Service in the same namespace as the Pod and with the same name as the subdomain, the cluster's DNS Server also returns an A or AAAA record for the Pod's fully qualified hostname. For example, given a Pod with the hostname set to "busybox-1" and the subdomain set to "default-subdomain", and a headless Service named "default-subdomain" in the same namespace, the Pod will see its own FQDN as "busybox-1.default-subdomain.my-namespace.svc.cluster-domain.example". DNS serves an A or AAAA record at that name, pointing to the Pod's IP. Both Pods "busybox1" and "busybox2" can have their distinct A or AAAA records.

The Endpoints object can specify the hostname for any endpoint addresses, along with its IP.

Note: Because A or AAAA records are not created for Pod names, hostname is required for the Pod's A or AAAA record to be created. A Pod with no hostname but with subdomain will only create the A or AAAA record for the headless Service (default-subdomain.my-namespace.svc.cluster-domain.example), pointing to the Pod's IP address. Also, Pod needs to become ready in order to have a record unless publishNotReadyAddresses=True is set on the Service.

Pod's setHostnameAsFQDN field

FEATURE STATE: Kubernetes v1.22 [stable]

When a Pod is configured to have fully qualified domain name (FQDN), its hostname is the short hostname. For example, if you have a Pod with the fully qualified domain name busybox-1.default-subdomain.my-namespace.svc.cluster-domain.example, then by default the hostname command inside that Pod returns busybox-1 and the hostname --fqdn command returns the FQDN.

When you set setHostnameAsFQDN: true in the Pod spec, the kubelet writes the Pod's FQDN into the hostname for that Pod's namespace. In this case, both hostname and hostname --fqdn return the Pod's FQDN.

Note:

In Linux, the hostname field of the kernel (the nodename field of struct utsname) is limited to 64 characters.

If a Pod enables this feature and its FQDN is longer than 64 character, it will fail to start. The Pod will remain in Pending status (ContainerCreating as seen by kubectl) generating error events, such as Failed to construct FQDN from Pod hostname and cluster domain, FQDN long-FQDN is too long (64 characters is the max, 70 characters requested). One way of improving user experience for this scenario is to create an admission webhook controller to control FQDN size when users create top level objects, for example, Deployment.

Pod's DNS Policy

DNS policies can be set on a per-Pod basis. Currently Kubernetes supports the following Pod-specific DNS policies. These policies are specified in the dnsPolicy field of a Pod Spec.

"Default": The Pod inherits the name resolution configuration from the node that the Pods run on. See related discussion for more details.
"ClusterFirst": Any DNS query that does not match the configured cluster domain suffix, such as "www.kubernetes.io", is forwarded to the upstream nameserver inherited from the node. Cluster administrators may have extra stub-domain and upstream DNS servers configured. See related discussion for details on how DNS queries are handled in those cases.
"ClusterFirstWithHostNet": For Pods running with hostNetwork, you should explicitly set its DNS policy "ClusterFirstWithHostNet".
- Note: This is not supported on Windows. See below for details
"None": It allows a Pod to ignore DNS settings from the Kubernetes environment. All DNS settings are supposed to be provided using the dnsConfig field in the Pod Spec. See Pod's DNS config subsection below.

Note: "Default" is not the default DNS policy. If dnsPolicy is not explicitly specified, then "ClusterFirst" is used.

The example below shows a Pod with its DNS policy set to "ClusterFirstWithHostNet" because it has hostNetwork set to true.

apiVersion: v1
kind: Pod
metadata:
  name: busybox
  namespace: default
spec:
  containers:
  - image: busybox:1.28
    command:
      - sleep
      - "3600"
    imagePullPolicy: IfNotPresent
    name: busybox
  restartPolicy: Always
  hostNetwork: true
  dnsPolicy: ClusterFirstWithHostNet

Pod's DNS Config

FEATURE STATE: Kubernetes v1.14 [stable]

Pod's DNS Config allows users more control on the DNS settings for a Pod.

The dnsConfig field is optional and it can work with any dnsPolicy settings. However, when a Pod's dnsPolicy is set to "None", the dnsConfig field has to be specified.

Below are the properties a user can specify in the dnsConfig field:

nameservers: a list of IP addresses that will be used as DNS servers for the Pod. There can be at most 3 IP addresses specified. When the Pod's dnsPolicy is set to "None", the list must contain at least one IP address, otherwise this property is optional. The servers listed will be combined to the base nameservers generated from the specified DNS policy with duplicate addresses removed.
searches: a list of DNS search domains for hostname lookup in the Pod. This property is optional. When specified, the provided list will be merged into the base search domain names generated from the chosen DNS policy. Duplicate domain names are removed. Kubernetes allows for at most 6 search domains.
options: an optional list of objects where each object may have a name property (required) and a value property (optional). The contents in this property will be merged to the options generated from the specified DNS policy. Duplicate entries are removed.

The following is an example Pod with custom DNS settings:

service/networking/custom-dns.yaml

apiVersion: v1
kind: Pod
metadata:
  namespace: default
  name: dns-example
spec:
  containers:
    - name: test
      image: nginx
  dnsPolicy: "None"
  dnsConfig:
    nameservers:
      - 1.2.3.4
    searches:
      - ns1.svc.cluster-domain.example
      - my.dns.search.suffix
    options:
      - name: ndots
        value: "2"
      - name: edns0

When the Pod above is created, the container test gets the following contents in its /etc/resolv.conf file:

nameserver 1.2.3.4
search ns1.svc.cluster-domain.example my.dns.search.suffix
options ndots:2 edns0

For IPv6 setup, search path and name server should be set up like this:

kubectl exec -it dns-example -- cat /etc/resolv.conf

The output is similar to this:

nameserver 2001:db8:30::a
search default.svc.cluster-domain.example svc.cluster-domain.example cluster-domain.example
options ndots:5

Expanded DNS Configuration

FEATURE STATE: Kubernetes 1.22 [alpha]

By default, for Pod's DNS Config, Kubernetes allows at most 6 search domains and a list of search domains of up to 256 characters.

If the feature gate ExpandedDNSConfig is enabled for the kube-apiserver and the kubelet, it is allowed for Kubernetes to have at most 32 search domains and a list of search domains of up to 2048 characters.

DNS resolution on Windows nodes

ClusterFirstWithHostNet is not supported for Pods that run on Windows nodes. Windows treats all names with a . as a FQDN and skips FQDN resolution.
On Windows, there are multiple DNS resolvers that can be used. As these come with slightly different behaviors, using the Resolve-DNSName powershell cmdlet for name query resolutions is recommended.
On Linux, you have a DNS suffix list, which is used after resolution of a name as fully qualified has failed. On Windows, you can only have 1 DNS suffix, which is the DNS suffix associated with that Pod's namespace (example: mydns.svc.cluster.local). Windows can resolve FQDNs, Services, or network name which can be resolved with this single suffix. For example, a Pod spawned in the default namespace, will have the DNS suffix default.svc.cluster.local. Inside a Windows Pod, you can resolve both kubernetes.default.svc.cluster.local and kubernetes, but not the partially qualified names (kubernetes.default or kubernetes.default.svc).

What's next

For guidance on administering DNS configurations, check Configure DNS Service

6.9 - IPv4/IPv6 dual-stack

Kubernetes lets you configure single-stack IPv4 networking, single-stack IPv6 networking, or dual stack networking with both network families active. This page explains how.

FEATURE STATE: Kubernetes v1.23 [stable]

IPv4/IPv6 dual-stack networking enables the allocation of both IPv4 and IPv6 addresses to Pods and Services.

IPv4/IPv6 dual-stack networking is enabled by default for your Kubernetes cluster starting in 1.21, allowing the simultaneous assignment of both IPv4 and IPv6 addresses.

Supported Features

IPv4/IPv6 dual-stack on your Kubernetes cluster provides the following features:

Dual-stack Pod networking (a single IPv4 and IPv6 address assignment per Pod)
IPv4 and IPv6 enabled Services
Pod off-cluster egress routing (eg. the Internet) via both IPv4 and IPv6 interfaces

Prerequisites

The following prerequisites are needed in order to utilize IPv4/IPv6 dual-stack Kubernetes clusters:

Kubernetes 1.20 or later

For information about using dual-stack services with earlier Kubernetes versions, refer to the documentation for that version of Kubernetes.
Provider support for dual-stack networking (Cloud provider or otherwise must be able to provide Kubernetes nodes with routable IPv4/IPv6 network interfaces)
A network plugin that supports dual-stack networking.

Configure IPv4/IPv6 dual-stack

To configure IPv4/IPv6 dual-stack, set dual-stack cluster network assignments:

kube-apiserver:
- --service-cluster-ip-range=<IPv4 CIDR>,<IPv6 CIDR>
kube-controller-manager:
- --cluster-cidr=<IPv4 CIDR>,<IPv6 CIDR>
- --service-cluster-ip-range=<IPv4 CIDR>,<IPv6 CIDR>
- --node-cidr-mask-size-ipv4|--node-cidr-mask-size-ipv6 defaults to /24 for IPv4 and /64 for IPv6
kube-proxy:
- --cluster-cidr=<IPv4 CIDR>,<IPv6 CIDR>
kubelet:
- when there is no --cloud-provider the administrator can pass a comma-separated pair of IP addresses via --node-ip to manually configure dual-stack .status.addresses for that Node. If a Pod runs on that node in HostNetwork mode, the Pod reports these IP addresses in its .status.podIPs field. All podIPs in a node match the IP family preference defined by the .status.addresses field for that Node.

Note:

An example of an IPv4 CIDR: 10.244.0.0/16 (though you would supply your own address range)

An example of an IPv6 CIDR: fdXY:IJKL:MNOP:15::/64 (this shows the format but is not a valid address - see RFC 4193)

Services

You can create Services which can use IPv4, IPv6, or both.

The address family of a Service defaults to the address family of the first service cluster IP range (configured via the --service-cluster-ip-range flag to the kube-apiserver).

When you define a Service you can optionally configure it as dual stack. To specify the behavior you want, you set the .spec.ipFamilyPolicy field to one of the following values:

SingleStack: Single-stack service. The control plane allocates a cluster IP for the Service, using the first configured service cluster IP range.
PreferDualStack:
- Allocates IPv4 and IPv6 cluster IPs for the Service.
RequireDualStack: Allocates Service .spec.ClusterIPs from both IPv4 and IPv6 address ranges.
- Selects the .spec.ClusterIP from the list of .spec.ClusterIPs based on the address family of the first element in the .spec.ipFamilies array.

If you would like to define which IP family to use for single stack or define the order of IP families for dual-stack, you can choose the address families by setting an optional field, .spec.ipFamilies, on the Service.

Note: The .spec.ipFamilies field is immutable because the .spec.ClusterIP cannot be reallocated on a Service that already exists. If you want to change .spec.ipFamilies, delete and recreate the Service.

You can set .spec.ipFamilies to any of the following array values:

["IPv4"]
["IPv6"]
["IPv4","IPv6"] (dual stack)
["IPv6","IPv4"] (dual stack)

The first family you list is used for the legacy .spec.ClusterIP field.

Dual-stack Service configuration scenarios

These examples demonstrate the behavior of various dual-stack Service configuration scenarios.

Dual-stack options on new Services

This Service specification does not explicitly define .spec.ipFamilyPolicy. When you create this Service, Kubernetes assigns a cluster IP for the Service from the first configured service-cluster-ip-range and sets the .spec.ipFamilyPolicy to SingleStack. (Services without selectors and headless Services with selectors will behave in this same way.)
service/networking/dual-stack-default-svc.yaml
```
apiVersion: v1
   kind: Service
   metadata:
     name: my-service
     labels:
       app.kubernetes.io/name: MyApp
   spec:
     selector:
       app.kubernetes.io/name: MyApp
     ports:
       - protocol: TCP
         port: 80
   
```
This Service specification explicitly defines PreferDualStack in .spec.ipFamilyPolicy. When you create this Service on a dual-stack cluster, Kubernetes assigns both IPv4 and IPv6 addresses for the service. The control plane updates the .spec for the Service to record the IP address assignments. The field .spec.ClusterIPs is the primary field, and contains both assigned IP addresses; .spec.ClusterIP is a secondary field with its value calculated from .spec.ClusterIPs.
- For the .spec.ClusterIP field, the control plane records the IP address that is from the same address family as the first service cluster IP range.
- On a single-stack cluster, the .spec.ClusterIPs and .spec.ClusterIP fields both only list one address.
- On a cluster with dual-stack enabled, specifying RequireDualStack in .spec.ipFamilyPolicy behaves the same as PreferDualStack.
service/networking/dual-stack-preferred-svc.yaml
```
apiVersion: v1
   kind: Service
   metadata:
     name: my-service
     labels:
       app.kubernetes.io/name: MyApp
   spec:
     ipFamilyPolicy: PreferDualStack
     selector:
       app.kubernetes.io/name: MyApp
     ports:
       - protocol: TCP
         port: 80
   
```

This Service specification explicitly defines IPv6 and IPv4 in .spec.ipFamilies as well as defining PreferDualStack in .spec.ipFamilyPolicy. When Kubernetes assigns an IPv6 and IPv4 address in .spec.ClusterIPs, .spec.ClusterIP is set to the IPv6 address because that is the first element in the .spec.ClusterIPs array, overriding the default.

service/networking/dual-stack-preferred-ipfamilies-svc.yaml

apiVersion: v1
   kind: Service
   metadata:
     name: my-service
     labels:
       app.kubernetes.io/name: MyApp
   spec:
     ipFamilyPolicy: PreferDualStack
     ipFamilies:
     - IPv6
     - IPv4
     selector:
       app.kubernetes.io/name: MyApp
     ports:
       - protocol: TCP
         port: 80

Dual-stack defaults on existing Services

These examples demonstrate the default behavior when dual-stack is newly enabled on a cluster where Services already exist. (Upgrading an existing cluster to 1.21 or beyond will enable dual-stack.)

When dual-stack is enabled on a cluster, existing Services (whether IPv4 or IPv6) are configured by the control plane to set .spec.ipFamilyPolicy to SingleStack and set .spec.ipFamilies to the address family of the existing Service. The existing Service cluster IP will be stored in .spec.ClusterIPs.

service/networking/dual-stack-default-svc.yaml

apiVersion: v1
   kind: Service
   metadata:
     name: my-service
     labels:
       app.kubernetes.io/name: MyApp
   spec:
     selector:
       app.kubernetes.io/name: MyApp
     ports:
       - protocol: TCP
         port: 80

You can validate this behavior by using kubectl to inspect an existing service.

kubectl get svc my-service -o yaml

apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: MyApp
  name: my-service
spec:
  clusterIP: 10.0.197.123
  clusterIPs:
  - 10.0.197.123
  ipFamilies:
  - IPv4
  ipFamilyPolicy: SingleStack
  ports:
  - port: 80
    protocol: TCP
    targetPort: 80
  selector:
    app.kubernetes.io/name: MyApp
  type: ClusterIP
status:
  loadBalancer: {}

When dual-stack is enabled on a cluster, existing headless Services with selectors are configured by the control plane to set .spec.ipFamilyPolicy to SingleStack and set .spec.ipFamilies to the address family of the first service cluster IP range (configured via the --service-cluster-ip-range flag to the kube-apiserver) even though .spec.ClusterIP is set to None.

service/networking/dual-stack-default-svc.yaml

apiVersion: v1
   kind: Service
   metadata:
     name: my-service
     labels:
       app.kubernetes.io/name: MyApp
   spec:
     selector:
       app.kubernetes.io/name: MyApp
     ports:
       - protocol: TCP
         port: 80

You can validate this behavior by using kubectl to inspect an existing headless service with selectors.

kubectl get svc my-service -o yaml

apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: MyApp
  name: my-service
spec:
  clusterIP: None
  clusterIPs:
  - None
  ipFamilies:
  - IPv4
  ipFamilyPolicy: SingleStack
  ports:
  - port: 80
    protocol: TCP
    targetPort: 80
  selector:
    app.kubernetes.io/name: MyApp

Switching Services between single-stack and dual-stack

Services can be changed from single-stack to dual-stack and from dual-stack to single-stack.

To change a Service from single-stack to dual-stack, change .spec.ipFamilyPolicy from SingleStack to PreferDualStack or RequireDualStack as desired. When you change this Service from single-stack to dual-stack, Kubernetes assigns the missing address family so that the Service now has IPv4 and IPv6 addresses.

Edit the Service specification updating the .spec.ipFamilyPolicy from SingleStack to PreferDualStack.

Before:
```
spec:
  ipFamilyPolicy: SingleStack
```
After:
```
spec:
  ipFamilyPolicy: PreferDualStack
```
To change a Service from dual-stack to single-stack, change .spec.ipFamilyPolicy from PreferDualStack or RequireDualStack to SingleStack. When you change this Service from dual-stack to single-stack, Kubernetes retains only the first element in the .spec.ClusterIPs array, and sets .spec.ClusterIP to that IP address and sets .spec.ipFamilies to the address family of .spec.ClusterIPs.

Headless Services without selector

For Headless Services without selectors and without .spec.ipFamilyPolicy explicitly set, the .spec.ipFamilyPolicy field defaults to RequireDualStack.

Service type LoadBalancer

To provision a dual-stack load balancer for your Service:

Set the .spec.type field to LoadBalancer
Set .spec.ipFamilyPolicy field to PreferDualStack or RequireDualStack

Note: To use a dual-stack LoadBalancer type Service, your cloud provider must support IPv4 and IPv6 load balancers.

Egress traffic

If you want to enable egress traffic in order to reach off-cluster destinations (eg. the public Internet) from a Pod that uses non-publicly routable IPv6 addresses, you need to enable the Pod to use a publicly routed IPv6 address via a mechanism such as transparent proxying or IP masquerading. The ip-masq-agent project supports IP masquerading on dual-stack clusters.

Note: Ensure your provider supports IPv6.

Windows support

Kubernetes on Windows does not support single-stack "IPv6-only" networking. However, dual-stack IPv4/IPv6 networking for pods and nodes with single-family services is supported.

You can use IPv4/IPv6 dual-stack networking with l2bridge networks.

Note: Overlay (VXLAN) networks on Windows do not support dual-stack networking.

You can read more about the different network modes for Windows within the Networking on Windows topic.

What's next

Validate IPv4/IPv6 dual-stack networking
Enable dual-stack networking using kubeadm

6.10 - Topology Aware Hints

Topology Aware Hints provides a mechanism to help keep network traffic within the zone where it originated. Preferring same-zone traffic between Pods in your cluster can help with reliability, performance (network latency and throughput), or cost.

FEATURE STATE: Kubernetes v1.23 [beta]

Topology Aware Hints enable topology aware routing by including suggestions for how clients should consume endpoints. This approach adds metadata to enable consumers of EndpointSlice and / or Endpoints objects, so that traffic to those network endpoints can be routed closer to where it originated.

For example, you can route traffic within a locality to reduce costs, or to improve network performance.

Motivation

Kubernetes clusters are increasingly deployed in multi-zone environments. Topology Aware Hints provides a mechanism to help keep traffic within the zone it originated from. This concept is commonly referred to as "Topology Aware Routing". When calculating the endpoints for a Service, the EndpointSlice controller considers the topology (region and zone) of each endpoint and populates the hints field to allocate it to a zone. Cluster components such as the kube-proxy can then consume those hints, and use them to influence how the traffic is routed (favoring topologically closer endpoints).

Using Topology Aware Hints

You can activate Topology Aware Hints for a Service by setting the service.kubernetes.io/topology-aware-hints annotation to auto. This tells the EndpointSlice controller to set topology hints if it is deemed safe. Importantly, this does not guarantee that hints will always be set.

How it works

The functionality enabling this feature is split into two components: The EndpointSlice controller and the kube-proxy. This section provides a high level overview of how each component implements this feature.

EndpointSlice controller

The EndpointSlice controller is responsible for setting hints on EndpointSlices when this feature is enabled. The controller allocates a proportional amount of endpoints to each zone. This proportion is based on the allocatable CPU cores for nodes running in that zone. For example, if one zone had 2 CPU cores and another zone only had 1 CPU core, the controller would allocate twice as many endpoints to the zone with 2 CPU cores.

The following example shows what an EndpointSlice looks like when hints have been populated:

apiVersion: discovery.k8s.io/v1
kind: EndpointSlice
metadata:
  name: example-hints
  labels:
    kubernetes.io/service-name: example-svc
addressType: IPv4
ports:
  - name: http
    protocol: TCP
    port: 80
endpoints:
  - addresses:
      - "10.1.2.3"
    conditions:
      ready: true
    hostname: pod-1
    zone: zone-a
    hints:
      forZones:
        - name: "zone-a"

kube-proxy

The kube-proxy component filters the endpoints it routes to based on the hints set by the EndpointSlice controller. In most cases, this means that the kube-proxy is able to route traffic to endpoints in the same zone. Sometimes the controller allocates endpoints from a different zone to ensure more even distribution of endpoints between zones. This would result in some traffic being routed to other zones.

Safeguards

The Kubernetes control plane and the kube-proxy on each node apply some safeguard rules before using Topology Aware Hints. If these don't check out, the kube-proxy selects endpoints from anywhere in your cluster, regardless of the zone.

Insufficient number of endpoints: If there are less endpoints than zones in a cluster, the controller will not assign any hints.
Impossible to achieve balanced allocation: In some cases, it will be impossible to achieve a balanced allocation of endpoints among zones. For example, if zone-a is twice as large as zone-b, but there are only 2 endpoints, an endpoint allocated to zone-a may receive twice as much traffic as zone-b. The controller does not assign hints if it can't get this "expected overload" value below an acceptable threshold for each zone. Importantly this is not based on real-time feedback. It is still possible for individual endpoints to become overloaded.
One or more Nodes has insufficient information: If any node does not have a topology.kubernetes.io/zone label or is not reporting a value for allocatable CPU, the control plane does not set any topology-aware endpoint hints and so kube-proxy does not filter endpoints by zone.
One or more endpoints does not have a zone hint: When this happens, the kube-proxy assumes that a transition from or to Topology Aware Hints is underway. Filtering endpoints for a Service in this state would be dangerous so the kube-proxy falls back to using all endpoints.
A zone is not represented in hints: If the kube-proxy is unable to find at least one endpoint with a hint targeting the zone it is running in, it falls to using endpoints from all zones. This is most likely to happen as you add a new zone into your existing cluster.

Constraints

Topology Aware Hints are not used when either externalTrafficPolicy or internalTrafficPolicy is set to Local on a Service. It is possible to use both features in the same cluster on different Services, just not on the same Service.
This approach will not work well for Services that have a large proportion of traffic originating from a subset of zones. Instead this assumes that incoming traffic will be roughly proportional to the capacity of the Nodes in each zone.
The EndpointSlice controller ignores unready nodes as it calculates the proportions of each zone. This could have unintended consequences if a large portion of nodes are unready.
The EndpointSlice controller does not take into account tolerations when deploying or calculating the proportions of each zone. If the Pods backing a Service are limited to a subset of Nodes in the cluster, this will not be taken into account.
This may not work well with autoscaling. For example, if a lot of traffic is originating from a single zone, only the endpoints allocated to that zone will be handling that traffic. That could result in Horizontal Pod Autoscaler either not picking up on this event, or newly added pods starting in a different zone.

What's next

Read Connecting Applications with Services

6.11 - Networking on Windows

Kubernetes supports running nodes on either Linux or Windows. You can mix both kinds of node within a single cluster. This page provides an overview to networking specific to the Windows operating system.

Container networking on Windows

Networking for Windows containers is exposed through CNI plugins. Windows containers function similarly to virtual machines in regards to networking. Each container has a virtual network adapter (vNIC) which is connected to a Hyper-V virtual switch (vSwitch). The Host Networking Service (HNS) and the Host Compute Service (HCS) work together to create containers and attach container vNICs to networks. HCS is responsible for the management of containers whereas HNS is responsible for the management of networking resources such as:

Virtual networks (including creation of vSwitches)
Endpoints / vNICs
Namespaces
Policies including packet encapsulations, load-balancing rules, ACLs, and NAT rules.

The Windows HNS and vSwitch implement namespacing and can create virtual NICs as needed for a pod or container. However, many configurations such as DNS, routes, and metrics are stored in the Windows registry database rather than as files inside /etc, which is how Linux stores those configurations. The Windows registry for the container is separate from that of the host, so concepts like mapping /etc/resolv.conf from the host into a container don't have the same effect they would on Linux. These must be configured using Windows APIs run in the context of that container. Therefore CNI implementations need to call the HNS instead of relying on file mappings to pass network details into the pod or container.

Network modes

Windows supports five different networking drivers/modes: L2bridge, L2tunnel, Overlay (Beta), Transparent, and NAT. In a heterogeneous cluster with Windows and Linux worker nodes, you need to select a networking solution that is compatible on both Windows and Linux. The following table lists the out-of-tree plugins are supported on Windows, with recommendations on when to use each CNI:

Network Driver	Description	Container Packet Modifications	Network Plugins	Network Plugin Characteristics
L2bridge	Containers are attached to an external vSwitch. Containers are attached to the underlay network, although the physical network doesn't need to learn the container MACs because they are rewritten on ingress/egress.	MAC is rewritten to host MAC, IP may be rewritten to host IP using HNS OutboundNAT policy.	win-bridge, Azure-CNI, Flannel host-gateway uses win-bridge	win-bridge uses L2bridge network mode, connects containers to the underlay of hosts, offering best performance. Requires user-defined routes (UDR) for inter-node connectivity.
L2Tunnel	This is a special case of l2bridge, but only used on Azure. All packets are sent to the virtualization host where SDN policy is applied.	MAC rewritten, IP visible on the underlay network	Azure-CNI	Azure-CNI allows integration of containers with Azure vNET, and allows them to leverage the set of capabilities that Azure Virtual Network provides. For example, securely connect to Azure services or use Azure NSGs. See azure-cni for some examples
Overlay	Containers are given a vNIC connected to an external vSwitch. Each overlay network gets its own IP subnet, defined by a custom IP prefix.The overlay network driver uses VXLAN encapsulation.	Encapsulated with an outer header.	win-overlay, Flannel VXLAN (uses win-overlay)	win-overlay should be used when virtual container networks are desired to be isolated from underlay of hosts (e.g. for security reasons). Allows for IPs to be re-used for different overlay networks (which have different VNID tags) if you are restricted on IPs in your datacenter. This option requires KB4489899 on Windows Server 2019.
Transparent (special use case for ovn-kubernetes)	Requires an external vSwitch. Containers are attached to an external vSwitch which enables intra-pod communication via logical networks (logical switches and routers).	Packet is encapsulated either via GENEVE or STT tunneling to reach pods which are not on the same host. Packets are forwarded or dropped via the tunnel metadata information supplied by the ovn network controller. NAT is done for north-south communication.	ovn-kubernetes	Deploy via ansible. Distributed ACLs can be applied via Kubernetes policies. IPAM support. Load-balancing can be achieved without kube-proxy. NATing is done without using iptables/netsh.
NAT (not used in Kubernetes)	Containers are given a vNIC connected to an internal vSwitch. DNS/DHCP is provided using an internal component called WinNAT	MAC and IP is rewritten to host MAC/IP.	nat	Included here for completeness

As outlined above, the Flannel CNI plugin is also supported on Windows via the VXLAN network backend (Beta support ; delegates to win-overlay) and host-gateway network backend (stable support; delegates to win-bridge).

This plugin supports delegating to one of the reference CNI plugins (win-overlay, win-bridge), to work in conjunction with Flannel daemon on Windows (Flanneld) for automatic node subnet lease assignment and HNS network creation. This plugin reads in its own configuration file (cni.conf), and aggregates it with the environment variables from the FlannelD generated subnet.env file. It then delegates to one of the reference CNI plugins for network plumbing, and sends the correct configuration containing the node-assigned subnet to the IPAM plugin (for example: host-local).

For Node, Pod, and Service objects, the following network flows are supported for TCP/UDP traffic:

Pod → Pod (IP)
Pod → Pod (Name)
Pod → Service (Cluster IP)
Pod → Service (PQDN, but only if there are no ".")
Pod → Service (FQDN)
Pod → external (IP)
Pod → external (DNS)
Node → Pod
Pod → Node

IP address management (IPAM)

The following IPAM options are supported on Windows:

host-local
azure-vnet-ipam (for azure-cni only)
Windows Server IPAM (fallback option if no IPAM is set)

Load balancing and Services

A Kubernetes Service is an abstraction that defines a logical set of Pods and a means to access them over a network. In a cluster that includes Windows nodes, you can use the following types of Service:

NodePort
ClusterIP
LoadBalancer
ExternalName

Windows container networking differs in some important ways from Linux networking. The Microsoft documentation for Windows Container Networking provides additional details and background.

On Windows, you can use the following settings to configure Services and load balancing behavior:

Windows Service Settings
Feature	Description	Minimum Supported Windows OS build	How to enable
Session affinity	Ensures that connections from a particular client are passed to the same Pod each time.	Windows Server 2022	Set `service.spec.sessionAffinity` to "ClientIP"
Direct Server Return (DSR)	Load balancing mode where the IP address fixups and the LBNAT occurs at the container vSwitch port directly; service traffic arrives with the source IP set as the originating pod IP.	Windows Server 2019	Set the following flags in kube-proxy: `--feature-gates="WinDSR=true" --enable-dsr=true`
Preserve-Destination	Skips DNAT of service traffic, thereby preserving the virtual IP of the target service in packets reaching the backend Pod. Also disables node-node forwarding.	Windows Server, version 1903	Set `"preserve-destination": "true"` in service annotations and enable DSR in kube-proxy.
IPv4/IPv6 dual-stack networking	Native IPv4-to-IPv4 in parallel with IPv6-to-IPv6 communications to, from, and within a cluster	Windows Server 2019	See IPv4/IPv6 dual-stack
Client IP preservation	Ensures that source IP of incoming ingress traffic gets preserved. Also disables node-node forwarding.	Windows Server 2019	Set `service.spec.externalTrafficPolicy` to "Local" and enable DSR in kube-proxy

Warning:

There are known issue with NodePort Services on overlay networking, if the destination node is running Windows Server 2022. To avoid the issue entirely, you can configure the service with externalTrafficPolicy: Local.

There are known issues with Pod to Pod connectivity on l2bridge network on Windows Server 2022 with KB5005619 or higher installed. To workaround the issue and restore Pod to Pod connectivity, you can disable the WinDSR feature in kube-proxy.

These issues require OS fixes. Please follow https://github.com/microsoft/Windows-Containers/issues/204 for updates.

Limitations

The following networking functionality is not supported on Windows nodes:

Host networking mode
Local NodePort access from the node itself (works for other nodes or external clients)
More than 64 backend pods (or unique destination addresses) for a single Service
IPv6 communication between Windows pods connected to overlay networks
Local Traffic Policy in non-DSR mode
Outbound communication using the ICMP protocol via the win-overlay, win-bridge, or using the Azure-CNI plugin.
Specifically, the Windows data plane (VFP) doesn't support ICMP packet transpositions, and this means:
- ICMP packets directed to destinations within the same network (such as pod to pod communication via ping) work as expected;
- TCP/UDP packets work as expected;
- ICMP packets directed to pass through a remote network (e.g. pod to external internet communication via ping) cannot be transposed and thus will not be routed back to their source;
- Since TCP/UDP packets can still be transposed, you can substitute ping <destination> with curl <destination> when debugging connectivity with the outside world.

Other limitations:

Windows reference network plugins win-bridge and win-overlay do not implement CNI spec v0.4.0, due to a missing CHECK implementation.
The Flannel VXLAN CNI plugin has the following limitations on Windows:
- Node-pod connectivity is only possible for local pods with Flannel v0.12.0 (or higher).
- Flannel is restricted to using VNI 4096 and UDP port 4789. See the official Flannel VXLAN backend docs for more details on these parameters.

6.12 - Service Internal Traffic Policy

If two Pods in your cluster want to communicate, and both Pods are actually running on the same node, Service Internal Traffic Policy to keep network traffic within that node. Avoiding a round trip via the cluster network can help with reliability, performance (network latency and throughput), or cost.

FEATURE STATE: Kubernetes v1.23 [beta]

Service Internal Traffic Policy enables internal traffic restrictions to only route internal traffic to endpoints within the node the traffic originated from. The "internal" traffic here refers to traffic originated from Pods in the current cluster. This can help to reduce costs and improve performance.

Using Service Internal Traffic Policy

The ServiceInternalTrafficPolicy feature gate is a Beta feature and enabled by default. When the feature is enabled, you can enable the internal-only traffic policy for a Service, by setting its .spec.internalTrafficPolicy to Local. This tells kube-proxy to only use node local endpoints for cluster internal traffic.

Note: For pods on nodes with no endpoints for a given Service, the Service behaves as if it has zero endpoints (for Pods on this node) even if the service does have endpoints on other nodes.

The following example shows what a Service looks like when you set .spec.internalTrafficPolicy to Local:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app.kubernetes.io/name: MyApp
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376
  internalTrafficPolicy: Local

How it works

The kube-proxy filters the endpoints it routes to based on the spec.internalTrafficPolicy setting. When it's set to Local, only node local endpoints are considered. When it's Cluster or missing, all endpoints are considered. When the feature gate ServiceInternalTrafficPolicy is enabled, spec.internalTrafficPolicy defaults to "Cluster".

What's next

Read about Topology Aware Hints
Read about Service External Traffic Policy
Read Connecting Applications with Services

7 - Storage

Ways to provide both long-term and temporary storage to Pods in your cluster.

7.1 - Volumes

On-disk files in a container are ephemeral, which presents some problems for non-trivial applications when running in containers. One problem is the loss of files when a container crashes. The kubelet restarts the container but with a clean state. A second problem occurs when sharing files between containers running together in a Pod. The Kubernetes volume abstraction solves both of these problems. Familiarity with Pods is suggested.

Background

Docker has a concept of volumes, though it is somewhat looser and less managed. A Docker volume is a directory on disk or in another container. Docker provides volume drivers, but the functionality is somewhat limited.

Kubernetes supports many types of volumes. A Pod can use any number of volume types simultaneously. Ephemeral volume types have a lifetime of a pod, but persistent volumes exist beyond the lifetime of a pod. When a pod ceases to exist, Kubernetes destroys ephemeral volumes; however, Kubernetes does not destroy persistent volumes. For any kind of volume in a given pod, data is preserved across container restarts.

At its core, a volume is a directory, possibly with some data in it, which is accessible to the containers in a pod. How that directory comes to be, the medium that backs it, and the contents of it are determined by the particular volume type used.

To use a volume, specify the volumes to provide for the Pod in .spec.volumes and declare where to mount those volumes into containers in .spec.containers[*].volumeMounts. A process in a container sees a filesystem view composed from the initial contents of the container image, plus volumes (if defined) mounted inside the container. The process sees a root filesystem that initially matches the contents of the container image. Any writes to within that filesystem hierarchy, if allowed, affect what that process views when it performs a subsequent filesystem access. Volumes mount at the specified paths within the image. For each container defined within a Pod, you must independently specify where to mount each volume that the container uses.

Volumes cannot mount within other volumes (but see Using subPath for a related mechanism). Also, a volume cannot contain a hard link to anything in a different volume.

Types of Volumes

Kubernetes supports several types of volumes.

awsElasticBlockStore (deprecated)

FEATURE STATE: Kubernetes v1.17 [deprecated]

An awsElasticBlockStore volume mounts an Amazon Web Services (AWS) EBS volume into your pod. Unlike emptyDir, which is erased when a pod is removed, the contents of an EBS volume are persisted and the volume is unmounted. This means that an EBS volume can be pre-populated with data, and that data can be shared between pods.

Note: You must create an EBS volume by using aws ec2 create-volume or the AWS API before you can use it.

There are some restrictions when using an awsElasticBlockStore volume:

the nodes on which pods are running must be AWS EC2 instances
those instances need to be in the same region and availability zone as the EBS volume
EBS only supports a single EC2 instance mounting a volume

Creating an AWS EBS volume

Before you can use an EBS volume with a pod, you need to create it.

aws ec2 create-volume --availability-zone=eu-west-1a --size=10 --volume-type=gp2

Make sure the zone matches the zone you brought up your cluster in. Check that the size and EBS volume type are suitable for your use.

AWS EBS configuration example

apiVersion: v1
kind: Pod
metadata:
  name: test-ebs
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-container
    volumeMounts:
    - mountPath: /test-ebs
      name: test-volume
  volumes:
  - name: test-volume
    # This AWS EBS volume must already exist.
    awsElasticBlockStore:
      volumeID: "<volume id>"
      fsType: ext4

If the EBS volume is partitioned, you can supply the optional field partition: "<partition number>" to specify which partition to mount on.

AWS EBS CSI migration

FEATURE STATE: Kubernetes v1.25 [stable]

The CSIMigration feature for awsElasticBlockStore, when enabled, redirects all plugin operations from the existing in-tree plugin to the ebs.csi.aws.com Container Storage Interface (CSI) driver. In order to use this feature, the AWS EBS CSI driver must be installed on the cluster.

AWS EBS CSI migration complete

FEATURE STATE: Kubernetes v1.17 [alpha]

To disable the awsElasticBlockStore storage plugin from being loaded by the controller manager and the kubelet, set the InTreePluginAWSUnregister flag to true.

azureDisk (deprecated)

FEATURE STATE: Kubernetes v1.19 [deprecated]

The azureDisk volume type mounts a Microsoft Azure Data Disk into a pod.

For more details, see the azureDisk volume plugin.

azureDisk CSI migration

FEATURE STATE: Kubernetes v1.24 [stable]

The CSIMigration feature for azureDisk, when enabled, redirects all plugin operations from the existing in-tree plugin to the disk.csi.azure.com Container Storage Interface (CSI) Driver. In order to use this feature, the Azure Disk CSI Driver must be installed on the cluster.

azureDisk CSI migration complete

FEATURE STATE: Kubernetes v1.21 [alpha]

To disable the azureDisk storage plugin from being loaded by the controller manager and the kubelet, set the InTreePluginAzureDiskUnregister flag to true.

azureFile (deprecated)

FEATURE STATE: Kubernetes v1.21 [deprecated]

The azureFile volume type mounts a Microsoft Azure File volume (SMB 2.1 and 3.0) into a pod.

For more details, see the azureFile volume plugin.

azureFile CSI migration

FEATURE STATE: Kubernetes v1.21 [beta]

The CSIMigration feature for azureFile, when enabled, redirects all plugin operations from the existing in-tree plugin to the file.csi.azure.com Container Storage Interface (CSI) Driver. In order to use this feature, the Azure File CSI Driver must be installed on the cluster and the CSIMigrationAzureFile feature gates must be enabled.

Azure File CSI driver does not support using same volume with different fsgroups. If CSIMigrationAzureFile is enabled, using same volume with different fsgroups won't be supported at all.

azureFile CSI migration complete

FEATURE STATE: Kubernetes v1.21 [alpha]

To disable the azureFile storage plugin from being loaded by the controller manager and the kubelet, set the InTreePluginAzureFileUnregister flag to true.

cephfs

A cephfs volume allows an existing CephFS volume to be mounted into your Pod. Unlike emptyDir, which is erased when a pod is removed, the contents of a cephfs volume are preserved and the volume is merely unmounted. This means that a cephfs volume can be pre-populated with data, and that data can be shared between pods. The cephfs volume can be mounted by multiple writers simultaneously.

Note: You must have your own Ceph server running with the share exported before you can use it.

See the CephFS example for more details.

cinder (deprecated)

FEATURE STATE: Kubernetes v1.18 [deprecated]

Note: Kubernetes must be configured with the OpenStack cloud provider.

The cinder volume type is used to mount the OpenStack Cinder volume into your pod.

Cinder volume configuration example

apiVersion: v1
kind: Pod
metadata:
  name: test-cinder
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-cinder-container
    volumeMounts:
    - mountPath: /test-cinder
      name: test-volume
  volumes:
  - name: test-volume
    # This OpenStack volume must already exist.
    cinder:
      volumeID: "<volume id>"
      fsType: ext4

OpenStack CSI migration

FEATURE STATE: Kubernetes v1.24 [stable]

The CSIMigration feature for Cinder is enabled by default since Kubernetes 1.21. It redirects all plugin operations from the existing in-tree plugin to the cinder.csi.openstack.org Container Storage Interface (CSI) Driver. OpenStack Cinder CSI Driver must be installed on the cluster.

To disable the in-tree Cinder plugin from being loaded by the controller manager and the kubelet, you can enable the InTreePluginOpenStackUnregister feature gate.

configMap

A ConfigMap provides a way to inject configuration data into pods. The data stored in a ConfigMap can be referenced in a volume of type configMap and then consumed by containerized applications running in a pod.

When referencing a ConfigMap, you provide the name of the ConfigMap in the volume. You can customize the path to use for a specific entry in the ConfigMap. The following configuration shows how to mount the log-config ConfigMap onto a Pod called configmap-pod:

apiVersion: v1
kind: Pod
metadata:
  name: configmap-pod
spec:
  containers:
    - name: test
      image: busybox:1.28
      volumeMounts:
        - name: config-vol
          mountPath: /etc/config
  volumes:
    - name: config-vol
      configMap:
        name: log-config
        items:
          - key: log_level
            path: log_level

The log-config ConfigMap is mounted as a volume, and all contents stored in its log_level entry are mounted into the Pod at path /etc/config/log_level. Note that this path is derived from the volume's mountPath and the path keyed with log_level.

Note:

You must create a ConfigMap before you can use it.
A container using a ConfigMap as a subPath volume mount will not receive ConfigMap updates.
Text data is exposed as files using the UTF-8 character encoding. For other character encodings, use binaryData.

downwardAPI

A downwardAPI volume makes downward API data available to applications. Within the volume, you can find the exposed data as read-only files in plain text format.

Note: A container using the downward API as a subPath volume mount does not receive updates when field values change.

See Expose Pod Information to Containers Through Files to learn more.

emptyDir

An emptyDir volume is first created when a Pod is assigned to a node, and exists as long as that Pod is running on that node. As the name says, the emptyDir volume is initially empty. All containers in the Pod can read and write the same files in the emptyDir volume, though that volume can be mounted at the same or different paths in each container. When a Pod is removed from a node for any reason, the data in the emptyDir is deleted permanently.

Note: A container crashing does not remove a Pod from a node. The data in an emptyDir volume is safe across container crashes.

Some uses for an emptyDir are:

scratch space, such as for a disk-based merge sort
checkpointing a long computation for recovery from crashes
holding files that a content-manager container fetches while a webserver container serves the data

Depending on your environment, emptyDir volumes are stored on whatever medium that backs the node such as disk or SSD, or network storage. However, if you set the emptyDir.medium field to "Memory", Kubernetes mounts a tmpfs (RAM-backed filesystem) for you instead. While tmpfs is very fast, be aware that unlike disks, tmpfs is cleared on node reboot and any files you write count against your container's memory limit.

Note: If the SizeMemoryBackedVolumes feature gate is enabled, you can specify a size for memory backed volumes. If no size is specified, memory backed volumes are sized to 50% of the memory on a Linux host.

emptyDir configuration example

apiVersion: v1
kind: Pod
metadata:
  name: test-pd
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-container
    volumeMounts:
    - mountPath: /cache
      name: cache-volume
  volumes:
  - name: cache-volume
    emptyDir: {}

fc (fibre channel)

An fc volume type allows an existing fibre channel block storage volume to mount in a Pod. You can specify single or multiple target world wide names (WWNs) using the parameter targetWWNs in your Volume configuration. If multiple WWNs are specified, targetWWNs expect that those WWNs are from multi-path connections.

Note: You must configure FC SAN Zoning to allocate and mask those LUNs (volumes) to the target WWNs beforehand so that Kubernetes hosts can access them.

See the fibre channel example for more details.

gcePersistentDisk (deprecated)

FEATURE STATE: Kubernetes v1.17 [deprecated]

A gcePersistentDisk volume mounts a Google Compute Engine (GCE) persistent disk (PD) into your Pod. Unlike emptyDir, which is erased when a pod is removed, the contents of a PD are preserved and the volume is merely unmounted. This means that a PD can be pre-populated with data, and that data can be shared between pods.

Note: You must create a PD using gcloud or the GCE API or UI before you can use it.

There are some restrictions when using a gcePersistentDisk:

the nodes on which Pods are running must be GCE VMs
those VMs need to be in the same GCE project and zone as the persistent disk

One feature of GCE persistent disk is concurrent read-only access to a persistent disk. A gcePersistentDisk volume permits multiple consumers to simultaneously mount a persistent disk as read-only. This means that you can pre-populate a PD with your dataset and then serve it in parallel from as many Pods as you need. Unfortunately, PDs can only be mounted by a single consumer in read-write mode. Simultaneous writers are not allowed.

Using a GCE persistent disk with a Pod controlled by a ReplicaSet will fail unless the PD is read-only or the replica count is 0 or 1.

Creating a GCE persistent disk

Before you can use a GCE persistent disk with a Pod, you need to create it.

gcloud compute disks create --size=500GB --zone=us-central1-a my-data-disk

GCE persistent disk configuration example

apiVersion: v1
kind: Pod
metadata:
  name: test-pd
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-container
    volumeMounts:
    - mountPath: /test-pd
      name: test-volume
  volumes:
  - name: test-volume
    # This GCE PD must already exist.
    gcePersistentDisk:
      pdName: my-data-disk
      fsType: ext4

Regional persistent disks

The Regional persistent disks feature allows the creation of persistent disks that are available in two zones within the same region. In order to use this feature, the volume must be provisioned as a PersistentVolume; referencing the volume directly from a pod is not supported.

Manually provisioning a Regional PD PersistentVolume

Dynamic provisioning is possible using a StorageClass for GCE PD. Before creating a PersistentVolume, you must create the persistent disk:

gcloud compute disks create --size=500GB my-data-disk
  --region us-central1
  --replica-zones us-central1-a,us-central1-b

Regional persistent disk configuration example

apiVersion: v1
kind: PersistentVolume
metadata:
  name: test-volume
spec:
  capacity:
    storage: 400Gi
  accessModes:
  - ReadWriteOnce
  gcePersistentDisk:
    pdName: my-data-disk
    fsType: ext4
  nodeAffinity:
    required:
      nodeSelectorTerms:
      - matchExpressions:
        # failure-domain.beta.kubernetes.io/zone should be used prior to 1.21
        - key: topology.kubernetes.io/zone
          operator: In
          values:
          - us-central1-a
          - us-central1-b

GCE CSI migration

FEATURE STATE: Kubernetes v1.25 [stable]

The CSIMigration feature for GCE PD, when enabled, redirects all plugin operations from the existing in-tree plugin to the pd.csi.storage.gke.io Container Storage Interface (CSI) Driver. In order to use this feature, the GCE PD CSI Driver must be installed on the cluster.

GCE CSI migration complete

FEATURE STATE: Kubernetes v1.21 [alpha]

To disable the gcePersistentDisk storage plugin from being loaded by the controller manager and the kubelet, set the InTreePluginGCEUnregister flag to true.

gitRepo (deprecated)

Warning: The gitRepo volume type is deprecated. To provision a container with a git repo, mount an EmptyDir into an InitContainer that clones the repo using git, then mount the EmptyDir into the Pod's container.

A gitRepo volume is an example of a volume plugin. This plugin mounts an empty directory and clones a git repository into this directory for your Pod to use.

Here is an example of a gitRepo volume:

apiVersion: v1
kind: Pod
metadata:
  name: server
spec:
  containers:
  - image: nginx
    name: nginx
    volumeMounts:
    - mountPath: /mypath
      name: git-volume
  volumes:
  - name: git-volume
    gitRepo:
      repository: "git@somewhere:me/my-git-repository.git"
      revision: "22f1d8406d464b0c0874075539c1f2e96c253775"

glusterfs (deprecated)

FEATURE STATE: Kubernetes v1.25 [deprecated]

A glusterfs volume allows a Glusterfs (an open source networked filesystem) volume to be mounted into your Pod. Unlike emptyDir, which is erased when a Pod is removed, the contents of a glusterfs volume are preserved and the volume is merely unmounted. This means that a glusterfs volume can be pre-populated with data, and that data can be shared between pods. GlusterFS can be mounted by multiple writers simultaneously.

Note: You must have your own GlusterFS installation running before you can use it.

See the GlusterFS example for more details.

hostPath

Warning:

HostPath volumes present many security risks, and it is a best practice to avoid the use of HostPaths when possible. When a HostPath volume must be used, it should be scoped to only the required file or directory, and mounted as ReadOnly.

If restricting HostPath access to specific directories through AdmissionPolicy, volumeMounts MUST be required to use readOnly mounts for the policy to be effective.

A hostPath volume mounts a file or directory from the host node's filesystem into your Pod. This is not something that most Pods will need, but it offers a powerful escape hatch for some applications.

For example, some uses for a hostPath are:

running a container that needs access to Docker internals; use a hostPath of /var/lib/docker
running cAdvisor in a container; use a hostPath of /sys
allowing a Pod to specify whether a given hostPath should exist prior to the Pod running, whether it should be created, and what it should exist as

In addition to the required path property, you can optionally specify a type for a hostPath volume.

The supported values for field type are:

Value	Behavior
	Empty string (default) is for backward compatibility, which means that no checks will be performed before mounting the hostPath volume.
`DirectoryOrCreate`	If nothing exists at the given path, an empty directory will be created there as needed with permission set to 0755, having the same group and ownership with Kubelet.
`Directory`	A directory must exist at the given path
`FileOrCreate`	If nothing exists at the given path, an empty file will be created there as needed with permission set to 0644, having the same group and ownership with Kubelet.
`File`	A file must exist at the given path
`Socket`	A UNIX socket must exist at the given path
`CharDevice`	A character device must exist at the given path
`BlockDevice`	A block device must exist at the given path

Watch out when using this type of volume, because:

HostPaths can expose privileged system credentials (such as for the Kubelet) or privileged APIs (such as container runtime socket), which can be used for container escape or to attack other parts of the cluster.
Pods with identical configuration (such as created from a PodTemplate) may behave differently on different nodes due to different files on the nodes
The files or directories created on the underlying hosts are only writable by root. You either need to run your process as root in a privileged Container or modify the file permissions on the host to be able to write to a hostPath volume

hostPath configuration example

apiVersion: v1
kind: Pod
metadata:
  name: test-pd
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-container
    volumeMounts:
    - mountPath: /test-pd
      name: test-volume
  volumes:
  - name: test-volume
    hostPath:
      # directory location on host
      path: /data
      # this field is optional
      type: Directory

Caution: The FileOrCreate mode does not create the parent directory of the file. If the parent directory of the mounted file does not exist, the pod fails to start. To ensure that this mode works, you can try to mount directories and files separately, as shown in the FileOrCreateconfiguration.

hostPath FileOrCreate configuration example

apiVersion: v1
kind: Pod
metadata:
  name: test-webserver
spec:
  containers:
  - name: test-webserver
    image: registry.k8s.io/test-webserver:latest
    volumeMounts:
    - mountPath: /var/local/aaa
      name: mydir
    - mountPath: /var/local/aaa/1.txt
      name: myfile
  volumes:
  - name: mydir
    hostPath:
      # Ensure the file directory is created.
      path: /var/local/aaa
      type: DirectoryOrCreate
  - name: myfile
    hostPath:
      path: /var/local/aaa/1.txt
      type: FileOrCreate

iscsi

An iscsi volume allows an existing iSCSI (SCSI over IP) volume to be mounted into your Pod. Unlike emptyDir, which is erased when a Pod is removed, the contents of an iscsi volume are preserved and the volume is merely unmounted. This means that an iscsi volume can be pre-populated with data, and that data can be shared between pods.

Note: You must have your own iSCSI server running with the volume created before you can use it.

A feature of iSCSI is that it can be mounted as read-only by multiple consumers simultaneously. This means that you can pre-populate a volume with your dataset and then serve it in parallel from as many Pods as you need. Unfortunately, iSCSI volumes can only be mounted by a single consumer in read-write mode. Simultaneous writers are not allowed.

See the iSCSI example for more details.

local

A local volume represents a mounted local storage device such as a disk, partition or directory.

Local volumes can only be used as a statically created PersistentVolume. Dynamic provisioning is not supported.

Compared to hostPath volumes, local volumes are used in a durable and portable manner without manually scheduling pods to nodes. The system is aware of the volume's node constraints by looking at the node affinity on the PersistentVolume.

However, local volumes are subject to the availability of the underlying node and are not suitable for all applications. If a node becomes unhealthy, then the local volume becomes inaccessible by the pod. The pod using this volume is unable to run. Applications using local volumes must be able to tolerate this reduced availability, as well as potential data loss, depending on the durability characteristics of the underlying disk.

The following example shows a PersistentVolume using a local volume and nodeAffinity:

apiVersion: v1
kind: PersistentVolume
metadata:
  name: example-pv
spec:
  capacity:
    storage: 100Gi
  volumeMode: Filesystem
  accessModes:
  - ReadWriteOnce
  persistentVolumeReclaimPolicy: Delete
  storageClassName: local-storage
  local:
    path: /mnt/disks/ssd1
  nodeAffinity:
    required:
      nodeSelectorTerms:
      - matchExpressions:
        - key: kubernetes.io/hostname
          operator: In
          values:
          - example-node

You must set a PersistentVolume nodeAffinity when using local volumes. The Kubernetes scheduler uses the PersistentVolume nodeAffinity to schedule these Pods to the correct node.

PersistentVolume volumeMode can be set to "Block" (instead of the default value "Filesystem") to expose the local volume as a raw block device.

When using local volumes, it is recommended to create a StorageClass with volumeBindingMode set to WaitForFirstConsumer. For more details, see the local StorageClass example. Delaying volume binding ensures that the PersistentVolumeClaim binding decision will also be evaluated with any other node constraints the Pod may have, such as node resource requirements, node selectors, Pod affinity, and Pod anti-affinity.

An external static provisioner can be run separately for improved management of the local volume lifecycle. Note that this provisioner does not support dynamic provisioning yet. For an example on how to run an external local provisioner, see the local volume provisioner user guide.

Note: The local PersistentVolume requires manual cleanup and deletion by the user if the external static provisioner is not used to manage the volume lifecycle.

nfs

An nfs volume allows an existing NFS (Network File System) share to be mounted into a Pod. Unlike emptyDir, which is erased when a Pod is removed, the contents of an nfs volume are preserved and the volume is merely unmounted. This means that an NFS volume can be pre-populated with data, and that data can be shared between pods. NFS can be mounted by multiple writers simultaneously.

apiVersion: v1
kind: Pod
metadata:
  name: test-pd
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-container
    volumeMounts:
    - mountPath: /my-nfs-data
      name: test-volume
  volumes:
  - name: test-volume
    nfs:
      server: my-nfs-server.example.com
      path: /my-nfs-volume
      readonly: true

Note:

You must have your own NFS server running with the share exported before you can use it.

Also note that you can't specify NFS mount options in a Pod spec. You can either set mount options server-side or use /etc/nfsmount.conf. You can also mount NFS volumes via PersistentVolumes which do allow you to set mount options.

See the NFS example for an example of mounting NFS volumes with PersistentVolumes.

persistentVolumeClaim

A persistentVolumeClaim volume is used to mount a PersistentVolume into a Pod. PersistentVolumeClaims are a way for users to "claim" durable storage (such as a GCE PersistentDisk or an iSCSI volume) without knowing the details of the particular cloud environment.

See the information about PersistentVolumes for more details.

portworxVolume (deprecated)

FEATURE STATE: Kubernetes v1.25 [deprecated]

A portworxVolume is an elastic block storage layer that runs hyperconverged with Kubernetes. Portworx fingerprints storage in a server, tiers based on capabilities, and aggregates capacity across multiple servers. Portworx runs in-guest in virtual machines or on bare metal Linux nodes.

A portworxVolume can be dynamically created through Kubernetes or it can also be pre-provisioned and referenced inside a Pod. Here is an example Pod referencing a pre-provisioned Portworx volume:

apiVersion: v1
kind: Pod
metadata:
  name: test-portworx-volume-pod
spec:
  containers:
  - image: registry.k8s.io/test-webserver
    name: test-container
    volumeMounts:
    - mountPath: /mnt
      name: pxvol
  volumes:
  - name: pxvol
    # This Portworx volume must already exist.
    portworxVolume:
      volumeID: "pxvol"
      fsType: "<fs-type>"

Note: Make sure you have an existing PortworxVolume with name pxvol before using it in the Pod.

For more details, see the Portworx volume examples.

Portworx CSI migration

FEATURE STATE: Kubernetes v1.25 [beta]

The CSIMigration feature for Portworx has been added but disabled by default in Kubernetes 1.23 since it's in alpha state. It has been beta now since v1.25 but it is still turned off by default. It redirects all plugin operations from the existing in-tree plugin to the pxd.portworx.com Container Storage Interface (CSI) Driver. Portworx CSI Driver must be installed on the cluster. To enable the feature, set CSIMigrationPortworx=true in kube-controller-manager and kubelet.

projected

A projected volume maps several existing volume sources into the same directory. For more details, see projected volumes.

rbd

An rbd volume allows a Rados Block Device (RBD) volume to mount into your Pod. Unlike emptyDir, which is erased when a pod is removed, the contents of an rbd volume are preserved and the volume is unmounted. This means that a RBD volume can be pre-populated with data, and that data can be shared between pods.

Note: You must have a Ceph installation running before you can use RBD.

A feature of RBD is that it can be mounted as read-only by multiple consumers simultaneously. This means that you can pre-populate a volume with your dataset and then serve it in parallel from as many pods as you need. Unfortunately, RBD volumes can only be mounted by a single consumer in read-write mode. Simultaneous writers are not allowed.

See the RBD example for more details.

RBD CSI migration

FEATURE STATE: Kubernetes v1.23 [alpha]

The CSIMigration feature for RBD, when enabled, redirects all plugin operations from the existing in-tree plugin to the rbd.csi.ceph.com CSI driver. In order to use this feature, the Ceph CSI driver must be installed on the cluster and the CSIMigrationRBD feature gate must be enabled. (Note that the csiMigrationRBD flag has been removed and replaced with CSIMigrationRBD in release v1.24)

Note:

As a Kubernetes cluster operator that administers storage, here are the prerequisites that you must complete before you attempt migration to the RBD CSI driver:

You must install the Ceph CSI driver (rbd.csi.ceph.com), v3.5.0 or above, into your Kubernetes cluster.
considering the clusterID field is a required parameter for CSI driver for its operations, but in-tree StorageClass has monitors field as a required parameter, a Kubernetes storage admin has to create a clusterID based on the monitors hash ( ex:#echo -n '<monitors_string>' | md5sum) in the CSI config map and keep the monitors under this clusterID configuration.
Also, if the value of adminId in the in-tree Storageclass is different from admin, the adminSecretName mentioned in the in-tree Storageclass has to be patched with the base64 value of the adminId parameter value, otherwise this step can be skipped.

secret

A secret volume is used to pass sensitive information, such as passwords, to Pods. You can store secrets in the Kubernetes API and mount them as files for use by pods without coupling to Kubernetes directly. secret volumes are backed by tmpfs (a RAM-backed filesystem) so they are never written to non-volatile storage.

Note: You must create a Secret in the Kubernetes API before you can use it.

Note: A container using a Secret as a subPath volume mount will not receive Secret updates.

For more details, see Configuring Secrets.

vsphereVolume (deprecated)

Note: We recommend to use vSphere CSI out-of-tree driver instead.

A vsphereVolume is used to mount a vSphere VMDK volume into your Pod. The contents of a volume are preserved when it is unmounted. It supports both VMFS and VSAN datastore.

For more information, see the vSphere volume examples.

vSphere CSI migration

FEATURE STATE: Kubernetes v1.19 [beta]

The CSIMigrationvSphere feature for vsphereVolume is enabled by default as of Kubernetes v1.25. All plugin operations from the in-tree vspherevolume will be redirected to the csi.vsphere.vmware.com CSI driver unless CSIMigrationvSphere feature gate is disabled.

vSphere CSI driver must be installed on the cluster. You can find additional advice on how to migrate in-tree vsphereVolume in VMware's documentation page Migrating In-Tree vSphere Volumes to vSphere Container Storage Plug-in.

As of Kubernetes v1.25, vSphere releases less than 7.0u2 are not supported for the (deprecated) in-tree vSphere storage driver. You must run vSphere 7.0u2 or later in order to either continue using the deprecated driver, or to migrate to the replacement CSI driver.

If you are running a version of Kubernetes other than v1.25, consult the documentation for that version of Kubernetes.

Note:

The following StorageClass parameters from the built-in vsphereVolume plugin are not supported by the vSphere CSI driver:

diskformat
hostfailurestotolerate
forceprovisioning
cachereservation
diskstripes
objectspacereservation
iopslimit

Existing volumes created using these parameters will be migrated to the vSphere CSI driver, but new volumes created by the vSphere CSI driver will not be honoring these parameters.

vSphere CSI migration complete

FEATURE STATE: Kubernetes v1.19 [beta]

To turn off the vsphereVolume plugin from being loaded by the controller manager and the kubelet, you need to set InTreePluginvSphereUnregister feature flag to true. You must install a csi.vsphere.vmware.com CSI driver on all worker nodes.

Using subPath

Sometimes, it is useful to share one volume for multiple uses in a single pod. The volumeMounts.subPath property specifies a sub-path inside the referenced volume instead of its root.

The following example shows how to configure a Pod with a LAMP stack (Linux Apache MySQL PHP) using a single, shared volume. This sample subPath configuration is not recommended for production use.

The PHP application's code and assets map to the volume's html folder and the MySQL database is stored in the volume's mysql folder. For example:

apiVersion: v1
kind: Pod
metadata:
  name: my-lamp-site
spec:
    containers:
    - name: mysql
      image: mysql
      env:
      - name: MYSQL_ROOT_PASSWORD
        value: "rootpasswd"
      volumeMounts:
      - mountPath: /var/lib/mysql
        name: site-data
        subPath: mysql
    - name: php
      image: php:7.0-apache
      volumeMounts:
      - mountPath: /var/www/html
        name: site-data
        subPath: html
    volumes:
    - name: site-data
      persistentVolumeClaim:
        claimName: my-lamp-site-data

Using subPath with expanded environment variables

FEATURE STATE: Kubernetes v1.17 [stable]

Use the subPathExpr field to construct subPath directory names from downward API environment variables. The subPath and subPathExpr properties are mutually exclusive.

In this example, a Pod uses subPathExpr to create a directory pod1 within the hostPath volume /var/log/pods. The hostPath volume takes the Pod name from the downwardAPI. The host directory /var/log/pods/pod1 is mounted at /logs in the container.

apiVersion: v1
kind: Pod
metadata:
  name: pod1
spec:
  containers:
  - name: container1
    env:
    - name: POD_NAME
      valueFrom:
        fieldRef:
          apiVersion: v1
          fieldPath: metadata.name
    image: busybox:1.28
    command: [ "sh", "-c", "while [ true ]; do echo 'Hello'; sleep 10; done | tee -a /logs/hello.txt" ]
    volumeMounts:
    - name: workdir1
      mountPath: /logs
      # The variable expansion uses round brackets (not curly brackets).
      subPathExpr: $(POD_NAME)
  restartPolicy: Never
  volumes:
  - name: workdir1
    hostPath:
      path: /var/log/pods

Resources

The storage media (such as Disk or SSD) of an emptyDir volume is determined by the medium of the filesystem holding the kubelet root dir (typically /var/lib/kubelet). There is no limit on how much space an emptyDir or hostPath volume can consume, and no isolation between containers or between pods.

To learn about requesting space using a resource specification, see how to manage resources.

Out-of-tree volume plugins

The out-of-tree volume plugins include Container Storage Interface (CSI), and also FlexVolume (which is deprecated). These plugins enable storage vendors to create custom storage plugins without adding their plugin source code to the Kubernetes repository.

Previously, all volume plugins were "in-tree". The "in-tree" plugins were built, linked, compiled, and shipped with the core Kubernetes binaries. This meant that adding a new storage system to Kubernetes (a volume plugin) required checking code into the core Kubernetes code repository.

Both CSI and FlexVolume allow volume plugins to be developed independent of the Kubernetes code base, and deployed (installed) on Kubernetes clusters as extensions.

For storage vendors looking to create an out-of-tree volume plugin, please refer to the volume plugin FAQ.

csi

Container Storage Interface (CSI) defines a standard interface for container orchestration systems (like Kubernetes) to expose arbitrary storage systems to their container workloads.

Please read the CSI design proposal for more information.

Note: Support for CSI spec versions 0.2 and 0.3 are deprecated in Kubernetes v1.13 and will be removed in a future release.

Note: CSI drivers may not be compatible across all Kubernetes releases. Please check the specific CSI driver's documentation for supported deployments steps for each Kubernetes release and a compatibility matrix.

Once a CSI compatible volume driver is deployed on a Kubernetes cluster, users may use the csi volume type to attach or mount the volumes exposed by the CSI driver.

A csi volume can be used in a Pod in three different ways:

through a reference to a PersistentVolumeClaim
with a generic ephemeral volume
with a CSI ephemeral volume if the driver supports that

The following fields are available to storage administrators to configure a CSI persistent volume:

driver: A string value that specifies the name of the volume driver to use. This value must correspond to the value returned in the GetPluginInfoResponse by the CSI driver as defined in the CSI spec. It is used by Kubernetes to identify which CSI driver to call out to, and by CSI driver components to identify which PV objects belong to the CSI driver.
volumeHandle: A string value that uniquely identifies the volume. This value must correspond to the value returned in the volume.id field of the CreateVolumeResponse by the CSI driver as defined in the CSI spec. The value is passed as volume_id on all calls to the CSI volume driver when referencing the volume.
readOnly: An optional boolean value indicating whether the volume is to be "ControllerPublished" (attached) as read only. Default is false. This value is passed to the CSI driver via the readonly field in the ControllerPublishVolumeRequest.
fsType: If the PV's VolumeMode is Filesystem then this field may be used to specify the filesystem that should be used to mount the volume. If the volume has not been formatted and formatting is supported, this value will be used to format the volume. This value is passed to the CSI driver via the VolumeCapability field of ControllerPublishVolumeRequest, NodeStageVolumeRequest, and NodePublishVolumeRequest.
volumeAttributes: A map of string to string that specifies static properties of a volume. This map must correspond to the map returned in the volume.attributes field of the CreateVolumeResponse by the CSI driver as defined in the CSI spec. The map is passed to the CSI driver via the volume_context field in the ControllerPublishVolumeRequest, NodeStageVolumeRequest, and NodePublishVolumeRequest.
controllerPublishSecretRef: A reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI ControllerPublishVolume and ControllerUnpublishVolume calls. This field is optional, and may be empty if no secret is required. If the Secret contains more than one secret, all secrets are passed. nodeExpandSecretRef: A reference to the secret containing sensitive information to pass to the CSI driver to complete the CSI NodeExpandVolume call. This field is optional, and may be empty if no secret is required. If the object contains more than one secret, all secrets are passed. When you have configured secret data for node-initiated volume expansion, the kubelet passes that data via the NodeExpandVolume() call to the CSI driver. In order to use the nodeExpandSecretRef field, your cluster should be running Kubernetes version 1.25 or later and you must enable the feature gate named CSINodeExpandSecret for each kube-apiserver and for the kubelet on every node. You must also be using a CSI driver that supports or requires secret data during node-initiated storage resize operations.
nodePublishSecretRef: A reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI NodePublishVolume call. This field is optional, and may be empty if no secret is required. If the secret object contains more than one secret, all secrets are passed.
nodeStageSecretRef: A reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI NodeStageVolume call. This field is optional, and may be empty if no secret is required. If the Secret contains more than one secret, all secrets are passed.

CSI raw block volume support

FEATURE STATE: Kubernetes v1.18 [stable]

Vendors with external CSI drivers can implement raw block volume support in Kubernetes workloads.

You can set up your PersistentVolume/PersistentVolumeClaim with raw block volume support as usual, without any CSI specific changes.

CSI ephemeral volumes

FEATURE STATE: Kubernetes v1.25 [stable]

You can directly configure CSI volumes within the Pod specification. Volumes specified in this way are ephemeral and do not persist across pod restarts. See Ephemeral Volumes for more information.

For more information on how to develop a CSI driver, refer to the kubernetes-csi documentation

Windows CSI proxy

FEATURE STATE: Kubernetes v1.22 [stable]

CSI node plugins need to perform various privileged operations like scanning of disk devices and mounting of file systems. These operations differ for each host operating system. For Linux worker nodes, containerized CSI node node plugins are typically deployed as privileged containers. For Windows worker nodes, privileged operations for containerized CSI node plugins is supported using csi-proxy, a community-managed, stand-alone binary that needs to be pre-installed on each Windows node.

For more details, refer to the deployment guide of the CSI plugin you wish to deploy.

Migrating to CSI drivers from in-tree plugins

FEATURE STATE: Kubernetes v1.25 [stable]

The CSIMigration feature directs operations against existing in-tree plugins to corresponding CSI plugins (which are expected to be installed and configured). As a result, operators do not have to make any configuration changes to existing Storage Classes, PersistentVolumes or PersistentVolumeClaims (referring to in-tree plugins) when transitioning to a CSI driver that supersedes an in-tree plugin.

The operations and features that are supported include: provisioning/delete, attach/detach, mount/unmount and resizing of volumes.

In-tree plugins that support CSIMigration and have a corresponding CSI driver implemented are listed in Types of Volumes.

The following in-tree plugins support persistent storage on Windows nodes:

awsElasticBlockStore
azureDisk
azureFile
gcePersistentDisk
vsphereVolume

flexVolume (deprecated)

FEATURE STATE: Kubernetes v1.23 [deprecated]

FlexVolume is an out-of-tree plugin interface that uses an exec-based model to interface with storage drivers. The FlexVolume driver binaries must be installed in a pre-defined volume plugin path on each node and in some cases the control plane nodes as well.

Pods interact with FlexVolume drivers through the flexVolume in-tree volume plugin. For more details, see the FlexVolume README document.

The following FlexVolume plugins, deployed as PowerShell scripts on the host, support Windows nodes:

SMB
iSCSI

Note:

FlexVolume is deprecated. Using an out-of-tree CSI driver is the recommended way to integrate external storage with Kubernetes.

Maintainers of FlexVolume driver should implement a CSI Driver and help to migrate users of FlexVolume drivers to CSI. Users of FlexVolume should move their workloads to use the equivalent CSI Driver.

Mount propagation

Mount propagation allows for sharing volumes mounted by a container to other containers in the same pod, or even to other pods on the same node.

Mount propagation of a volume is controlled by the mountPropagation field in Container.volumeMounts. Its values are:

None - This volume mount will not receive any subsequent mounts that are mounted to this volume or any of its subdirectories by the host. In similar fashion, no mounts created by the container will be visible on the host. This is the default mode.

This mode is equal to private mount propagation as described in the Linux kernel documentation
HostToContainer - This volume mount will receive all subsequent mounts that are mounted to this volume or any of its subdirectories.

In other words, if the host mounts anything inside the volume mount, the container will see it mounted there.

Similarly, if any Pod with Bidirectional mount propagation to the same volume mounts anything there, the container with HostToContainer mount propagation will see it.

This mode is equal to rslave mount propagation as described in the Linux kernel documentation
Bidirectional - This volume mount behaves the same the HostToContainer mount. In addition, all volume mounts created by the container will be propagated back to the host and to all containers of all pods that use the same volume.

A typical use case for this mode is a Pod with a FlexVolume or CSI driver or a Pod that needs to mount something on the host using a hostPath volume.

This mode is equal to rshared mount propagation as described in the Linux kernel documentation

Warning: Bidirectional mount propagation can be dangerous. It can damage the host operating system and therefore it is allowed only in privileged containers. Familiarity with Linux kernel behavior is strongly recommended. In addition, any volume mounts created by containers in pods must be destroyed (unmounted) by the containers on termination.

Configuration

Before mount propagation can work properly on some deployments (CoreOS, RedHat/Centos, Ubuntu) mount share must be configured correctly in Docker as shown below.

Edit your Docker's systemd service file. Set MountFlags as follows:

MountFlags=shared

Or, remove MountFlags=slave if present. Then restart the Docker daemon:

sudo systemctl daemon-reload
sudo systemctl restart docker

What's next

Follow an example of deploying WordPress and MySQL with Persistent Volumes.

7.2 - Persistent Volumes

This document describes persistent volumes in Kubernetes. Familiarity with volumes is suggested.

Introduction

Managing storage is a distinct problem from managing compute instances. The PersistentVolume subsystem provides an API for users and administrators that abstracts details of how storage is provided from how it is consumed. To do this, we introduce two new API resources: PersistentVolume and PersistentVolumeClaim.

A PersistentVolume (PV) is a piece of storage in the cluster that has been provisioned by an administrator or dynamically provisioned using Storage Classes. It is a resource in the cluster just like a node is a cluster resource. PVs are volume plugins like Volumes, but have a lifecycle independent of any individual Pod that uses the PV. This API object captures the details of the implementation of the storage, be that NFS, iSCSI, or a cloud-provider-specific storage system.

A PersistentVolumeClaim (PVC) is a request for storage by a user. It is similar to a Pod. Pods consume node resources and PVCs consume PV resources. Pods can request specific levels of resources (CPU and Memory). Claims can request specific size and access modes (e.g., they can be mounted ReadWriteOnce, ReadOnlyMany or ReadWriteMany, see AccessModes).

While PersistentVolumeClaims allow a user to consume abstract storage resources, it is common that users need PersistentVolumes with varying properties, such as performance, for different problems. Cluster administrators need to be able to offer a variety of PersistentVolumes that differ in more ways than size and access modes, without exposing users to the details of how those volumes are implemented. For these needs, there is the StorageClass resource.

See the detailed walkthrough with working examples.

Lifecycle of a volume and claim

PVs are resources in the cluster. PVCs are requests for those resources and also act as claim checks to the resource. The interaction between PVs and PVCs follows this lifecycle:

Provisioning

There are two ways PVs may be provisioned: statically or dynamically.

Static

A cluster administrator creates a number of PVs. They carry the details of the real storage, which is available for use by cluster users. They exist in the Kubernetes API and are available for consumption.

Dynamic

When none of the static PVs the administrator created match a user's PersistentVolumeClaim, the cluster may try to dynamically provision a volume specially for the PVC. This provisioning is based on StorageClasses: the PVC must request a storage class and the administrator must have created and configured that class for dynamic provisioning to occur. Claims that request the class "" effectively disable dynamic provisioning for themselves.

To enable dynamic storage provisioning based on storage class, the cluster administrator needs to enable the DefaultStorageClass admission controller on the API server. This can be done, for example, by ensuring that DefaultStorageClass is among the comma-delimited, ordered list of values for the --enable-admission-plugins flag of the API server component. For more information on API server command-line flags, check kube-apiserver documentation.

Binding

A user creates, or in the case of dynamic provisioning, has already created, a PersistentVolumeClaim with a specific amount of storage requested and with certain access modes. A control loop in the master watches for new PVCs, finds a matching PV (if possible), and binds them together. If a PV was dynamically provisioned for a new PVC, the loop will always bind that PV to the PVC. Otherwise, the user will always get at least what they asked for, but the volume may be in excess of what was requested. Once bound, PersistentVolumeClaim binds are exclusive, regardless of how they were bound. A PVC to PV binding is a one-to-one mapping, using a ClaimRef which is a bi-directional binding between the PersistentVolume and the PersistentVolumeClaim.

Claims will remain unbound indefinitely if a matching volume does not exist. Claims will be bound as matching volumes become available. For example, a cluster provisioned with many 50Gi PVs would not match a PVC requesting 100Gi. The PVC can be bound when a 100Gi PV is added to the cluster.

Using

Pods use claims as volumes. The cluster inspects the claim to find the bound volume and mounts that volume for a Pod. For volumes that support multiple access modes, the user specifies which mode is desired when using their claim as a volume in a Pod.

Once a user has a claim and that claim is bound, the bound PV belongs to the user for as long as they need it. Users schedule Pods and access their claimed PVs by including a persistentVolumeClaim section in a Pod's volumes block. See Claims As Volumes for more details on this.

Storage Object in Use Protection

The purpose of the Storage Object in Use Protection feature is to ensure that PersistentVolumeClaims (PVCs) in active use by a Pod and PersistentVolume (PVs) that are bound to PVCs are not removed from the system, as this may result in data loss.

Note: PVC is in active use by a Pod when a Pod object exists that is using the PVC.

If a user deletes a PVC in active use by a Pod, the PVC is not removed immediately. PVC removal is postponed until the PVC is no longer actively used by any Pods. Also, if an admin deletes a PV that is bound to a PVC, the PV is not removed immediately. PV removal is postponed until the PV is no longer bound to a PVC.

You can see that a PVC is protected when the PVC's status is Terminating and the Finalizers list includes kubernetes.io/pvc-protection:

kubectl describe pvc hostpath
Name:          hostpath
Namespace:     default
StorageClass:  example-hostpath
Status:        Terminating
Volume:
Labels:        <none>
Annotations:   volume.beta.kubernetes.io/storage-class=example-hostpath
               volume.beta.kubernetes.io/storage-provisioner=example.com/hostpath
Finalizers:    [kubernetes.io/pvc-protection]
...

You can see that a PV is protected when the PV's status is Terminating and the Finalizers list includes kubernetes.io/pv-protection too:

kubectl describe pv task-pv-volume
Name:            task-pv-volume
Labels:          type=local
Annotations:     <none>
Finalizers:      [kubernetes.io/pv-protection]
StorageClass:    standard
Status:          Terminating
Claim:
Reclaim Policy:  Delete
Access Modes:    RWO
Capacity:        1Gi
Message:
Source:
    Type:          HostPath (bare host directory volume)
    Path:          /tmp/data
    HostPathType:
Events:            <none>

Reclaiming

When a user is done with their volume, they can delete the PVC objects from the API that allows reclamation of the resource. The reclaim policy for a PersistentVolume tells the cluster what to do with the volume after it has been released of its claim. Currently, volumes can either be Retained, Recycled, or Deleted.

Retain

The Retain reclaim policy allows for manual reclamation of the resource. When the PersistentVolumeClaim is deleted, the PersistentVolume still exists and the volume is considered "released". But it is not yet available for another claim because the previous claimant's data remains on the volume. An administrator can manually reclaim the volume with the following steps.

Delete the PersistentVolume. The associated storage asset in external infrastructure (such as an AWS EBS, GCE PD, Azure Disk, or Cinder volume) still exists after the PV is deleted.
Manually clean up the data on the associated storage asset accordingly.
Manually delete the associated storage asset.

If you want to reuse the same storage asset, create a new PersistentVolume with the same storage asset definition.

Delete

For volume plugins that support the Delete reclaim policy, deletion removes both the PersistentVolume object from Kubernetes, as well as the associated storage asset in the external infrastructure, such as an AWS EBS, GCE PD, Azure Disk, or Cinder volume. Volumes that were dynamically provisioned inherit the reclaim policy of their StorageClass, which defaults to Delete. The administrator should configure the StorageClass according to users' expectations; otherwise, the PV must be edited or patched after it is created. See Change the Reclaim Policy of a PersistentVolume.

Recycle

Warning: The Recycle reclaim policy is deprecated. Instead, the recommended approach is to use dynamic provisioning.

If supported by the underlying volume plugin, the Recycle reclaim policy performs a basic scrub (rm -rf /thevolume/*) on the volume and makes it available again for a new claim.

However, an administrator can configure a custom recycler Pod template using the Kubernetes controller manager command line arguments as described in the reference. The custom recycler Pod template must contain a volumes specification, as shown in the example below:

apiVersion: v1
kind: Pod
metadata:
  name: pv-recycler
  namespace: default
spec:
  restartPolicy: Never
  volumes:
  - name: vol
    hostPath:
      path: /any/path/it/will/be/replaced
  containers:
  - name: pv-recycler
    image: "registry.k8s.io/busybox"
    command: ["/bin/sh", "-c", "test -e /scrub && rm -rf /scrub/..?* /scrub/.[!.]* /scrub/*  && test -z \"$(ls -A /scrub)\" || exit 1"]
    volumeMounts:
    - name: vol
      mountPath: /scrub

However, the particular path specified in the custom recycler Pod template in the volumes part is replaced with the particular path of the volume that is being recycled.

PersistentVolume deletion protection finalizer

FEATURE STATE: Kubernetes v1.23 [alpha]

Finalizers can be added on a PersistentVolume to ensure that PersistentVolumes having Delete reclaim policy are deleted only after the backing storage are deleted.

The newly introduced finalizers kubernetes.io/pv-controller and external-provisioner.volume.kubernetes.io/finalizer are only added to dynamically provisioned volumes.

The finalizer kubernetes.io/pv-controller is added to in-tree plugin volumes. The following is an example

kubectl describe pv pvc-74a498d6-3929-47e8-8c02-078c1ece4d78
Name:            pvc-74a498d6-3929-47e8-8c02-078c1ece4d78
Labels:          <none>
Annotations:     kubernetes.io/createdby: vsphere-volume-dynamic-provisioner
                 pv.kubernetes.io/bound-by-controller: yes
                 pv.kubernetes.io/provisioned-by: kubernetes.io/vsphere-volume
Finalizers:      [kubernetes.io/pv-protection kubernetes.io/pv-controller]
StorageClass:    vcp-sc
Status:          Bound
Claim:           default/vcp-pvc-1
Reclaim Policy:  Delete
Access Modes:    RWO
VolumeMode:      Filesystem
Capacity:        1Gi
Node Affinity:   <none>
Message:         
Source:
    Type:               vSphereVolume (a Persistent Disk resource in vSphere)
    VolumePath:         [vsanDatastore] d49c4a62-166f-ce12-c464-020077ba5d46/kubernetes-dynamic-pvc-74a498d6-3929-47e8-8c02-078c1ece4d78.vmdk
    FSType:             ext4
    StoragePolicyName:  vSAN Default Storage Policy
Events:                 <none>

The finalizer external-provisioner.volume.kubernetes.io/finalizer is added for CSI volumes. The following is an example:

Name:            pvc-2f0bab97-85a8-4552-8044-eb8be45cf48d
Labels:          <none>
Annotations:     pv.kubernetes.io/provisioned-by: csi.vsphere.vmware.com
Finalizers:      [kubernetes.io/pv-protection external-provisioner.volume.kubernetes.io/finalizer]
StorageClass:    fast
Status:          Bound
Claim:           demo-app/nginx-logs
Reclaim Policy:  Delete
Access Modes:    RWO
VolumeMode:      Filesystem
Capacity:        200Mi
Node Affinity:   <none>
Message:         
Source:
    Type:              CSI (a Container Storage Interface (CSI) volume source)
    Driver:            csi.vsphere.vmware.com
    FSType:            ext4
    VolumeHandle:      44830fa8-79b4-406b-8b58-621ba25353fd
    ReadOnly:          false
    VolumeAttributes:      storage.kubernetes.io/csiProvisionerIdentity=1648442357185-8081-csi.vsphere.vmware.com
                           type=vSphere CNS Block Volume
Events:                <none>

When the CSIMigration{provider} feature flag is enabled for a specific in-tree volume plugin, the kubernetes.io/pv-controller finalizer is replaced by the external-provisioner.volume.kubernetes.io/finalizer finalizer.

Reserving a PersistentVolume

The control plane can bind PersistentVolumeClaims to matching PersistentVolumes in the cluster. However, if you want a PVC to bind to a specific PV, you need to pre-bind them.

By specifying a PersistentVolume in a PersistentVolumeClaim, you declare a binding between that specific PV and PVC. If the PersistentVolume exists and has not reserved PersistentVolumeClaims through its claimRef field, then the PersistentVolume and PersistentVolumeClaim will be bound.

The binding happens regardless of some volume matching criteria, including node affinity. The control plane still checks that storage class, access modes, and requested storage size are valid.

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: foo-pvc
  namespace: foo
spec:
  storageClassName: "" # Empty string must be explicitly set otherwise default StorageClass will be set
  volumeName: foo-pv
  ...

This method does not guarantee any binding privileges to the PersistentVolume. If other PersistentVolumeClaims could use the PV that you specify, you first need to reserve that storage volume. Specify the relevant PersistentVolumeClaim in the claimRef field of the PV so that other PVCs can not bind to it.

apiVersion: v1
kind: PersistentVolume
metadata:
  name: foo-pv
spec:
  storageClassName: ""
  claimRef:
    name: foo-pvc
    namespace: foo
  ...

This is useful if you want to consume PersistentVolumes that have their claimPolicy set to Retain, including cases where you are reusing an existing PV.

Expanding Persistent Volumes Claims

FEATURE STATE: Kubernetes v1.24 [stable]

Support for expanding PersistentVolumeClaims (PVCs) is enabled by default. You can expand the following types of volumes:

azureDisk
azureFile
awsElasticBlockStore
cinder (deprecated)
csi
flexVolume (deprecated)
gcePersistentDisk
glusterfs
rbd
portworxVolume

You can only expand a PVC if its storage class's allowVolumeExpansion field is set to true.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: gluster-vol-default
provisioner: kubernetes.io/glusterfs
parameters:
  resturl: "http://192.168.10.100:8080"
  restuser: ""
  secretNamespace: ""
  secretName: ""
allowVolumeExpansion: true

To request a larger volume for a PVC, edit the PVC object and specify a larger size. This triggers expansion of the volume that backs the underlying PersistentVolume. A new PersistentVolume is never created to satisfy the claim. Instead, an existing volume is resized.

Warning: Directly editing the size of a PersistentVolume can prevent an automatic resize of that volume. If you edit the capacity of a PersistentVolume, and then edit the .spec of a matching PersistentVolumeClaim to make the size of the PersistentVolumeClaim match the PersistentVolume, then no storage resize happens. The Kubernetes control plane will see that the desired state of both resources matches, conclude that the backing volume size has been manually increased and that no resize is necessary.

CSI Volume expansion

FEATURE STATE: Kubernetes v1.24 [stable]

Support for expanding CSI volumes is enabled by default but it also requires a specific CSI driver to support volume expansion. Refer to documentation of the specific CSI driver for more information.

Resizing a volume containing a file system

You can only resize volumes containing a file system if the file system is XFS, Ext3, or Ext4.

When a volume contains a file system, the file system is only resized when a new Pod is using the PersistentVolumeClaim in ReadWrite mode. File system expansion is either done when a Pod is starting up or when a Pod is running and the underlying file system supports online expansion.

FlexVolumes (deprecated since Kubernetes v1.23) allow resize if the driver is configured with the RequiresFSResize capability to true. The FlexVolume can be resized on Pod restart.

Resizing an in-use PersistentVolumeClaim

FEATURE STATE: Kubernetes v1.24 [stable]

In this case, you don't need to delete and recreate a Pod or deployment that is using an existing PVC. Any in-use PVC automatically becomes available to its Pod as soon as its file system has been expanded. This feature has no effect on PVCs that are not in use by a Pod or deployment. You must create a Pod that uses the PVC before the expansion can complete.

Similar to other volume types - FlexVolume volumes can also be expanded when in-use by a Pod.

Note: FlexVolume resize is possible only when the underlying driver supports resize.

Note: Expanding EBS volumes is a time-consuming operation. Also, there is a per-volume quota of one modification every 6 hours.

Recovering from Failure when Expanding Volumes

If a user specifies a new size that is too big to be satisfied by underlying storage system, expansion of PVC will be continuously retried until user or cluster administrator takes some action. This can be undesirable and hence Kubernetes provides following methods of recovering from such failures.

Manually with Cluster Administrator access
By requesting expansion to smaller size

If expanding underlying storage fails, the cluster administrator can manually recover the Persistent Volume Claim (PVC) state and cancel the resize requests. Otherwise, the resize requests are continuously retried by the controller without administrator intervention.

Mark the PersistentVolume(PV) that is bound to the PersistentVolumeClaim(PVC) with Retain reclaim policy.
Delete the PVC. Since PV has Retain reclaim policy - we will not lose any data when we recreate the PVC.
Delete the claimRef entry from PV specs, so as new PVC can bind to it. This should make the PV Available.
Re-create the PVC with smaller size than PV and set volumeName field of the PVC to the name of the PV. This should bind new PVC to existing PV.
Don't forget to restore the reclaim policy of the PV.

FEATURE STATE: Kubernetes v1.23 [alpha]

Note: Recovery from failing PVC expansion by users is available as an alpha feature since Kubernetes 1.23. The RecoverVolumeExpansionFailure feature must be enabled for this feature to work. Refer to the feature gate documentation for more information.

If the feature gates RecoverVolumeExpansionFailure is enabled in your cluster, and expansion has failed for a PVC, you can retry expansion with a smaller size than the previously requested value. To request a new expansion attempt with a smaller proposed size, edit .spec.resources for that PVC and choose a value that is less than the value you previously tried. This is useful if expansion to a higher value did not succeed because of capacity constraint. If that has happened, or you suspect that it might have, you can retry expansion by specifying a size that is within the capacity limits of underlying storage provider. You can monitor status of resize operation by watching .status.resizeStatus and events on the PVC.

Note that, although you can specify a lower amount of storage than what was requested previously, the new value must still be higher than .status.capacity. Kubernetes does not support shrinking a PVC to less than its current size.

Types of Persistent Volumes

PersistentVolume types are implemented as plugins. Kubernetes currently supports the following plugins:

cephfs - CephFS volume
csi - Container Storage Interface (CSI)
fc - Fibre Channel (FC) storage
hostPath - HostPath volume (for single node testing only; WILL NOT WORK in a multi-node cluster; consider using local volume instead)
iscsi - iSCSI (SCSI over IP) storage
local - local storage devices mounted on nodes.
nfs - Network File System (NFS) storage
rbd - Rados Block Device (RBD) volume

The following types of PersistentVolume are deprecated. This means that support is still available but will be removed in a future Kubernetes release.

awsElasticBlockStore - AWS Elastic Block Store (EBS) (deprecated in v1.17)
azureDisk - Azure Disk (deprecated in v1.19)
azureFile - Azure File (deprecated in v1.21)
cinder - Cinder (OpenStack block storage) (deprecated in v1.18)
flexVolume - FlexVolume (deprecated in v1.23)
gcePersistentDisk - GCE Persistent Disk (deprecated in v1.17)
glusterfs - Glusterfs volume (deprecated in v1.25)
portworxVolume - Portworx volume (deprecated in v1.25)
vsphereVolume - vSphere VMDK volume (deprecated in v1.19)

Older versions of Kubernetes also supported the following in-tree PersistentVolume types:

photonPersistentDisk - Photon controller persistent disk. (not available starting v1.15)
scaleIO - ScaleIO volume (not available starting v1.21)
flocker - Flocker storage (not available starting v1.25)
quobyte - Quobyte volume (not available starting v1.25)
storageos - StorageOS volume (not available starting v1.25)

Persistent Volumes

Each PV contains a spec and status, which is the specification and status of the volume. The name of a PersistentVolume object must be a valid DNS subdomain name.

apiVersion: v1
kind: PersistentVolume
metadata:
  name: pv0003
spec:
  capacity:
    storage: 5Gi
  volumeMode: Filesystem
  accessModes:
    - ReadWriteOnce
  persistentVolumeReclaimPolicy: Recycle
  storageClassName: slow
  mountOptions:
    - hard
    - nfsvers=4.1
  nfs:
    path: /tmp
    server: 172.17.0.2

Note: Helper programs relating to the volume type may be required for consumption of a PersistentVolume within a cluster. In this example, the PersistentVolume is of type NFS and the helper program /sbin/mount.nfs is required to support the mounting of NFS filesystems.

Capacity

Generally, a PV will have a specific storage capacity. This is set using the PV's capacity attribute. Read the glossary term Quantity to understand the units expected by capacity.

Currently, storage size is the only resource that can be set or requested. Future attributes may include IOPS, throughput, etc.

Volume Mode

FEATURE STATE: Kubernetes v1.18 [stable]

Kubernetes supports two volumeModes of PersistentVolumes: Filesystem and Block.

volumeMode is an optional API parameter. Filesystem is the default mode used when volumeMode parameter is omitted.

A volume with volumeMode: Filesystem is mounted into Pods into a directory. If the volume is backed by a block device and the device is empty, Kubernetes creates a filesystem on the device before mounting it for the first time.

You can set the value of volumeMode to Block to use a volume as a raw block device. Such volume is presented into a Pod as a block device, without any filesystem on it. This mode is useful to provide a Pod the fastest possible way to access a volume, without any filesystem layer between the Pod and the volume. On the other hand, the application running in the Pod must know how to handle a raw block device. See Raw Block Volume Support for an example on how to use a volume with volumeMode: Block in a Pod.

Access Modes

A PersistentVolume can be mounted on a host in any way supported by the resource provider. As shown in the table below, providers will have different capabilities and each PV's access modes are set to the specific modes supported by that particular volume. For example, NFS can support multiple read/write clients, but a specific NFS PV might be exported on the server as read-only. Each PV gets its own set of access modes describing that specific PV's capabilities.

The access modes are:

ReadWriteOnce: the volume can be mounted as read-write by a single node. ReadWriteOnce access mode still can allow multiple pods to access the volume when the pods are running on the same node.
ReadOnlyMany: the volume can be mounted as read-only by many nodes.
ReadWriteMany: the volume can be mounted as read-write by many nodes.
ReadWriteOncePod: the volume can be mounted as read-write by a single Pod. Use ReadWriteOncePod access mode if you want to ensure that only one pod across whole cluster can read that PVC or write to it. This is only supported for CSI volumes and Kubernetes version 1.22+.

The blog article Introducing Single Pod Access Mode for PersistentVolumes covers this in more detail.

In the CLI, the access modes are abbreviated to:

RWO - ReadWriteOnce
ROX - ReadOnlyMany
RWX - ReadWriteMany
RWOP - ReadWriteOncePod

Note: Kubernetes uses volume access modes to match PersistentVolumeClaims and PersistentVolumes. In some cases, the volume access modes also constrain where the PersistentVolume can be mounted. Volume access modes do not enforce write protection once the storage has been mounted. Even if the access modes are specified as ReadWriteOnce, ReadOnlyMany, or ReadWriteMany, they don't set any constraints on the volume. For example, even if a PersistentVolume is created as ReadOnlyMany, it is no guarantee that it will be read-only. If the access modes are specified as ReadWriteOncePod, the volume is constrained and can be mounted on only a single Pod.

Important! A volume can only be mounted using one access mode at a time, even if it supports many. For example, a GCEPersistentDisk can be mounted as ReadWriteOnce by a single node or ReadOnlyMany by many nodes, but not at the same time.

Volume Plugin	ReadWriteOnce	ReadOnlyMany	ReadWriteMany	ReadWriteOncePod
AWSElasticBlockStore	✓	-	-	-
AzureFile	✓	✓	✓	-
AzureDisk	✓	-	-	-
CephFS	✓	✓	✓	-
Cinder	✓	-	(if multi-attach volumes are available)	-
CSI	depends on the driver	depends on the driver	depends on the driver	depends on the driver
FC	✓	✓	-	-
FlexVolume	✓	✓	depends on the driver	-
GCEPersistentDisk	✓	✓	-	-
Glusterfs	✓	✓	✓	-
HostPath	✓	-	-	-
iSCSI	✓	✓	-	-
NFS	✓	✓	✓	-
RBD	✓	✓	-	-
VsphereVolume	✓	-	- (works when Pods are collocated)	-
PortworxVolume	✓	-	✓	-

Class

A PV can have a class, which is specified by setting the storageClassName attribute to the name of a StorageClass. A PV of a particular class can only be bound to PVCs requesting that class. A PV with no storageClassName has no class and can only be bound to PVCs that request no particular class.

In the past, the annotation volume.beta.kubernetes.io/storage-class was used instead of the storageClassName attribute. This annotation is still working; however, it will become fully deprecated in a future Kubernetes release.

Reclaim Policy

Current reclaim policies are:

Retain -- manual reclamation
Recycle -- basic scrub (rm -rf /thevolume/*)
Delete -- associated storage asset such as AWS EBS, GCE PD, Azure Disk, or OpenStack Cinder volume is deleted

Currently, only NFS and HostPath support recycling. AWS EBS, GCE PD, Azure Disk, and Cinder volumes support deletion.

Mount Options

A Kubernetes administrator can specify additional mount options for when a Persistent Volume is mounted on a node.

Note: Not all Persistent Volume types support mount options.

The following volume types support mount options:

awsElasticBlockStore
azureDisk
azureFile
cephfs
cinder (deprecated in v1.18)
gcePersistentDisk
glusterfs
iscsi
nfs
rbd
vsphereVolume

Mount options are not validated. If a mount option is invalid, the mount fails.

In the past, the annotation volume.beta.kubernetes.io/mount-options was used instead of the mountOptions attribute. This annotation is still working; however, it will become fully deprecated in a future Kubernetes release.

Node Affinity

Note: For most volume types, you do not need to set this field. It is automatically populated for AWS EBS, GCE PD and Azure Disk volume block types. You need to explicitly set this for local volumes.

A PV can specify node affinity to define constraints that limit what nodes this volume can be accessed from. Pods that use a PV will only be scheduled to nodes that are selected by the node affinity. To specify node affinity, set nodeAffinity in the .spec of a PV. The PersistentVolume API reference has more details on this field.

Phase

A volume will be in one of the following phases:

Available -- a free resource that is not yet bound to a claim
Bound -- the volume is bound to a claim
Released -- the claim has been deleted, but the resource is not yet reclaimed by the cluster
Failed -- the volume has failed its automatic reclamation

The CLI will show the name of the PVC bound to the PV.

PersistentVolumeClaims

Each PVC contains a spec and status, which is the specification and status of the claim. The name of a PersistentVolumeClaim object must be a valid DNS subdomain name.

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: myclaim
spec:
  accessModes:
    - ReadWriteOnce
  volumeMode: Filesystem
  resources:
    requests:
      storage: 8Gi
  storageClassName: slow
  selector:
    matchLabels:
      release: "stable"
    matchExpressions:
      - {key: environment, operator: In, values: [dev]}

Access Modes

Claims use the same conventions as volumes when requesting storage with specific access modes.

Volume Modes

Claims use the same convention as volumes to indicate the consumption of the volume as either a filesystem or block device.

Resources

Claims, like Pods, can request specific quantities of a resource. In this case, the request is for storage. The same resource model applies to both volumes and claims.

Selector

Claims can specify a label selector to further filter the set of volumes. Only the volumes whose labels match the selector can be bound to the claim. The selector can consist of two fields:

matchLabels - the volume must have a label with this value
matchExpressions - a list of requirements made by specifying key, list of values, and operator that relates the key and values. Valid operators include In, NotIn, Exists, and DoesNotExist.

All of the requirements, from both matchLabels and matchExpressions, are ANDed together – they must all be satisfied in order to match.

Class

A claim can request a particular class by specifying the name of a StorageClass using the attribute storageClassName. Only PVs of the requested class, ones with the same storageClassName as the PVC, can be bound to the PVC.

PVCs don't necessarily have to request a class. A PVC with its storageClassName set equal to "" is always interpreted to be requesting a PV with no class, so it can only be bound to PVs with no class (no annotation or one set equal to ""). A PVC with no storageClassName is not quite the same and is treated differently by the cluster, depending on whether the DefaultStorageClass admission plugin is turned on.

If the admission plugin is turned on, the administrator may specify a default StorageClass. All PVCs that have no storageClassName can be bound only to PVs of that default. Specifying a default StorageClass is done by setting the annotation storageclass.kubernetes.io/is-default-class equal to true in a StorageClass object. If the administrator does not specify a default, the cluster responds to PVC creation as if the admission plugin were turned off. If more than one default is specified, the admission plugin forbids the creation of all PVCs.
If the admission plugin is turned off, there is no notion of a default StorageClass. All PVCs that have storageClassName set to "" can be bound only to PVs that have storageClassName also set to "". However, PVCs with missing storageClassName can be updated later once default StorageClass becomes available. If the PVC gets updated it will no longer bind to PVs that have storageClassName also set to "".

See retroactive default StorageClass assignment for more details.

Depending on installation method, a default StorageClass may be deployed to a Kubernetes cluster by addon manager during installation.

When a PVC specifies a selector in addition to requesting a StorageClass, the requirements are ANDed together: only a PV of the requested class and with the requested labels may be bound to the PVC.

Note: Currently, a PVC with a non-empty selector can't have a PV dynamically provisioned for it.

In the past, the annotation volume.beta.kubernetes.io/storage-class was used instead of storageClassName attribute. This annotation is still working; however, it won't be supported in a future Kubernetes release.

Retroactive default StorageClass assignment

FEATURE STATE: Kubernetes v1.25 [alpha]

You can create a PersistentVolumeClaim without specifying a storageClassName for the new PVC, and you can do so even when no default StorageClass exists in your cluster. In this case, the new PVC creates as you defined it, and the storageClassName of that PVC remains unset until default becomes available. However, if you enable the RetroactiveDefaultStorageClass feature gate then Kubernetes behaves differently: existing PVCs without storageClassName update to use the new default StorageClass.

When a default StorageClass becomes available, the control plane identifies any existing PVCs without storageClassName. For the PVCs that either have an empty value for storageClassName or do not have this key, the control plane then updates those PVCs to set storageClassName to match the new default StorageClass. If you have an existing PVC where the storageClassName is "", and you configure a default StorageClass, then this PVC will not get updated.

In order to keep binding to PVs with storageClassName set to "" (while a default StorageClass is present), you need to set the storageClassName of the associated PVC to "".

This behavior helps administrators change default StorageClass by removing the old one first and then creating or setting another one. This brief window while there is no default causes PVCs without storageClassName created at that time to not have any default, but due to the retroactive default StorageClass assignment this way of changing defaults is safe.

Claims As Volumes

Pods access storage by using the claim as a volume. Claims must exist in the same namespace as the Pod using the claim. The cluster finds the claim in the Pod's namespace and uses it to get the PersistentVolume backing the claim. The volume is then mounted to the host and into the Pod.

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
    - name: myfrontend
      image: nginx
      volumeMounts:
      - mountPath: "/var/www/html"
        name: mypd
  volumes:
    - name: mypd
      persistentVolumeClaim:
        claimName: myclaim

A Note on Namespaces

PersistentVolumes binds are exclusive, and since PersistentVolumeClaims are namespaced objects, mounting claims with "Many" modes (ROX, RWX) is only possible within one namespace.

PersistentVolumes typed `hostPath`

A hostPath PersistentVolume uses a file or directory on the Node to emulate network-attached storage. See an example of hostPath typed volume.

Raw Block Volume Support

FEATURE STATE: Kubernetes v1.18 [stable]

The following volume plugins support raw block volumes, including dynamic provisioning where applicable:

AWSElasticBlockStore
AzureDisk
CSI
FC (Fibre Channel)
GCEPersistentDisk
iSCSI
Local volume
OpenStack Cinder
RBD (Ceph Block Device)
VsphereVolume

PersistentVolume using a Raw Block Volume

apiVersion: v1
kind: PersistentVolume
metadata:
  name: block-pv
spec:
  capacity:
    storage: 10Gi
  accessModes:
    - ReadWriteOnce
  volumeMode: Block
  persistentVolumeReclaimPolicy: Retain
  fc:
    targetWWNs: ["50060e801049cfd1"]
    lun: 0
    readOnly: false

PersistentVolumeClaim requesting a Raw Block Volume

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: block-pvc
spec:
  accessModes:
    - ReadWriteOnce
  volumeMode: Block
  resources:
    requests:
      storage: 10Gi

Pod specification adding Raw Block Device path in container

apiVersion: v1
kind: Pod
metadata:
  name: pod-with-block-volume
spec:
  containers:
    - name: fc-container
      image: fedora:26
      command: ["/bin/sh", "-c"]
      args: [ "tail -f /dev/null" ]
      volumeDevices:
        - name: data
          devicePath: /dev/xvda
  volumes:
    - name: data
      persistentVolumeClaim:
        claimName: block-pvc

Note: When adding a raw block device for a Pod, you specify the device path in the container instead of a mount path.

Binding Block Volumes

If a user requests a raw block volume by indicating this using the volumeMode field in the PersistentVolumeClaim spec, the binding rules differ slightly from previous releases that didn't consider this mode as part of the spec. Listed is a table of possible combinations the user and admin might specify for requesting a raw block device. The table indicates if the volume will be bound or not given the combinations: Volume binding matrix for statically provisioned volumes:

PV volumeMode	PVC volumeMode	Result
unspecified	unspecified	BIND
unspecified	Block	NO BIND
unspecified	Filesystem	BIND
Block	unspecified	NO BIND
Block	Block	BIND
Block	Filesystem	NO BIND
Filesystem	Filesystem	BIND
Filesystem	Block	NO BIND
Filesystem	unspecified	BIND

Note: Only statically provisioned volumes are supported for alpha release. Administrators should take care to consider these values when working with raw block devices.

Volume Snapshot and Restore Volume from Snapshot Support

FEATURE STATE: Kubernetes v1.20 [stable]

Volume snapshots only support the out-of-tree CSI volume plugins. For details, see Volume Snapshots. In-tree volume plugins are deprecated. You can read about the deprecated volume plugins in the Volume Plugin FAQ.

Create a PersistentVolumeClaim from a Volume Snapshot

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: restore-pvc
spec:
  storageClassName: csi-hostpath-sc
  dataSource:
    name: new-snapshot-test
    kind: VolumeSnapshot
    apiGroup: snapshot.storage.k8s.io
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

Volume Cloning

Volume Cloning only available for CSI volume plugins.

Create PersistentVolumeClaim from an existing PVC

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: cloned-pvc
spec:
  storageClassName: my-csi-plugin
  dataSource:
    name: existing-src-pvc-name
    kind: PersistentVolumeClaim
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

Volume populators and data sources

FEATURE STATE: Kubernetes v1.24 [beta]

Kubernetes supports custom volume populators. To use custom volume populators, you must enable the AnyVolumeDataSource feature gate for the kube-apiserver and kube-controller-manager.

Volume populators take advantage of a PVC spec field called dataSourceRef. Unlike the dataSource field, which can only contain either a reference to another PersistentVolumeClaim or to a VolumeSnapshot, the dataSourceRef field can contain a reference to any object in the same namespace, except for core objects other than PVCs. For clusters that have the feature gate enabled, use of the dataSourceRef is preferred over dataSource.

Data source references

The dataSourceRef field behaves almost the same as the dataSource field. If either one is specified while the other is not, the API server will give both fields the same value. Neither field can be changed after creation, and attempting to specify different values for the two fields will result in a validation error. Therefore the two fields will always have the same contents.

There are two differences between the dataSourceRef field and the dataSource field that users should be aware of:

The dataSource field ignores invalid values (as if the field was blank) while the dataSourceRef field never ignores values and will cause an error if an invalid value is used. Invalid values are any core object (objects with no apiGroup) except for PVCs.
The dataSourceRef field may contain different types of objects, while the dataSource field only allows PVCs and VolumeSnapshots.

Users should always use dataSourceRef on clusters that have the feature gate enabled, and fall back to dataSource on clusters that do not. It is not necessary to look at both fields under any circumstance. The duplicated values with slightly different semantics exist only for backwards compatibility. In particular, a mixture of older and newer controllers are able to interoperate because the fields are the same.

Using volume populators

Volume populators are controllers that can create non-empty volumes, where the contents of the volume are determined by a Custom Resource. Users create a populated volume by referring to a Custom Resource using the dataSourceRef field:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: populated-pvc
spec:
  dataSourceRef:
    name: example-name
    kind: ExampleDataSource
    apiGroup: example.storage.k8s.io
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

Because volume populators are external components, attempts to create a PVC that uses one can fail if not all the correct components are installed. External controllers should generate events on the PVC to provide feedback on the status of the creation, including warnings if the PVC cannot be created due to some missing component.

You can install the alpha volume data source validator controller into your cluster. That controller generates warning Events on a PVC in the case that no populator is registered to handle that kind of data source. When a suitable populator is installed for a PVC, it's the responsibility of that populator controller to report Events that relate to volume creation and issues during the process.

Writing Portable Configuration

If you're writing configuration templates or examples that run on a wide range of clusters and need persistent storage, it is recommended that you use the following pattern:

Include PersistentVolumeClaim objects in your bundle of config (alongside Deployments, ConfigMaps, etc).
Do not include PersistentVolume objects in the config, since the user instantiating the config may not have permission to create PersistentVolumes.
Give the user the option of providing a storage class name when instantiating the template.
- If the user provides a storage class name, put that value into the persistentVolumeClaim.storageClassName field. This will cause the PVC to match the right storage class if the cluster has StorageClasses enabled by the admin.
- If the user does not provide a storage class name, leave the persistentVolumeClaim.storageClassName field as nil. This will cause a PV to be automatically provisioned for the user with the default StorageClass in the cluster. Many cluster environments have a default StorageClass installed, or administrators can create their own default StorageClass.
In your tooling, watch for PVCs that are not getting bound after some time and surface this to the user, as this may indicate that the cluster has no dynamic storage support (in which case the user should create a matching PV) or the cluster has no storage system (in which case the user cannot deploy config requiring PVCs).

What's next

Learn more about Creating a PersistentVolume.
Learn more about Creating a PersistentVolumeClaim.
Read the Persistent Storage design document.

API references

Read about the APIs described in this page:

7.3 - Projected Volumes

This document describes projected volumes in Kubernetes. Familiarity with volumes is suggested.

Introduction

A projected volume maps several existing volume sources into the same directory.

Currently, the following types of volume sources can be projected:

All sources are required to be in the same namespace as the Pod. For more details, see the all-in-one volume design document.

Example configuration with a secret, a downwardAPI, and a configMap

pods/storage/projected-secret-downwardapi-configmap.yaml

apiVersion: v1
kind: Pod
metadata:
  name: volume-test
spec:
  containers:
  - name: container-test
    image: busybox:1.28
    volumeMounts:
    - name: all-in-one
      mountPath: "/projected-volume"
      readOnly: true
  volumes:
  - name: all-in-one
    projected:
      sources:
      - secret:
          name: mysecret
          items:
            - key: username
              path: my-group/my-username
      - downwardAPI:
          items:
            - path: "labels"
              fieldRef:
                fieldPath: metadata.labels
            - path: "cpu_limit"
              resourceFieldRef:
                containerName: container-test
                resource: limits.cpu
      - configMap:
          name: myconfigmap
          items:
            - key: config
              path: my-group/my-config

Example configuration: secrets with a non-default permission mode set

pods/storage/projected-secrets-nondefault-permission-mode.yaml

apiVersion: v1
kind: Pod
metadata:
  name: volume-test
spec:
  containers:
  - name: container-test
    image: busybox:1.28
    volumeMounts:
    - name: all-in-one
      mountPath: "/projected-volume"
      readOnly: true
  volumes:
  - name: all-in-one
    projected:
      sources:
      - secret:
          name: mysecret
          items:
            - key: username
              path: my-group/my-username
      - secret:
          name: mysecret2
          items:
            - key: password
              path: my-group/my-password
              mode: 511

Each projected volume source is listed in the spec under sources. The parameters are nearly the same with two exceptions:

For secrets, the secretName field has been changed to name to be consistent with ConfigMap naming.
The defaultMode can only be specified at the projected level and not for each volume source. However, as illustrated above, you can explicitly set the mode for each individual projection.

serviceAccountToken projected volumes

When the TokenRequestProjection feature is enabled, you can inject the token for the current service account into a Pod at a specified path. For example:

pods/storage/projected-service-account-token.yaml

apiVersion: v1
kind: Pod
metadata:
  name: sa-token-test
spec:
  containers:
  - name: container-test
    image: busybox:1.28
    volumeMounts:
    - name: token-vol
      mountPath: "/service-account"
      readOnly: true
  serviceAccountName: default
  volumes:
  - name: token-vol
    projected:
      sources:
      - serviceAccountToken:
          audience: api
          expirationSeconds: 3600
          path: token

The example Pod has a projected volume containing the injected service account token. Containers in this Pod can use that token to access the Kubernetes API server, authenticating with the identity of the pod's ServiceAccount. The audience field contains the intended audience of the token. A recipient of the token must identify itself with an identifier specified in the audience of the token, and otherwise should reject the token. This field is optional and it defaults to the identifier of the API server.

The expirationSeconds is the expected duration of validity of the service account token. It defaults to 1 hour and must be at least 10 minutes (600 seconds). An administrator can also limit its maximum value by specifying the --service-account-max-token-expiration option for the API server. The path field specifies a relative path to the mount point of the projected volume.

Note: A container using a projected volume source as a subPath volume mount will not receive updates for those volume sources.

SecurityContext interactions

The proposal for file permission handling in projected service account volume enhancement introduced the projected files having the correct owner permissions set.

Linux

In Linux pods that have a projected volume and RunAsUser set in the Pod SecurityContext, the projected files have the correct ownership set including container user ownership.

Windows

In Windows pods that have a projected volume and RunAsUsername set in the Pod SecurityContext, the ownership is not enforced due to the way user accounts are managed in Windows. Windows stores and manages local user and group accounts in a database file called Security Account Manager (SAM). Each container maintains its own instance of the SAM database, to which the host has no visibility into while the container is running. Windows containers are designed to run the user mode portion of the OS in isolation from the host, hence the maintenance of a virtual SAM database. As a result, the kubelet running on the host does not have the ability to dynamically configure host file ownership for virtualized container accounts. It is recommended that if files on the host machine are to be shared with the container then they should be placed into their own volume mount outside of C:\.

By default, the projected files will have the following ownership as shown for an example projected volume file:

PS C:\> Get-Acl C:\var\run\secrets\kubernetes.io\serviceaccount\..2021_08_31_22_22_18.318230061\ca.crt | Format-List

Path   : Microsoft.PowerShell.Core\FileSystem::C:\var\run\secrets\kubernetes.io\serviceaccount\..2021_08_31_22_22_18.318230061\ca.crt
Owner  : BUILTIN\Administrators
Group  : NT AUTHORITY\SYSTEM
Access : NT AUTHORITY\SYSTEM Allow  FullControl
         BUILTIN\Administrators Allow  FullControl
         BUILTIN\Users Allow  ReadAndExecute, Synchronize
Audit  :
Sddl   : O:BAG:SYD:AI(A;ID;FA;;;SY)(A;ID;FA;;;BA)(A;ID;0x1200a9;;;BU)

This implies all administrator users like ContainerAdministrator will have read, write and execute access while, non-administrator users will have read and execute access.

Note:

In general, granting the container access to the host is discouraged as it can open the door for potential security exploits.

Creating a Windows Pod with RunAsUser in it's SecurityContext will result in the Pod being stuck at ContainerCreating forever. So it is advised to not use the Linux only RunAsUser option with Windows Pods.

7.4 - Ephemeral Volumes

This document describes ephemeral volumes in Kubernetes. Familiarity with volumes is suggested, in particular PersistentVolumeClaim and PersistentVolume.

Some application need additional storage but don't care whether that data is stored persistently across restarts. For example, caching services are often limited by memory size and can move infrequently used data into storage that is slower than memory with little impact on overall performance.

Other applications expect some read-only input data to be present in files, like configuration data or secret keys.

Ephemeral volumes are designed for these use cases. Because volumes follow the Pod's lifetime and get created and deleted along with the Pod, Pods can be stopped and restarted without being limited to where some persistent volume is available.

Ephemeral volumes are specified inline in the Pod spec, which simplifies application deployment and management.

Types of ephemeral volumes

Kubernetes supports several different kinds of ephemeral volumes for different purposes:

emptyDir: empty at Pod startup, with storage coming locally from the kubelet base directory (usually the root disk) or RAM
configMap, downwardAPI, secret: inject different kinds of Kubernetes data into a Pod
CSI ephemeral volumes: similar to the previous volume kinds, but provided by special CSI drivers which specifically support this feature
generic ephemeral volumes, which can be provided by all storage drivers that also support persistent volumes

emptyDir, configMap, downwardAPI, secret are provided as local ephemeral storage. They are managed by kubelet on each node.

CSI ephemeral volumes must be provided by third-party CSI storage drivers.

Generic ephemeral volumes can be provided by third-party CSI storage drivers, but also by any other storage driver that supports dynamic provisioning. Some CSI drivers are written specifically for CSI ephemeral volumes and do not support dynamic provisioning: those then cannot be used for generic ephemeral volumes.

The advantage of using third-party drivers is that they can offer functionality that Kubernetes itself does not support, for example storage with different performance characteristics than the disk that is managed by kubelet, or injecting different data.

CSI ephemeral volumes

FEATURE STATE: Kubernetes v1.25 [stable]

Note: CSI ephemeral volumes are only supported by a subset of CSI drivers. The Kubernetes CSI Drivers list shows which drivers support ephemeral volumes.

Conceptually, CSI ephemeral volumes are similar to configMap, downwardAPI and secret volume types: the storage is managed locally on each node and is created together with other local resources after a Pod has been scheduled onto a node. Kubernetes has no concept of rescheduling Pods anymore at this stage. Volume creation has to be unlikely to fail, otherwise Pod startup gets stuck. In particular, storage capacity aware Pod scheduling is not supported for these volumes. They are currently also not covered by the storage resource usage limits of a Pod, because that is something that kubelet can only enforce for storage that it manages itself.

Here's an example manifest for a Pod that uses CSI ephemeral storage:

kind: Pod
apiVersion: v1
metadata:
  name: my-csi-app
spec:
  containers:
    - name: my-frontend
      image: busybox:1.28
      volumeMounts:
      - mountPath: "/data"
        name: my-csi-inline-vol
      command: [ "sleep", "1000000" ]
  volumes:
    - name: my-csi-inline-vol
      csi:
        driver: inline.storage.kubernetes.io
        volumeAttributes:
          foo: bar

The volumeAttributes determine what volume is prepared by the driver. These attributes are specific to each driver and not standardized. See the documentation of each CSI driver for further instructions.

CSI driver restrictions

CSI ephemeral volumes allow users to provide volumeAttributes directly to the CSI driver as part of the Pod spec. A CSI driver allowing volumeAttributes that are typically restricted to administrators is NOT suitable for use in an inline ephemeral volume. For example, parameters that are normally defined in the StorageClass should not be exposed to users through the use of inline ephemeral volumes.

Cluster administrators who need to restrict the CSI drivers that are allowed to be used as inline volumes within a Pod spec may do so by:

Removing Ephemeral from volumeLifecycleModes in the CSIDriver spec, which prevents the driver from being used as an inline ephemeral volume.
Using an admission webhook to restrict how this driver is used.

Generic ephemeral volumes

FEATURE STATE: Kubernetes v1.23 [stable]

Generic ephemeral volumes are similar to emptyDir volumes in the sense that they provide a per-pod directory for scratch data that is usually empty after provisioning. But they may also have additional features:

Storage can be local or network-attached.
Volumes can have a fixed size that Pods are not able to exceed.
Volumes may have some initial data, depending on the driver and parameters.
Typical operations on volumes are supported assuming that the driver supports them, including snapshotting, cloning, resizing, and storage capacity tracking.

Example:

kind: Pod
apiVersion: v1
metadata:
  name: my-app
spec:
  containers:
    - name: my-frontend
      image: busybox:1.28
      volumeMounts:
      - mountPath: "/scratch"
        name: scratch-volume
      command: [ "sleep", "1000000" ]
  volumes:
    - name: scratch-volume
      ephemeral:
        volumeClaimTemplate:
          metadata:
            labels:
              type: my-frontend-volume
          spec:
            accessModes: [ "ReadWriteOnce" ]
            storageClassName: "scratch-storage-class"
            resources:
              requests:
                storage: 1Gi

Lifecycle and PersistentVolumeClaim

The key design idea is that the parameters for a volume claim are allowed inside a volume source of the Pod. Labels, annotations and the whole set of fields for a PersistentVolumeClaim are supported. When such a Pod gets created, the ephemeral volume controller then creates an actual PersistentVolumeClaim object in the same namespace as the Pod and ensures that the PersistentVolumeClaim gets deleted when the Pod gets deleted.

That triggers volume binding and/or provisioning, either immediately if the StorageClass uses immediate volume binding or when the Pod is tentatively scheduled onto a node (WaitForFirstConsumer volume binding mode). The latter is recommended for generic ephemeral volumes because then the scheduler is free to choose a suitable node for the Pod. With immediate binding, the scheduler is forced to select a node that has access to the volume once it is available.

In terms of resource ownership, a Pod that has generic ephemeral storage is the owner of the PersistentVolumeClaim(s) that provide that ephemeral storage. When the Pod is deleted, the Kubernetes garbage collector deletes the PVC, which then usually triggers deletion of the volume because the default reclaim policy of storage classes is to delete volumes. You can create quasi-ephemeral local storage using a StorageClass with a reclaim policy of retain: the storage outlives the Pod, and in this case you need to ensure that volume clean up happens separately.

While these PVCs exist, they can be used like any other PVC. In particular, they can be referenced as data source in volume cloning or snapshotting. The PVC object also holds the current status of the volume.

PersistentVolumeClaim naming

Naming of the automatically created PVCs is deterministic: the name is a combination of Pod name and volume name, with a hyphen (-) in the middle. In the example above, the PVC name will be my-app-scratch-volume. This deterministic naming makes it easier to interact with the PVC because one does not have to search for it once the Pod name and volume name are known.

The deterministic naming also introduces a potential conflict between different Pods (a Pod "pod-a" with volume "scratch" and another Pod with name "pod" and volume "a-scratch" both end up with the same PVC name "pod-a-scratch") and between Pods and manually created PVCs.

Such conflicts are detected: a PVC is only used for an ephemeral volume if it was created for the Pod. This check is based on the ownership relationship. An existing PVC is not overwritten or modified. But this does not resolve the conflict because without the right PVC, the Pod cannot start.

Caution: Take care when naming Pods and volumes inside the same namespace, so that these conflicts can't occur.

Security

Enabling the GenericEphemeralVolume feature allows users to create PVCs indirectly if they can create Pods, even if they do not have permission to create PVCs directly. Cluster administrators must be aware of this. If this does not fit their security model, they should use an admission webhook that rejects objects like Pods that have a generic ephemeral volume.

The normal namespace quota for PVCs still applies, so even if users are allowed to use this new mechanism, they cannot use it to circumvent other policies.

What's next

Ephemeral volumes managed by kubelet

See local ephemeral storage.

CSI ephemeral volumes

For more information on the design, see the Ephemeral Inline CSI volumes KEP.
For more information on further development of this feature, see the enhancement tracking issue #596.

Generic ephemeral volumes

For more information on the design, see the Generic ephemeral inline volumes KEP.

7.5 - Storage Classes

This document describes the concept of a StorageClass in Kubernetes. Familiarity with volumes and persistent volumes is suggested.

Introduction

A StorageClass provides a way for administrators to describe the "classes" of storage they offer. Different classes might map to quality-of-service levels, or to backup policies, or to arbitrary policies determined by the cluster administrators. Kubernetes itself is unopinionated about what classes represent. This concept is sometimes called "profiles" in other storage systems.

The StorageClass Resource

Each StorageClass contains the fields provisioner, parameters, and reclaimPolicy, which are used when a PersistentVolume belonging to the class needs to be dynamically provisioned.

The name of a StorageClass object is significant, and is how users can request a particular class. Administrators set the name and other parameters of a class when first creating StorageClass objects, and the objects cannot be updated once they are created.

Administrators can specify a default StorageClass only for PVCs that don't request any particular class to bind to: see the PersistentVolumeClaim section for details.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: standard
provisioner: kubernetes.io/aws-ebs
parameters:
  type: gp2
reclaimPolicy: Retain
allowVolumeExpansion: true
mountOptions:
  - debug
volumeBindingMode: Immediate

Provisioner

Each StorageClass has a provisioner that determines what volume plugin is used for provisioning PVs. This field must be specified.

Volume Plugin	Internal Provisioner	Config Example
AWSElasticBlockStore	✓	AWS EBS
AzureFile	✓	Azure File
AzureDisk	✓	Azure Disk
CephFS	-	-
Cinder	✓	OpenStack Cinder
FC	-	-
FlexVolume	-	-
GCEPersistentDisk	✓	GCE PD
Glusterfs	✓	Glusterfs
iSCSI	-	-
NFS	-	NFS
RBD	✓	Ceph RBD
VsphereVolume	✓	vSphere
PortworxVolume	✓	Portworx Volume
Local	-	Local

You are not restricted to specifying the "internal" provisioners listed here (whose names are prefixed with "kubernetes.io" and shipped alongside Kubernetes). You can also run and specify external provisioners, which are independent programs that follow a specification defined by Kubernetes. Authors of external provisioners have full discretion over where their code lives, how the provisioner is shipped, how it needs to be run, what volume plugin it uses (including Flex), etc. The repository kubernetes-sigs/sig-storage-lib-external-provisioner houses a library for writing external provisioners that implements the bulk of the specification. Some external provisioners are listed under the repository kubernetes-sigs/sig-storage-lib-external-provisioner.

For example, NFS doesn't provide an internal provisioner, but an external provisioner can be used. There are also cases when 3rd party storage vendors provide their own external provisioner.

Reclaim Policy

PersistentVolumes that are dynamically created by a StorageClass will have the reclaim policy specified in the reclaimPolicy field of the class, which can be either Delete or Retain. If no reclaimPolicy is specified when a StorageClass object is created, it will default to Delete.

PersistentVolumes that are created manually and managed via a StorageClass will have whatever reclaim policy they were assigned at creation.

Allow Volume Expansion

FEATURE STATE: Kubernetes v1.11 [beta]

PersistentVolumes can be configured to be expandable. This feature when set to true, allows the users to resize the volume by editing the corresponding PVC object.

The following types of volumes support volume expansion, when the underlying StorageClass has the field allowVolumeExpansion set to true.

Table of Volume types and the version of Kubernetes they require
Volume type	Required Kubernetes version
gcePersistentDisk	1.11
awsElasticBlockStore	1.11
Cinder	1.11
glusterfs	1.11
rbd	1.11
Azure File	1.11
Azure Disk	1.11
Portworx	1.11
FlexVolume	1.13
CSI	1.14 (alpha), 1.16 (beta)

Note: You can only use the volume expansion feature to grow a Volume, not to shrink it.

Mount Options

PersistentVolumes that are dynamically created by a StorageClass will have the mount options specified in the mountOptions field of the class.

If the volume plugin does not support mount options but mount options are specified, provisioning will fail. Mount options are not validated on either the class or PV. If a mount option is invalid, the PV mount fails.

Volume Binding Mode

The volumeBindingMode field controls when volume binding and dynamic provisioning should occur. When unset, "Immediate" mode is used by default.

The Immediate mode indicates that volume binding and dynamic provisioning occurs once the PersistentVolumeClaim is created. For storage backends that are topology-constrained and not globally accessible from all Nodes in the cluster, PersistentVolumes will be bound or provisioned without knowledge of the Pod's scheduling requirements. This may result in unschedulable Pods.

A cluster administrator can address this issue by specifying the WaitForFirstConsumer mode which will delay the binding and provisioning of a PersistentVolume until a Pod using the PersistentVolumeClaim is created. PersistentVolumes will be selected or provisioned conforming to the topology that is specified by the Pod's scheduling constraints. These include, but are not limited to, resource requirements, node selectors, pod affinity and anti-affinity, and taints and tolerations.

The following plugins support WaitForFirstConsumer with dynamic provisioning:

AWSElasticBlockStore
GCEPersistentDisk
AzureDisk

The following plugins support WaitForFirstConsumer with pre-created PersistentVolume binding:

All of the above
Local

FEATURE STATE: Kubernetes v1.17 [stable]

CSI volumes are also supported with dynamic provisioning and pre-created PVs, but you'll need to look at the documentation for a specific CSI driver to see its supported topology keys and examples.

Note:

If you choose to use WaitForFirstConsumer, do not use nodeName in the Pod spec to specify node affinity. If nodeName is used in this case, the scheduler will be bypassed and PVC will remain in pending state.

Instead, you can use node selector for hostname in this case as shown below.

apiVersion: v1
kind: Pod
metadata:
  name: task-pv-pod
spec:
  nodeSelector:
    kubernetes.io/hostname: kube-01
  volumes:
    - name: task-pv-storage
      persistentVolumeClaim:
        claimName: task-pv-claim
  containers:
    - name: task-pv-container
      image: nginx
      ports:
        - containerPort: 80
          name: "http-server"
      volumeMounts:
        - mountPath: "/usr/share/nginx/html"
          name: task-pv-storage

Allowed Topologies

When a cluster operator specifies the WaitForFirstConsumer volume binding mode, it is no longer necessary to restrict provisioning to specific topologies in most situations. However, if still required, allowedTopologies can be specified.

This example demonstrates how to restrict the topology of provisioned volumes to specific zones and should be used as a replacement for the zone and zones parameters for the supported plugins.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: standard
provisioner: kubernetes.io/gce-pd
parameters:
  type: pd-standard
volumeBindingMode: WaitForFirstConsumer
allowedTopologies:
- matchLabelExpressions:
  - key: failure-domain.beta.kubernetes.io/zone
    values:
    - us-central-1a
    - us-central-1b

Parameters

Storage Classes have parameters that describe volumes belonging to the storage class. Different parameters may be accepted depending on the provisioner. For example, the value io1, for the parameter type, and the parameter iopsPerGB are specific to EBS. When a parameter is omitted, some default is used.

There can be at most 512 parameters defined for a StorageClass. The total length of the parameters object including its keys and values cannot exceed 256 KiB.

AWS EBS

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: slow
provisioner: kubernetes.io/aws-ebs
parameters:
  type: io1
  iopsPerGB: "10"
  fsType: ext4

type: io1, gp2, sc1, st1. See AWS docs for details. Default: gp2.
zone (Deprecated): AWS zone. If neither zone nor zones is specified, volumes are generally round-robin-ed across all active zones where Kubernetes cluster has a node. zone and zones parameters must not be used at the same time.
zones (Deprecated): A comma separated list of AWS zone(s). If neither zone nor zones is specified, volumes are generally round-robin-ed across all active zones where Kubernetes cluster has a node. zone and zones parameters must not be used at the same time.
iopsPerGB: only for io1 volumes. I/O operations per second per GiB. AWS volume plugin multiplies this with size of requested volume to compute IOPS of the volume and caps it at 20 000 IOPS (maximum supported by AWS, see AWS docs. A string is expected here, i.e. "10", not 10.
fsType: fsType that is supported by kubernetes. Default: "ext4".
encrypted: denotes whether the EBS volume should be encrypted or not. Valid values are "true" or "false". A string is expected here, i.e. "true", not true.
kmsKeyId: optional. The full Amazon Resource Name of the key to use when encrypting the volume. If none is supplied but encrypted is true, a key is generated by AWS. See AWS docs for valid ARN value.

Note: zone and zones parameters are deprecated and replaced with allowedTopologies

GCE PD

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: slow
provisioner: kubernetes.io/gce-pd
parameters:
  type: pd-standard
  fstype: ext4
  replication-type: none

type: pd-standard or pd-ssd. Default: pd-standard
zone (Deprecated): GCE zone. If neither zone nor zones is specified, volumes are generally round-robin-ed across all active zones where Kubernetes cluster has a node. zone and zones parameters must not be used at the same time.
zones (Deprecated): A comma separated list of GCE zone(s). If neither zone nor zones is specified, volumes are generally round-robin-ed across all active zones where Kubernetes cluster has a node. zone and zones parameters must not be used at the same time.
fstype: ext4 or xfs. Default: ext4. The defined filesystem type must be supported by the host operating system.
replication-type: none or regional-pd. Default: none.

If replication-type is set to none, a regular (zonal) PD will be provisioned.

If replication-type is set to regional-pd, a Regional Persistent Disk will be provisioned. It's highly recommended to have volumeBindingMode: WaitForFirstConsumer set, in which case when you create a Pod that consumes a PersistentVolumeClaim which uses this StorageClass, a Regional Persistent Disk is provisioned with two zones. One zone is the same as the zone that the Pod is scheduled in. The other zone is randomly picked from the zones available to the cluster. Disk zones can be further constrained using allowedTopologies.

Note: zone and zones parameters are deprecated and replaced with allowedTopologies

Glusterfs

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: slow
provisioner: kubernetes.io/glusterfs
parameters:
  resturl: "http://127.0.0.1:8081"
  clusterid: "630372ccdc720a92c681fb928f27b53f"
  restauthenabled: "true"
  restuser: "admin"
  secretNamespace: "default"
  secretName: "heketi-secret"
  gidMin: "40000"
  gidMax: "50000"
  volumetype: "replicate:3"

resturl: Gluster REST service/Heketi service url which provision gluster volumes on demand. The general format should be IPaddress:Port and this is a mandatory parameter for GlusterFS dynamic provisioner. If Heketi service is exposed as a routable service in openshift/kubernetes setup, this can have a format similar to http://heketi-storage-project.cloudapps.mystorage.com where the fqdn is a resolvable Heketi service url.
restauthenabled : Gluster REST service authentication boolean that enables authentication to the REST server. If this value is "true", restuser and restuserkey or secretNamespace + secretName have to be filled. This option is deprecated, authentication is enabled when any of restuser, restuserkey, secretName or secretNamespace is specified.
restuser : Gluster REST service/Heketi user who has access to create volumes in the Gluster Trusted Pool.
restuserkey : Gluster REST service/Heketi user's password which will be used for authentication to the REST server. This parameter is deprecated in favor of secretNamespace + secretName.
secretNamespace, secretName : Identification of Secret instance that contains user password to use when talking to Gluster REST service. These parameters are optional, empty password will be used when both secretNamespace and secretName are omitted. The provided secret must have type "kubernetes.io/glusterfs", for example created in this way:
```
kubectl create secret generic heketi-secret \
  --type="kubernetes.io/glusterfs" --from-literal=key='opensesame' \
  --namespace=default
```
Example of a secret can be found in glusterfs-provisioning-secret.yaml.
clusterid: 630372ccdc720a92c681fb928f27b53f is the ID of the cluster which will be used by Heketi when provisioning the volume. It can also be a list of clusterids, for example: "8452344e2becec931ece4e33c4674e4e,42982310de6c63381718ccfa6d8cf397". This is an optional parameter.
gidMin, gidMax : The minimum and maximum value of GID range for the StorageClass. A unique value (GID) in this range ( gidMin-gidMax ) will be used for dynamically provisioned volumes. These are optional values. If not specified, the volume will be provisioned with a value between 2000-2147483647 which are defaults for gidMin and gidMax respectively.
volumetype : The volume type and its parameters can be configured with this optional value. If the volume type is not mentioned, it's up to the provisioner to decide the volume type.

For example:
- Replica volume: volumetype: replicate:3 where '3' is replica count.
- Disperse/EC volume: volumetype: disperse:4:2 where '4' is data and '2' is the redundancy count.
- Distribute volume: volumetype: none
For available volume types and administration options, refer to the Administration Guide.

For further reference information, see How to configure Heketi.

When persistent volumes are dynamically provisioned, the Gluster plugin automatically creates an endpoint and a headless service in the name gluster-dynamic-<claimname>. The dynamic endpoint and service are automatically deleted when the persistent volume claim is deleted.

NFS

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: example-nfs
provisioner: example.com/external-nfs
parameters:
  server: nfs-server.example.com
  path: /share
  readOnly: "false"

server: Server is the hostname or IP address of the NFS server.
path: Path that is exported by the NFS server.
readOnly: A flag indicating whether the storage will be mounted as read only (default false).

Kubernetes doesn't include an internal NFS provisioner. You need to use an external provisioner to create a StorageClass for NFS. Here are some examples:

OpenStack Cinder

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: gold
provisioner: kubernetes.io/cinder
parameters:
  availability: nova

availability: Availability Zone. If not specified, volumes are generally round-robin-ed across all active zones where Kubernetes cluster has a node.

Note:

FEATURE STATE: Kubernetes v1.11 [deprecated]

This internal provisioner of OpenStack is deprecated. Please use the external cloud provider for OpenStack.

vSphere

There are two types of provisioners for vSphere storage classes:

CSI provisioner: csi.vsphere.vmware.com
vCP provisioner: kubernetes.io/vsphere-volume

In-tree provisioners are deprecated. For more information on the CSI provisioner, see Kubernetes vSphere CSI Driver and vSphereVolume CSI migration.

CSI Provisioner

The vSphere CSI StorageClass provisioner works with Tanzu Kubernetes clusters. For an example, refer to the vSphere CSI repository.

vCP Provisioner

The following examples use the VMware Cloud Provider (vCP) StorageClass provisioner.

Create a StorageClass with a user specified disk format.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast
provisioner: kubernetes.io/vsphere-volume
parameters:
  diskformat: zeroedthick

diskformat: thin, zeroedthick and eagerzeroedthick. Default: "thin".

Create a StorageClass with a disk format on a user specified datastore.
```
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast
provisioner: kubernetes.io/vsphere-volume
parameters:
    diskformat: zeroedthick
    datastore: VSANDatastore
```
datastore: The user can also specify the datastore in the StorageClass. The volume will be created on the datastore specified in the StorageClass, which in this case is VSANDatastore. This field is optional. If the datastore is not specified, then the volume will be created on the datastore specified in the vSphere config file used to initialize the vSphere Cloud Provider.
Storage Policy Management inside kubernetes
- Using existing vCenter SPBM policy
  
  One of the most important features of vSphere for Storage Management is policy based Management. Storage Policy Based Management (SPBM) is a storage policy framework that provides a single unified control plane across a broad range of data services and storage solutions. SPBM enables vSphere administrators to overcome upfront storage provisioning challenges, such as capacity planning, differentiated service levels and managing capacity headroom.
  
  The SPBM policies can be specified in the StorageClass using the storagePolicyName parameter.
- Virtual SAN policy support inside Kubernetes
  
  Vsphere Infrastructure (VI) Admins will have the ability to specify custom Virtual SAN Storage Capabilities during dynamic volume provisioning. You can now define storage requirements, such as performance and availability, in the form of storage capabilities during dynamic volume provisioning. The storage capability requirements are converted into a Virtual SAN policy which are then pushed down to the Virtual SAN layer when a persistent volume (virtual disk) is being created. The virtual disk is distributed across the Virtual SAN datastore to meet the requirements.
  
  You can see Storage Policy Based Management for dynamic provisioning of volumes for more details on how to use storage policies for persistent volumes management.

There are few vSphere examples which you try out for persistent volume management inside Kubernetes for vSphere.

Ceph RBD

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast
provisioner: kubernetes.io/rbd
parameters:
  monitors: 10.16.153.105:6789
  adminId: kube
  adminSecretName: ceph-secret
  adminSecretNamespace: kube-system
  pool: kube
  userId: kube
  userSecretName: ceph-secret-user
  userSecretNamespace: default
  fsType: ext4
  imageFormat: "2"
  imageFeatures: "layering"

monitors: Ceph monitors, comma delimited. This parameter is required.
adminId: Ceph client ID that is capable of creating images in the pool. Default is "admin".
adminSecretName: Secret Name for adminId. This parameter is required. The provided secret must have type "kubernetes.io/rbd".
adminSecretNamespace: The namespace for adminSecretName. Default is "default".
pool: Ceph RBD pool. Default is "rbd".
userId: Ceph client ID that is used to map the RBD image. Default is the same as adminId.
userSecretName: The name of Ceph Secret for userId to map RBD image. It must exist in the same namespace as PVCs. This parameter is required. The provided secret must have type "kubernetes.io/rbd", for example created in this way:
```
kubectl create secret generic ceph-secret --type="kubernetes.io/rbd" \
  --from-literal=key='QVFEQ1pMdFhPUnQrSmhBQUFYaERWNHJsZ3BsMmNjcDR6RFZST0E9PQ==' \
  --namespace=kube-system
```
userSecretNamespace: The namespace for userSecretName.
fsType: fsType that is supported by kubernetes. Default: "ext4".
imageFormat: Ceph RBD image format, "1" or "2". Default is "2".
imageFeatures: This parameter is optional and should only be used if you set imageFormat to "2". Currently supported features are layering only. Default is "", and no features are turned on.

Azure Disk

Azure Unmanaged Disk storage class

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: slow
provisioner: kubernetes.io/azure-disk
parameters:
  skuName: Standard_LRS
  location: eastus
  storageAccount: azure_storage_account_name

skuName: Azure storage account Sku tier. Default is empty.
location: Azure storage account location. Default is empty.
storageAccount: Azure storage account name. If a storage account is provided, it must reside in the same resource group as the cluster, and location is ignored. If a storage account is not provided, a new storage account will be created in the same resource group as the cluster.

Azure Disk storage class (starting from v1.7.2)

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: slow
provisioner: kubernetes.io/azure-disk
parameters:
  storageaccounttype: Standard_LRS
  kind: managed

storageaccounttype: Azure storage account Sku tier. Default is empty.
kind: Possible values are shared, dedicated, and managed (default). When kind is shared, all unmanaged disks are created in a few shared storage accounts in the same resource group as the cluster. When kind is dedicated, a new dedicated storage account will be created for the new unmanaged disk in the same resource group as the cluster. When kind is managed, all managed disks are created in the same resource group as the cluster.
resourceGroup: Specify the resource group in which the Azure disk will be created. It must be an existing resource group name. If it is unspecified, the disk will be placed in the same resource group as the current Kubernetes cluster.

Premium VM can attach both Standard_LRS and Premium_LRS disks, while Standard VM can only attach Standard_LRS disks.
Managed VM can only attach managed disks and unmanaged VM can only attach unmanaged disks.

Azure File

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: azurefile
provisioner: kubernetes.io/azure-file
parameters:
  skuName: Standard_LRS
  location: eastus
  storageAccount: azure_storage_account_name

skuName: Azure storage account Sku tier. Default is empty.
location: Azure storage account location. Default is empty.
storageAccount: Azure storage account name. Default is empty. If a storage account is not provided, all storage accounts associated with the resource group are searched to find one that matches skuName and location. If a storage account is provided, it must reside in the same resource group as the cluster, and skuName and location are ignored.
secretNamespace: the namespace of the secret that contains the Azure Storage Account Name and Key. Default is the same as the Pod.
secretName: the name of the secret that contains the Azure Storage Account Name and Key. Default is azure-storage-account-<accountName>-secret
readOnly: a flag indicating whether the storage will be mounted as read only. Defaults to false which means a read/write mount. This setting will impact the ReadOnly setting in VolumeMounts as well.

During storage provisioning, a secret named by secretName is created for the mounting credentials. If the cluster has enabled both RBAC and Controller Roles, add the create permission of resource secret for clusterrole system:controller:persistent-volume-binder.

In a multi-tenancy context, it is strongly recommended to set the value for secretNamespace explicitly, otherwise the storage account credentials may be read by other users.

Portworx Volume

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: portworx-io-priority-high
provisioner: kubernetes.io/portworx-volume
parameters:
  repl: "1"
  snap_interval:   "70"
  priority_io:  "high"

fs: filesystem to be laid out: none/xfs/ext4 (default: ext4).
block_size: block size in Kbytes (default: 32).
repl: number of synchronous replicas to be provided in the form of replication factor 1..3 (default: 1) A string is expected here i.e. "1" and not 1.
priority_io: determines whether the volume will be created from higher performance or a lower priority storage high/medium/low (default: low).
snap_interval: clock/time interval in minutes for when to trigger snapshots. Snapshots are incremental based on difference with the prior snapshot, 0 disables snaps (default: 0). A string is expected here i.e. "70" and not 70.
aggregation_level: specifies the number of chunks the volume would be distributed into, 0 indicates a non-aggregated volume (default: 0). A string is expected here i.e. "0" and not 0
ephemeral: specifies whether the volume should be cleaned-up after unmount or should be persistent. emptyDir use case can set this value to true and persistent volumes use case such as for databases like Cassandra should set to false, true/false (default false). A string is expected here i.e. "true" and not true.

Local

FEATURE STATE: Kubernetes v1.14 [stable]

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: local-storage
provisioner: kubernetes.io/no-provisioner
volumeBindingMode: WaitForFirstConsumer

Local volumes do not currently support dynamic provisioning, however a StorageClass should still be created to delay volume binding until Pod scheduling. This is specified by the WaitForFirstConsumer volume binding mode.

Delaying volume binding allows the scheduler to consider all of a Pod's scheduling constraints when choosing an appropriate PersistentVolume for a PersistentVolumeClaim.

7.6 - Dynamic Volume Provisioning

Dynamic volume provisioning allows storage volumes to be created on-demand. Without dynamic provisioning, cluster administrators have to manually make calls to their cloud or storage provider to create new storage volumes, and then create PersistentVolume objects to represent them in Kubernetes. The dynamic provisioning feature eliminates the need for cluster administrators to pre-provision storage. Instead, it automatically provisions storage when it is requested by users.

Background

The implementation of dynamic volume provisioning is based on the API object StorageClass from the API group storage.k8s.io. A cluster administrator can define as many StorageClass objects as needed, each specifying a volume plugin (aka provisioner) that provisions a volume and the set of parameters to pass to that provisioner when provisioning. A cluster administrator can define and expose multiple flavors of storage (from the same or different storage systems) within a cluster, each with a custom set of parameters. This design also ensures that end users don't have to worry about the complexity and nuances of how storage is provisioned, but still have the ability to select from multiple storage options.

More information on storage classes can be found here.

Enabling Dynamic Provisioning

To enable dynamic provisioning, a cluster administrator needs to pre-create one or more StorageClass objects for users. StorageClass objects define which provisioner should be used and what parameters should be passed to that provisioner when dynamic provisioning is invoked. The name of a StorageClass object must be a valid DNS subdomain name.

The following manifest creates a storage class "slow" which provisions standard disk-like persistent disks.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: slow
provisioner: kubernetes.io/gce-pd
parameters:
  type: pd-standard

The following manifest creates a storage class "fast" which provisions SSD-like persistent disks.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast
provisioner: kubernetes.io/gce-pd
parameters:
  type: pd-ssd

Using Dynamic Provisioning

Users request dynamically provisioned storage by including a storage class in their PersistentVolumeClaim. Before Kubernetes v1.6, this was done via the volume.beta.kubernetes.io/storage-class annotation. However, this annotation is deprecated since v1.9. Users now can and should instead use the storageClassName field of the PersistentVolumeClaim object. The value of this field must match the name of a StorageClass configured by the administrator (see below).

To select the "fast" storage class, for example, a user would create the following PersistentVolumeClaim:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: claim1
spec:
  accessModes:
    - ReadWriteOnce
  storageClassName: fast
  resources:
    requests:
      storage: 30Gi

This claim results in an SSD-like Persistent Disk being automatically provisioned. When the claim is deleted, the volume is destroyed.

Defaulting Behavior

Dynamic provisioning can be enabled on a cluster such that all claims are dynamically provisioned if no storage class is specified. A cluster administrator can enable this behavior by:

Marking one StorageClass object as default;
Making sure that the DefaultStorageClass admission controller is enabled on the API server.

An administrator can mark a specific StorageClass as default by adding the storageclass.kubernetes.io/is-default-class annotation to it. When a default StorageClass exists in a cluster and a user creates a PersistentVolumeClaim with storageClassName unspecified, the DefaultStorageClass admission controller automatically adds the storageClassName field pointing to the default storage class.

Note that there can be at most one default storage class on a cluster, or a PersistentVolumeClaim without storageClassName explicitly specified cannot be created.

Topology Awareness

In Multi-Zone clusters, Pods can be spread across Zones in a Region. Single-Zone storage backends should be provisioned in the Zones where Pods are scheduled. This can be accomplished by setting the Volume Binding Mode.

7.7 - Volume Snapshots

In Kubernetes, a VolumeSnapshot represents a snapshot of a volume on a storage system. This document assumes that you are already familiar with Kubernetes persistent volumes.

Introduction

Similar to how API resources PersistentVolume and PersistentVolumeClaim are used to provision volumes for users and administrators, VolumeSnapshotContent and VolumeSnapshot API resources are provided to create volume snapshots for users and administrators.

A VolumeSnapshotContent is a snapshot taken from a volume in the cluster that has been provisioned by an administrator. It is a resource in the cluster just like a PersistentVolume is a cluster resource.

A VolumeSnapshot is a request for snapshot of a volume by a user. It is similar to a PersistentVolumeClaim.

VolumeSnapshotClass allows you to specify different attributes belonging to a VolumeSnapshot. These attributes may differ among snapshots taken from the same volume on the storage system and therefore cannot be expressed by using the same StorageClass of a PersistentVolumeClaim.

Volume snapshots provide Kubernetes users with a standardized way to copy a volume's contents at a particular point in time without creating an entirely new volume. This functionality enables, for example, database administrators to backup databases before performing edit or delete modifications.

Users need to be aware of the following when using this feature:

API Objects VolumeSnapshot, VolumeSnapshotContent, and VolumeSnapshotClass are CRDs, not part of the core API.
VolumeSnapshot support is only available for CSI drivers.
As part of the deployment process of VolumeSnapshot, the Kubernetes team provides a snapshot controller to be deployed into the control plane, and a sidecar helper container called csi-snapshotter to be deployed together with the CSI driver. The snapshot controller watches VolumeSnapshot and VolumeSnapshotContent objects and is responsible for the creation and deletion of VolumeSnapshotContent object. The sidecar csi-snapshotter watches VolumeSnapshotContent objects and triggers CreateSnapshot and DeleteSnapshot operations against a CSI endpoint.
There is also a validating webhook server which provides tightened validation on snapshot objects. This should be installed by the Kubernetes distros along with the snapshot controller and CRDs, not CSI drivers. It should be installed in all Kubernetes clusters that has the snapshot feature enabled.
CSI drivers may or may not have implemented the volume snapshot functionality. The CSI drivers that have provided support for volume snapshot will likely use the csi-snapshotter. See CSI Driver documentation for details.
The CRDs and snapshot controller installations are the responsibility of the Kubernetes distribution.

Lifecycle of a volume snapshot and volume snapshot content

VolumeSnapshotContents are resources in the cluster. VolumeSnapshots are requests for those resources. The interaction between VolumeSnapshotContents and VolumeSnapshots follow this lifecycle:

Provisioning Volume Snapshot

There are two ways snapshots may be provisioned: pre-provisioned or dynamically provisioned.

Pre-provisioned

A cluster administrator creates a number of VolumeSnapshotContents. They carry the details of the real volume snapshot on the storage system which is available for use by cluster users. They exist in the Kubernetes API and are available for consumption.

Dynamic

Instead of using a pre-existing snapshot, you can request that a snapshot to be dynamically taken from a PersistentVolumeClaim. The VolumeSnapshotClass specifies storage provider-specific parameters to use when taking a snapshot.

Binding

The snapshot controller handles the binding of a VolumeSnapshot object with an appropriate VolumeSnapshotContent object, in both pre-provisioned and dynamically provisioned scenarios. The binding is a one-to-one mapping.

In the case of pre-provisioned binding, the VolumeSnapshot will remain unbound until the requested VolumeSnapshotContent object is created.

Persistent Volume Claim as Snapshot Source Protection

The purpose of this protection is to ensure that in-use PersistentVolumeClaim API objects are not removed from the system while a snapshot is being taken from it (as this may result in data loss).

While a snapshot is being taken of a PersistentVolumeClaim, that PersistentVolumeClaim is in-use. If you delete a PersistentVolumeClaim API object in active use as a snapshot source, the PersistentVolumeClaim object is not removed immediately. Instead, removal of the PersistentVolumeClaim object is postponed until the snapshot is readyToUse or aborted.

Delete

Deletion is triggered by deleting the VolumeSnapshot object, and the DeletionPolicy will be followed. If the DeletionPolicy is Delete, then the underlying storage snapshot will be deleted along with the VolumeSnapshotContent object. If the DeletionPolicy is Retain, then both the underlying snapshot and VolumeSnapshotContent remain.

VolumeSnapshots

Each VolumeSnapshot contains a spec and a status.

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshot
metadata:
  name: new-snapshot-test
spec:
  volumeSnapshotClassName: csi-hostpath-snapclass
  source:
    persistentVolumeClaimName: pvc-test

persistentVolumeClaimName is the name of the PersistentVolumeClaim data source for the snapshot. This field is required for dynamically provisioning a snapshot.

A volume snapshot can request a particular class by specifying the name of a VolumeSnapshotClass using the attribute volumeSnapshotClassName. If nothing is set, then the default class is used if available.

For pre-provisioned snapshots, you need to specify a volumeSnapshotContentName as the source for the snapshot as shown in the following example. The volumeSnapshotContentName source field is required for pre-provisioned snapshots.

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshot
metadata:
  name: test-snapshot
spec:
  source:
    volumeSnapshotContentName: test-content

Volume Snapshot Contents

Each VolumeSnapshotContent contains a spec and status. In dynamic provisioning, the snapshot common controller creates VolumeSnapshotContent objects. Here is an example:

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshotContent
metadata:
  name: snapcontent-72d9a349-aacd-42d2-a240-d775650d2455
spec:
  deletionPolicy: Delete
  driver: hostpath.csi.k8s.io
  source:
    volumeHandle: ee0cfb94-f8d4-11e9-b2d8-0242ac110002
  sourceVolumeMode: Filesystem
  volumeSnapshotClassName: csi-hostpath-snapclass
  volumeSnapshotRef:
    name: new-snapshot-test
    namespace: default
    uid: 72d9a349-aacd-42d2-a240-d775650d2455

volumeHandle is the unique identifier of the volume created on the storage backend and returned by the CSI driver during the volume creation. This field is required for dynamically provisioning a snapshot. It specifies the volume source of the snapshot.

For pre-provisioned snapshots, you (as cluster administrator) are responsible for creating the VolumeSnapshotContent object as follows.

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshotContent
metadata:
  name: new-snapshot-content-test
spec:
  deletionPolicy: Delete
  driver: hostpath.csi.k8s.io
  source:
    snapshotHandle: 7bdd0de3-aaeb-11e8-9aae-0242ac110002
  sourceVolumeMode: Filesystem
  volumeSnapshotRef:
    name: new-snapshot-test
    namespace: default

snapshotHandle is the unique identifier of the volume snapshot created on the storage backend. This field is required for the pre-provisioned snapshots. It specifies the CSI snapshot id on the storage system that this VolumeSnapshotContent represents.

sourceVolumeMode is the mode of the volume whose snapshot is taken. The value of the sourceVolumeMode field can be either Filesystem or Block. If the source volume mode is not specified, Kubernetes treats the snapshot as if the source volume's mode is unknown.

volumeSnapshotRef is the reference of the corresponding VolumeSnapshot. Note that when the VolumeSnapshotContent is being created as a pre-provisioned snapshot, the VolumeSnapshot referenced in volumeSnapshotRef might not exist yet.

Converting the volume mode of a Snapshot

If the VolumeSnapshots API installed on your cluster supports the sourceVolumeMode field, then the API has the capability to prevent unauthorized users from converting the mode of a volume.

To check if your cluster has capability for this feature, run the following command:

$ kubectl get crd volumesnapshotcontent -o yaml

If you want to allow users to create a PersistentVolumeClaim from an existing VolumeSnapshot, but with a different volume mode than the source, the annotation snapshot.storage.kubernetes.io/allowVolumeModeChange: "true"needs to be added to the VolumeSnapshotContent that corresponds to the VolumeSnapshot.

For pre-provisioned snapshots, Spec.SourceVolumeMode needs to be populated by the cluster administrator.

An example VolumeSnapshotContent resource with this feature enabled would look like:

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshotContent
metadata:
  name: new-snapshot-content-test
  annotations:
    - snapshot.storage.kubernetes.io/allowVolumeModeChange: "true"
spec:
  deletionPolicy: Delete
  driver: hostpath.csi.k8s.io
  source:
    snapshotHandle: 7bdd0de3-aaeb-11e8-9aae-0242ac110002
  sourceVolumeMode: Filesystem
  volumeSnapshotRef:
    name: new-snapshot-test
    namespace: default

Provisioning Volumes from Snapshots

You can provision a new volume, pre-populated with data from a snapshot, by using the dataSource field in the PersistentVolumeClaim object.

For more details, see Volume Snapshot and Restore Volume from Snapshot.

7.8 - Volume Snapshot Classes

This document describes the concept of VolumeSnapshotClass in Kubernetes. Familiarity with volume snapshots and storage classes is suggested.

Introduction

Just like StorageClass provides a way for administrators to describe the "classes" of storage they offer when provisioning a volume, VolumeSnapshotClass provides a way to describe the "classes" of storage when provisioning a volume snapshot.

The VolumeSnapshotClass Resource

Each VolumeSnapshotClass contains the fields driver, deletionPolicy, and parameters, which are used when a VolumeSnapshot belonging to the class needs to be dynamically provisioned.

The name of a VolumeSnapshotClass object is significant, and is how users can request a particular class. Administrators set the name and other parameters of a class when first creating VolumeSnapshotClass objects, and the objects cannot be updated once they are created.

Note: Installation of the CRDs is the responsibility of the Kubernetes distribution. Without the required CRDs present, the creation of a VolumeSnapshotClass fails.

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshotClass
metadata:
  name: csi-hostpath-snapclass
driver: hostpath.csi.k8s.io
deletionPolicy: Delete
parameters:

Administrators can specify a default VolumeSnapshotClass for VolumeSnapshots that don't request any particular class to bind to by adding the snapshot.storage.kubernetes.io/is-default-class: "true" annotation:

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshotClass
metadata:
  name: csi-hostpath-snapclass
  annotations:
    snapshot.storage.kubernetes.io/is-default-class: "true"
driver: hostpath.csi.k8s.io
deletionPolicy: Delete
parameters:

Driver

Volume snapshot classes have a driver that determines what CSI volume plugin is used for provisioning VolumeSnapshots. This field must be specified.

DeletionPolicy

Volume snapshot classes have a deletionPolicy. It enables you to configure what happens to a VolumeSnapshotContent when the VolumeSnapshot object it is bound to is to be deleted. The deletionPolicy of a volume snapshot class can either be Retain or Delete. This field must be specified.

If the deletionPolicy is Delete, then the underlying storage snapshot will be deleted along with the VolumeSnapshotContent object. If the deletionPolicy is Retain, then both the underlying snapshot and VolumeSnapshotContent remain.

Parameters

Volume snapshot classes have parameters that describe volume snapshots belonging to the volume snapshot class. Different parameters may be accepted depending on the driver.

7.9 - CSI Volume Cloning

This document describes the concept of cloning existing CSI Volumes in Kubernetes. Familiarity with Volumes is suggested.

Introduction

The CSI Volume Cloning feature adds support for specifying existing PVCs in the dataSource field to indicate a user would like to clone a Volume.

A Clone is defined as a duplicate of an existing Kubernetes Volume that can be consumed as any standard Volume would be. The only difference is that upon provisioning, rather than creating a "new" empty Volume, the back end device creates an exact duplicate of the specified Volume.

The implementation of cloning, from the perspective of the Kubernetes API, adds the ability to specify an existing PVC as a dataSource during new PVC creation. The source PVC must be bound and available (not in use).

Users need to be aware of the following when using this feature:

Cloning support (VolumePVCDataSource) is only available for CSI drivers.
Cloning support is only available for dynamic provisioners.
CSI drivers may or may not have implemented the volume cloning functionality.
You can only clone a PVC when it exists in the same namespace as the destination PVC (source and destination must be in the same namespace).
Cloning is supported with a different Storage Class.
- Destination volume can be the same or a different storage class as the source.
- Default storage class can be used and storageClassName omitted in the spec.
Cloning can only be performed between two volumes that use the same VolumeMode setting (if you request a block mode volume, the source MUST also be block mode)

Provisioning

Clones are provisioned like any other PVC with the exception of adding a dataSource that references an existing PVC in the same namespace.

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
    name: clone-of-pvc-1
    namespace: myns
spec:
  accessModes:
  - ReadWriteOnce
  storageClassName: cloning
  resources:
    requests:
      storage: 5Gi
  dataSource:
    kind: PersistentVolumeClaim
    name: pvc-1

Note: You must specify a capacity value for spec.resources.requests.storage, and the value you specify must be the same or larger than the capacity of the source volume.

The result is a new PVC with the name clone-of-pvc-1 that has the exact same content as the specified source pvc-1.

Usage

Upon availability of the new PVC, the cloned PVC is consumed the same as other PVC. It's also expected at this point that the newly created PVC is an independent object. It can be consumed, cloned, snapshotted, or deleted independently and without consideration for it's original dataSource PVC. This also implies that the source is not linked in any way to the newly created clone, it may also be modified or deleted without affecting the newly created clone.

7.10 - Storage Capacity

Storage capacity is limited and may vary depending on the node on which a pod runs: network-attached storage might not be accessible by all nodes, or storage is local to a node to begin with.

FEATURE STATE: Kubernetes v1.24 [stable]

This page describes how Kubernetes keeps track of storage capacity and how the scheduler uses that information to schedule Pods onto nodes that have access to enough storage capacity for the remaining missing volumes. Without storage capacity tracking, the scheduler may choose a node that doesn't have enough capacity to provision a volume and multiple scheduling retries will be needed.

Before you begin

Kubernetes v1.25 includes cluster-level API support for storage capacity tracking. To use this you must also be using a CSI driver that supports capacity tracking. Consult the documentation for the CSI drivers that you use to find out whether this support is available and, if so, how to use it. If you are not running Kubernetes v1.25, check the documentation for that version of Kubernetes.

API

There are two API extensions for this feature:

CSIStorageCapacity objects: these get produced by a CSI driver in the namespace where the driver is installed. Each object contains capacity information for one storage class and defines which nodes have access to that storage.
The CSIDriverSpec.StorageCapacity field: when set to true, the Kubernetes scheduler will consider storage capacity for volumes that use the CSI driver.

Scheduling

Storage capacity information is used by the Kubernetes scheduler if:

a Pod uses a volume that has not been created yet,
that volume uses a StorageClass which references a CSI driver and uses WaitForFirstConsumer volume binding mode, and
the CSIDriver object for the driver has StorageCapacity set to true.

In that case, the scheduler only considers nodes for the Pod which have enough storage available to them. This check is very simplistic and only compares the size of the volume against the capacity listed in CSIStorageCapacity objects with a topology that includes the node.

For volumes with Immediate volume binding mode, the storage driver decides where to create the volume, independently of Pods that will use the volume. The scheduler then schedules Pods onto nodes where the volume is available after the volume has been created.

For CSI ephemeral volumes, scheduling always happens without considering storage capacity. This is based on the assumption that this volume type is only used by special CSI drivers which are local to a node and do not need significant resources there.

Rescheduling

When a node has been selected for a Pod with WaitForFirstConsumer volumes, that decision is still tentative. The next step is that the CSI storage driver gets asked to create the volume with a hint that the volume is supposed to be available on the selected node.

Because Kubernetes might have chosen a node based on out-dated capacity information, it is possible that the volume cannot really be created. The node selection is then reset and the Kubernetes scheduler tries again to find a node for the Pod.

Limitations

Storage capacity tracking increases the chance that scheduling works on the first try, but cannot guarantee this because the scheduler has to decide based on potentially out-dated information. Usually, the same retry mechanism as for scheduling without any storage capacity information handles scheduling failures.

One situation where scheduling can fail permanently is when a Pod uses multiple volumes: one volume might have been created already in a topology segment which then does not have enough capacity left for another volume. Manual intervention is necessary to recover from this, for example by increasing capacity or deleting the volume that was already created.

What's next

For more information on the design, see the Storage Capacity Constraints for Pod Scheduling KEP.

7.11 - Node-specific Volume Limits

This page describes the maximum number of volumes that can be attached to a Node for various cloud providers.

Cloud providers like Google, Amazon, and Microsoft typically have a limit on how many volumes can be attached to a Node. It is important for Kubernetes to respect those limits. Otherwise, Pods scheduled on a Node could get stuck waiting for volumes to attach.

Kubernetes default limits

The Kubernetes scheduler has default limits on the number of volumes that can be attached to a Node:

Cloud service	Maximum volumes per Node
Amazon Elastic Block Store (EBS)	39
Google Persistent Disk	16
Microsoft Azure Disk Storage	16

Custom limits

You can change these limits by setting the value of the KUBE_MAX_PD_VOLS environment variable, and then starting the scheduler. CSI drivers might have a different procedure, see their documentation on how to customize their limits.

Use caution if you set a limit that is higher than the default limit. Consult the cloud provider's documentation to make sure that Nodes can actually support the limit you set.

The limit applies to the entire cluster, so it affects all Nodes.

Dynamic volume limits

FEATURE STATE: Kubernetes v1.17 [stable]

Dynamic volume limits are supported for following volume types.

Amazon EBS
Google Persistent Disk
Azure Disk
CSI

For volumes managed by in-tree volume plugins, Kubernetes automatically determines the Node type and enforces the appropriate maximum number of volumes for the node. For example:

On Google Compute Engine, up to 127 volumes can be attached to a node, depending on the node type.
For Amazon EBS disks on M5,C5,R5,T3 and Z1D instance types, Kubernetes allows only 25 volumes to be attached to a Node. For other instance types on Amazon Elastic Compute Cloud (EC2), Kubernetes allows 39 volumes to be attached to a Node.
On Azure, up to 64 disks can be attached to a node, depending on the node type. For more details, refer to Sizes for virtual machines in Azure.
If a CSI storage driver advertises a maximum number of volumes for a Node (using NodeGetInfo), the kube-scheduler honors that limit. Refer to the CSI specifications for details.
For volumes managed by in-tree plugins that have been migrated to a CSI driver, the maximum number of volumes will be the one reported by the CSI driver.

7.12 - Volume Health Monitoring

FEATURE STATE: Kubernetes v1.21 [alpha]

CSI volume health monitoring allows CSI Drivers to detect abnormal volume conditions from the underlying storage systems and report them as events on PVCs or Pods.

Volume health monitoring

Kubernetes volume health monitoring is part of how Kubernetes implements the Container Storage Interface (CSI). Volume health monitoring feature is implemented in two components: an External Health Monitor controller, and the kubelet.

If a CSI Driver supports Volume Health Monitoring feature from the controller side, an event will be reported on the related PersistentVolumeClaim (PVC) when an abnormal volume condition is detected on a CSI volume.

The External Health Monitor controller also watches for node failure events. You can enable node failure monitoring by setting the enable-node-watcher flag to true. When the external health monitor detects a node failure event, the controller reports an Event will be reported on the PVC to indicate that pods using this PVC are on a failed node.

If a CSI Driver supports Volume Health Monitoring feature from the node side, an Event will be reported on every Pod using the PVC when an abnormal volume condition is detected on a CSI volume. In addition, Volume Health information is exposed as Kubelet VolumeStats metrics. A new metric kubelet_volume_stats_health_status_abnormal is added. This metric includes two labels: namespace and persistentvolumeclaim. The count is either 1 or 0. 1 indicates the volume is unhealthy, 0 indicates volume is healthy. For more information, please check KEP.

Note: You need to enable the CSIVolumeHealth feature gate to use this feature from the node side.

What's next

See the CSI driver documentation to find out which CSI drivers have implemented this feature.

7.13 - Windows Storage

This page provides an storage overview specific to the Windows operating system.

Persistent storage

Windows has a layered filesystem driver to mount container layers and create a copy filesystem based on NTFS. All file paths in the container are resolved only within the context of that container.

With Docker, volume mounts can only target a directory in the container, and not an individual file. This limitation does not apply to containerd.
Volume mounts cannot project files or directories back to the host filesystem.
Read-only filesystems are not supported because write access is always required for the Windows registry and SAM database. However, read-only volumes are supported.
Volume user-masks and permissions are not available. Because the SAM is not shared between the host & container, there's no mapping between them. All permissions are resolved within the context of the container.

As a result, the following storage functionality is not supported on Windows nodes:

Volume subpath mounts: only the entire volume can be mounted in a Windows container
Subpath volume mounting for Secrets
Host mount projection
Read-only root filesystem (mapped volumes still support readOnly)
Block device mapping
Memory as the storage medium (for example, emptyDir.medium set to Memory)
File system features like uid/gid; per-user Linux filesystem permissions
Setting secret permissions with DefaultMode (due to UID/GID dependency)
NFS based storage/volume support
Expanding the mounted volume (resizefs)

Kubernetes volumes enable complex applications, with data persistence and Pod volume sharing requirements, to be deployed on Kubernetes. Management of persistent volumes associated with a specific storage back-end or protocol includes actions such as provisioning/de-provisioning/resizing of volumes, attaching/detaching a volume to/from a Kubernetes node and mounting/dismounting a volume to/from individual containers in a pod that needs to persist data.

Volume management components are shipped as Kubernetes volume plugin. The following broad classes of Kubernetes volume plugins are supported on Windows:

FlexVolume plugins
- Please note that FlexVolumes have been deprecated as of 1.23
CSI Plugins

In-tree volume plugins

The following in-tree plugins support persistent storage on Windows nodes:

8 - Configuration

Resources that Kubernetes provides for configuring Pods.

8.1 - Configuration Best Practices

This document highlights and consolidates configuration best practices that are introduced throughout the user guide, Getting Started documentation, and examples.

This is a living document. If you think of something that is not on this list but might be useful to others, please don't hesitate to file an issue or submit a PR.

General Configuration Tips

When defining configurations, specify the latest stable API version.
Configuration files should be stored in version control before being pushed to the cluster. This allows you to quickly roll back a configuration change if necessary. It also aids cluster re-creation and restoration.
Write your configuration files using YAML rather than JSON. Though these formats can be used interchangeably in almost all scenarios, YAML tends to be more user-friendly.
Group related objects into a single file whenever it makes sense. One file is often easier to manage than several. See the guestbook-all-in-one.yaml file as an example of this syntax.
Note also that many kubectl commands can be called on a directory. For example, you can call kubectl apply on a directory of config files.
Don't specify default values unnecessarily: simple, minimal configuration will make errors less likely.
Put object descriptions in annotations, to allow better introspection.

"Naked" Pods versus ReplicaSets, Deployments, and Jobs

Don't use naked Pods (that is, Pods not bound to a ReplicaSet or Deployment) if you can avoid it. Naked Pods will not be rescheduled in the event of a node failure.

A Deployment, which both creates a ReplicaSet to ensure that the desired number of Pods is always available, and specifies a strategy to replace Pods (such as RollingUpdate), is almost always preferable to creating Pods directly, except for some explicit restartPolicy: Never scenarios. A Job may also be appropriate.

Services

Create a Service before its corresponding backend workloads (Deployments or ReplicaSets), and before any workloads that need to access it. When Kubernetes starts a container, it provides environment variables pointing to all the Services which were running when the container was started. For example, if a Service named foo exists, all containers will get the following variables in their initial environment:
```
FOO_SERVICE_HOST=<the host the Service is running on>
FOO_SERVICE_PORT=<the port the Service is running on>
```
This does imply an ordering requirement - any Service that a Pod wants to access must be created before the Pod itself, or else the environment variables will not be populated. DNS does not have this restriction.
An optional (though strongly recommended) cluster add-on is a DNS server. The DNS server watches the Kubernetes API for new Services and creates a set of DNS records for each. If DNS has been enabled throughout the cluster then all Pods should be able to do name resolution of Services automatically.
Don't specify a hostPort for a Pod unless it is absolutely necessary. When you bind a Pod to a hostPort, it limits the number of places the Pod can be scheduled, because each <hostIP, hostPort, protocol> combination must be unique. If you don't specify the hostIP and protocol explicitly, Kubernetes will use 0.0.0.0 as the default hostIP and TCP as the default protocol.

If you only need access to the port for debugging purposes, you can use the apiserver proxy or kubectl port-forward.

If you explicitly need to expose a Pod's port on the node, consider using a NodePort Service before resorting to hostPort.
Avoid using hostNetwork, for the same reasons as hostPort.
Use headless Services (which have a ClusterIP of None) for service discovery when you don't need kube-proxy load balancing.

Using Labels

Define and use labels that identify semantic attributes of your application or Deployment, such as { app.kubernetes.io/name: MyApp, tier: frontend, phase: test, deployment: v3 }. You can use these labels to select the appropriate Pods for other resources; for example, a Service that selects all tier: frontend Pods, or all phase: test components of app.kubernetes.io/name: MyApp. See the guestbook app for examples of this approach.

A Service can be made to span multiple Deployments by omitting release-specific labels from its selector. When you need to update a running service without downtime, use a Deployment.

A desired state of an object is described by a Deployment, and if changes to that spec are applied, the deployment controller changes the actual state to the desired state at a controlled rate.

Use the Kubernetes common labels for common use cases. These standardized labels enrich the metadata in a way that allows tools, including kubectl and dashboard, to work in an interoperable way.
You can manipulate labels for debugging. Because Kubernetes controllers (such as ReplicaSet) and Services match to Pods using selector labels, removing the relevant labels from a Pod will stop it from being considered by a controller or from being served traffic by a Service. If you remove the labels of an existing Pod, its controller will create a new Pod to take its place. This is a useful way to debug a previously "live" Pod in a "quarantine" environment. To interactively remove or add labels, use kubectl label.

Using kubectl

Use kubectl apply -f <directory>. This looks for Kubernetes configuration in all .yaml, .yml, and .json files in <directory> and passes it to apply.
Use label selectors for get and delete operations instead of specific object names. See the sections on label selectors and using labels effectively.
Use kubectl create deployment and kubectl expose to quickly create single-container Deployments and Services. See Use a Service to Access an Application in a Cluster for an example.

8.2 - ConfigMaps

A ConfigMap is an API object used to store non-confidential data in key-value pairs. Pods can consume ConfigMaps as environment variables, command-line arguments, or as configuration files in a volume.

A ConfigMap allows you to decouple environment-specific configuration from your container images, so that your applications are easily portable.

Caution: ConfigMap does not provide secrecy or encryption. If the data you want to store are confidential, use a rather than a ConfigMap, or use additional (third party) tools to keep your data private.

Motivation

Use a ConfigMap for setting configuration data separately from application code.

For example, imagine that you are developing an application that you can run on your own computer (for development) and in the cloud (to handle real traffic). You write the code to look in an environment variable named DATABASE_HOST. Locally, you set that variable to localhost. In the cloud, you set it to refer to a Kubernetes Service that exposes the database component to your cluster. This lets you fetch a container image running in the cloud and debug the exact same code locally if needed.

A ConfigMap is not designed to hold large chunks of data. The data stored in a ConfigMap cannot exceed 1 MiB. If you need to store settings that are larger than this limit, you may want to consider mounting a volume or use a separate database or file service.

ConfigMap object

A ConfigMap is an API object that lets you store configuration for other objects to use. Unlike most Kubernetes objects that have a spec, a ConfigMap has data and binaryData fields. These fields accept key-value pairs as their values. Both the data field and the binaryData are optional. The data field is designed to contain UTF-8 strings while the binaryData field is designed to contain binary data as base64-encoded strings.

The name of a ConfigMap must be a valid DNS subdomain name.

Each key under the data or the binaryData field must consist of alphanumeric characters, -, _ or .. The keys stored in data must not overlap with the keys in the binaryData field.

Starting from v1.19, you can add an immutable field to a ConfigMap definition to create an immutable ConfigMap.

ConfigMaps and Pods

You can write a Pod spec that refers to a ConfigMap and configures the container(s) in that Pod based on the data in the ConfigMap. The Pod and the ConfigMap must be in the same namespace.

Note: The spec of a cannot refer to a ConfigMap or any other API objects.

Here's an example ConfigMap that has some keys with single values, and other keys where the value looks like a fragment of a configuration format.

apiVersion: v1
kind: ConfigMap
metadata:
  name: game-demo
data:
  # property-like keys; each key maps to a simple value
  player_initial_lives: "3"
  ui_properties_file_name: "user-interface.properties"

  # file-like keys
  game.properties: |
    enemy.types=aliens,monsters
    player.maximum-lives=5    
  user-interface.properties: |
    color.good=purple
    color.bad=yellow
    allow.textmode=true

There are four different ways that you can use a ConfigMap to configure a container inside a Pod:

Inside a container command and args
Environment variables for a container
Add a file in read-only volume, for the application to read
Write code to run inside the Pod that uses the Kubernetes API to read a ConfigMap

These different methods lend themselves to different ways of modeling the data being consumed. For the first three methods, the kubelet uses the data from the ConfigMap when it launches container(s) for a Pod.

The fourth method means you have to write code to read the ConfigMap and its data. However, because you're using the Kubernetes API directly, your application can subscribe to get updates whenever the ConfigMap changes, and react when that happens. By accessing the Kubernetes API directly, this technique also lets you access a ConfigMap in a different namespace.

Here's an example Pod that uses values from game-demo to configure a Pod:

apiVersion: v1
kind: Pod
metadata:
  name: configmap-demo-pod
spec:
  containers:
    - name: demo
      image: alpine
      command: ["sleep", "3600"]
      env:
        # Define the environment variable
        - name: PLAYER_INITIAL_LIVES # Notice that the case is different here
                                     # from the key name in the ConfigMap.
          valueFrom:
            configMapKeyRef:
              name: game-demo           # The ConfigMap this value comes from.
              key: player_initial_lives # The key to fetch.
        - name: UI_PROPERTIES_FILE_NAME
          valueFrom:
            configMapKeyRef:
              name: game-demo
              key: ui_properties_file_name
      volumeMounts:
      - name: config
        mountPath: "/config"
        readOnly: true
  volumes:
    # You set volumes at the Pod level, then mount them into containers inside that Pod
    - name: config
      configMap:
        # Provide the name of the ConfigMap you want to mount.
        name: game-demo
        # An array of keys from the ConfigMap to create as files
        items:
        - key: "game.properties"
          path: "game.properties"
        - key: "user-interface.properties"
          path: "user-interface.properties"

A ConfigMap doesn't differentiate between single line property values and multi-line file-like values. What matters is how Pods and other objects consume those values.

For this example, defining a volume and mounting it inside the demo container as /config creates two files, /config/game.properties and /config/user-interface.properties, even though there are four keys in the ConfigMap. This is because the Pod definition specifies an items array in the volumes section. If you omit the items array entirely, every key in the ConfigMap becomes a file with the same name as the key, and you get 4 files.

Using ConfigMaps

ConfigMaps can be mounted as data volumes. ConfigMaps can also be used by other parts of the system, without being directly exposed to the Pod. For example, ConfigMaps can hold data that other parts of the system should use for configuration.

The most common way to use ConfigMaps is to configure settings for containers running in a Pod in the same namespace. You can also use a ConfigMap separately.

For example, you might encounter addons or operators that adjust their behavior based on a ConfigMap.

Using ConfigMaps as files from a Pod

To consume a ConfigMap in a volume in a Pod:

Create a ConfigMap or use an existing one. Multiple Pods can reference the same ConfigMap.
Modify your Pod definition to add a volume under .spec.volumes[]. Name the volume anything, and have a .spec.volumes[].configMap.name field set to reference your ConfigMap object.
Add a .spec.containers[].volumeMounts[] to each container that needs the ConfigMap. Specify .spec.containers[].volumeMounts[].readOnly = true and .spec.containers[].volumeMounts[].mountPath to an unused directory name where you would like the ConfigMap to appear.
Modify your image or command line so that the program looks for files in that directory. Each key in the ConfigMap data map becomes the filename under mountPath.

This is an example of a Pod that mounts a ConfigMap in a volume:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
  - name: mypod
    image: redis
    volumeMounts:
    - name: foo
      mountPath: "/etc/foo"
      readOnly: true
  volumes:
  - name: foo
    configMap:
      name: myconfigmap

Each ConfigMap you want to use needs to be referred to in .spec.volumes.

If there are multiple containers in the Pod, then each container needs its own volumeMounts block, but only one .spec.volumes is needed per ConfigMap.

Mounted ConfigMaps are updated automatically

When a ConfigMap currently consumed in a volume is updated, projected keys are eventually updated as well. The kubelet checks whether the mounted ConfigMap is fresh on every periodic sync. However, the kubelet uses its local cache for getting the current value of the ConfigMap. The type of the cache is configurable using the ConfigMapAndSecretChangeDetectionStrategy field in the KubeletConfiguration struct. A ConfigMap can be either propagated by watch (default), ttl-based, or by redirecting all requests directly to the API server. As a result, the total delay from the moment when the ConfigMap is updated to the moment when new keys are projected to the Pod can be as long as the kubelet sync period + cache propagation delay, where the cache propagation delay depends on the chosen cache type (it equals to watch propagation delay, ttl of cache, or zero correspondingly).

ConfigMaps consumed as environment variables are not updated automatically and require a pod restart.

Note: A container using a ConfigMap as a subPath volume mount will not receive ConfigMap updates.

Immutable ConfigMaps

FEATURE STATE: Kubernetes v1.21 [stable]

The Kubernetes feature Immutable Secrets and ConfigMaps provides an option to set individual Secrets and ConfigMaps as immutable. For clusters that extensively use ConfigMaps (at least tens of thousands of unique ConfigMap to Pod mounts), preventing changes to their data has the following advantages:

protects you from accidental (or unwanted) updates that could cause applications outages
improves performance of your cluster by significantly reducing load on kube-apiserver, by closing watches for ConfigMaps marked as immutable.

This feature is controlled by the ImmutableEphemeralVolumes feature gate. You can create an immutable ConfigMap by setting the immutable field to true. For example:

apiVersion: v1
kind: ConfigMap
metadata:
  ...
data:
  ...
immutable: true

Once a ConfigMap is marked as immutable, it is not possible to revert this change nor to mutate the contents of the data or the binaryData field. You can only delete and recreate the ConfigMap. Because existing Pods maintain a mount point to the deleted ConfigMap, it is recommended to recreate these pods.

What's next

Read about Secrets.
Read Configure a Pod to Use a ConfigMap.
Read about changing a ConfigMap (or any other Kubernetes object)
Read The Twelve-Factor App to understand the motivation for separating code from configuration.

8.3 - Secrets

A Secret is an object that contains a small amount of sensitive data such as a password, a token, or a key. Such information might otherwise be put in a Pod specification or in a container image. Using a Secret means that you don't need to include confidential data in your application code.

Because Secrets can be created independently of the Pods that use them, there is less risk of the Secret (and its data) being exposed during the workflow of creating, viewing, and editing Pods. Kubernetes, and applications that run in your cluster, can also take additional precautions with Secrets, such as avoiding writing secret data to nonvolatile storage.

Secrets are similar to ConfigMaps but are specifically intended to hold confidential data.

Caution:

Kubernetes Secrets are, by default, stored unencrypted in the API server's underlying data store (etcd). Anyone with API access can retrieve or modify a Secret, and so can anyone with access to etcd. Additionally, anyone who is authorized to create a Pod in a namespace can use that access to read any Secret in that namespace; this includes indirect access such as the ability to create a Deployment.

In order to safely use Secrets, take at least the following steps:

Enable Encryption at Rest for Secrets.
Enable or configure RBAC rules with least-privilege access to Secrets.
Restrict Secret access to specific containers.
Consider using external Secret store providers.

For more guidelines to manage and improve the security of your Secrets, refer to Good practices for Kubernetes Secrets.

See Information security for Secrets for more details.

Uses for Secrets

There are three main ways for a Pod to use a Secret:

As files in a volume mounted on one or more of its containers.
As container environment variable.
By the kubelet when pulling images for the Pod.

The Kubernetes control plane also uses Secrets; for example, bootstrap token Secrets are a mechanism to help automate node registration.

Alternatives to Secrets

Rather than using a Secret to protect confidential data, you can pick from alternatives.

Here are some of your options:

if your cloud-native component needs to authenticate to another application that you know is running within the same Kubernetes cluster, you can use a ServiceAccount and its tokens to identify your client.
there are third-party tools that you can run, either within or outside your cluster, that provide secrets management. For example, a service that Pods access over HTTPS, that reveals a secret if the client correctly authenticates (for example, with a ServiceAccount token).
for authentication, you can implement a custom signer for X.509 certificates, and use CertificateSigningRequests to let that custom signer issue certificates to Pods that need them.
you can use a device plugin to expose node-local encryption hardware to a specific Pod. For example, you can schedule trusted Pods onto nodes that provide a Trusted Platform Module, configured out-of-band.

You can also combine two or more of those options, including the option to use Secret objects themselves.

For example: implement (or deploy) an operator that fetches short-lived session tokens from an external service, and then creates Secrets based on those short-lived session tokens. Pods running in your cluster can make use of the session tokens, and operator ensures they are valid. This separation means that you can run Pods that are unaware of the exact mechanisms for issuing and refreshing those session tokens.

Working with Secrets

Creating a Secret

There are several options to create a Secret:

Constraints on Secret names and data

The name of a Secret object must be a valid DNS subdomain name.

You can specify the data and/or the stringData field when creating a configuration file for a Secret. The data and the stringData fields are optional. The values for all keys in the data field have to be base64-encoded strings. If the conversion to base64 string is not desirable, you can choose to specify the stringData field instead, which accepts arbitrary strings as values.

The keys of data and stringData must consist of alphanumeric characters, -, _ or .. All key-value pairs in the stringData field are internally merged into the data field. If a key appears in both the data and the stringData field, the value specified in the stringData field takes precedence.

Size limit

Individual secrets are limited to 1MiB in size. This is to discourage creation of very large secrets that could exhaust the API server and kubelet memory. However, creation of many smaller secrets could also exhaust memory. You can use a resource quota to limit the number of Secrets (or other resources) in a namespace.

Editing a Secret

You can edit an existing Secret using kubectl:

kubectl edit secrets mysecret

This opens your default editor and allows you to update the base64 encoded Secret values in the data field; for example:

# Please edit the object below. Lines beginning with a '#' will be ignored,
# and an empty file will abort the edit. If an error occurs while saving this file, it will be
# reopened with the relevant failures.
#
apiVersion: v1
data:
  username: YWRtaW4=
  password: MWYyZDFlMmU2N2Rm
kind: Secret
metadata:
  annotations:
    kubectl.kubernetes.io/last-applied-configuration: { ... }
  creationTimestamp: 2020-01-22T18:41:56Z
  name: mysecret
  namespace: default
  resourceVersion: "164619"
  uid: cfee02d6-c137-11e5-8d73-42010af00002
type: Opaque

That example manifest defines a Secret with two keys in the data field: username and password. The values are Base64 strings in the manifest; however, when you use the Secret with a Pod then the kubelet provides the decoded data to the Pod and its containers.

You can package many keys and values into one Secret, or use many Secrets, whichever is convenient.

Using a Secret

Secrets can be mounted as data volumes or exposed as environment variables to be used by a container in a Pod. Secrets can also be used by other parts of the system, without being directly exposed to the Pod. For example, Secrets can hold credentials that other parts of the system should use to interact with external systems on your behalf.

Secret volume sources are validated to ensure that the specified object reference actually points to an object of type Secret. Therefore, a Secret needs to be created before any Pods that depend on it.

If the Secret cannot be fetched (perhaps because it does not exist, or due to a temporary lack of connection to the API server) the kubelet periodically retries running that Pod. The kubelet also reports an Event for that Pod, including details of the problem fetching the Secret.

Optional Secrets

When you define a container environment variable based on a Secret, you can mark it as optional. The default is for the Secret to be required.

None of a Pod's containers will start until all non-optional Secrets are available.

If a Pod references a specific key in a Secret and that Secret does exist, but is missing the named key, the Pod fails during startup.

Using Secrets as files from a Pod

If you want to access data from a Secret in a Pod, one way to do that is to have Kubernetes make the value of that Secret be available as a file inside the filesystem of one or more of the Pod's containers.

To configure that, you:

Create a secret or use an existing one. Multiple Pods can reference the same secret.
Modify your Pod definition to add a volume under .spec.volumes[]. Name the volume anything, and have a .spec.volumes[].secret.secretName field equal to the name of the Secret object.
Add a .spec.containers[].volumeMounts[] to each container that needs the secret. Specify .spec.containers[].volumeMounts[].readOnly = true and .spec.containers[].volumeMounts[].mountPath to an unused directory name where you would like the secrets to appear.
Modify your image or command line so that the program looks for files in that directory. Each key in the secret data map becomes the filename under mountPath.

This is an example of a Pod that mounts a Secret named mysecret in a volume:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
  - name: mypod
    image: redis
    volumeMounts:
    - name: foo
      mountPath: "/etc/foo"
      readOnly: true
  volumes:
  - name: foo
    secret:
      secretName: mysecret
      optional: false # default setting; "mysecret" must exist

Each Secret you want to use needs to be referred to in .spec.volumes.

If there are multiple containers in the Pod, then each container needs its own volumeMounts block, but only one .spec.volumes is needed per Secret.

Note:

Versions of Kubernetes before v1.22 automatically created credentials for accessing the Kubernetes API. This older mechanism was based on creating token Secrets that could then be mounted into running Pods. In more recent versions, including Kubernetes v1.25, API credentials are obtained directly by using the TokenRequest API, and are mounted into Pods using a projected volume. The tokens obtained using this method have bounded lifetimes, and are automatically invalidated when the Pod they are mounted into is deleted.

You can still manually create a service account token Secret; for example, if you need a token that never expires. However, using the TokenRequest subresource to obtain a token to access the API is recommended instead. You can use the kubectl create token command to obtain a token from the TokenRequest API.

Projection of Secret keys to specific paths

You can also control the paths within the volume where Secret keys are projected. You can use the .spec.volumes[].secret.items field to change the target path of each key:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
  - name: mypod
    image: redis
    volumeMounts:
    - name: foo
      mountPath: "/etc/foo"
      readOnly: true
  volumes:
  - name: foo
    secret:
      secretName: mysecret
      items:
      - key: username
        path: my-group/my-username

What will happen:

the username key from mysecret is available to the container at the path /etc/foo/my-group/my-username instead of at /etc/foo/username.
the password key from that Secret object is not projected.

If .spec.volumes[].secret.items is used, only keys specified in items are projected. To consume all keys from the Secret, all of them must be listed in the items field.

If you list keys explicitly, then all listed keys must exist in the corresponding Secret. Otherwise, the volume is not created.

Secret files permissions

You can set the POSIX file access permission bits for a single Secret key. If you don't specify any permissions, 0644 is used by default. You can also set a default mode for the entire Secret volume and override per key if needed.

For example, you can specify a default mode like this:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  containers:
  - name: mypod
    image: redis
    volumeMounts:
    - name: foo
      mountPath: "/etc/foo"
  volumes:
  - name: foo
    secret:
      secretName: mysecret
      defaultMode: 0400

The secret is mounted on /etc/foo; all the files created by the secret volume mount have permission 0400.

Note: If you're defining a Pod or a Pod template using JSON, beware that the JSON specification doesn't support octal notation. You can use the decimal value for the defaultMode (for example, 0400 in octal is 256 in decimal) instead. If you're writing YAML, you can write the defaultMode in octal.

Consuming Secret values from volumes

Inside the container that mounts a secret volume, the secret keys appear as files. The secret values are base64 decoded and stored inside these files.

This is the result of commands executed inside the container from the example above:

ls /etc/foo/

The output is similar to:

username
password

cat /etc/foo/username

The output is similar to:

admin

cat /etc/foo/password

The output is similar to:

1f2d1e2e67df

The program in a container is responsible for reading the secret data from these files, as needed.

Mounted Secrets are updated automatically

When a volume contains data from a Secret, and that Secret is updated, Kubernetes tracks this and updates the data in the volume, using an eventually-consistent approach.

Note: A container using a Secret as a subPath volume mount does not receive automated Secret updates.

The kubelet keeps a cache of the current keys and values for the Secrets that are used in volumes for pods on that node. You can configure the way that the kubelet detects changes from the cached values. The configMapAndSecretChangeDetectionStrategy field in the kubelet configuration controls which strategy the kubelet uses. The default strategy is Watch.

Updates to Secrets can be either propagated by an API watch mechanism (the default), based on a cache with a defined time-to-live, or polled from the cluster API server on each kubelet synchronisation loop.

As a result, the total delay from the moment when the Secret is updated to the moment when new keys are projected to the Pod can be as long as the kubelet sync period + cache propagation delay, where the cache propagation delay depends on the chosen cache type (following the same order listed in the previous paragraph, these are: watch propagation delay, the configured cache TTL, or zero for direct polling).

Using Secrets as environment variables

To use a Secret in an environment variable in a Pod:

Create a Secret (or use an existing one). Multiple Pods can reference the same Secret.
Modify your Pod definition in each container that you wish to consume the value of a secret key to add an environment variable for each secret key you wish to consume. The environment variable that consumes the secret key should populate the secret's name and key in env[].valueFrom.secretKeyRef.
Modify your image and/or command line so that the program looks for values in the specified environment variables.

This is an example of a Pod that uses a Secret via environment variables:

apiVersion: v1
kind: Pod
metadata:
  name: secret-env-pod
spec:
  containers:
  - name: mycontainer
    image: redis
    env:
      - name: SECRET_USERNAME
        valueFrom:
          secretKeyRef:
            name: mysecret
            key: username
            optional: false # same as default; "mysecret" must exist
                            # and include a key named "username"
      - name: SECRET_PASSWORD
        valueFrom:
          secretKeyRef:
            name: mysecret
            key: password
            optional: false # same as default; "mysecret" must exist
                            # and include a key named "password"
  restartPolicy: Never

Invalid environment variables

Secrets used to populate environment variables by the envFrom field that have keys that are considered invalid environment variable names will have those keys skipped. The Pod is allowed to start.

If you define a Pod with an invalid variable name, the failed Pod startup includes an event with the reason set to InvalidVariableNames and a message that lists the skipped invalid keys. The following example shows a Pod that refers to a Secret named mysecret, where mysecret contains 2 invalid keys: 1badkey and 2alsobad.

kubectl get events

The output is similar to:

LASTSEEN   FIRSTSEEN   COUNT     NAME            KIND      SUBOBJECT                         TYPE      REASON
0s         0s          1         dapi-test-pod   Pod                                         Warning   InvalidEnvironmentVariableNames   kubelet, 127.0.0.1      Keys [1badkey, 2alsobad] from the EnvFrom secret default/mysecret were skipped since they are considered invalid environment variable names.

Consuming Secret values from environment variables

Inside a container that consumes a Secret using environment variables, the secret keys appear as normal environment variables. The values of those variables are the base64 decoded values of the secret data.

This is the result of commands executed inside the container from the example above:

echo "$SECRET_USERNAME"

The output is similar to:

admin

echo "$SECRET_PASSWORD"

The output is similar to:

1f2d1e2e67df

Note: If a container already consumes a Secret in an environment variable, a Secret update will not be seen by the container unless it is restarted. There are third party solutions for triggering restarts when secrets change.

Container image pull secrets

If you want to fetch container images from a private repository, you need a way for the kubelet on each node to authenticate to that repository. You can configure image pull secrets to make this possible. These secrets are configured at the Pod level.

The imagePullSecrets field for a Pod is a list of references to Secrets in the same namespace as the Pod. You can use an imagePullSecrets to pass image registry access credentials to the kubelet. The kubelet uses this information to pull a private image on behalf of your Pod. See PodSpec in the Pod API reference for more information about the imagePullSecrets field.

Using imagePullSecrets

The imagePullSecrets field is a list of references to secrets in the same namespace. You can use an imagePullSecrets to pass a secret that contains a Docker (or other) image registry password to the kubelet. The kubelet uses this information to pull a private image on behalf of your Pod. See the PodSpec API for more information about the imagePullSecrets field.

Manually specifying an imagePullSecret

You can learn how to specify imagePullSecrets from the container images documentation.

Arranging for imagePullSecrets to be automatically attached

You can manually create imagePullSecrets, and reference these from a ServiceAccount. Any Pods created with that ServiceAccount or created with that ServiceAccount by default, will get their imagePullSecrets field set to that of the service account. See Add ImagePullSecrets to a service account for a detailed explanation of that process.

Using Secrets with static Pods

You cannot use ConfigMaps or Secrets with static Pods.

Use cases

Use case: As container environment variables

Create a secret

apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
data:
  USER_NAME: YWRtaW4=
  PASSWORD: MWYyZDFlMmU2N2Rm

Create the Secret:

kubectl apply -f mysecret.yaml

Use envFrom to define all of the Secret's data as container environment variables. The key from the Secret becomes the environment variable name in the Pod.

apiVersion: v1
kind: Pod
metadata:
  name: secret-test-pod
spec:
  containers:
    - name: test-container
      image: registry.k8s.io/busybox
      command: [ "/bin/sh", "-c", "env" ]
      envFrom:
      - secretRef:
          name: mysecret
  restartPolicy: Never

Use case: Pod with SSH keys

Create a Secret containing some SSH keys:

kubectl create secret generic ssh-key-secret --from-file=ssh-privatekey=/path/to/.ssh/id_rsa --from-file=ssh-publickey=/path/to/.ssh/id_rsa.pub

The output is similar to:

secret "ssh-key-secret" created

You can also create a kustomization.yaml with a secretGenerator field containing ssh keys.

Caution:

Think carefully before sending your own SSH keys: other users of the cluster may have access to the Secret.

You could instead create an SSH private key representing a service identity that you want to be accessible to all the users with whom you share the Kubernetes cluster, and that you can revoke if the credentials are compromised.

Now you can create a Pod which references the secret with the SSH key and consumes it in a volume:

apiVersion: v1
kind: Pod
metadata:
  name: secret-test-pod
  labels:
    name: secret-test
spec:
  volumes:
  - name: secret-volume
    secret:
      secretName: ssh-key-secret
  containers:
  - name: ssh-test-container
    image: mySshImage
    volumeMounts:
    - name: secret-volume
      readOnly: true
      mountPath: "/etc/secret-volume"

When the container's command runs, the pieces of the key will be available in:

/etc/secret-volume/ssh-publickey
/etc/secret-volume/ssh-privatekey

The container is then free to use the secret data to establish an SSH connection.

Use case: Pods with prod / test credentials

This example illustrates a Pod which consumes a secret containing production credentials and another Pod which consumes a secret with test environment credentials.

You can create a kustomization.yaml with a secretGenerator field or run kubectl create secret.

kubectl create secret generic prod-db-secret --from-literal=username=produser --from-literal=password=Y4nys7f11

The output is similar to:

secret "prod-db-secret" created

You can also create a secret for test environment credentials.

kubectl create secret generic test-db-secret --from-literal=username=testuser --from-literal=password=iluvtests

The output is similar to:

secret "test-db-secret" created

Note:

Special characters such as $, \, *, =, and ! will be interpreted by your shell and require escaping.

In most shells, the easiest way to escape the password is to surround it with single quotes ('). For example, if your actual password is S!B\*d$zDsb=, you should execute the command this way:

kubectl create secret generic dev-db-secret --from-literal=username=devuser --from-literal=password='S!B\*d$zDsb='

You do not need to escape special characters in passwords from files (--from-file).

Now make the Pods:

cat <<EOF > pod.yaml
apiVersion: v1
kind: List
items:
- kind: Pod
  apiVersion: v1
  metadata:
    name: prod-db-client-pod
    labels:
      name: prod-db-client
  spec:
    volumes:
    - name: secret-volume
      secret:
        secretName: prod-db-secret
    containers:
    - name: db-client-container
      image: myClientImage
      volumeMounts:
      - name: secret-volume
        readOnly: true
        mountPath: "/etc/secret-volume"
- kind: Pod
  apiVersion: v1
  metadata:
    name: test-db-client-pod
    labels:
      name: test-db-client
  spec:
    volumes:
    - name: secret-volume
      secret:
        secretName: test-db-secret
    containers:
    - name: db-client-container
      image: myClientImage
      volumeMounts:
      - name: secret-volume
        readOnly: true
        mountPath: "/etc/secret-volume"
EOF

Add the pods to the same kustomization.yaml:

cat <<EOF >> kustomization.yaml
resources:
- pod.yaml
EOF

Apply all those objects on the API server by running:

kubectl apply -k .

Both containers will have the following files present on their filesystems with the values for each container's environment:

/etc/secret-volume/username
/etc/secret-volume/password

Note how the specs for the two Pods differ only in one field; this facilitates creating Pods with different capabilities from a common Pod template.

You could further simplify the base Pod specification by using two service accounts:

prod-user with the prod-db-secret
test-user with the test-db-secret

The Pod specification is shortened to:

apiVersion: v1
kind: Pod
metadata:
  name: prod-db-client-pod
  labels:
    name: prod-db-client
spec:
  serviceAccount: prod-db-client
  containers:
  - name: db-client-container
    image: myClientImage

Use case: dotfiles in a secret volume

You can make your data "hidden" by defining a key that begins with a dot. This key represents a dotfile or "hidden" file. For example, when the following secret is mounted into a volume, secret-volume:

apiVersion: v1
kind: Secret
metadata:
  name: dotfile-secret
data:
  .secret-file: dmFsdWUtMg0KDQo=
---
apiVersion: v1
kind: Pod
metadata:
  name: secret-dotfiles-pod
spec:
  volumes:
  - name: secret-volume
    secret:
      secretName: dotfile-secret
  containers:
  - name: dotfile-test-container
    image: registry.k8s.io/busybox
    command:
    - ls
    - "-l"
    - "/etc/secret-volume"
    volumeMounts:
    - name: secret-volume
      readOnly: true
      mountPath: "/etc/secret-volume"

The volume will contain a single file, called .secret-file, and the dotfile-test-container will have this file present at the path /etc/secret-volume/.secret-file.

Note: Files beginning with dot characters are hidden from the output of ls -l; you must use ls -la to see them when listing directory contents.

Use case: Secret visible to one container in a Pod

Consider a program that needs to handle HTTP requests, do some complex business logic, and then sign some messages with an HMAC. Because it has complex application logic, there might be an unnoticed remote file reading exploit in the server, which could expose the private key to an attacker.

This could be divided into two processes in two containers: a frontend container which handles user interaction and business logic, but which cannot see the private key; and a signer container that can see the private key, and responds to simple signing requests from the frontend (for example, over localhost networking).

With this partitioned approach, an attacker now has to trick the application server into doing something rather arbitrary, which may be harder than getting it to read a file.

Types of Secret

When creating a Secret, you can specify its type using the type field of the Secret resource, or certain equivalent kubectl command line flags (if available). The Secret type is used to facilitate programmatic handling of the Secret data.

Kubernetes provides several built-in types for some common usage scenarios. These types vary in terms of the validations performed and the constraints Kubernetes imposes on them.

Built-in Type	Usage
`Opaque`	arbitrary user-defined data
`kubernetes.io/service-account-token`	ServiceAccount token
`kubernetes.io/dockercfg`	serialized `~/.dockercfg` file
`kubernetes.io/dockerconfigjson`	serialized `~/.docker/config.json` file
`kubernetes.io/basic-auth`	credentials for basic authentication
`kubernetes.io/ssh-auth`	credentials for SSH authentication
`kubernetes.io/tls`	data for a TLS client or server
`bootstrap.kubernetes.io/token`	bootstrap token data

You can define and use your own Secret type by assigning a non-empty string as the type value for a Secret object (an empty string is treated as an Opaque type).

Kubernetes doesn't impose any constraints on the type name. However, if you are using one of the built-in types, you must meet all the requirements defined for that type.

If you are defining a type of secret that's for public use, follow the convention and structure the secret type to have your domain name before the name, separated by a /. For example: cloud-hosting.example.net/cloud-api-credentials.

Opaque secrets

Opaque is the default Secret type if omitted from a Secret configuration file. When you create a Secret using kubectl, you will use the generic subcommand to indicate an Opaque Secret type. For example, the following command creates an empty Secret of type Opaque.

kubectl create secret generic empty-secret
kubectl get secret empty-secret

The output looks like:

NAME           TYPE     DATA   AGE
empty-secret   Opaque   0      2m6s

The DATA column shows the number of data items stored in the Secret. In this case, 0 means you have created an empty Secret.

Service account token Secrets

A kubernetes.io/service-account-token type of Secret is used to store a token credential that identifies a service account.

Since 1.22, this type of Secret is no longer used to mount credentials into Pods, and obtaining tokens via the TokenRequest API is recommended instead of using service account token Secret objects. Tokens obtained from the TokenRequest API are more secure than ones stored in Secret objects, because they have a bounded lifetime and are not readable by other API clients. You can use the kubectl create token command to obtain a token from the TokenRequest API.

You should only create a service account token Secret object if you can't use the TokenRequest API to obtain a token, and the security exposure of persisting a non-expiring token credential in a readable API object is acceptable to you.

When using this Secret type, you need to ensure that the kubernetes.io/service-account.name annotation is set to an existing service account name. If you are creating both the ServiceAccount and the Secret objects, you should create the ServiceAccount object first.

After the Secret is created, a Kubernetes controller fills in some other fields such as the kubernetes.io/service-account.uid annotation, and the token key in the data field, which is populated with an authentication token.

The following example configuration declares a service account token Secret:

apiVersion: v1
kind: Secret
metadata:
  name: secret-sa-sample
  annotations:
    kubernetes.io/service-account.name: "sa-name"
type: kubernetes.io/service-account-token
data:
  # You can include additional key value pairs as you do with Opaque Secrets
  extra: YmFyCg==

After creating the Secret, wait for Kubernetes to populate the token key in the data field.

See the ServiceAccount documentation for more information on how service accounts work. You can also check the automountServiceAccountToken field and the serviceAccountName field of the Pod for information on referencing service account credentials from within Pods.

Docker config Secrets

You can use one of the following type values to create a Secret to store the credentials for accessing a container image registry:

kubernetes.io/dockercfg
kubernetes.io/dockerconfigjson

The kubernetes.io/dockercfg type is reserved to store a serialized ~/.dockercfg which is the legacy format for configuring Docker command line. When using this Secret type, you have to ensure the Secret data field contains a .dockercfg key whose value is content of a ~/.dockercfg file encoded in the base64 format.

The kubernetes.io/dockerconfigjson type is designed for storing a serialized JSON that follows the same format rules as the ~/.docker/config.json file which is a new format for ~/.dockercfg. When using this Secret type, the data field of the Secret object must contain a .dockerconfigjson key, in which the content for the ~/.docker/config.json file is provided as a base64 encoded string.

Below is an example for a kubernetes.io/dockercfg type of Secret:

apiVersion: v1
kind: Secret
metadata:
  name: secret-dockercfg
type: kubernetes.io/dockercfg
data:
  .dockercfg: |
        "<base64 encoded ~/.dockercfg file>"

Note: If you do not want to perform the base64 encoding, you can choose to use the stringData field instead.

When you create these types of Secrets using a manifest, the API server checks whether the expected key exists in the data field, and it verifies if the value provided can be parsed as a valid JSON. The API server doesn't validate if the JSON actually is a Docker config file.

When you do not have a Docker config file, or you want to use kubectl to create a Secret for accessing a container registry, you can do:

kubectl create secret docker-registry secret-tiger-docker \
  --docker-email=tiger@acme.example \
  --docker-username=tiger \
  --docker-password=pass1234 \
  --docker-server=my-registry.example:5000

That command creates a Secret of type kubernetes.io/dockerconfigjson. If you dump the .data.dockerconfigjson field from that new Secret and then decode it from base64:

kubectl get secret secret-tiger-docker -o jsonpath='{.data.*}' | base64 -d

then the output is equivalent to this JSON document (which is also a valid Docker configuration file):

{
  "auths": {
    "my-registry.example:5000": {
      "username": "tiger",
      "password": "pass1234",
      "email": "tiger@acme.example",
      "auth": "dGlnZXI6cGFzczEyMzQ="
    }
  }
}

Note: The auth value there is base64 encoded; it is obscured but not secret. Anyone who can read that Secret can learn the registry access bearer token.

Basic authentication Secret

The kubernetes.io/basic-auth type is provided for storing credentials needed for basic authentication. When using this Secret type, the data field of the Secret must contain one of the following two keys:

username: the user name for authentication
password: the password or token for authentication

Both values for the above two keys are base64 encoded strings. You can, of course, provide the clear text content using the stringData for Secret creation.

The following manifest is an example of a basic authentication Secret:

apiVersion: v1
kind: Secret
metadata:
  name: secret-basic-auth
type: kubernetes.io/basic-auth
stringData:
  username: admin      # required field for kubernetes.io/basic-auth
  password: t0p-Secret # required field for kubernetes.io/basic-auth

The basic authentication Secret type is provided only for convenience. You can create an Opaque type for credentials used for basic authentication. However, using the defined and public Secret type (kubernetes.io/basic-auth) helps other people to understand the purpose of your Secret, and sets a convention for what key names to expect. The Kubernetes API verifies that the required keys are set for a Secret of this type.

SSH authentication secrets

The builtin type kubernetes.io/ssh-auth is provided for storing data used in SSH authentication. When using this Secret type, you will have to specify a ssh-privatekey key-value pair in the data (or stringData) field as the SSH credential to use.

The following manifest is an example of a Secret used for SSH public/private key authentication:

apiVersion: v1
kind: Secret
metadata:
  name: secret-ssh-auth
type: kubernetes.io/ssh-auth
data:
  # the data is abbreviated in this example
  ssh-privatekey: |
          MIIEpQIBAAKCAQEAulqb/Y ...

The SSH authentication Secret type is provided only for user's convenience. You could instead create an Opaque type Secret for credentials used for SSH authentication. However, using the defined and public Secret type (kubernetes.io/ssh-auth) helps other people to understand the purpose of your Secret, and sets a convention for what key names to expect. and the API server does verify if the required keys are provided in a Secret configuration.

Caution: SSH private keys do not establish trusted communication between an SSH client and host server on their own. A secondary means of establishing trust is needed to mitigate "man in the middle" attacks, such as a known_hosts file added to a ConfigMap.

TLS secrets

Kubernetes provides a builtin Secret type kubernetes.io/tls for storing a certificate and its associated key that are typically used for TLS.

One common use for TLS secrets is to configure encryption in transit for an Ingress, but you can also use it with other resources or directly in your workload. When using this type of Secret, the tls.key and the tls.crt key must be provided in the data (or stringData) field of the Secret configuration, although the API server doesn't actually validate the values for each key.

The following YAML contains an example config for a TLS Secret:

apiVersion: v1
kind: Secret
metadata:
  name: secret-tls
type: kubernetes.io/tls
data:
  # the data is abbreviated in this example
  tls.crt: |
        MIIC2DCCAcCgAwIBAgIBATANBgkqh ...
  tls.key: |
        MIIEpgIBAAKCAQEA7yn3bRHQ5FHMQ ...

The TLS Secret type is provided for user's convenience. You can create an Opaque for credentials used for TLS server and/or client. However, using the builtin Secret type helps ensure the consistency of Secret format in your project; the API server does verify if the required keys are provided in a Secret configuration.

When creating a TLS Secret using kubectl, you can use the tls subcommand as shown in the following example:

kubectl create secret tls my-tls-secret \
  --cert=path/to/cert/file \
  --key=path/to/key/file

The public/private key pair must exist before hand. The public key certificate for --cert must be DER format as per Section 5.1 of RFC 7468, and must match the given private key for --key (PKCS #8 in DER format; Section 11 of RFC 7468).

Note:

A kubernetes.io/tls Secret stores the Base64-encoded DER data for keys and certificates. If you're familiar with PEM format for private keys and for certificates, the base64 data are the same as that format except that you omit the initial and the last lines that are used in PEM.

For example, for a certificate, you do not include --------BEGIN CERTIFICATE----- and -------END CERTIFICATE----.

Bootstrap token Secrets

A bootstrap token Secret can be created by explicitly specifying the Secret type to bootstrap.kubernetes.io/token. This type of Secret is designed for tokens used during the node bootstrap process. It stores tokens used to sign well-known ConfigMaps.

A bootstrap token Secret is usually created in the kube-system namespace and named in the form bootstrap-token-<token-id> where <token-id> is a 6 character string of the token ID.

As a Kubernetes manifest, a bootstrap token Secret might look like the following:

apiVersion: v1
kind: Secret
metadata:
  name: bootstrap-token-5emitj
  namespace: kube-system
type: bootstrap.kubernetes.io/token
data:
  auth-extra-groups: c3lzdGVtOmJvb3RzdHJhcHBlcnM6a3ViZWFkbTpkZWZhdWx0LW5vZGUtdG9rZW4=
  expiration: MjAyMC0wOS0xM1QwNDozOToxMFo=
  token-id: NWVtaXRq
  token-secret: a3E0Z2lodnN6emduMXAwcg==
  usage-bootstrap-authentication: dHJ1ZQ==
  usage-bootstrap-signing: dHJ1ZQ==

A bootstrap type Secret has the following keys specified under data:

token-id: A random 6 character string as the token identifier. Required.
token-secret: A random 16 character string as the actual token secret. Required.
description: A human-readable string that describes what the token is used for. Optional.
expiration: An absolute UTC time using RFC3339 specifying when the token should be expired. Optional.
usage-bootstrap-<usage>: A boolean flag indicating additional usage for the bootstrap token.
auth-extra-groups: A comma-separated list of group names that will be authenticated as in addition to the system:bootstrappers group.

The above YAML may look confusing because the values are all in base64 encoded strings. In fact, you can create an identical Secret using the following YAML:

apiVersion: v1
kind: Secret
metadata:
  # Note how the Secret is named
  name: bootstrap-token-5emitj
  # A bootstrap token Secret usually resides in the kube-system namespace
  namespace: kube-system
type: bootstrap.kubernetes.io/token
stringData:
  auth-extra-groups: "system:bootstrappers:kubeadm:default-node-token"
  expiration: "2020-09-13T04:39:10Z"
  # This token ID is used in the name
  token-id: "5emitj"
  token-secret: "kq4gihvszzgn1p0r"
  # This token can be used for authentication
  usage-bootstrap-authentication: "true"
  # and it can be used for signing
  usage-bootstrap-signing: "true"

Immutable Secrets

FEATURE STATE: Kubernetes v1.21 [stable]

Kubernetes lets you mark specific Secrets (and ConfigMaps) as immutable. Preventing changes to the data of an existing Secret has the following benefits:

protects you from accidental (or unwanted) updates that could cause applications outages
(for clusters that extensively use Secrets - at least tens of thousands of unique Secret to Pod mounts), switching to immutable Secrets improves the performance of your cluster by significantly reducing load on kube-apiserver. The kubelet does not need to maintain a [watch] on any Secrets that are marked as immutable.

Marking a Secret as immutable

You can create an immutable Secret by setting the immutable field to true. For example,

apiVersion: v1
kind: Secret
metadata:
  ...
data:
  ...
immutable: true

You can also update any existing mutable Secret to make it immutable.

Note: Once a Secret or ConfigMap is marked as immutable, it is not possible to revert this change nor to mutate the contents of the data field. You can only delete and recreate the Secret. Existing Pods maintain a mount point to the deleted Secret - it is recommended to recreate these pods.

Information security for Secrets

Although ConfigMap and Secret work similarly, Kubernetes applies some additional protection for Secret objects.

Secrets often hold values that span a spectrum of importance, many of which can cause escalations within Kubernetes (e.g. service account tokens) and to external systems. Even if an individual app can reason about the power of the Secrets it expects to interact with, other apps within the same namespace can render those assumptions invalid.

A Secret is only sent to a node if a Pod on that node requires it. For mounting secrets into Pods, the kubelet stores a copy of the data into a tmpfs so that the confidential data is not written to durable storage. Once the Pod that depends on the Secret is deleted, the kubelet deletes its local copy of the confidential data from the Secret.

There may be several containers in a Pod. By default, containers you define only have access to the default ServiceAccount and its related Secret. You must explicitly define environment variables or map a volume into a container in order to provide access to any other Secret.

There may be Secrets for several Pods on the same node. However, only the Secrets that a Pod requests are potentially visible within its containers. Therefore, one Pod does not have access to the Secrets of another Pod.

Warning: Any containers that run with privileged: true on a node can access all Secrets used on that node.

What's next

For guidelines to manage and improve the security of your Secrets, refer to Good practices for Kubernetes Secrets.
Learn how to manage Secrets using kubectl
Learn how to manage Secrets using config file
Learn how to manage Secrets using kustomize
Read the API reference for Secret

8.4 - Resource Management for Pods and Containers

When you specify a Pod, you can optionally specify how much of each resource a container needs. The most common resources to specify are CPU and memory (RAM); there are others.

When you specify the resource request for containers in a Pod, the kube-scheduler uses this information to decide which node to place the Pod on. When you specify a resource limit for a container, the kubelet enforces those limits so that the running container is not allowed to use more of that resource than the limit you set. The kubelet also reserves at least the request amount of that system resource specifically for that container to use.

Requests and limits

If the node where a Pod is running has enough of a resource available, it's possible (and allowed) for a container to use more resource than its request for that resource specifies. However, a container is not allowed to use more than its resource limit.

For example, if you set a memory request of 256 MiB for a container, and that container is in a Pod scheduled to a Node with 8GiB of memory and no other Pods, then the container can try to use more RAM.

If you set a memory limit of 4GiB for that container, the kubelet (and container runtime) enforce the limit. The runtime prevents the container from using more than the configured resource limit. For example: when a process in the container tries to consume more than the allowed amount of memory, the system kernel terminates the process that attempted the allocation, with an out of memory (OOM) error.

Limits can be implemented either reactively (the system intervenes once it sees a violation) or by enforcement (the system prevents the container from ever exceeding the limit). Different runtimes can have different ways to implement the same restrictions.

Note: If you specify a limit for a resource, but do not specify any request, and no admission-time mechanism has applied a default request for that resource, then Kubernetes copies the limit you specified and uses it as the requested value for the resource.

Resource types

CPU and memory are each a resource type. A resource type has a base unit. CPU represents compute processing and is specified in units of Kubernetes CPUs. Memory is specified in units of bytes. For Linux workloads, you can specify huge page resources. Huge pages are a Linux-specific feature where the node kernel allocates blocks of memory that are much larger than the default page size.

For example, on a system where the default page size is 4KiB, you could specify a limit, hugepages-2Mi: 80Mi. If the container tries allocating over 40 2MiB huge pages (a total of 80 MiB), that allocation fails.

Note: You cannot overcommit hugepages-* resources. This is different from the memory and cpu resources.

CPU and memory are collectively referred to as compute resources, or resources. Compute resources are measurable quantities that can be requested, allocated, and consumed. They are distinct from API resources. API resources, such as Pods and Services are objects that can be read and modified through the Kubernetes API server.

Resource requests and limits of Pod and container

For each container, you can specify resource limits and requests, including the following:

spec.containers[].resources.limits.cpu
spec.containers[].resources.limits.memory
spec.containers[].resources.limits.hugepages-<size>
spec.containers[].resources.requests.cpu
spec.containers[].resources.requests.memory
spec.containers[].resources.requests.hugepages-<size>

Although you can only specify requests and limits for individual containers, it is also useful to think about the overall resource requests and limits for a Pod. For a particular resource, a Pod resource request/limit is the sum of the resource requests/limits of that type for each container in the Pod.

Resource units in Kubernetes

CPU resource units

Limits and requests for CPU resources are measured in cpu units. In Kubernetes, 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core, depending on whether the node is a physical host or a virtual machine running inside a physical machine.

Fractional requests are allowed. When you define a container with spec.containers[].resources.requests.cpu set to 0.5, you are requesting half as much CPU time compared to if you asked for 1.0 CPU. For CPU resource units, the quantity expression 0.1 is equivalent to the expression 100m, which can be read as "one hundred millicpu". Some people say "one hundred millicores", and this is understood to mean the same thing.

CPU resource is always specified as an absolute amount of resource, never as a relative amount. For example, 500m CPU represents the roughly same amount of computing power whether that container runs on a single-core, dual-core, or 48-core machine.

Note: Kubernetes doesn't allow you to specify CPU resources with a precision finer than 1m. Because of this, it's useful to specify CPU units less than 1.0 or 1000m using the milliCPU form; for example, 5m rather than 0.005.

Memory resource units

Limits and requests for memory are measured in bytes. You can express memory as a plain integer or as a fixed-point number using one of these quantity suffixes: E, P, T, G, M, k. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki. For example, the following represent roughly the same value:

128974848, 129e6, 129M,  128974848000m, 123Mi

Pay attention to the case of the suffixes. If you request 400m of memory, this is a request for 0.4 bytes. Someone who types that probably meant to ask for 400 mebibytes (400Mi) or 400 megabytes (400M).

Container resources example

The following Pod has two containers. Both containers are defined with a request for 0.25 CPU and 64MiB (2²⁶ bytes) of memory. Each container has a limit of 0.5 CPU and 128MiB of memory. You can say the Pod has a request of 0.5 CPU and 128 MiB of memory, and a limit of 1 CPU and 256MiB of memory.

---
apiVersion: v1
kind: Pod
metadata:
  name: frontend
spec:
  containers:
  - name: app
    image: images.my-company.example/app:v4
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
      limits:
        memory: "128Mi"
        cpu: "500m"
  - name: log-aggregator
    image: images.my-company.example/log-aggregator:v6
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
      limits:
        memory: "128Mi"
        cpu: "500m"

How Pods with resource requests are scheduled

When you create a Pod, the Kubernetes scheduler selects a node for the Pod to run on. Each node has a maximum capacity for each of the resource types: the amount of CPU and memory it can provide for Pods. The scheduler ensures that, for each resource type, the sum of the resource requests of the scheduled containers is less than the capacity of the node. Note that although actual memory or CPU resource usage on nodes is very low, the scheduler still refuses to place a Pod on a node if the capacity check fails. This protects against a resource shortage on a node when resource usage later increases, for example, during a daily peak in request rate.

How Kubernetes applies resource requests and limits

When the kubelet starts a container as part of a Pod, the kubelet passes that container's requests and limits for memory and CPU to the container runtime.

On Linux, the container runtime typically configures kernel cgroups that apply and enforce the limits you defined.

The CPU limit defines a hard ceiling on how much CPU time that the container can use. During each scheduling interval (time slice), the Linux kernel checks to see if this limit is exceeded; if so, the kernel waits before allowing that cgroup to resume execution.
The CPU request typically defines a weighting. If several different containers (cgroups) want to run on a contended system, workloads with larger CPU requests are allocated more CPU time than workloads with small requests.
The memory request is mainly used during (Kubernetes) Pod scheduling. On a node that uses cgroups v2, the container runtime might use the memory request as a hint to set memory.min and memory.low.
The memory limit defines a memory limit for that cgroup. If the container tries to allocate more memory than this limit, the Linux kernel out-of-memory subsystem activates and, typically, intervenes by stopping one of the processes in the container that tried to allocate memory. If that process is the container's PID 1, and the container is marked as restartable, Kubernetes restarts the container.
The memory limit for the Pod or container can also apply to pages in memory backed volumes, such as an emptyDir. The kubelet tracks tmpfs emptyDir volumes as container memory use, rather than as local ephemeral storage.

If a container exceeds its memory request and the node that it runs on becomes short of memory overall, it is likely that the Pod the container belongs to will be evicted.

A container might or might not be allowed to exceed its CPU limit for extended periods of time. However, container runtimes don't terminate Pods or containers for excessive CPU usage.

To determine whether a container cannot be scheduled or is being killed due to resource limits, see the Troubleshooting section.

Monitoring compute & memory resource usage

The kubelet reports the resource usage of a Pod as part of the Pod status.

If optional tools for monitoring are available in your cluster, then Pod resource usage can be retrieved either from the Metrics API directly or from your monitoring tools.

Local ephemeral storage

FEATURE STATE: Kubernetes v1.25 [stable]

Nodes have local ephemeral storage, backed by locally-attached writeable devices or, sometimes, by RAM. "Ephemeral" means that there is no long-term guarantee about durability.

Pods use ephemeral local storage for scratch space, caching, and for logs. The kubelet can provide scratch space to Pods using local ephemeral storage to mount emptyDir volumes into containers.

The kubelet also uses this kind of storage to hold node-level container logs, container images, and the writable layers of running containers.

Caution: If a node fails, the data in its ephemeral storage can be lost. Your applications cannot expect any performance SLAs (disk IOPS for example) from local ephemeral storage.

As a beta feature, Kubernetes lets you track, reserve and limit the amount of ephemeral local storage a Pod can consume.

Configurations for local ephemeral storage

Kubernetes supports two ways to configure local ephemeral storage on a node:

Single filesystem
Two filesystems

In this configuration, you place all different kinds of ephemeral local data (emptyDir volumes, writeable layers, container images, logs) into one filesystem. The most effective way to configure the kubelet means dedicating this filesystem to Kubernetes (kubelet) data.

The kubelet also writes node-level container logs and treats these similarly to ephemeral local storage.

The kubelet writes logs to files inside its configured log directory (/var/log by default); and has a base directory for other locally stored data (/var/lib/kubelet by default).

Typically, both /var/lib/kubelet and /var/log are on the system root filesystem, and the kubelet is designed with that layout in mind.

Your node can have as many other filesystems, not used for Kubernetes, as you like.

You have a filesystem on the node that you're using for ephemeral data that comes from running Pods: logs, and emptyDir volumes. You can use this filesystem for other data (for example: system logs not related to Kubernetes); it can even be the root filesystem.

The kubelet also writes node-level container logs into the first filesystem, and treats these similarly to ephemeral local storage.

You also use a separate filesystem, backed by a different logical storage device. In this configuration, the directory where you tell the kubelet to place container image layers and writeable layers is on this second filesystem.

The first filesystem does not hold any image layers or writeable layers.

Your node can have as many other filesystems, not used for Kubernetes, as you like.

The kubelet can measure how much local storage it is using. It does this provided that you have set up the node using one of the supported configurations for local ephemeral storage.

If you have a different configuration, then the kubelet does not apply resource limits for ephemeral local storage.

Note: The kubelet tracks tmpfs emptyDir volumes as container memory use, rather than as local ephemeral storage.

Note: The kubelet will only track the root filesystem for ephemeral storage. OS layouts that mount a separate disk to /var/lib/kubelet or /var/lib/containers will not report ephemeral storage correctly.

Setting requests and limits for local ephemeral storage

You can specify ephemeral-storage for managing local ephemeral storage. Each container of a Pod can specify either or both of the following:

spec.containers[].resources.limits.ephemeral-storage
spec.containers[].resources.requests.ephemeral-storage

Limits and requests for ephemeral-storage are measured in byte quantities. You can express storage as a plain integer or as a fixed-point number using one of these suffixes: E, P, T, G, M, k. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki. For example, the following quantities all represent roughly the same value:

128974848
129e6
129M
123Mi

Pay attention to the case of the suffixes. If you request 400m of ephemeral-storage, this is a request for 0.4 bytes. Someone who types that probably meant to ask for 400 mebibytes (400Mi) or 400 megabytes (400M).

In the following example, the Pod has two containers. Each container has a request of 2GiB of local ephemeral storage. Each container has a limit of 4GiB of local ephemeral storage. Therefore, the Pod has a request of 4GiB of local ephemeral storage, and a limit of 8GiB of local ephemeral storage.

apiVersion: v1
kind: Pod
metadata:
  name: frontend
spec:
  containers:
  - name: app
    image: images.my-company.example/app:v4
    resources:
      requests:
        ephemeral-storage: "2Gi"
      limits:
        ephemeral-storage: "4Gi"
    volumeMounts:
    - name: ephemeral
      mountPath: "/tmp"
  - name: log-aggregator
    image: images.my-company.example/log-aggregator:v6
    resources:
      requests:
        ephemeral-storage: "2Gi"
      limits:
        ephemeral-storage: "4Gi"
    volumeMounts:
    - name: ephemeral
      mountPath: "/tmp"
  volumes:
    - name: ephemeral
      emptyDir: {}

How Pods with ephemeral-storage requests are scheduled

When you create a Pod, the Kubernetes scheduler selects a node for the Pod to run on. Each node has a maximum amount of local ephemeral storage it can provide for Pods. For more information, see Node Allocatable.

The scheduler ensures that the sum of the resource requests of the scheduled containers is less than the capacity of the node.

Ephemeral storage consumption management

If the kubelet is managing local ephemeral storage as a resource, then the kubelet measures storage use in:

emptyDir volumes, except tmpfs emptyDir volumes
directories holding node-level logs
writeable container layers

If a Pod is using more ephemeral storage than you allow it to, the kubelet sets an eviction signal that triggers Pod eviction.

For container-level isolation, if a container's writable layer and log usage exceeds its storage limit, the kubelet marks the Pod for eviction.

For pod-level isolation the kubelet works out an overall Pod storage limit by summing the limits for the containers in that Pod. In this case, if the sum of the local ephemeral storage usage from all containers and also the Pod's emptyDir volumes exceeds the overall Pod storage limit, then the kubelet also marks the Pod for eviction.

Caution:

If the kubelet is not measuring local ephemeral storage, then a Pod that exceeds its local storage limit will not be evicted for breaching local storage resource limits.

However, if the filesystem space for writeable container layers, node-level logs, or emptyDir volumes falls low, the node taints itself as short on local storage and this taint triggers eviction for any Pods that don't specifically tolerate the taint.

See the supported configurations for ephemeral local storage.

The kubelet supports different ways to measure Pod storage use:

Periodic scanning
Filesystem project quota

The kubelet performs regular, scheduled checks that scan each emptyDir volume, container log directory, and writeable container layer.

The scan measures how much space is used.

Note:

In this mode, the kubelet does not track open file descriptors for deleted files.

If you (or a container) create a file inside an emptyDir volume, something then opens that file, and you delete the file while it is still open, then the inode for the deleted file stays until you close that file but the kubelet does not categorize the space as in use.

FEATURE STATE: Kubernetes v1.15 [alpha]

Project quotas are an operating-system level feature for managing storage use on filesystems. With Kubernetes, you can enable project quotas for monitoring storage use. Make sure that the filesystem backing the emptyDir volumes, on the node, provides project quota support. For example, XFS and ext4fs offer project quotas.

Note: Project quotas let you monitor storage use; they do not enforce limits.

Kubernetes uses project IDs starting from 1048576. The IDs in use are registered in /etc/projects and /etc/projid. If project IDs in this range are used for other purposes on the system, those project IDs must be registered in /etc/projects and /etc/projid so that Kubernetes does not use them.

Quotas are faster and more accurate than directory scanning. When a directory is assigned to a project, all files created under a directory are created in that project, and the kernel merely has to keep track of how many blocks are in use by files in that project. If a file is created and deleted, but has an open file descriptor, it continues to consume space. Quota tracking records that space accurately whereas directory scans overlook the storage used by deleted files.

If you want to use project quotas, you should:

Enable the LocalStorageCapacityIsolationFSQuotaMonitoring=true feature gate using the featureGates field in the kubelet configuration or the --feature-gates command line flag.
Ensure that the root filesystem (or optional runtime filesystem) has project quotas enabled. All XFS filesystems support project quotas. For ext4 filesystems, you need to enable the project quota tracking feature while the filesystem is not mounted.
```
# For ext4, with /dev/block-device not mounted
sudo tune2fs -O project -Q prjquota /dev/block-device
```
Ensure that the root filesystem (or optional runtime filesystem) is mounted with project quotas enabled. For both XFS and ext4fs, the mount option is named prjquota.

Extended resources

Extended resources are fully-qualified resource names outside the kubernetes.io domain. They allow cluster operators to advertise and users to consume the non-Kubernetes-built-in resources.

There are two steps required to use Extended Resources. First, the cluster operator must advertise an Extended Resource. Second, users must request the Extended Resource in Pods.

Managing extended resources

Node-level extended resources

Node-level extended resources are tied to nodes.

Device plugin managed resources

See Device Plugin for how to advertise device plugin managed resources on each node.

Other resources

To advertise a new node-level extended resource, the cluster operator can submit a PATCH HTTP request to the API server to specify the available quantity in the status.capacity for a node in the cluster. After this operation, the node's status.capacity will include a new resource. The status.allocatable field is updated automatically with the new resource asynchronously by the kubelet.

Because the scheduler uses the node's status.allocatable value when evaluating Pod fitness, the scheduler only takes account of the new value after that asynchronous update. There may be a short delay between patching the node capacity with a new resource and the time when the first Pod that requests the resource can be scheduled on that node.

Example:

Here is an example showing how to use curl to form an HTTP request that advertises five "example.com/foo" resources on node k8s-node-1 whose master is k8s-master.

curl --header "Content-Type: application/json-patch+json" \
--request PATCH \
--data '[{"op": "add", "path": "/status/capacity/example.com~1foo", "value": "5"}]' \
http://k8s-master:8080/api/v1/nodes/k8s-node-1/status

Note: In the preceding request, ~1 is the encoding for the character / in the patch path. The operation path value in JSON-Patch is interpreted as a JSON-Pointer. For more details, see IETF RFC 6901, section 3.

Cluster-level extended resources

Cluster-level extended resources are not tied to nodes. They are usually managed by scheduler extenders, which handle the resource consumption and resource quota.

You can specify the extended resources that are handled by scheduler extenders in scheduler configuration

Example:

The following configuration for a scheduler policy indicates that the cluster-level extended resource "example.com/foo" is handled by the scheduler extender.

The scheduler sends a Pod to the scheduler extender only if the Pod requests "example.com/foo".
The ignoredByScheduler field specifies that the scheduler does not check the "example.com/foo" resource in its PodFitsResources predicate.

{
  "kind": "Policy",
  "apiVersion": "v1",
  "extenders": [
    {
      "urlPrefix":"<extender-endpoint>",
      "bindVerb": "bind",
      "managedResources": [
        {
          "name": "example.com/foo",
          "ignoredByScheduler": true
        }
      ]
    }
  ]
}

Consuming extended resources

Users can consume extended resources in Pod specs like CPU and memory. The scheduler takes care of the resource accounting so that no more than the available amount is simultaneously allocated to Pods.

The API server restricts quantities of extended resources to whole numbers. Examples of valid quantities are 3, 3000m and 3Ki. Examples of invalid quantities are 0.5 and 1500m.

Note: Extended resources replace Opaque Integer Resources. Users can use any domain name prefix other than kubernetes.io which is reserved.

To consume an extended resource in a Pod, include the resource name as a key in the spec.containers[].resources.limits map in the container spec.

Note: Extended resources cannot be overcommitted, so request and limit must be equal if both are present in a container spec.

A Pod is scheduled only if all of the resource requests are satisfied, including CPU, memory and any extended resources. The Pod remains in the PENDING state as long as the resource request cannot be satisfied.

Example:

The Pod below requests 2 CPUs and 1 "example.com/foo" (an extended resource).

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  containers:
  - name: my-container
    image: myimage
    resources:
      requests:
        cpu: 2
        example.com/foo: 1
      limits:
        example.com/foo: 1

PID limiting

Process ID (PID) limits allow for the configuration of a kubelet to limit the number of PIDs that a given Pod can consume. See PID Limiting for information.

Troubleshooting

My Pods are pending with event message `FailedScheduling`

If the scheduler cannot find any node where a Pod can fit, the Pod remains unscheduled until a place can be found. An Event is produced each time the scheduler fails to find a place for the Pod. You can use kubectl to view the events for a Pod; for example:

kubectl describe pod frontend | grep -A 9999999999 Events

Events:
  Type     Reason            Age   From               Message
  ----     ------            ----  ----               -------
  Warning  FailedScheduling  23s   default-scheduler  0/42 nodes available: insufficient cpu

In the preceding example, the Pod named "frontend" fails to be scheduled due to insufficient CPU resource on any node. Similar error messages can also suggest failure due to insufficient memory (PodExceedsFreeMemory). In general, if a Pod is pending with a message of this type, there are several things to try:

Add more nodes to the cluster.
Terminate unneeded Pods to make room for pending Pods.
Check that the Pod is not larger than all the nodes. For example, if all the nodes have a capacity of cpu: 1, then a Pod with a request of cpu: 1.1 will never be scheduled.
Check for node taints. If most of your nodes are tainted, and the new Pod does not tolerate that taint, the scheduler only considers placements onto the remaining nodes that don't have that taint.

You can check node capacities and amounts allocated with the kubectl describe nodes command. For example:

kubectl describe nodes e2e-test-node-pool-4lw4

Name:            e2e-test-node-pool-4lw4
[ ... lines removed for clarity ...]
Capacity:
 cpu:                               2
 memory:                            7679792Ki
 pods:                              110
Allocatable:
 cpu:                               1800m
 memory:                            7474992Ki
 pods:                              110
[ ... lines removed for clarity ...]
Non-terminated Pods:        (5 in total)
  Namespace    Name                                  CPU Requests  CPU Limits  Memory Requests  Memory Limits
  ---------    ----                                  ------------  ----------  ---------------  -------------
  kube-system  fluentd-gcp-v1.38-28bv1               100m (5%)     0 (0%)      200Mi (2%)       200Mi (2%)
  kube-system  kube-dns-3297075139-61lj3             260m (13%)    0 (0%)      100Mi (1%)       170Mi (2%)
  kube-system  kube-proxy-e2e-test-...               100m (5%)     0 (0%)      0 (0%)           0 (0%)
  kube-system  monitoring-influxdb-grafana-v4-z1m12  200m (10%)    200m (10%)  600Mi (8%)       600Mi (8%)
  kube-system  node-problem-detector-v0.1-fj7m3      20m (1%)      200m (10%)  20Mi (0%)        100Mi (1%)
Allocated resources:
  (Total limits may be over 100 percent, i.e., overcommitted.)
  CPU Requests    CPU Limits    Memory Requests    Memory Limits
  ------------    ----------    ---------------    -------------
  680m (34%)      400m (20%)    920Mi (11%)        1070Mi (13%)

In the preceding output, you can see that if a Pod requests more than 1.120 CPUs or more than 6.23Gi of memory, that Pod will not fit on the node.

By looking at the “Pods” section, you can see which Pods are taking up space on the node.

The amount of resources available to Pods is less than the node capacity because system daemons use a portion of the available resources. Within the Kubernetes API, each Node has a .status.allocatable field (see NodeStatus for details).

The .status.allocatable field describes the amount of resources that are available to Pods on that node (for example: 15 virtual CPUs and 7538 MiB of memory). For more information on node allocatable resources in Kubernetes, see Reserve Compute Resources for System Daemons.

You can configure resource quotas to limit the total amount of resources that a namespace can consume. Kubernetes enforces quotas for objects in particular namespace when there is a ResourceQuota in that namespace. For example, if you assign specific namespaces to different teams, you can add ResourceQuotas into those namespaces. Setting resource quotas helps to prevent one team from using so much of any resource that this over-use affects other teams.

You should also consider what access you grant to that namespace: full write access to a namespace allows someone with that access to remove any resource, including a configured ResourceQuota.

My container is terminated

Your container might get terminated because it is resource-starved. To check whether a container is being killed because it is hitting a resource limit, call kubectl describe pod on the Pod of interest:

kubectl describe pod simmemleak-hra99

The output is similar to:

Name:                           simmemleak-hra99
Namespace:                      default
Image(s):                       saadali/simmemleak
Node:                           kubernetes-node-tf0f/10.240.216.66
Labels:                         name=simmemleak
Status:                         Running
Reason:
Message:
IP:                             10.244.2.75
Containers:
  simmemleak:
    Image:  saadali/simmemleak:latest
    Limits:
      cpu:          100m
      memory:       50Mi
    State:          Running
      Started:      Tue, 07 Jul 2019 12:54:41 -0700
    Last State:     Terminated
      Reason:       OOMKilled
      Exit Code:    137
      Started:      Fri, 07 Jul 2019 12:54:30 -0700
      Finished:     Fri, 07 Jul 2019 12:54:33 -0700
    Ready:          False
    Restart Count:  5
Conditions:
  Type      Status
  Ready     False
Events:
  Type    Reason     Age   From               Message
  ----    ------     ----  ----               -------
  Normal  Scheduled  42s   default-scheduler  Successfully assigned simmemleak-hra99 to kubernetes-node-tf0f
  Normal  Pulled     41s   kubelet            Container image "saadali/simmemleak:latest" already present on machine
  Normal  Created    41s   kubelet            Created container simmemleak
  Normal  Started    40s   kubelet            Started container simmemleak
  Normal  Killing    32s   kubelet            Killing container with id ead3fb35-5cf5-44ed-9ae1-488115be66c6: Need to kill Pod

In the preceding example, the Restart Count: 5 indicates that the simmemleak container in the Pod was terminated and restarted five times (so far). The OOMKilled reason shows that the container tried to use more memory than its limit.

Your next step might be to check the application code for a memory leak. If you find that the application is behaving how you expect, consider setting a higher memory limit (and possibly request) for that container.

What's next

Get hands-on experience assigning Memory resources to containers and Pods.
Get hands-on experience assigning CPU resources to containers and Pods.
Read how the API reference defines a container and its resource requirements
Read about project quotas in XFS
Read more about the kube-scheduler configuration reference (v1beta3)

8.5 - Organizing Cluster Access Using kubeconfig Files

Use kubeconfig files to organize information about clusters, users, namespaces, and authentication mechanisms. The kubectl command-line tool uses kubeconfig files to find the information it needs to choose a cluster and communicate with the API server of a cluster.

Note: A file that is used to configure access to clusters is called a kubeconfig file. This is a generic way of referring to configuration files. It does not mean that there is a file named kubeconfig.

Warning: Only use kubeconfig files from trusted sources. Using a specially-crafted kubeconfig file could result in malicious code execution or file exposure. If you must use an untrusted kubeconfig file, inspect it carefully first, much as you would a shell script.

By default, kubectl looks for a file named config in the $HOME/.kube directory. You can specify other kubeconfig files by setting the KUBECONFIG environment variable or by setting the --kubeconfig flag.

For step-by-step instructions on creating and specifying kubeconfig files, see Configure Access to Multiple Clusters.

Supporting multiple clusters, users, and authentication mechanisms

Suppose you have several clusters, and your users and components authenticate in a variety of ways. For example:

A running kubelet might authenticate using certificates.
A user might authenticate using tokens.
Administrators might have sets of certificates that they provide to individual users.

With kubeconfig files, you can organize your clusters, users, and namespaces. You can also define contexts to quickly and easily switch between clusters and namespaces.

Context

A context element in a kubeconfig file is used to group access parameters under a convenient name. Each context has three parameters: cluster, namespace, and user. By default, the kubectl command-line tool uses parameters from the current context to communicate with the cluster.

To choose the current context:

kubectl config use-context

The KUBECONFIG environment variable

The KUBECONFIG environment variable holds a list of kubeconfig files. For Linux and Mac, the list is colon-delimited. For Windows, the list is semicolon-delimited. The KUBECONFIG environment variable is not required. If the KUBECONFIG environment variable doesn't exist, kubectl uses the default kubeconfig file, $HOME/.kube/config.

If the KUBECONFIG environment variable does exist, kubectl uses an effective configuration that is the result of merging the files listed in the KUBECONFIG environment variable.

Merging kubeconfig files

To see your configuration, enter this command:

kubectl config view

As described previously, the output might be from a single kubeconfig file, or it might be the result of merging several kubeconfig files.

Here are the rules that kubectl uses when it merges kubeconfig files:

If the --kubeconfig flag is set, use only the specified file. Do not merge. Only one instance of this flag is allowed.

Otherwise, if the KUBECONFIG environment variable is set, use it as a list of files that should be merged. Merge the files listed in the KUBECONFIG environment variable according to these rules:
- Ignore empty filenames.
- Produce errors for files with content that cannot be deserialized.
- The first file to set a particular value or map key wins.
- Never change the value or map key. Example: Preserve the context of the first file to set current-context. Example: If two files specify a red-user, use only values from the first file's red-user. Even if the second file has non-conflicting entries under red-user, discard them.
For an example of setting the KUBECONFIG environment variable, see Setting the KUBECONFIG environment variable.

Otherwise, use the default kubeconfig file, $HOME/.kube/config, with no merging.
Determine the context to use based on the first hit in this chain:
1. Use the --context command-line flag if it exists.
2. Use the current-context from the merged kubeconfig files.
An empty context is allowed at this point.
Determine the cluster and user. At this point, there might or might not be a context. Determine the cluster and user based on the first hit in this chain, which is run twice: once for user and once for cluster:
1. Use a command-line flag if it exists: --user or --cluster.
2. If the context is non-empty, take the user or cluster from the context.
The user and cluster can be empty at this point.
Determine the actual cluster information to use. At this point, there might or might not be cluster information. Build each piece of the cluster information based on this chain; the first hit wins:
1. Use command line flags if they exist: --server, --certificate-authority, --insecure-skip-tls-verify.
2. If any cluster information attributes exist from the merged kubeconfig files, use them.
3. If there is no server location, fail.
Determine the actual user information to use. Build user information using the same rules as cluster information, except allow only one authentication technique per user:
1. Use command line flags if they exist: --client-certificate, --client-key, --username, --password, --token.
2. Use the user fields from the merged kubeconfig files.
3. If there are two conflicting techniques, fail.
For any information still missing, use default values and potentially prompt for authentication information.

File references

File and path references in a kubeconfig file are relative to the location of the kubeconfig file. File references on the command line are relative to the current working directory. In $HOME/.kube/config, relative paths are stored relatively, and absolute paths are stored absolutely.

Proxy

You can configure kubectl to use a proxy per cluster using proxy-url in your kubeconfig file, like this:

apiVersion: v1
kind: Config

clusters:
- cluster:
    proxy-url: http://proxy.example.org:3128
    server: https://k8s.example.org/k8s/clusters/c-xxyyzz
  name: development

users:
- name: developer

contexts:
- context:
  name: development

What's next

8.6 - Resource Management for Windows nodes

This page outlines the differences in how resources are managed between Linux and Windows.

On Linux nodes, cgroups are used as a pod boundary for resource control. Containers are created within that boundary for network, process and file system isolation. The Linux cgroup APIs can be used to gather CPU, I/O, and memory use statistics.

In contrast, Windows uses a job object per container with a system namespace filter to contain all processes in a container and provide logical isolation from the host. (Job objects are a Windows process isolation mechanism and are different from what Kubernetes refers to as a Job).

There is no way to run a Windows container without the namespace filtering in place. This means that system privileges cannot be asserted in the context of the host, and thus privileged containers are not available on Windows. Containers cannot assume an identity from the host because the Security Account Manager (SAM) is separate.

Memory management

Windows does not have an out-of-memory process killer as Linux does. Windows always treats all user-mode memory allocations as virtual, and pagefiles are mandatory.

Windows nodes do not overcommit memory for processes. The net effect is that Windows won't reach out of memory conditions the same way Linux does, and processes page to disk instead of being subject to out of memory (OOM) termination. If memory is over-provisioned and all physical memory is exhausted, then paging can slow down performance.

CPU management

Windows can limit the amount of CPU time allocated for different processes but cannot guarantee a minimum amount of CPU time.

On Windows, the kubelet supports a command-line flag to set the scheduling priority of the kubelet process: --windows-priorityclass. This flag allows the kubelet process to get more CPU time slices when compared to other processes running on the Windows host. More information on the allowable values and their meaning is available at Windows Priority Classes. To ensure that running Pods do not starve the kubelet of CPU cycles, set this flag to ABOVE_NORMAL_PRIORITY_CLASS or above.

Resource reservation

To account for memory and CPU used by the operating system, the container runtime, and by Kubernetes host processes such as the kubelet, you can (and should) reserve memory and CPU resources with the --kube-reserved and/or --system-reserved kubelet flags. On Windows these values are only used to calculate the node's allocatable resources.

Caution:

As you deploy workloads, set resource memory and CPU limits on containers. This also subtracts from NodeAllocatable and helps the cluster-wide scheduler in determining which pods to place on which nodes.

Scheduling pods without limits may over-provision the Windows nodes and in extreme cases can cause the nodes to become unhealthy.

On Windows, a good practice is to reserve at least 2GiB of memory.

To determine how much CPU to reserve, identify the maximum pod density for each node and monitor the CPU usage of the system services running there, then choose a value that meets your workload needs.

9 - Security

Concepts for keeping your cloud-native workload secure.

9.1 - Overview of Cloud Native Security

A model for thinking about Kubernetes security in the context of Cloud Native security.

This overview defines a model for thinking about Kubernetes security in the context of Cloud Native security.

Warning: This container security model provides suggestions, not proven information security policies.

The 4C's of Cloud Native security

You can think about security in layers. The 4C's of Cloud Native security are Cloud, Clusters, Containers, and Code.

Note: This layered approach augments the defense in depth computing approach to security, which is widely regarded as a best practice for securing software systems.

Each layer of the Cloud Native security model builds upon the next outermost layer. The Code layer benefits from strong base (Cloud, Cluster, Container) security layers. You cannot safeguard against poor security standards in the base layers by addressing security at the Code level.

Cloud

In many ways, the Cloud (or co-located servers, or the corporate datacenter) is the trusted computing base of a Kubernetes cluster. If the Cloud layer is vulnerable (or configured in a vulnerable way) then there is no guarantee that the components built on top of this base are secure. Each cloud provider makes security recommendations for running workloads securely in their environment.

Cloud provider security

If you are running a Kubernetes cluster on your own hardware or a different cloud provider, consult your documentation for security best practices. Here are links to some of the popular cloud providers' security documentation:

Cloud provider security
IaaS Provider	Link
Alibaba Cloud	https://www.alibabacloud.com/trust-center
Amazon Web Services	https://aws.amazon.com/security
Google Cloud Platform	https://cloud.google.com/security
Huawei Cloud	https://www.huaweicloud.com/securecenter/overallsafety
IBM Cloud	https://www.ibm.com/cloud/security
Microsoft Azure	https://docs.microsoft.com/en-us/azure/security/azure-security
Oracle Cloud Infrastructure	https://www.oracle.com/security
VMware vSphere	https://www.vmware.com/security/hardening-guides

Infrastructure security

Suggestions for securing your infrastructure in a Kubernetes cluster:

Infrastructure security
Area of Concern for Kubernetes Infrastructure	Recommendation
Network access to API Server (Control plane)	All access to the Kubernetes control plane is not allowed publicly on the internet and is controlled by network access control lists restricted to the set of IP addresses needed to administer the cluster.
Network access to Nodes (nodes)	Nodes should be configured to only accept connections (via network access control lists) from the control plane on the specified ports, and accept connections for services in Kubernetes of type NodePort and LoadBalancer. If possible, these nodes should not be exposed on the public internet entirely.
Kubernetes access to Cloud Provider API	Each cloud provider needs to grant a different set of permissions to the Kubernetes control plane and nodes. It is best to provide the cluster with cloud provider access that follows the principle of least privilege for the resources it needs to administer. The Kops documentation provides information about IAM policies and roles.
Access to etcd	Access to etcd (the datastore of Kubernetes) should be limited to the control plane only. Depending on your configuration, you should attempt to use etcd over TLS. More information can be found in the etcd documentation.
etcd Encryption	Wherever possible it's a good practice to encrypt all storage at rest, and since etcd holds the state of the entire cluster (including Secrets) its disk should especially be encrypted at rest.

Cluster

There are two areas of concern for securing Kubernetes:

Securing the cluster components that are configurable
Securing the applications which run in the cluster

Components of the Cluster

If you want to protect your cluster from accidental or malicious access and adopt good information practices, read and follow the advice about securing your cluster.

Components in the cluster (your application)

Depending on the attack surface of your application, you may want to focus on specific aspects of security. For example: If you are running a service (Service A) that is critical in a chain of other resources and a separate workload (Service B) which is vulnerable to a resource exhaustion attack, then the risk of compromising Service A is high if you do not limit the resources of Service B. The following table lists areas of security concerns and recommendations for securing workloads running in Kubernetes:

Area of Concern for Workload Security	Recommendation
RBAC Authorization (Access to the Kubernetes API)	https://kubernetes.io/docs/reference/access-authn-authz/rbac/
Authentication	https://kubernetes.io/docs/concepts/security/controlling-access/
Application secrets management (and encrypting them in etcd at rest)	https://kubernetes.io/docs/concepts/configuration/secret/ https://kubernetes.io/docs/tasks/administer-cluster/encrypt-data/
Ensuring that pods meet defined Pod Security Standards	https://kubernetes.io/docs/concepts/security/pod-security-standards/#policy-instantiation
Quality of Service (and Cluster resource management)	https://kubernetes.io/docs/tasks/configure-pod-container/quality-service-pod/
Network Policies	https://kubernetes.io/docs/concepts/services-networking/network-policies/
TLS for Kubernetes Ingress	https://kubernetes.io/docs/concepts/services-networking/ingress/#tls

Container

Container security is outside the scope of this guide. Here are general recommendations and links to explore this topic:

Area of Concern for Containers	Recommendation
Container Vulnerability Scanning and OS Dependency Security	As part of an image build step, you should scan your containers for known vulnerabilities.
Image Signing and Enforcement	Sign container images to maintain a system of trust for the content of your containers.
Disallow privileged users	When constructing containers, consult your documentation for how to create users inside of the containers that have the least level of operating system privilege necessary in order to carry out the goal of the container.
Use container runtime with stronger isolation	Select container runtime classes that provide stronger isolation.

Code

Application code is one of the primary attack surfaces over which you have the most control. While securing application code is outside of the Kubernetes security topic, here are recommendations to protect application code:

Code security

Code security
Area of Concern for Code	Recommendation
Access over TLS only	If your code needs to communicate by TCP, perform a TLS handshake with the client ahead of time. With the exception of a few cases, encrypt everything in transit. Going one step further, it's a good idea to encrypt network traffic between services. This can be done through a process known as mutual TLS authentication or mTLS which performs a two sided verification of communication between two certificate holding services.
Limiting port ranges of communication	This recommendation may be a bit self-explanatory, but wherever possible you should only expose the ports on your service that are absolutely essential for communication or metric gathering.
3rd Party Dependency Security	It is a good practice to regularly scan your application's third party libraries for known security vulnerabilities. Each programming language has a tool for performing this check automatically.
Static Code Analysis	Most languages provide a way for a snippet of code to be analyzed for any potentially unsafe coding practices. Whenever possible you should perform checks using automated tooling that can scan codebases for common security errors. Some of the tools can be found at: https://owasp.org/www-community/Source_Code_Analysis_Tools
Dynamic probing attacks	There are a few automated tools that you can run against your service to try some of the well known service attacks. These include SQL injection, CSRF, and XSS. One of the most popular dynamic analysis tools is the OWASP Zed Attack proxy tool.

What's next

Learn about related Kubernetes security topics:

Pod security standards
Network policies for Pods
Controlling Access to the Kubernetes API
Securing your cluster
Data encryption in transit for the control plane
Data encryption at rest
Secrets in Kubernetes
Runtime class

9.2 - Pod Security Standards

A detailed look at the different policy levels defined in the Pod Security Standards.

The Pod Security Standards define three different policies to broadly cover the security spectrum. These policies are cumulative and range from highly-permissive to highly-restrictive. This guide outlines the requirements of each policy.

Profile	Description
Privileged	Unrestricted policy, providing the widest possible level of permissions. This policy allows for known privilege escalations.
Baseline	Minimally restrictive policy which prevents known privilege escalations. Allows the default (minimally specified) Pod configuration.
Restricted	Heavily restricted policy, following current Pod hardening best practices.

Profile Details

Privileged

The Privileged policy is purposely-open, and entirely unrestricted. This type of policy is typically aimed at system- and infrastructure-level workloads managed by privileged, trusted users.

The Privileged policy is defined by an absence of restrictions. Allow-by-default mechanisms (such as gatekeeper) may be Privileged by default. In contrast, for a deny-by-default mechanism (such as Pod Security Policy) the Privileged policy should disable all restrictions.

Baseline

The Baseline policy is aimed at ease of adoption for common containerized workloads while preventing known privilege escalations. This policy is targeted at application operators and developers of non-critical applications. The following listed controls should be enforced/disallowed:

Note: In this table, wildcards (*) indicate all elements in a list. For example, spec.containers[*].securityContext refers to the Security Context object for all defined containers. If any of the listed containers fails to meet the requirements, the entire pod will fail validation.

Baseline policy specification
Control	Policy
HostProcess	Windows pods offer the ability to run HostProcess containers which enables privileged access to the Windows node. Privileged access to the host is disallowed in the baseline policy. FEATURE STATE: `Kubernetes v1.23 [beta]` Restricted Fields `spec.securityContext.windowsOptions.hostProcess` `spec.containers[].securityContext.windowsOptions.hostProcess` `spec.initContainers[].securityContext.windowsOptions.hostProcess` `spec.ephemeralContainers[].securityContext.windowsOptions.hostProcess` Allowed Values* Undefined/nil `false`
Host Namespaces	Sharing the host namespaces must be disallowed. Restricted Fields `spec.hostNetwork` `spec.hostPID` `spec.hostIPC` Allowed Values Undefined/nil `false`
Privileged Containers	Privileged Pods disable most security mechanisms and must be disallowed. Restricted Fields `spec.containers[].securityContext.privileged` `spec.initContainers[].securityContext.privileged` `spec.ephemeralContainers[].securityContext.privileged` Allowed Values* Undefined/nil `false`
Capabilities	Adding additional capabilities beyond those listed below must be disallowed. Restricted Fields `spec.containers[].securityContext.capabilities.add` `spec.initContainers[].securityContext.capabilities.add` `spec.ephemeralContainers[].securityContext.capabilities.add` Allowed Values* Undefined/nil `AUDIT_WRITE` `CHOWN` `DAC_OVERRIDE` `FOWNER` `FSETID` `KILL` `MKNOD` `NET_BIND_SERVICE` `SETFCAP` `SETGID` `SETPCAP` `SETUID` `SYS_CHROOT`
HostPath Volumes	HostPath volumes must be forbidden. Restricted Fields `spec.volumes[].hostPath` Allowed Values* Undefined/nil
Host Ports	HostPorts should be disallowed, or at minimum restricted to a known list. Restricted Fields `spec.containers[].ports[].hostPort` `spec.initContainers[].ports[].hostPort` `spec.ephemeralContainers[].ports[].hostPort` Allowed Values Undefined/nil Known list `0`
AppArmor	On supported hosts, the `runtime/default` AppArmor profile is applied by default. The baseline policy should prevent overriding or disabling the default AppArmor profile, or restrict overrides to an allowed set of profiles. Restricted Fields `metadata.annotations["container.apparmor.security.beta.kubernetes.io/"]` Allowed Values* Undefined/nil `runtime/default` `localhost/*`
SELinux	Setting the SELinux type is restricted, and setting a custom SELinux user or role option is forbidden. Restricted Fields `spec.securityContext.seLinuxOptions.type` `spec.containers[].securityContext.seLinuxOptions.type` `spec.initContainers[].securityContext.seLinuxOptions.type` `spec.ephemeralContainers[].securityContext.seLinuxOptions.type` Allowed Values* Undefined/"" `container_t` `container_init_t` `container_kvm_t` Restricted Fields `spec.securityContext.seLinuxOptions.user` `spec.containers[].securityContext.seLinuxOptions.user` `spec.initContainers[].securityContext.seLinuxOptions.user` `spec.ephemeralContainers[].securityContext.seLinuxOptions.user` `spec.securityContext.seLinuxOptions.role` `spec.containers[].securityContext.seLinuxOptions.role` `spec.initContainers[].securityContext.seLinuxOptions.role` `spec.ephemeralContainers[].securityContext.seLinuxOptions.role` Allowed Values Undefined/""
`/proc` Mount Type	The default `/proc` masks are set up to reduce attack surface, and should be required. Restricted Fields `spec.containers[].securityContext.procMount` `spec.initContainers[].securityContext.procMount` `spec.ephemeralContainers[].securityContext.procMount` Allowed Values* Undefined/nil `Default`
Seccomp	Seccomp profile must not be explicitly set to `Unconfined`. Restricted Fields `spec.securityContext.seccompProfile.type` `spec.containers[].securityContext.seccompProfile.type` `spec.initContainers[].securityContext.seccompProfile.type` `spec.ephemeralContainers[].securityContext.seccompProfile.type` Allowed Values* Undefined/nil `RuntimeDefault` `Localhost`
Sysctls	Sysctls can disable security mechanisms or affect all containers on a host, and should be disallowed except for an allowed "safe" subset. A sysctl is considered safe if it is namespaced in the container or the Pod, and it is isolated from other Pods or processes on the same Node. Restricted Fields `spec.securityContext.sysctls[].name` Allowed Values* Undefined/nil `kernel.shm_rmid_forced` `net.ipv4.ip_local_port_range` `net.ipv4.ip_unprivileged_port_start` `net.ipv4.tcp_syncookies` `net.ipv4.ping_group_range`

Restricted

The Restricted policy is aimed at enforcing current Pod hardening best practices, at the expense of some compatibility. It is targeted at operators and developers of security-critical applications, as well as lower-trust users. The following listed controls should be enforced/disallowed:

Restricted policy specification
Control	Policy
Everything from the baseline profile.
Volume Types	The restricted policy only permits the following volume types. Restricted Fields `spec.volumes[]` Allowed Values* Every item in the `spec.volumes[]` list must set one of the following fields to a non-null value: `spec.volumes[].configMap` `spec.volumes[].csi` `spec.volumes[].downwardAPI` `spec.volumes[].emptyDir` `spec.volumes[].ephemeral` `spec.volumes[].persistentVolumeClaim` `spec.volumes[].projected` `spec.volumes[*].secret`
Privilege Escalation (v1.8+)	Privilege escalation (such as via set-user-ID or set-group-ID file mode) should not be allowed. This is Linux only policy in v1.25+ `(spec.os.name != windows)` Restricted Fields `spec.containers[].securityContext.allowPrivilegeEscalation` `spec.initContainers[].securityContext.allowPrivilegeEscalation` `spec.ephemeralContainers[].securityContext.allowPrivilegeEscalation` Allowed Values* `false`
Running as Non-root	Containers must be required to run as non-root users. Restricted Fields `spec.securityContext.runAsNonRoot` `spec.containers[].securityContext.runAsNonRoot` `spec.initContainers[].securityContext.runAsNonRoot` `spec.ephemeralContainers[].securityContext.runAsNonRoot` Allowed Values* `true` The container fields may be undefined/`nil` if the pod-level `spec.securityContext.runAsNonRoot` is set to `true`.
Running as Non-root user (v1.23+)	Containers must not set `runAsUser` to 0 Restricted Fields `spec.securityContext.runAsUser` `spec.containers[].securityContext.runAsUser` `spec.initContainers[].securityContext.runAsUser` `spec.ephemeralContainers[].securityContext.runAsUser` Allowed Values* any non-zero value `undefined/null`
Seccomp (v1.19+)	Seccomp profile must be explicitly set to one of the allowed values. Both the `Unconfined` profile and the absence of a profile are prohibited. This is Linux only policy in v1.25+ `(spec.os.name != windows)` Restricted Fields `spec.securityContext.seccompProfile.type` `spec.containers[].securityContext.seccompProfile.type` `spec.initContainers[].securityContext.seccompProfile.type` `spec.ephemeralContainers[].securityContext.seccompProfile.type` Allowed Values* `RuntimeDefault` `Localhost` The container fields may be undefined/`nil` if the pod-level `spec.securityContext.seccompProfile.type` field is set appropriately. Conversely, the pod-level field may be undefined/`nil` if _all_ container- level fields are set.
Capabilities (v1.22+)	Containers must drop `ALL` capabilities, and are only permitted to add back the `NET_BIND_SERVICE` capability. This is Linux only policy in v1.25+ `(.spec.os.name != "windows")` Restricted Fields `spec.containers[].securityContext.capabilities.drop` `spec.initContainers[].securityContext.capabilities.drop` `spec.ephemeralContainers[].securityContext.capabilities.drop` Allowed Values* Any list of capabilities that includes `ALL` Restricted Fields `spec.containers[].securityContext.capabilities.add` `spec.initContainers[].securityContext.capabilities.add` `spec.ephemeralContainers[].securityContext.capabilities.add` Allowed Values* Undefined/nil `NET_BIND_SERVICE`

Policy Instantiation

Decoupling policy definition from policy instantiation allows for a common understanding and consistent language of policies across clusters, independent of the underlying enforcement mechanism.

As mechanisms mature, they will be defined below on a per-policy basis. The methods of enforcement of individual policies are not defined here.

Pod Security Admission Controller

Alternatives

Other alternatives for enforcing policies are being developed in the Kubernetes ecosystem, such as:

Pod OS field

Kubernetes lets you use nodes that run either Linux or Windows. You can mix both kinds of node in one cluster. Windows in Kubernetes has some limitations and differentiators from Linux-based workloads. Specifically, many of the Pod securityContext fields have no effect on Windows.

Note: Kubelets prior to v1.24 don't enforce the pod OS field, and if a cluster has nodes on versions earlier than v1.24 the restricted policies should be pinned to a version prior to v1.25.

Restricted Pod Security Standard changes

Another important change, made in Kubernetes v1.25 is that the restricted Pod security has been updated to use the pod.spec.os.name field. Based on the OS name, certain policies that are specific to a particular OS can be relaxed for the other OS.

OS-specific policy controls

Restrictions on the following controls are only required if .spec.os.name is not windows:

Privilege Escalation
Seccomp
Linux Capabilities

FAQ

Why isn't there a profile between privileged and baseline?

The three profiles defined here have a clear linear progression from most secure (restricted) to least secure (privileged), and cover a broad set of workloads. Privileges required above the baseline policy are typically very application specific, so we do not offer a standard profile in this niche. This is not to say that the privileged profile should always be used in this case, but that policies in this space need to be defined on a case-by-case basis.

SIG Auth may reconsider this position in the future, should a clear need for other profiles arise.

What's the difference between a security profile and a security context?

Security Contexts configure Pods and Containers at runtime. Security contexts are defined as part of the Pod and container specifications in the Pod manifest, and represent parameters to the container runtime.

Security profiles are control plane mechanisms to enforce specific settings in the Security Context, as well as other related parameters outside the Security Context. As of July 2021, Pod Security Policies are deprecated in favor of the built-in Pod Security Admission Controller.

What about sandboxed Pods?

There is not currently an API standard that controls whether a Pod is considered sandboxed or not. Sandbox Pods may be identified by the use of a sandboxed runtime (such as gVisor or Kata Containers), but there is no standard definition of what a sandboxed runtime is.

The protections necessary for sandboxed workloads can differ from others. For example, the need to restrict privileged permissions is lessened when the workload is isolated from the underlying kernel. This allows for workloads requiring heightened permissions to still be isolated.

Additionally, the protection of sandboxed workloads is highly dependent on the method of sandboxing. As such, no single recommended profile is recommended for all sandboxed workloads.

9.3 - Pod Security Admission

An overview of the Pod Security Admission Controller, which can enforce the Pod Security Standards.

FEATURE STATE: Kubernetes v1.25 [stable]

The Kubernetes Pod Security Standards define different isolation levels for Pods. These standards let you define how you want to restrict the behavior of pods in a clear, consistent fashion.

Kubernetes offers a built-in Pod Security admission controller to enforce the Pod Security Standards. Pod security restrictions are applied at the namespace level when pods are created.

Built-in Pod Security admission enforcement

This page is part of the documentation for Kubernetes v1.25. If you are running a different version of Kubernetes, consult the documentation for that release.

Pod Security levels

Pod Security admission places requirements on a Pod's Security Context and other related fields according to the three levels defined by the Pod Security Standards: privileged, baseline, and restricted. Refer to the Pod Security Standards page for an in-depth look at those requirements.

Pod Security Admission labels for namespaces

Once the feature is enabled or the webhook is installed, you can configure namespaces to define the admission control mode you want to use for pod security in each namespace. Kubernetes defines a set of labels that you can set to define which of the predefined Pod Security Standard levels you want to use for a namespace. The label you select defines what action the control plane takes if a potential violation is detected:

Pod Security Admission modes
Mode	Description
enforce	Policy violations will cause the pod to be rejected.
audit	Policy violations will trigger the addition of an audit annotation to the event recorded in the audit log, but are otherwise allowed.
warn	Policy violations will trigger a user-facing warning, but are otherwise allowed.

A namespace can configure any or all modes, or even set a different level for different modes.

For each mode, there are two labels that determine the policy used:

# The per-mode level label indicates which policy level to apply for the mode.
#
# MODE must be one of `enforce`, `audit`, or `warn`.
# LEVEL must be one of `privileged`, `baseline`, or `restricted`.
pod-security.kubernetes.io/<MODE>: <LEVEL>

# Optional: per-mode version label that can be used to pin the policy to the
# version that shipped with a given Kubernetes minor version (for example v1.25).
#
# MODE must be one of `enforce`, `audit`, or `warn`.
# VERSION must be a valid Kubernetes minor version, or `latest`.
pod-security.kubernetes.io/<MODE>-version: <VERSION>

Check out Enforce Pod Security Standards with Namespace Labels to see example usage.

Workload resources and Pod templates

Pods are often created indirectly, by creating a workload object such as a Deployment or Job. The workload object defines a Pod template and a controller for the workload resource creates Pods based on that template. To help catch violations early, both the audit and warning modes are applied to the workload resources. However, enforce mode is not applied to workload resources, only to the resulting pod objects.

Exemptions

You can define exemptions from pod security enforcement in order to allow the creation of pods that would have otherwise been prohibited due to the policy associated with a given namespace. Exemptions can be statically configured in the Admission Controller configuration.

Exemptions must be explicitly enumerated. Requests meeting exemption criteria are ignored by the Admission Controller (all enforce, audit and warn behaviors are skipped). Exemption dimensions include:

Usernames: requests from users with an exempt authenticated (or impersonated) username are ignored.
RuntimeClassNames: pods and workload resources specifying an exempt runtime class name are ignored.
Namespaces: pods and workload resources in an exempt namespace are ignored.

Caution: Most pods are created by a controller in response to a workload resource, meaning that exempting an end user will only exempt them from enforcement when creating pods directly, but not when creating a workload resource. Controller service accounts (such as system:serviceaccount:kube-system:replicaset-controller) should generally not be exempted, as doing so would implicitly exempt any user that can create the corresponding workload resource.

Updates to the following pod fields are exempt from policy checks, meaning that if a pod update request only changes these fields, it will not be denied even if the pod is in violation of the current policy level:

Any metadata updates except changes to the seccomp or AppArmor annotations:
- seccomp.security.alpha.kubernetes.io/pod (deprecated)
- container.seccomp.security.alpha.kubernetes.io/* (deprecated)
- container.apparmor.security.beta.kubernetes.io/*
Valid updates to .spec.activeDeadlineSeconds
Valid updates to .spec.tolerations

What's next

9.4 - Pod Security Policies

FEATURE STATE: Kubernetes v1.21 [deprecated]

Note:

PodSecurityPolicy was deprecated in Kubernetes v1.21, and removed from Kubernetes in v1.25. Instead of using PodSecurityPolicy, you can enforce similar restrictions on Pods using either or both:

Pod Security Admission
a 3rd party admission plugin, that you deploy and configure yourself

For a migration guide, see Migrate from PodSecurityPolicy to the Built-In PodSecurity Admission Controller. For more information on the removal of this API, see PodSecurityPolicy Deprecation: Past, Present, and Future.

If you are not running Kubernetes v1.25, check the documentation for your version of Kubernetes.

9.5 - Security For Windows Nodes

This page describes security considerations and best practices specific to the Windows operating system.

Protection for Secret data on nodes

On Windows, data from Secrets are written out in clear text onto the node's local storage (as compared to using tmpfs / in-memory filesystems on Linux). As a cluster operator, you should take both of the following additional measures:

Use file ACLs to secure the Secrets' file location.
Apply volume-level encryption using BitLocker.

Container users

RunAsUsername can be specified for Windows Pods or containers to execute the container processes as specific user. This is roughly equivalent to RunAsUser.

Windows containers offer two default user accounts, ContainerUser and ContainerAdministrator. The differences between these two user accounts are covered in When to use ContainerAdmin and ContainerUser user accounts within Microsoft's Secure Windows containers documentation.

Local users can be added to container images during the container build process.

Note:

Nano Server based images run as ContainerUser by default
Server Core based images run as ContainerAdministrator by default

Windows containers can also run as Active Directory identities by utilizing Group Managed Service Accounts

Pod-level security isolation

Linux-specific pod security context mechanisms (such as SELinux, AppArmor, Seccomp, or custom POSIX capabilities) are not supported on Windows nodes.

Privileged containers are not supported on Windows. Instead HostProcess containers can be used on Windows to perform many of the tasks performed by privileged containers on Linux.

9.6 - Controlling Access to the Kubernetes API

This page provides an overview of controlling access to the Kubernetes API.

Users access the Kubernetes API using kubectl, client libraries, or by making REST requests. Both human users and Kubernetes service accounts can be authorized for API access. When a request reaches the API, it goes through several stages, illustrated in the following diagram:

Diagram of request handling steps for Kubernetes API request

Transport security

By default, the Kubernetes API server listens on port 6443 on the first non-localhost network interface, protected by TLS. In a typical production Kubernetes cluster, the API serves on port 443. The port can be changed with the --secure-port, and the listening IP address with the --bind-address flag.

The API server presents a certificate. This certificate may be signed using a private certificate authority (CA), or based on a public key infrastructure linked to a generally recognized CA. The certificate and corresponding private key can be set by using the --tls-cert-file and --tls-private-key-file flags.

If your cluster uses a private certificate authority, you need a copy of that CA certificate configured into your ~/.kube/config on the client, so that you can trust the connection and be confident it was not intercepted.

Your client can present a TLS client certificate at this stage.

Authentication

Once TLS is established, the HTTP request moves to the Authentication step. This is shown as step 1 in the diagram. The cluster creation script or cluster admin configures the API server to run one or more Authenticator modules. Authenticators are described in more detail in Authentication.

The input to the authentication step is the entire HTTP request; however, it typically examines the headers and/or client certificate.

Authentication modules include client certificates, password, and plain tokens, bootstrap tokens, and JSON Web Tokens (used for service accounts).

Multiple authentication modules can be specified, in which case each one is tried in sequence, until one of them succeeds.

If the request cannot be authenticated, it is rejected with HTTP status code 401. Otherwise, the user is authenticated as a specific username, and the user name is available to subsequent steps to use in their decisions. Some authenticators also provide the group memberships of the user, while other authenticators do not.

While Kubernetes uses usernames for access control decisions and in request logging, it does not have a User object nor does it store usernames or other information about users in its API.

Authorization

After the request is authenticated as coming from a specific user, the request must be authorized. This is shown as step 2 in the diagram.

A request must include the username of the requester, the requested action, and the object affected by the action. The request is authorized if an existing policy declares that the user has permissions to complete the requested action.

For example, if Bob has the policy below, then he can read pods only in the namespace projectCaribou:

{
    "apiVersion": "abac.authorization.kubernetes.io/v1beta1",
    "kind": "Policy",
    "spec": {
        "user": "bob",
        "namespace": "projectCaribou",
        "resource": "pods",
        "readonly": true
    }
}

If Bob makes the following request, the request is authorized because he is allowed to read objects in the projectCaribou namespace:

{
  "apiVersion": "authorization.k8s.io/v1beta1",
  "kind": "SubjectAccessReview",
  "spec": {
    "resourceAttributes": {
      "namespace": "projectCaribou",
      "verb": "get",
      "group": "unicorn.example.org",
      "resource": "pods"
    }
  }
}

If Bob makes a request to write (create or update) to the objects in the projectCaribou namespace, his authorization is denied. If Bob makes a request to read (get) objects in a different namespace such as projectFish, then his authorization is denied.

Kubernetes authorization requires that you use common REST attributes to interact with existing organization-wide or cloud-provider-wide access control systems. It is important to use REST formatting because these control systems might interact with other APIs besides the Kubernetes API.

Kubernetes supports multiple authorization modules, such as ABAC mode, RBAC Mode, and Webhook mode. When an administrator creates a cluster, they configure the authorization modules that should be used in the API server. If more than one authorization modules are configured, Kubernetes checks each module, and if any module authorizes the request, then the request can proceed. If all of the modules deny the request, then the request is denied (HTTP status code 403).

To learn more about Kubernetes authorization, including details about creating policies using the supported authorization modules, see Authorization.

Admission control

Admission Control modules are software modules that can modify or reject requests. In addition to the attributes available to Authorization modules, Admission Control modules can access the contents of the object that is being created or modified.

Admission controllers act on requests that create, modify, delete, or connect to (proxy) an object. Admission controllers do not act on requests that merely read objects. When multiple admission controllers are configured, they are called in order.

This is shown as step 3 in the diagram.

Unlike Authentication and Authorization modules, if any admission controller module rejects, then the request is immediately rejected.

In addition to rejecting objects, admission controllers can also set complex defaults for fields.

The available Admission Control modules are described in Admission Controllers.

Once a request passes all admission controllers, it is validated using the validation routines for the corresponding API object, and then written to the object store (shown as step 4).

Auditing

Kubernetes auditing provides a security-relevant, chronological set of records documenting the sequence of actions in a cluster. The cluster audits the activities generated by users, by applications that use the Kubernetes API, and by the control plane itself.

For more information, see Auditing.

What's next

Read more documentation on authentication, authorization and API access control:

Authenticating
- Authenticating with Bootstrap Tokens
Admission Controllers
- Dynamic Admission Control
Authorization
Certificate Signing Requests
- including CSR approval and certificate signing
Service accounts
- Developer guide
- Administration

You can learn about:

how Pods can use Secrets to obtain API credentials.

9.7 - Role Based Access Control Good Practices

Principles and practices for good RBAC design for cluster operators.

Kubernetes RBAC is a key security control to ensure that cluster users and workloads have only the access to resources required to execute their roles. It is important to ensure that, when designing permissions for cluster users, the cluster administrator understands the areas where privilege escalation could occur, to reduce the risk of excessive access leading to security incidents.

The good practices laid out here should be read in conjunction with the general RBAC documentation.

General good practice

Least privilege

Ideally, minimal RBAC rights should be assigned to users and service accounts. Only permissions explicitly required for their operation should be used. While each cluster will be different, some general rules that can be applied are :

Assign permissions at the namespace level where possible. Use RoleBindings as opposed to ClusterRoleBindings to give users rights only within a specific namespace.
Avoid providing wildcard permissions when possible, especially to all resources. As Kubernetes is an extensible system, providing wildcard access gives rights not just to all object types that currently exist in the cluster, but also to all object types which are created in the future.
Administrators should not use cluster-admin accounts except where specifically needed. Providing a low privileged account with impersonation rights can avoid accidental modification of cluster resources.
Avoid adding users to the system:masters group. Any user who is a member of this group bypasses all RBAC rights checks and will always have unrestricted superuser access, which cannot be revoked by removing RoleBindings or ClusterRoleBindings. As an aside, if a cluster is using an authorization webhook, membership of this group also bypasses that webhook (requests from users who are members of that group are never sent to the webhook)

Minimize distribution of privileged tokens

Ideally, pods shouldn't be assigned service accounts that have been granted powerful permissions (for example, any of the rights listed under privilege escalation risks). In cases where a workload requires powerful permissions, consider the following practices:

Limit the number of nodes running powerful pods. Ensure that any DaemonSets you run are necessary and are run with least privilege to limit the blast radius of container escapes.
Avoid running powerful pods alongside untrusted or publicly-exposed ones. Consider using Taints and Toleration, NodeAffinity, or PodAntiAffinity to ensure pods don't run alongside untrusted or less-trusted Pods. Pay especial attention to situations where less-trustworthy Pods are not meeting the Restricted Pod Security Standard.

Hardening

Kubernetes defaults to providing access which may not be required in every cluster. Reviewing the RBAC rights provided by default can provide opportunities for security hardening. In general, changes should not be made to rights provided to system: accounts some options to harden cluster rights exist:

Review bindings for the system:unauthenticated group and remove them where possible, as this gives access to anyone who can contact the API server at a network level.
Avoid the default auto-mounting of service account tokens by setting automountServiceAccountToken: false. For more details, see using default service account token. Setting this value for a Pod will overwrite the service account setting, workloads which require service account tokens can still mount them.

Periodic review

It is vital to periodically review the Kubernetes RBAC settings for redundant entries and possible privilege escalations. If an attacker is able to create a user account with the same name as a deleted user, they can automatically inherit all the rights of the deleted user, especially the rights assigned to that user.

Kubernetes RBAC - privilege escalation risks

Within Kubernetes RBAC there are a number of privileges which, if granted, can allow a user or a service account to escalate their privileges in the cluster or affect systems outside the cluster.

This section is intended to provide visibility of the areas where cluster operators should take care, to ensure that they do not inadvertently allow for more access to clusters than intended.

Listing secrets

It is generally clear that allowing get access on Secrets will allow a user to read their contents. It is also important to note that list and watch access also effectively allow for users to reveal the Secret contents. For example, when a List response is returned (for example, via kubectl get secrets -A -o yaml), the response includes the contents of all Secrets.

Workload creation

Permission to create workloads (either Pods, or workload resources that manage Pods) in a namespace implicitly grants access to many other resources in that namespace, such as Secrets, ConfigMaps, and PersistentVolumes that can be mounted in Pods. Additionally, since Pods can run as any ServiceAccount, granting permission to create workloads also implicitly grants the API access levels of any service account in that namespace.

Users who can run privileged Pods can use that access to gain node access and potentially to further elevate their privileges. Where you do not fully trust a user or other principal with the ability to create suitably secure and isolated Pods, you should enforce either the Baseline or Restricted Pod Security Standard. You can use Pod Security admission or other (third party) mechanisms to implement that enforcement.

For these reasons, namespaces should be used to separate resources requiring different levels of trust or tenancy. It is still considered best practice to follow least privilege principles and assign the minimum set of permissions, but boundaries within a namespace should be considered weak.

Persistent volume creation

As noted in the PodSecurityPolicy documentation, access to create PersistentVolumes can allow for escalation of access to the underlying host. Where access to persistent storage is required trusted administrators should create PersistentVolumes, and constrained users should use PersistentVolumeClaims to access that storage.

Access to `proxy` subresource of Nodes

Users with access to the proxy sub-resource of node objects have rights to the Kubelet API, which allows for command execution on every pod on the node(s) to which they have rights. This access bypasses audit logging and admission control, so care should be taken before granting rights to this resource.

Escalate verb

Generally, the RBAC system prevents users from creating clusterroles with more rights than the user possesses. The exception to this is the escalate verb. As noted in the RBAC documentation, users with this right can effectively escalate their privileges.

Bind verb

Similar to the escalate verb, granting users this right allows for the bypass of Kubernetes in-built protections against privilege escalation, allowing users to create bindings to roles with rights they do not already have.

Impersonate verb

This verb allows users to impersonate and gain the rights of other users in the cluster. Care should be taken when granting it, to ensure that excessive permissions cannot be gained via one of the impersonated accounts.

CSRs and certificate issuing

The CSR API allows for users with create rights to CSRs and update rights on certificatesigningrequests/approval where the signer is kubernetes.io/kube-apiserver-client to create new client certificates which allow users to authenticate to the cluster. Those client certificates can have arbitrary names including duplicates of Kubernetes system components. This will effectively allow for privilege escalation.

Token request

Users with create rights on serviceaccounts/token can create TokenRequests to issue tokens for existing service accounts.

Control admission webhooks

Users with control over validatingwebhookconfigurations or mutatingwebhookconfigurations can control webhooks that can read any object admitted to the cluster, and in the case of mutating webhooks, also mutate admitted objects.

Kubernetes RBAC - denial of service risks

Object creation denial-of-service

Users who have rights to create objects in a cluster may be able to create sufficient large objects to create a denial of service condition either based on the size or number of objects, as discussed in etcd used by Kubernetes is vulnerable to OOM attack. This may be specifically relevant in multi-tenant clusters if semi-trusted or untrusted users are allowed limited access to a system.

One option for mitigation of this issue would be to use resource quotas to limit the quantity of objects which can be created.

What's next

To learn more about RBAC, see the RBAC documentation.

9.8 - Good practices for Kubernetes Secrets

Principles and practices for good Secret management for cluster administrators and application developers.

In Kubernetes, a Secret is an object that stores sensitive information, such as passwords, OAuth tokens, and SSH keys.

Secrets give you more control over how sensitive information is used and reduces the risk of accidental exposure. Secret values are encoded as base64 strings and are stored unencrypted by default, but can be configured to be encrypted at rest.

A Pod can reference the Secret in a variety of ways, such as in a volume mount or as an environment variable. Secrets are designed for confidential data and ConfigMaps are designed for non-confidential data.

The following good practices are intended for both cluster administrators and application developers. Use these guidelines to improve the security of your sensitive information in Secret objects, as well as to more effectively manage your Secrets.

Cluster administrators

This section provides good practices that cluster administrators can use to improve the security of confidential information in the cluster.

Configure encryption at rest

By default, Secret objects are stored unencrypted in etcd. You should configure encryption of your Secret data in etcd. For instructions, refer to Encrypt Secret Data at Rest.

Configure least-privilege access to Secrets

When planning your access control mechanism, such as Kubernetes Role-based Access Control (RBAC), consider the following guidelines for access to Secret objects. You should also follow the other guidelines in RBAC good practices.

Components: Restrict watch or list access to only the most privileged, system-level components. Only grant get access for Secrets if the component's normal behavior requires it.
Humans: Restrict get, watch, or list access to Secrets. Only allow cluster admistrators to access etcd. This includes read-only access. For more complex access control, such as restricting access to Secrets with specific annotations, consider using third-party authorization mechanisms.

Caution: Granting list access to Secrets implicitly lets the subject fetch the contents of the Secrets.

A user who can create a Pod that uses a Secret can also see the value of that Secret. Even if cluster policies do not allow a user to read the Secret directly, the same user could have access to run a Pod that then exposes the Secret. You can detect or limit the impact caused by Secret data being exposed, either intentionally or unintentionally, by a user with this access. Some recommendations include:

Use short-lived Secrets
Implement audit rules that alert on specific events, such as concurrent reading of multiple Secrets by a single user

Improve etcd management policies

Consider wiping or shredding the durable storage used by etcd once it is no longer in use.

If there are multiple etcd instances, configure encrypted SSL/TLS communication between the instances to protect the Secret data in transit.

Configure access to external Secrets

You can use third-party Secrets store providers to keep your confidential data outside your cluster and then configure Pods to access that information. The Kubernetes Secrets Store CSI Driver is a DaemonSet that lets the kubelet retrieve Secrets from external stores, and mount the Secrets as a volume into specific Pods that you authorize to access the data.

For a list of supported providers, refer to Providers for the Secret Store CSI Driver.

Developers

This section provides good practices for developers to use to improve the security of confidential data when building and deploying Kubernetes resources.

Restrict Secret access to specific containers

If you are defining multiple containers in a Pod, and only one of those containers needs access to a Secret, define the volume mount or environment variable configuration so that the other containers do not have access to that Secret.

Protect Secret data after reading

Applications still need to protect the value of confidential information after reading it from an environment variable or volume. For example, your application must avoid logging the secret data in the clear or transmitting it to an untrusted party.

If you configure a Secret through a manifest, with the secret data encoded as base64, sharing this file or checking it in to a source repository means the secret is available to everyone who can read the manifest.

Caution: Base64 encoding is not an encryption method, it provides no additional confidentiality over plain text.

9.9 - Multi-tenancy

This page provides an overview of available configuration options and best practices for cluster multi-tenancy.

Sharing clusters saves costs and simplifies administration. However, sharing clusters also presents challenges such as security, fairness, and managing noisy neighbors.

Clusters can be shared in many ways. In some cases, different applications may run in the same cluster. In other cases, multiple instances of the same application may run in the same cluster, one for each end user. All these types of sharing are frequently described using the umbrella term multi-tenancy.

While Kubernetes does not have first-class concepts of end users or tenants, it provides several features to help manage different tenancy requirements. These are discussed below.

Use cases

The first step to determining how to share your cluster is understanding your use case, so you can evaluate the patterns and tools available. In general, multi-tenancy in Kubernetes clusters falls into two broad categories, though many variations and hybrids are also possible.

Multiple teams

A common form of multi-tenancy is to share a cluster between multiple teams within an organization, each of whom may operate one or more workloads. These workloads frequently need to communicate with each other, and with other workloads located on the same or different clusters.

In this scenario, members of the teams often have direct access to Kubernetes resources via tools such as kubectl, or indirect access through GitOps controllers or other types of release automation tools. There is often some level of trust between members of different teams, but Kubernetes policies such as RBAC, quotas, and network policies are essential to safely and fairly share clusters.

Multiple customers

The other major form of multi-tenancy frequently involves a Software-as-a-Service (SaaS) vendor running multiple instances of a workload for customers. This business model is so strongly associated with this deployment style that many people call it "SaaS tenancy." However, a better term might be "multi-customer tenancy,” since SaaS vendors may also use other deployment models, and this deployment model can also be used outside of SaaS.

In this scenario, the customers do not have access to the cluster; Kubernetes is invisible from their perspective and is only used by the vendor to manage the workloads. Cost optimization is frequently a critical concern, and Kubernetes policies are used to ensure that the workloads are strongly isolated from each other.

Terminology

Tenants

When discussing multi-tenancy in Kubernetes, there is no single definition for a "tenant". Rather, the definition of a tenant will vary depending on whether multi-team or multi-customer tenancy is being discussed.

In multi-team usage, a tenant is typically a team, where each team typically deploys a small number of workloads that scales with the complexity of the service. However, the definition of "team" may itself be fuzzy, as teams may be organized into higher-level divisions or subdivided into smaller teams.

By contrast, if each team deploys dedicated workloads for each new client, they are using a multi-customer model of tenancy. In this case, a "tenant" is simply a group of users who share a single workload. This may be as large as an entire company, or as small as a single team at that company.

In many cases, the same organization may use both definitions of "tenants" in different contexts. For example, a platform team may offer shared services such as security tools and databases to multiple internal “customers” and a SaaS vendor may also have multiple teams sharing a development cluster. Finally, hybrid architectures are also possible, such as a SaaS provider using a combination of per-customer workloads for sensitive data, combined with multi-tenant shared services.

A cluster showing coexisting tenancy models

Isolation

There are several ways to design and build multi-tenant solutions with Kubernetes. Each of these methods comes with its own set of tradeoffs that impact the isolation level, implementation effort, operational complexity, and cost of service.

A Kubernetes cluster consists of a control plane which runs Kubernetes software, and a data plane consisting of worker nodes where tenant workloads are executed as pods. Tenant isolation can be applied in both the control plane and the data plane based on organizational requirements.

The level of isolation offered is sometimes described using terms like “hard” multi-tenancy, which implies strong isolation, and “soft” multi-tenancy, which implies weaker isolation. In particular, "hard" multi-tenancy is often used to describe cases where the tenants do not trust each other, often from security and resource sharing perspectives (e.g. guarding against attacks such as data exfiltration or DoS). Since data planes typically have much larger attack surfaces, "hard" multi-tenancy often requires extra attention to isolating the data-plane, though control plane isolation also remains critical.

However, the terms "hard" and "soft" can often be confusing, as there is no single definition that will apply to all users. Rather, "hardness" or "softness" is better understood as a broad spectrum, with many different techniques that can be used to maintain different types of isolation in your clusters, based on your requirements.

In more extreme cases, it may be easier or necessary to forgo any cluster-level sharing at all and assign each tenant their dedicated cluster, possibly even running on dedicated hardware if VMs are not considered an adequate security boundary. This may be easier with managed Kubernetes clusters, where the overhead of creating and operating clusters is at least somewhat taken on by a cloud provider. The benefit of stronger tenant isolation must be evaluated against the cost and complexity of managing multiple clusters. The Multi-cluster SIG is responsible for addressing these types of use cases.

The remainder of this page focuses on isolation techniques used for shared Kubernetes clusters. However, even if you are considering dedicated clusters, it may be valuable to review these recommendations, as it will give you the flexibility to shift to shared clusters in the future if your needs or capabilities change.

Control plane isolation

Control plane isolation ensures that different tenants cannot access or affect each others' Kubernetes API resources.

Namespaces

In Kubernetes, a Namespace provides a mechanism for isolating groups of API resources within a single cluster. This isolation has two key dimensions:

Object names within a namespace can overlap with names in other namespaces, similar to files in folders. This allows tenants to name their resources without having to consider what other tenants are doing.
Many Kubernetes security policies are scoped to namespaces. For example, RBAC Roles and Network Policies are namespace-scoped resources. Using RBAC, Users and Service Accounts can be restricted to a namespace.

In a multi-tenant environment, a Namespace helps segment a tenant's workload into a logical and distinct management unit. In fact, a common practice is to isolate every workload in its own namespace, even if multiple workloads are operated by the same tenant. This ensures that each workload has its own identity and can be configured with an appropriate security policy.

The namespace isolation model requires configuration of several other Kubernetes resources, networking plugins, and adherence to security best practices to properly isolate tenant workloads. These considerations are discussed below.

Access controls

The most important type of isolation for the control plane is authorization. If teams or their workloads can access or modify each others' API resources, they can change or disable all other types of policies thereby negating any protection those policies may offer. As a result, it is critical to ensure that each tenant has the appropriate access to only the namespaces they need, and no more. This is known as the "Principle of Least Privilege."

Role-based access control (RBAC) is commonly used to enforce authorization in the Kubernetes control plane, for both users and workloads (service accounts). Roles and RoleBindings are Kubernetes objects that are used at a namespace level to enforce access control in your application; similar objects exist for authorizing access to cluster-level objects, though these are less useful for multi-tenant clusters.

In a multi-team environment, RBAC must be used to restrict tenants' access to the appropriate namespaces, and ensure that cluster-wide resources can only be accessed or modified by privileged users such as cluster administrators.

If a policy ends up granting a user more permissions than they need, this is likely a signal that the namespace containing the affected resources should be refactored into finer-grained namespaces. Namespace management tools may simplify the management of these finer-grained namespaces by applying common RBAC policies to different namespaces, while still allowing fine-grained policies where necessary.

Quotas

Kubernetes workloads consume node resources, like CPU and memory. In a multi-tenant environment, you can use Resource Quotas to manage resource usage of tenant workloads. For the multiple teams use case, where tenants have access to the Kubernetes API, you can use resource quotas to limit the number of API resources (for example: the number of Pods, or the number of ConfigMaps) that a tenant can create. Limits on object count ensure fairness and aim to avoid noisy neighbor issues from affecting other tenants that share a control plane.

Resource quotas are namespaced objects. By mapping tenants to namespaces, cluster admins can use quotas to ensure that a tenant cannot monopolize a cluster's resources or overwhelm its control plane. Namespace management tools simplify the administration of quotas. In addition, while Kubernetes quotas only apply within a single namespace, some namespace management tools allow groups of namespaces to share quotas, giving administrators far more flexibility with less effort than built-in quotas.

Quotas prevent a single tenant from consuming greater than their allocated share of resources hence minimizing the “noisy neighbor” issue, where one tenant negatively impacts the performance of other tenants' workloads.

When you apply a quota to namespace, Kubernetes requires you to also specify resource requests and limits for each container. Limits are the upper bound for the amount of resources that a container can consume. Containers that attempt to consume resources that exceed the configured limits will either be throttled or killed, based on the resource type. When resource requests are set lower than limits, each container is guaranteed the requested amount but there may still be some potential for impact across workloads.

Quotas cannot protect against all kinds of resource sharing, such as network traffic. Node isolation (described below) may be a better solution for this problem.

Data Plane Isolation

Data plane isolation ensures that pods and workloads for different tenants are sufficiently isolated.

Network isolation

By default, all pods in a Kubernetes cluster are allowed to communicate with each other, and all network traffic is unencrypted. This can lead to security vulnerabilities where traffic is accidentally or maliciously sent to an unintended destination, or is intercepted by a workload on a compromised node.

Pod-to-pod communication can be controlled using Network Policies, which restrict communication between pods using namespace labels or IP address ranges. In a multi-tenant environment where strict network isolation between tenants is required, starting with a default policy that denies communication between pods is recommended with another rule that allows all pods to query the DNS server for name resolution. With such a default policy in place, you can begin adding more permissive rules that allow for communication within a namespace. It is also recommended not to use empty label selector '{}' for namespaceSelector field in network policy definition, in case traffic need to be allowed between namespaces. This scheme can be further refined as required. Note that this only applies to pods within a single control plane; pods that belong to different virtual control planes cannot talk to each other via Kubernetes networking.

Namespace management tools may simplify the creation of default or common network policies. In addition, some of these tools allow you to enforce a consistent set of namespace labels across your cluster, ensuring that they are a trusted basis for your policies.

Warning: Network policies require a CNI plugin that supports the implementation of network policies. Otherwise, NetworkPolicy resources will be ignored.

More advanced network isolation may be provided by service meshes, which provide OSI Layer 7 policies based on workload identity, in addition to namespaces. These higher-level policies can make it easier to manage namespace-based multi-tenancy, especially when multiple namespaces are dedicated to a single tenant. They frequently also offer encryption using mutual TLS, protecting your data even in the presence of a compromised node, and work across dedicated or virtual clusters. However, they can be significantly more complex to manage and may not be appropriate for all users.

Storage isolation

Kubernetes offers several types of volumes that can be used as persistent storage for workloads. For security and data-isolation, dynamic volume provisioning is recommended and volume types that use node resources should be avoided.

StorageClasses allow you to describe custom "classes" of storage offered by your cluster, based on quality-of-service levels, backup policies, or custom policies determined by the cluster administrators.

Pods can request storage using a PersistentVolumeClaim. A PersistentVolumeClaim is a namespaced resource, which enables isolating portions of the storage system and dedicating it to tenants within the shared Kubernetes cluster. However, it is important to note that a PersistentVolume is a cluster-wide resource and has a lifecycle independent of workloads and namespaces.

For example, you can configure a separate StorageClass for each tenant and use this to strengthen isolation. If a StorageClass is shared, you should set a reclaim policy of Delete to ensure that a PersistentVolume cannot be reused across different namespaces.

Sandboxing containers

Kubernetes pods are composed of one or more containers that execute on worker nodes. Containers utilize OS-level virtualization and hence offer a weaker isolation boundary than virtual machines that utilize hardware-based virtualization.

In a shared environment, unpatched vulnerabilities in the application and system layers can be exploited by attackers for container breakouts and remote code execution that allow access to host resources. In some applications, like a Content Management System (CMS), customers may be allowed the ability to upload and execute untrusted scripts or code. In either case, mechanisms to further isolate and protect workloads using strong isolation are desirable.

Sandboxing provides a way to isolate workloads running in a shared cluster. It typically involves running each pod in a separate execution environment such as a virtual machine or a userspace kernel. Sandboxing is often recommended when you are running untrusted code, where workloads are assumed to be malicious. Part of the reason this type of isolation is necessary is because containers are processes running on a shared kernel; they mount file systems like /sys and /proc from the underlying host, making them less secure than an application that runs on a virtual machine which has its own kernel. While controls such as seccomp, AppArmor, and SELinux can be used to strengthen the security of containers, it is hard to apply a universal set of rules to all workloads running in a shared cluster. Running workloads in a sandbox environment helps to insulate the host from container escapes, where an attacker exploits a vulnerability to gain access to the host system and all the processes/files running on that host.

Virtual machines and userspace kernels are 2 popular approaches to sandboxing. The following sandboxing implementations are available:

gVisor intercepts syscalls from containers and runs them through a userspace kernel, written in Go, with limited access to the underlying host.
Kata Containers is an OCI compliant runtime that allows you to run containers in a VM. The hardware virtualization available in Kata offers an added layer of security for containers running untrusted code.

Node Isolation

Node isolation is another technique that you can use to isolate tenant workloads from each other. With node isolation, a set of nodes is dedicated to running pods from a particular tenant and co-mingling of tenant pods is prohibited. This configuration reduces the noisy tenant issue, as all pods running on a node will belong to a single tenant. The risk of information disclosure is slightly lower with node isolation because an attacker that manages to escape from a container will only have access to the containers and volumes mounted to that node.

Although workloads from different tenants are running on different nodes, it is important to be aware that the kubelet and (unless using virtual control planes) the API service are still shared services. A skilled attacker could use the permissions assigned to the kubelet or other pods running on the node to move laterally within the cluster and gain access to tenant workloads running on other nodes. If this is a major concern, consider implementing compensating controls such as seccomp, AppArmor or SELinux or explore using sandboxed containers or creating separate clusters for each tenant.

Node isolation is a little easier to reason about from a billing standpoint than sandboxing containers since you can charge back per node rather than per pod. It also has fewer compatibility and performance issues and may be easier to implement than sandboxing containers. For example, nodes for each tenant can be configured with taints so that only pods with the corresponding toleration can run on them. A mutating webhook could then be used to automatically add tolerations and node affinities to pods deployed into tenant namespaces so that they run on a specific set of nodes designated for that tenant.

Node isolation can be implemented using an pod node selectors or a Virtual Kubelet.

Additional Considerations

This section discusses other Kubernetes constructs and patterns that are relevant for multi-tenancy.

API Priority and Fairness

API priority and fairness is a Kubernetes feature that allows you to assign a priority to certain pods running within the cluster. When an application calls the Kubernetes API, the API server evaluates the priority assigned to pod. Calls from pods with higher priority are fulfilled before those with a lower priority. When contention is high, lower priority calls can be queued until the server is less busy or you can reject the requests.

Using API priority and fairness will not be very common in SaaS environments unless you are allowing customers to run applications that interface with the Kubernetes API, for example, a controller.

Quality-of-Service (QoS)

When you’re running a SaaS application, you may want the ability to offer different Quality-of-Service (QoS) tiers of service to different tenants. For example, you may have freemium service that comes with fewer performance guarantees and features and a for-fee service tier with specific performance guarantees. Fortunately, there are several Kubernetes constructs that can help you accomplish this within a shared cluster, including network QoS, storage classes, and pod priority and preemption. The idea with each of these is to provide tenants with the quality of service that they paid for. Let’s start by looking at networking QoS.

Typically, all pods on a node share a network interface. Without network QoS, some pods may consume an unfair share of the available bandwidth at the expense of other pods. The Kubernetes bandwidth plugin creates an extended resource for networking that allows you to use Kubernetes resources constructs, i.e. requests/limits, to apply rate limits to pods by using Linux tc queues. Be aware that the plugin is considered experimental as per the Network Plugins documentation and should be thoroughly tested before use in production environments.

For storage QoS, you will likely want to create different storage classes or profiles with different performance characteristics. Each storage profile can be associated with a different tier of service that is optimized for different workloads such IO, redundancy, or throughput. Additional logic might be necessary to allow the tenant to associate the appropriate storage profile with their workload.

Finally, there’s pod priority and preemption where you can assign priority values to pods. When scheduling pods, the scheduler will try evicting pods with lower priority when there are insufficient resources to schedule pods that are assigned a higher priority. If you have a use case where tenants have different service tiers in a shared cluster e.g. free and paid, you may want to give higher priority to certain tiers using this feature.

DNS

Kubernetes clusters include a Domain Name System (DNS) service to provide translations from names to IP addresses, for all Services and Pods. By default, the Kubernetes DNS service allows lookups across all namespaces in the cluster.

In multi-tenant environments where tenants can access pods and other Kubernetes resources, or where stronger isolation is required, it may be necessary to prevent pods from looking up services in other Namespaces. You can restrict cross-namespace DNS lookups by configuring security rules for the DNS service. For example, CoreDNS (the default DNS service for Kubernetes) can leverage Kubernetes metadata to restrict queries to Pods and Services within a namespace. For more information, read an example of configuring this within the CoreDNS documentation.

When a Virtual Control Plane per tenant model is used, a DNS service must be configured per tenant or a multi-tenant DNS service must be used. Here is an example of a customized version of CoreDNS that supports multiple tenants.

Operators

Operators are Kubernetes controllers that manage applications. Operators can simplify the management of multiple instances of an application, like a database service, which makes them a common building block in the multi-consumer (SaaS) multi-tenancy use case.

Operators used in a multi-tenant environment should follow a stricter set of guidelines. Specifically, the Operator should:

Support creating resources within different tenant namespaces, rather than just in the namespace in which the Operator is deployed.
Ensure that the Pods are configured with resource requests and limits, to ensure scheduling and fairness.
Support configuration of Pods for data-plane isolation techniques such as node isolation and sandboxed containers.

Implementations

There are two primary ways to share a Kubernetes cluster for multi-tenancy: using Namespaces (that is, a Namespace per tenant) or by virtualizing the control plane (that is, virtual control plane per tenant).

In both cases, data plane isolation, and management of additional considerations such as API Priority and Fairness, is also recommended.

Namespace isolation is well-supported by Kubernetes, has a negligible resource cost, and provides mechanisms to allow tenants to interact appropriately, such as by allowing service-to-service communication. However, it can be difficult to configure, and doesn't apply to Kubernetes resources that can't be namespaced, such as Custom Resource Definitions, Storage Classes, and Webhooks.

Control plane virtualization allows for isolation of non-namespaced resources at the cost of somewhat higher resource usage and more difficult cross-tenant sharing. It is a good option when namespace isolation is insufficient but dedicated clusters are undesirable, due to the high cost of maintaining them (especially on-prem) or due to their higher overhead and lack of resource sharing. However, even within a virtualized control plane, you will likely see benefits by using namespaces as well.

The two options are discussed in more detail in the following sections.

Namespace per tenant

As previously mentioned, you should consider isolating each workload in its own namespace, even if you are using dedicated clusters or virtualized control planes. This ensures that each workload only has access to its own resources, such as Config Maps and Secrets, and allows you to tailor dedicated security policies for each workload. In addition, it is a best practice to give each namespace names that are unique across your entire fleet (that is, even if they are in separate clusters), as this gives you the flexibility to switch between dedicated and shared clusters in the future, or to use multi-cluster tooling such as service meshes.

Conversely, there are also advantages to assigning namespaces at the tenant level, not just the workload level, since there are often policies that apply to all workloads owned by a single tenant. However, this raises its own problems. Firstly, this makes it difficult or impossible to customize policies to individual workloads, and secondly, it may be challenging to come up with a single level of "tenancy" that should be given a namespace. For example, an organization may have divisions, teams, and subteams - which should be assigned a namespace?

To solve this, Kubernetes provides the Hierarchical Namespace Controller (HNC), which allows you to organize your namespaces into hierarchies, and share certain policies and resources between them. It also helps you manage namespace labels, namespace lifecycles, and delegated management, and share resource quotas across related namespaces. These capabilities can be useful in both multi-team and multi-customer scenarios.

Other projects that provide similar capabilities and aid in managing namespaced resources are listed below.

Multi-team tenancy

Multi-customer tenancy

Kubeplus

Policy engines

Policy engines provide features to validate and generate tenant configurations:

Virtual control plane per tenant

Another form of control-plane isolation is to use Kubernetes extensions to provide each tenant a virtual control-plane that enables segmentation of cluster-wide API resources. Data plane isolation techniques can be used with this model to securely manage worker nodes across tenants.

The virtual control plane based multi-tenancy model extends namespace-based multi-tenancy by providing each tenant with dedicated control plane components, and hence complete control over cluster-wide resources and add-on services. Worker nodes are shared across all tenants, and are managed by a Kubernetes cluster that is normally inaccessible to tenants. This cluster is often referred to as a super-cluster (or sometimes as a host-cluster). Since a tenant’s control-plane is not directly associated with underlying compute resources it is referred to as a virtual control plane.

A virtual control plane typically consists of the Kubernetes API server, the controller manager, and the etcd data store. It interacts with the super cluster via a metadata synchronization controller which coordinates changes across tenant control planes and the control plane of the super-cluster.

By using per-tenant dedicated control planes, most of the isolation problems due to sharing one API server among all tenants are solved. Examples include noisy neighbors in the control plane, uncontrollable blast radius of policy misconfigurations, and conflicts between cluster scope objects such as webhooks and CRDs. Hence, the virtual control plane model is particularly suitable for cases where each tenant requires access to a Kubernetes API server and expects the full cluster manageability.

The improved isolation comes at the cost of running and maintaining an individual virtual control plane per tenant. In addition, per-tenant control planes do not solve isolation problems in the data plane, such as node-level noisy neighbors or security threats. These must still be addressed separately.

The Kubernetes Cluster API - Nested (CAPN) project provides an implementation of virtual control planes.

Other implementations

9.10 - Kubernetes API Server Bypass Risks

Security architecture information relating to the API server and other components

The Kubernetes API server is the main point of entry to a cluster for external parties (users and services) interacting with it.

As part of this role, the API server has several key built-in security controls, such as audit logging and admission controllers. However, there are ways to modify the configuration or content of the cluster that bypass these controls.

This page describes the ways in which the security controls built into the Kubernetes API server can be bypassed, so that cluster operators and security architects can ensure that these bypasses are appropriately restricted.

Static Pods

The kubelet on each node loads and directly manages any manifests that are stored in a named directory or fetched from a specific URL as static Pods in your cluster. The API server doesn't manage these static Pods. An attacker with write access to this location could modify the configuration of static pods loaded from that source, or could introduce new static Pods.

Static Pods are restricted from accessing other objects in the Kubernetes API. For example, you can't configure a static Pod to mount a Secret from the cluster. However, these Pods can take other security sensitive actions, such as using hostPath mounts from the underlying node.

By default, the kubelet creates a mirror pod so that the static Pods are visible in the Kubernetes API. However, if the attacker uses an invalid namespace name when creating the Pod, it will not be visible in the Kubernetes API and can only be discovered by tooling that has access to the affected host(s).

If a static Pod fails admission control, the kubelet won't register the Pod with the API server. However, the Pod still runs on the node. For more information, refer to kubeadm issue #1541.

Mitigations

Only enable the kubelet static Pod manifest functionality if required by the node.
If a node uses the static Pod functionality, restrict filesystem access to the static Pod manifest directory or URL to users who need the access.
Restrict access to kubelet configuration parameters and files to prevent an attacker setting a static Pod path or URL.
Regularly audit and centrally report all access to directories or web storage locations that host static Pod manifests and kubelet configuration files.

The kubelet API

The kubelet provides an HTTP API that is typically exposed on TCP port 10250 on cluster worker nodes. The API might also be exposed on control plane nodes depending on the Kubernetes distribution in use. Direct access to the API allows for disclosure of information about the pods running on a node, the logs from those pods, and execution of commands in every container running on the node.

When Kubernetes cluster users have RBAC access to Node object sub-resources, that access serves as authorization to interact with the kubelet API. The exact access depends on which sub-resource access has been granted, as detailed in kubelet authorization.

Direct access to the kubelet API is not subject to admission control and is not logged by Kubernetes audit logging. An attacker with direct access to this API may be able to bypass controls that detect or prevent certain actions.

The kubelet API can be configured to authenticate requests in a number of ways. By default, the kubelet configuration allows anonymous access. Most Kubernetes providers change the default to use webhook and certificate authentication. This lets the control plane ensure that the caller is authorized to access the nodes API resource or sub-resources. The default anonymous access doesn't make this assertion with the control plane.

Mitigations

Restrict access to sub-resources of the nodes API object using mechanisms such as RBAC. Only grant this access when required, such as by monitoring services.
Restrict access to the kubelet port. Only allow specified and trusted IP address ranges to access the port.
Ensure that kubelet authentication is set to webhook or certificate mode.
Ensure that the unauthenticated "read-only" Kubelet port is not enabled on the cluster.

The etcd API

Kubernetes clusters use etcd as a datastore. The etcd service listens on TCP port 2379. The only clients that need access are the Kubernetes API server and any backup tooling that you use. Direct access to this API allows for disclosure or modification of any data held in the cluster.

Access to the etcd API is typically managed by client certificate authentication. Any certificate issued by a certificate authority that etcd trusts allows full access to the data stored inside etcd.

Direct access to etcd is not subject to Kubernetes admission control and is not logged by Kubernetes audit logging. An attacker who has read access to the API server's etcd client certificate private key (or can create a new trusted client certificate) can gain cluster admin rights by accessing cluster secrets or modifying access rules. Even without elevating their Kubernetes RBAC privileges, an attacker who can modify etcd can retrieve any API object or create new workloads inside the cluster.

Many Kubernetes providers configure etcd to use mutual TLS (both client and server verify each other's certificate for authentication). There is no widely accepted implementation of authorization for the etcd API, although the feature exists. Since there is no authorization model, any certificate with client access to etcd can be used to gain full access to etcd. Typically, etcd client certificates that are only used for health checking can also grant full read and write access.

Mitigations

Ensure that the certificate authority trusted by etcd is used only for the purposes of authentication to that service.
Control access to the private key for the etcd server certificate, and to the API server's client certificate and key.
Consider restricting access to the etcd port at a network level, to only allow access from specified and trusted IP address ranges.

Container runtime socket

On each node in a Kubernetes cluster, access to interact with containers is controlled by the container runtime (or runtimes, if you have configured more than one). Typically, the container runtime exposes a Unix socket that the kubelet can access. An attacker with access to this socket can launch new containers or interact with running containers.

At the cluster level, the impact of this access depends on whether the containers that run on the compromised node have access to Secrets or other confidential data that an attacker could use to escalate privileges to other worker nodes or to control plane components.

Mitigations

Ensure that you tightly control filesystem access to container runtime sockets. When possible, restrict this access to the root user.
Isolate the kubelet from other components running on the node, using mechanisms such as Linux kernel namespaces.
Ensure that you restrict or forbid the use of hostPath mounts that include the container runtime socket, either directly or by mounting a parent directory. Also hostPath mounts must be set as read-only to mitigate risks of attackers bypassing directory restrictions.
Restrict user access to nodes, and especially restrict superuser access to nodes.

9.11 - Security Checklist

Baseline checklist for ensuring security in Kubernetes clusters.

This checklist aims at providing a basic list of guidance with links to more comprehensive documentation on each topic. It does not claim to be exhaustive and is meant to evolve.

On how to read and use this document:

The order of topics does not reflect an order of priority.
Some checklist items are detailed in the paragraph below the list of each section.

Caution: Checklists are not sufficient for attaining a good security posture on their own. A good security posture requires constant attention and improvement, but a checklist can be the first step on the never-ending journey towards security preparedness. Some of the recommendations in this checklist may be too restrictive or too lax for your specific security needs. Since Kubernetes security is not "one size fits all", each category of checklist items should be evaluated on its merits.

Authentication & Authorization

system:masters group is not used for user or component authentication after bootstrapping.
The kube-controller-manager is running with --use-service-account-credentials enabled.
The root certificate is protected (either an offline CA, or a managed online CA with effective access controls).
Intermediate and leaf certificates have an expiry date no more than 3 years in the future.
A process exists for periodic access review, and reviews occur no more than 24 months apart.
The Role Based Access Control Good Practices is followed for guidance related to authentication and authorization.

After bootstrapping, neither users nor components should authenticate to the Kubernetes API as system:masters. Similarly, running all of kube-controller-manager as system:masters should be avoided. In fact, system:masters should only be used as a break-glass mechanism, as opposed to an admin user.

Network security

CNI plugins in-use supports network policies.
Ingress and egress network policies are applied to all workloads in the cluster.
Default network policies within each namespace, selecting all pods, denying everything, are in place.
If appropriate, a service mesh is used to encrypt all communications inside of the cluster.
The Kubernetes API, kubelet API and etcd are not exposed publicly on Internet.
Access from the workloads to the cloud metadata API is filtered.
Use of LoadBalancer and ExternalIPs is restricted.

A number of Container Network Interface (CNI) plugins plugins provide the functionality to restrict network resources that pods may communicate with. This is most commonly done through Network Policies which provide a namespaced resource to define rules. Default network policies blocking everything egress and ingress, in each namespace, selecting all the pods, can be useful to adopt an allow list approach, ensuring that no workloads is missed.

Not all CNI plugins provide encryption in transit. If the chosen plugin lacks this feature, an alternative solution could be to use a service mesh to provide that functionality.

The etcd datastore of the control plane should have controls to limit access and not be publicly exposed on the Internet. Furthermore, mutual TLS (mTLS) should be used to communicate securely with it. The certificate authority for this should be unique to etcd.

External Internet access to the Kubernetes API server should be restricted to not expose the API publicly. Be careful as many managed Kubernetes distribution are publicly exposing the API server by default. You can then use a bastion host to access the server.

The kubelet API access should be restricted and not publicly exposed, the defaults authentication and authorization settings, when no configuration file specified with the --config flag, are overly permissive.

If a cloud provider is used for hosting Kubernetes, the access from pods to the cloud metadata API 169.254.169.254 should also be restricted or blocked if not needed because it may leak information.

For restricted LoadBalancer and ExternalIPs use, see CVE-2020-8554: Man in the middle using LoadBalancer or ExternalIPs and the DenyServiceExternalIPs admission controller for further information.

Pod security

RBAC rights to create, update, patch, delete workloads is only granted if necessary.
Appropriate Pod Security Standards policy is applied for all namespaces and enforced.
Memory limit is set for the workloads with a limit equal or inferior to the request.
CPU limit might be set on sensitive workloads.
For nodes that support it, Seccomp is enabled with appropriate syscalls profile for programs.
For nodes that support it, AppArmor or SELinux is enabled with appropriate profile for programs.

RBAC authorization is crucial but cannot be granular enough to have authorization on the Pods' resources (or on any resource that manages Pods). The only granularity is the API verbs on the resource itself, for example, create on Pods. Without additional admission, the authorization to create these resources allows direct unrestricted access to the schedulable nodes of a cluster.

The Pod Security Standards define three different policies, privileged, baseline and restricted that limit how fields can be set in the PodSpec regarding security. These standards can be enforced at the namespace level with the new Pod Security admission, enabled by default, or by third-party admission webhook. Please note that, contrary to the removed PodSecurityPolicy admission it replaces, Pod Security admission can be easily combined with admission webhooks and external services.

Pod Security admission restricted policy, the most restrictive policy of the Pod Security Standards set, can operate in several modes, warn, audit or enforce to gradually apply the most appropriate security context according to security best practices. Nevertheless, pods' security context should be separately investigated to limit the privileges and access pods may have on top of the predefined security standards, for specific use cases.

For a hands-on tutorial on Pod Security, see the blog post Kubernetes 1.23: Pod Security Graduates to Beta.

Memory and CPU limits should be set in order to restrict the memory and CPU resources a pod can consume on a node, and therefore prevent potential DoS attacks from malicious or breached workloads. Such policy can be enforced by an admission controller. Please note that CPU limits will throttle usage and thus can have unintended effects on auto-scaling features or efficiency i.e. running the process in best effort with the CPU resource available.

Caution: Memory limit superior to request can expose the whole node to OOM issues.

Enabling Seccomp

Seccomp can improve the security of your workloads by reducing the Linux kernel syscall attack surface available inside containers. The seccomp filter mode leverages BPF to create an allow or deny list of specific syscalls, named profiles. Those seccomp profiles can be enabled on individual workloads, a security tutorial is available. In addition, the Kubernetes Security Profiles Operator is a project to facilitate the management and use of seccomp in clusters.

For historical context, please note that Docker has been using a default seccomp profile to only allow a restricted set of syscalls since 2016 from Docker Engine 1.10, but Kubernetes is still not confining workloads by default. The default seccomp profile can be found in containerd as well. Fortunately, Seccomp Default, a new alpha feature to use a default seccomp profile for all workloads can now be enabled and tested.

Note: Seccomp is only available on Linux nodes.

Enabling AppArmor or SELinux

AppArmor

AppArmor is a Linux kernel security module that can provide an easy way to implement Mandatory Access Control (MAC) and better auditing through system logs. To enable AppArmor in Kubernetes, at least version 1.4 is required. Like seccomp, AppArmor is also configured through profiles, where each profile is either running in enforcing mode, which blocks access to disallowed resources or complain mode, which only reports violations. AppArmor profiles are enforced on a per-container basis, with an annotation, allowing for processes to gain just the right privileges.

Note: AppArmor is only available on Linux nodes, and enabled in some Linux distributions.

SELinux

SELinux is also a Linux kernel security module that can provide a mechanism for supporting access control security policies, including Mandatory Access Controls (MAC). SELinux labels can be assigned to containers or pods via their securityContext section.

Note: SELinux is only available on Linux nodes, and enabled in some Linux distributions.

Pod placement

Pod placement is done in accordance with the tiers of sensitivity of the application.
Sensitive applications are running isolated on nodes or with specific sandboxed runtimes.

Pods that are on different tiers of sensitivity, for example, an application pod and the Kubernetes API server, should be deployed onto separate nodes. The purpose of node isolation is to prevent an application container breakout to directly providing access to applications with higher level of sensitivity to easily pivot within the cluster. This separation should be enforced to prevent pods accidentally being deployed onto the same node. This could be enforced with the following features:

Node Selectors: Key-value pairs, as part of the pod specification, that specify which nodes to deploy onto. These can be enforced at the namespace and cluster level with the PodNodeSelector admission controller.
PodTolerationRestriction: An admission controller that allows administrators to restrict permitted tolerations within a namespace. Pods within a namespace may only utilize the tolerations specified on the namespace object annotation keys that provide a set of default and allowed tolerations.
RuntimeClass: RuntimeClass is a feature for selecting the container runtime configuration. The container runtime configuration is used to run a Pod's containers and can provide more or less isolation from the host at the cost of performance overhead.

Secrets

ConfigMaps are not used to hold confidential data.
Encryption at rest is configured for the Secret API.
If appropriate, a mechanism to inject secrets stored in third-party storage is deployed and available.
Service account tokens are not mounted in pods that don't require them.
Bound service account token volume is in-use instead of non-expiring tokens.

Secrets required for pods should be stored within Kubernetes Secrets as opposed to alternatives such as ConfigMap. Secret resources stored within etcd should be encrypted at rest.

Pods needing secrets should have these automatically mounted through volumes, preferably stored in memory like with the emptyDir.medium option. Mechanism can be used to also inject secrets from third-party storages as volume, like the Secrets Store CSI Driver. This should be done preferentially as compared to providing the pods service account RBAC access to secrets. This would allow adding secrets into the pod as environment variables or files. Please note that the environment variable method might be more prone to leakage due to crash dumps in logs and the non-confidential nature of environment variable in Linux, as opposed to the permission mechanism on files.

Service account tokens should not be mounted into pods that do not require them. This can be configured by setting automountServiceAccountToken to false either within the service account to apply throughout the namespace or specifically for a pod. For Kubernetes v1.22 and above, use Bound Service Accounts for time-bound service account credentials.

Images

Minimize unnecessary content in container images.
Container images are configured to be run as unprivileged user.
References to container images are made by sha256 digests (rather than tags) or the provenance of the image is validated by verifying the image's digital signature at deploy time via admission control.
Container images are regularly scanned during creation and in deployment, and known vulnerable software is patched.

Container image should contain the bare minimum to run the program they package. Preferably, only the program and its dependencies, building the image from the minimal possible base. In particular, image used in production should not contain shells or debugging utilities, as an ephemeral debug container can be used for troubleshooting.

Build images to directly start with an unprivileged user by using the USER instruction in Dockerfile. The Security Context allows a container image to be started with a specific user and group with runAsUser and runAsGroup, even if not specified in the image manifest. However, the file permissions in the image layers might make it impossible to just start the process with a new unprivileged user without image modification.

Avoid using image tags to reference an image, especially the latest tag, the image behind a tag can be easily modified in a registry. Prefer using the complete sha256 digest which is unique to the image manifest. This policy can be enforced via an ImagePolicyWebhook. Image signatures can also be automatically verified with an admission controller at deploy time to validate their authenticity and integrity.

Scanning a container image can prevent critical vulnerabilities from being deployed to the cluster alongside the container image. Image scanning should be completed before deploying a container image to a cluster and is usually done as part of the deployment process in a CI/CD pipeline. The purpose of an image scan is to obtain information about possible vulnerabilities and their prevention in the container image, such as a Common Vulnerability Scoring System (CVSS) score. If the result of the image scans is combined with the pipeline compliance rules, only properly patched container images will end up in Production.

Admission controllers

An appropriate selection of admission controllers is enabled.
A pod security policy is enforced by the Pod Security Admission or/and a webhook admission controller.
The admission chain plugins and webhooks are securely configured.

Admission controllers can help to improve the security of the cluster. However, they can present risks themselves as they extend the API server and should be properly secured.

The following lists present a number of admission controllers that could be considered to enhance the security posture of your cluster and application. It includes controllers that may be referenced in other parts of this document.

This first group of admission controllers includes plugins enabled by default, consider to leave them enabled unless you know what you are doing:

CertificateApproval: Performs additional authorization checks to ensure the approving user has permission to approve certificate request.
CertificateSigning: Performs additional authorization checks to ensure the signing user has permission to sign certificate requests.
CertificateSubjectRestriction: Rejects any certificate request that specifies a 'group' (or 'organization attribute') of system:masters.
LimitRanger: Enforce the LimitRange API constraints.
MutatingAdmissionWebhook: Allows the use of custom controllers through webhooks, these controllers may mutate requests that it reviews.
PodSecurity: Replacement for Pod Security Policy, restricts security contexts of deployed Pods.
ResourceQuota: Enforces resource quotas to prevent over-usage of resources.
ValidatingAdmissionWebhook: Allows the use of custom controllers through webhooks, these controllers do not mutate requests that it reviews.

The second group includes plugin that are not enabled by default but in general availability state and recommended to improve your security posture:

DenyServiceExternalIPs: Rejects all net-new usage of the Service.spec.externalIPs field. This is a mitigation for CVE-2020-8554: Man in the middle using LoadBalancer or ExternalIPs.
NodeRestriction: Restricts kubelet's permissions to only modify the pods API resources they own or the node API ressource that represent themselves. It also prevents kubelet from using the node-restriction.kubernetes.io/ annotation, which can be used by an attacker with access to the kubelet's credentials to influence pod placement to the controlled node.

The third group includes plugins that are not enabled by default but could be considered for certain use cases:

AlwaysPullImages: Enforces the usage of the latest version of a tagged image and ensures that the deployer has permissions to use the image.
ImagePolicyWebhook: Allows enforcing additional controls for images through webhooks.

What's next

RBAC Good Practices for further information on authorization.
Cluster Multi-tenancy guide for configuration options recommendations and best practices on multi-tenancy.
Blog post "A Closer Look at NSA/CISA Kubernetes Hardening Guidance" for complementary resource on hardening Kubernetes clusters.

10 - Policies

Policies you can configure that apply to groups of resources.

Note: See Network Policies for documentation about NetworkPolicy in Kubernetes.

10.1 - Limit Ranges

By default, containers run with unbounded compute resources on a Kubernetes cluster. With resource quotas, cluster administrators can restrict resource consumption and creation on a namespace basis. Within a namespace, a Pod or Container can consume as much CPU and memory as defined by the namespace's resource quota. There is a concern that one Pod or Container could monopolize all available resources. A LimitRange is a policy to constrain resource allocations (to Pods or Containers) in a namespace.

A LimitRange provides constraints that can:

Enforce minimum and maximum compute resources usage per Pod or Container in a namespace.
Enforce minimum and maximum storage request per PersistentVolumeClaim in a namespace.
Enforce a ratio between request and limit for a resource in a namespace.
Set default request/limit for compute resources in a namespace and automatically inject them to Containers at runtime.

Enabling LimitRange

LimitRange support has been enabled by default since Kubernetes 1.10.

A LimitRange is enforced in a particular namespace when there is a LimitRange object in that namespace.

The name of a LimitRange object must be a valid DNS subdomain name.

Overview of Limit Range

The administrator creates one LimitRange in one namespace.
Users create resources like Pods, Containers, and PersistentVolumeClaims in the namespace.
The LimitRanger admission controller enforces defaults and limits for all Pods and Containers that do not set compute resource requirements and tracks usage to ensure it does not exceed resource minimum, maximum and ratio defined in any LimitRange present in the namespace.
If creating or updating a resource (Pod, Container, PersistentVolumeClaim) that violates a LimitRange constraint, the request to the API server will fail with an HTTP status code 403 FORBIDDEN and a message explaining the constraint that have been violated.
If a LimitRange is activated in a namespace for compute resources like cpu and memory, users must specify requests or limits for those values. Otherwise, the system may reject Pod creation.
LimitRange validations occurs only at Pod Admission stage, not on Running Pods.

Examples of policies that could be created using limit range are:

In a 2 node cluster with a capacity of 8 GiB RAM and 16 cores, constrain Pods in a namespace to request 100m of CPU with a max limit of 500m for CPU and request 200Mi for Memory with a max limit of 600Mi for Memory.
Define default CPU limit and request to 150m and memory default request to 300Mi for Containers started with no cpu and memory requests in their specs.

In the case where the total limits of the namespace is less than the sum of the limits of the Pods/Containers, there may be contention for resources. In this case, the Containers or Pods will not be created.

Neither contention nor changes to a LimitRange will affect already created resources.

What's next

Refer to the LimitRanger design document for more information.

For examples on using limits, see:

10.2 - Resource Quotas

When several users or teams share a cluster with a fixed number of nodes, there is a concern that one team could use more than its fair share of resources.

Resource quotas are a tool for administrators to address this concern.

A resource quota, defined by a ResourceQuota object, provides constraints that limit aggregate resource consumption per namespace. It can limit the quantity of objects that can be created in a namespace by type, as well as the total amount of compute resources that may be consumed by resources in that namespace.

Resource quotas work like this:

Different teams work in different namespaces. This can be enforced with RBAC.
The administrator creates one ResourceQuota for each namespace.
Users create resources (pods, services, etc.) in the namespace, and the quota system tracks usage to ensure it does not exceed hard resource limits defined in a ResourceQuota.
If creating or updating a resource violates a quota constraint, the request will fail with HTTP status code 403 FORBIDDEN with a message explaining the constraint that would have been violated.
If quota is enabled in a namespace for compute resources like cpu and memory, users must specify requests or limits for those values; otherwise, the quota system may reject pod creation. Hint: Use the LimitRanger admission controller to force defaults for pods that make no compute resource requirements.

See the walkthrough for an example of how to avoid this problem.

The name of a ResourceQuota object must be a valid DNS subdomain name.

Examples of policies that could be created using namespaces and quotas are:

In a cluster with a capacity of 32 GiB RAM, and 16 cores, let team A use 20 GiB and 10 cores, let B use 10GiB and 4 cores, and hold 2GiB and 2 cores in reserve for future allocation.
Limit the "testing" namespace to using 1 core and 1GiB RAM. Let the "production" namespace use any amount.

In the case where the total capacity of the cluster is less than the sum of the quotas of the namespaces, there may be contention for resources. This is handled on a first-come-first-served basis.

Neither contention nor changes to quota will affect already created resources.

Enabling Resource Quota

Resource Quota support is enabled by default for many Kubernetes distributions. It is enabled when the API server --enable-admission-plugins= flag has ResourceQuota as one of its arguments.

A resource quota is enforced in a particular namespace when there is a ResourceQuota in that namespace.

Compute Resource Quota

You can limit the total sum of compute resources that can be requested in a given namespace.

The following resource types are supported:

Resource Name	Description
`limits.cpu`	Across all pods in a non-terminal state, the sum of CPU limits cannot exceed this value.
`limits.memory`	Across all pods in a non-terminal state, the sum of memory limits cannot exceed this value.
`requests.cpu`	Across all pods in a non-terminal state, the sum of CPU requests cannot exceed this value.
`requests.memory`	Across all pods in a non-terminal state, the sum of memory requests cannot exceed this value.
`hugepages-<size>`	Across all pods in a non-terminal state, the number of huge page requests of the specified size cannot exceed this value.
`cpu`	Same as `requests.cpu`
`memory`	Same as `requests.memory`

Resource Quota For Extended Resources

In addition to the resources mentioned above, in release 1.10, quota support for extended resources is added.

As overcommit is not allowed for extended resources, it makes no sense to specify both requests and limits for the same extended resource in a quota. So for extended resources, only quota items with prefix requests. is allowed for now.

Take the GPU resource as an example, if the resource name is nvidia.com/gpu, and you want to limit the total number of GPUs requested in a namespace to 4, you can define a quota as follows:

requests.nvidia.com/gpu: 4

See Viewing and Setting Quotas for more detail information.

Storage Resource Quota

You can limit the total sum of storage resources that can be requested in a given namespace.

In addition, you can limit consumption of storage resources based on associated storage-class.

Resource Name	Description
`requests.storage`	Across all persistent volume claims, the sum of storage requests cannot exceed this value.
`persistentvolumeclaims`	The total number of PersistentVolumeClaims that can exist in the namespace.
`<storage-class-name>.storageclass.storage.k8s.io/requests.storage`	Across all persistent volume claims associated with the `<storage-class-name>`, the sum of storage requests cannot exceed this value.
`<storage-class-name>.storageclass.storage.k8s.io/persistentvolumeclaims`	Across all persistent volume claims associated with the storage-class-name, the total number of persistent volume claims that can exist in the namespace.

For example, if an operator wants to quota storage with gold storage class separate from bronze storage class, the operator can define a quota as follows:

gold.storageclass.storage.k8s.io/requests.storage: 500Gi
bronze.storageclass.storage.k8s.io/requests.storage: 100Gi

In release 1.8, quota support for local ephemeral storage is added as an alpha feature:

Resource Name	Description
`requests.ephemeral-storage`	Across all pods in the namespace, the sum of local ephemeral storage requests cannot exceed this value.
`limits.ephemeral-storage`	Across all pods in the namespace, the sum of local ephemeral storage limits cannot exceed this value.
`ephemeral-storage`	Same as `requests.ephemeral-storage`.

Note: When using a CRI container runtime, container logs will count against the ephemeral storage quota. This can result in the unexpected eviction of pods that have exhausted their storage quotas. Refer to Logging Architecture for details.

Object Count Quota

You can set quota for the total number of certain resources of all standard, namespaced resource types using the following syntax:

count/<resource>.<group> for resources from non-core groups
count/<resource> for resources from the core group

Here is an example set of resources users may want to put under object count quota:

count/persistentvolumeclaims
count/services
count/secrets
count/configmaps
count/replicationcontrollers
count/deployments.apps
count/replicasets.apps
count/statefulsets.apps
count/jobs.batch
count/cronjobs.batch

The same syntax can be used for custom resources. For example, to create a quota on a widgets custom resource in the example.com API group, use count/widgets.example.com.

When using count/* resource quota, an object is charged against the quota if it exists in server storage. These types of quotas are useful to protect against exhaustion of storage resources. For example, you may want to limit the number of Secrets in a server given their large size. Too many Secrets in a cluster can actually prevent servers and controllers from starting. You can set a quota for Jobs to protect against a poorly configured CronJob. CronJobs that create too many Jobs in a namespace can lead to a denial of service.

It is also possible to do generic object count quota on a limited set of resources. The following types are supported:

Resource Name	Description
`configmaps`	The total number of ConfigMaps that can exist in the namespace.
`persistentvolumeclaims`	The total number of PersistentVolumeClaims that can exist in the namespace.
`pods`	The total number of Pods in a non-terminal state that can exist in the namespace. A pod is in a terminal state if `.status.phase in (Failed, Succeeded)` is true.
`replicationcontrollers`	The total number of ReplicationControllers that can exist in the namespace.
`resourcequotas`	The total number of ResourceQuotas that can exist in the namespace.
`services`	The total number of Services that can exist in the namespace.
`services.loadbalancers`	The total number of Services of type `LoadBalancer` that can exist in the namespace.
`services.nodeports`	The total number of Services of type `NodePort` that can exist in the namespace.
`secrets`	The total number of Secrets that can exist in the namespace.

For example, pods quota counts and enforces a maximum on the number of pods created in a single namespace that are not terminal. You might want to set a pods quota on a namespace to avoid the case where a user creates many small pods and exhausts the cluster's supply of Pod IPs.

Quota Scopes

Each quota can have an associated set of scopes. A quota will only measure usage for a resource if it matches the intersection of enumerated scopes.

When a scope is added to the quota, it limits the number of resources it supports to those that pertain to the scope. Resources specified on the quota outside of the allowed set results in a validation error.

Scope	Description
`Terminating`	Match pods where `.spec.activeDeadlineSeconds >= 0`
`NotTerminating`	Match pods where `.spec.activeDeadlineSeconds is nil`
`BestEffort`	Match pods that have best effort quality of service.
`NotBestEffort`	Match pods that do not have best effort quality of service.
`PriorityClass`	Match pods that references the specified priority class.
`CrossNamespacePodAffinity`	Match pods that have cross-namespace pod (anti)affinity terms.

The BestEffort scope restricts a quota to tracking the following resource:

pods

The Terminating, NotTerminating, NotBestEffort and PriorityClass scopes restrict a quota to tracking the following resources:

pods
cpu
memory
requests.cpu
requests.memory
limits.cpu
limits.memory

Note that you cannot specify both the Terminating and the NotTerminating scopes in the same quota, and you cannot specify both the BestEffort and NotBestEffort scopes in the same quota either.

The scopeSelector supports the following values in the operator field:

In
NotIn
Exists
DoesNotExist

When using one of the following values as the scopeName when defining the scopeSelector, the operator must be Exists.

Terminating
NotTerminating
BestEffort
NotBestEffort

If the operator is In or NotIn, the values field must have at least one value. For example:

  scopeSelector:
    matchExpressions:
      - scopeName: PriorityClass
        operator: In
        values:
          - middle

If the operator is Exists or DoesNotExist, the values field must NOT be specified.

Resource Quota Per PriorityClass

FEATURE STATE: Kubernetes v1.17 [stable]

Pods can be created at a specific priority. You can control a pod's consumption of system resources based on a pod's priority, by using the scopeSelector field in the quota spec.

A quota is matched and consumed only if scopeSelector in the quota spec selects the pod.

When quota is scoped for priority class using scopeSelector field, quota object is restricted to track only following resources:

pods
cpu
memory
ephemeral-storage
limits.cpu
limits.memory
limits.ephemeral-storage
requests.cpu
requests.memory
requests.ephemeral-storage

This example creates a quota object and matches it with pods at specific priorities. The example works as follows:

Pods in the cluster have one of the three priority classes, "low", "medium", "high".
One quota object is created for each priority.

Save the following YAML to a file quota.yml.

apiVersion: v1
kind: List
items:
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: pods-high
  spec:
    hard:
      cpu: "1000"
      memory: 200Gi
      pods: "10"
    scopeSelector:
      matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["high"]
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: pods-medium
  spec:
    hard:
      cpu: "10"
      memory: 20Gi
      pods: "10"
    scopeSelector:
      matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["medium"]
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: pods-low
  spec:
    hard:
      cpu: "5"
      memory: 10Gi
      pods: "10"
    scopeSelector:
      matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["low"]

Apply the YAML using kubectl create.

kubectl create -f ./quota.yml

resourcequota/pods-high created
resourcequota/pods-medium created
resourcequota/pods-low created

Verify that Used quota is 0 using kubectl describe quota.

kubectl describe quota

Name:       pods-high
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     1k
memory      0     200Gi
pods        0     10


Name:       pods-low
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     5
memory      0     10Gi
pods        0     10


Name:       pods-medium
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     10
memory      0     20Gi
pods        0     10

Create a pod with priority "high". Save the following YAML to a file high-priority-pod.yml.

apiVersion: v1
kind: Pod
metadata:
  name: high-priority
spec:
  containers:
  - name: high-priority
    image: ubuntu
    command: ["/bin/sh"]
    args: ["-c", "while true; do echo hello; sleep 10;done"]
    resources:
      requests:
        memory: "10Gi"
        cpu: "500m"
      limits:
        memory: "10Gi"
        cpu: "500m"
  priorityClassName: high

Apply it with kubectl create.

kubectl create -f ./high-priority-pod.yml

Verify that "Used" stats for "high" priority quota, pods-high, has changed and that the other two quotas are unchanged.

kubectl describe quota

Name:       pods-high
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         500m  1k
memory      10Gi  200Gi
pods        1     10


Name:       pods-low
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     5
memory      0     10Gi
pods        0     10


Name:       pods-medium
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     10
memory      0     20Gi
pods        0     10

Cross-namespace Pod Affinity Quota

FEATURE STATE: Kubernetes v1.24 [stable]

Operators can use CrossNamespacePodAffinity quota scope to limit which namespaces are allowed to have pods with affinity terms that cross namespaces. Specifically, it controls which pods are allowed to set namespaces or namespaceSelector fields in pod affinity terms.

Preventing users from using cross-namespace affinity terms might be desired since a pod with anti-affinity constraints can block pods from all other namespaces from getting scheduled in a failure domain.

Using this scope operators can prevent certain namespaces (foo-ns in the example below) from having pods that use cross-namespace pod affinity by creating a resource quota object in that namespace with CrossNamespaceAffinity scope and hard limit of 0:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: disable-cross-namespace-affinity
  namespace: foo-ns
spec:
  hard:
    pods: "0"
  scopeSelector:
    matchExpressions:
    - scopeName: CrossNamespaceAffinity

If operators want to disallow using namespaces and namespaceSelector by default, and only allow it for specific namespaces, they could configure CrossNamespaceAffinity as a limited resource by setting the kube-apiserver flag --admission-control-config-file to the path of the following configuration file:

apiVersion: apiserver.config.k8s.io/v1
kind: AdmissionConfiguration
plugins:
- name: "ResourceQuota"
  configuration:
    apiVersion: apiserver.config.k8s.io/v1
    kind: ResourceQuotaConfiguration
    limitedResources:
    - resource: pods
      matchScopes:
      - scopeName: CrossNamespaceAffinity

With the above configuration, pods can use namespaces and namespaceSelector in pod affinity only if the namespace where they are created have a resource quota object with CrossNamespaceAffinity scope and a hard limit greater than or equal to the number of pods using those fields.

Requests compared to Limits

When allocating compute resources, each container may specify a request and a limit value for either CPU or memory. The quota can be configured to quota either value.

If the quota has a value specified for requests.cpu or requests.memory, then it requires that every incoming container makes an explicit request for those resources. If the quota has a value specified for limits.cpu or limits.memory, then it requires that every incoming container specifies an explicit limit for those resources.

Viewing and Setting Quotas

Kubectl supports creating, updating, and viewing quotas:

kubectl create namespace myspace

cat <<EOF > compute-resources.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
  name: compute-resources
spec:
  hard:
    requests.cpu: "1"
    requests.memory: 1Gi
    limits.cpu: "2"
    limits.memory: 2Gi
    requests.nvidia.com/gpu: 4
EOF

kubectl create -f ./compute-resources.yaml --namespace=myspace

cat <<EOF > object-counts.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
  name: object-counts
spec:
  hard:
    configmaps: "10"
    persistentvolumeclaims: "4"
    pods: "4"
    replicationcontrollers: "20"
    secrets: "10"
    services: "10"
    services.loadbalancers: "2"
EOF

kubectl create -f ./object-counts.yaml --namespace=myspace

kubectl get quota --namespace=myspace

NAME                    AGE
compute-resources       30s
object-counts           32s

kubectl describe quota compute-resources --namespace=myspace

Name:                    compute-resources
Namespace:               myspace
Resource                 Used  Hard
--------                 ----  ----
limits.cpu               0     2
limits.memory            0     2Gi
requests.cpu             0     1
requests.memory          0     1Gi
requests.nvidia.com/gpu  0     4

kubectl describe quota object-counts --namespace=myspace

Name:                   object-counts
Namespace:              myspace
Resource                Used    Hard
--------                ----    ----
configmaps              0       10
persistentvolumeclaims  0       4
pods                    0       4
replicationcontrollers  0       20
secrets                 1       10
services                0       10
services.loadbalancers  0       2

Kubectl also supports object count quota for all standard namespaced resources using the syntax count/<resource>.<group>:

kubectl create namespace myspace

kubectl create quota test --hard=count/deployments.apps=2,count/replicasets.apps=4,count/pods=3,count/secrets=4 --namespace=myspace

kubectl create deployment nginx --image=nginx --namespace=myspace --replicas=2

kubectl describe quota --namespace=myspace

Name:                         test
Namespace:                    myspace
Resource                      Used  Hard
--------                      ----  ----
count/deployments.apps        1     2
count/pods                    2     3
count/replicasets.apps        1     4
count/secrets                 1     4

Quota and Cluster Capacity

ResourceQuotas are independent of the cluster capacity. They are expressed in absolute units. So, if you add nodes to your cluster, this does not automatically give each namespace the ability to consume more resources.

Sometimes more complex policies may be desired, such as:

Proportionally divide total cluster resources among several teams.
Allow each tenant to grow resource usage as needed, but have a generous limit to prevent accidental resource exhaustion.
Detect demand from one namespace, add nodes, and increase quota.

Such policies could be implemented using ResourceQuotas as building blocks, by writing a "controller" that watches the quota usage and adjusts the quota hard limits of each namespace according to other signals.

Note that resource quota divides up aggregate cluster resources, but it creates no restrictions around nodes: pods from several namespaces may run on the same node.

Limit Priority Class consumption by default

It may be desired that pods at a particular priority, eg. "cluster-services", should be allowed in a namespace, if and only if, a matching quota object exists.

With this mechanism, operators are able to restrict usage of certain high priority classes to a limited number of namespaces and not every namespace will be able to consume these priority classes by default.

To enforce this, kube-apiserver flag --admission-control-config-file should be used to pass path to the following configuration file:

apiVersion: apiserver.config.k8s.io/v1
kind: AdmissionConfiguration
plugins:
- name: "ResourceQuota"
  configuration:
    apiVersion: apiserver.config.k8s.io/v1
    kind: ResourceQuotaConfiguration
    limitedResources:
    - resource: pods
      matchScopes:
      - scopeName: PriorityClass
        operator: In
        values: ["cluster-services"]

Then, create a resource quota object in the kube-system namespace:

policy/priority-class-resourcequota.yaml

apiVersion: v1
kind: ResourceQuota
metadata:
  name: pods-cluster-services
spec:
  scopeSelector:
    matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["cluster-services"]

kubectl apply -f https://k8s.io/examples/policy/priority-class-resourcequota.yaml -n kube-system

resourcequota/pods-cluster-services created

In this case, a pod creation will be allowed if:

the Pod's priorityClassName is not specified.
the Pod's priorityClassName is specified to a value other than cluster-services.
the Pod's priorityClassName is set to cluster-services, it is to be created in the kube-system namespace, and it has passed the resource quota check.

A Pod creation request is rejected if its priorityClassName is set to cluster-services and it is to be created in a namespace other than kube-system.

What's next

See ResourceQuota design doc for more information.
See a detailed example for how to use resource quota.
Read Quota support for priority class design doc.
See LimitedResources

10.3 - Process ID Limits And Reservations

FEATURE STATE: Kubernetes v1.20 [stable]

Kubernetes allow you to limit the number of process IDs (PIDs) that a Pod can use. You can also reserve a number of allocatable PIDs for each node for use by the operating system and daemons (rather than by Pods).

Process IDs (PIDs) are a fundamental resource on nodes. It is trivial to hit the task limit without hitting any other resource limits, which can then cause instability to a host machine.

Cluster administrators require mechanisms to ensure that Pods running in the cluster cannot induce PID exhaustion that prevents host daemons (such as the kubelet or kube-proxy, and potentially also the container runtime) from running. In addition, it is important to ensure that PIDs are limited among Pods in order to ensure they have limited impact on other workloads on the same node.

Note: On certain Linux installations, the operating system sets the PIDs limit to a low default, such as 32768. Consider raising the value of /proc/sys/kernel/pid_max.

You can configure a kubelet to limit the number of PIDs a given Pod can consume. For example, if your node's host OS is set to use a maximum of 262144 PIDs and expect to host less than 250 Pods, one can give each Pod a budget of 1000 PIDs to prevent using up that node's overall number of available PIDs. If the admin wants to overcommit PIDs similar to CPU or memory, they may do so as well with some additional risks. Either way, a single Pod will not be able to bring the whole machine down. This kind of resource limiting helps to prevent simple fork bombs from affecting operation of an entire cluster.

Per-Pod PID limiting allows administrators to protect one Pod from another, but does not ensure that all Pods scheduled onto that host are unable to impact the node overall. Per-Pod limiting also does not protect the node agents themselves from PID exhaustion.

You can also reserve an amount of PIDs for node overhead, separate from the allocation to Pods. This is similar to how you can reserve CPU, memory, or other resources for use by the operating system and other facilities outside of Pods and their containers.

PID limiting is a an important sibling to compute resource requests and limits. However, you specify it in a different way: rather than defining a Pod's resource limit in the .spec for a Pod, you configure the limit as a setting on the kubelet. Pod-defined PID limits are not currently supported.

Caution: This means that the limit that applies to a Pod may be different depending on where the Pod is scheduled. To make things simple, it's easiest if all Nodes use the same PID resource limits and reservations.

Node PID limits

Kubernetes allows you to reserve a number of process IDs for the system use. To configure the reservation, use the parameter pid=<number> in the --system-reserved and --kube-reserved command line options to the kubelet. The value you specified declares that the specified number of process IDs will be reserved for the system as a whole and for Kubernetes system daemons respectively.

Note: Before Kubernetes version 1.20, PID resource limiting with Node-level reservations required enabling the feature gate SupportNodePidsLimit to work.

Pod PID limits

Kubernetes allows you to limit the number of processes running in a Pod. You specify this limit at the node level, rather than configuring it as a resource limit for a particular Pod. Each Node can have a different PID limit.
To configure the limit, you can specify the command line parameter --pod-max-pids to the kubelet, or set PodPidsLimit in the kubelet configuration file.

Note: Before Kubernetes version 1.20, PID resource limiting for Pods required enabling the feature gate SupportPodPidsLimit to work.

PID based eviction

You can configure kubelet to start terminating a Pod when it is misbehaving and consuming abnormal amount of resources. This feature is called eviction. You can Configure Out of Resource Handling for various eviction signals. Use pid.available eviction signal to configure the threshold for number of PIDs used by Pod. You can set soft and hard eviction policies. However, even with the hard eviction policy, if the number of PIDs growing very fast, node can still get into unstable state by hitting the node PIDs limit. Eviction signal value is calculated periodically and does NOT enforce the limit.

PID limiting - per Pod and per Node sets the hard limit. Once the limit is hit, workload will start experiencing failures when trying to get a new PID. It may or may not lead to rescheduling of a Pod, depending on how workload reacts on these failures and how liveleness and readiness probes are configured for the Pod. However, if limits were set correctly, you can guarantee that other Pods workload and system processes will not run out of PIDs when one Pod is misbehaving.

What's next

Refer to the PID Limiting enhancement document for more information.
For historical context, read Process ID Limiting for Stability Improvements in Kubernetes 1.14.
Read Managing Resources for Containers.
Learn how to Configure Out of Resource Handling.

10.4 - Node Resource Managers

In order to support latency-critical and high-throughput workloads, Kubernetes offers a suite of Resource Managers. The managers aim to co-ordinate and optimise node's resources alignment for pods configured with a specific requirement for CPUs, devices, and memory (hugepages) resources.

The main manager, the Topology Manager, is a Kubelet component that co-ordinates the overall resource management process through its policy.

The configuration of individual managers is elaborated in dedicated documents:

11 - Scheduling, Preemption and Eviction

In Kubernetes, scheduling refers to making sure that Pods are matched to Nodes so that the kubelet can run them. Preemption is the process of terminating Pods with lower Priority so that Pods with higher Priority can schedule on Nodes. Eviction is the process of proactively terminating one or more Pods on resource-starved Nodes.

Scheduling

Pod Disruption

Pod disruption is the process by which Pods on Nodes are terminated either voluntarily or involuntarily.

Voluntary disruptions are started intentionally by application owners or cluster administrators. Involuntary disruptions are unintentional and can be triggered by unavoidable issues like Nodes running out of resources, or by accidental deletions.

11.1 - Kubernetes Scheduler

In Kubernetes, scheduling refers to making sure that Pods are matched to Nodes so that Kubelet can run them.

Scheduling overview

A scheduler watches for newly created Pods that have no Node assigned. For every Pod that the scheduler discovers, the scheduler becomes responsible for finding the best Node for that Pod to run on. The scheduler reaches this placement decision taking into account the scheduling principles described below.

If you want to understand why Pods are placed onto a particular Node, or if you're planning to implement a custom scheduler yourself, this page will help you learn about scheduling.

kube-scheduler

kube-scheduler is the default scheduler for Kubernetes and runs as part of the control plane. kube-scheduler is designed so that, if you want and need to, you can write your own scheduling component and use that instead.

For every newly created pod or other unscheduled pods, kube-scheduler selects an optimal node for them to run on. However, every container in pods has different requirements for resources and every pod also has different requirements. Therefore, existing nodes need to be filtered according to the specific scheduling requirements.

In a cluster, Nodes that meet the scheduling requirements for a Pod are called feasible nodes. If none of the nodes are suitable, the pod remains unscheduled until the scheduler is able to place it.

The scheduler finds feasible Nodes for a Pod and then runs a set of functions to score the feasible Nodes and picks a Node with the highest score among the feasible ones to run the Pod. The scheduler then notifies the API server about this decision in a process called binding.

Factors that need to be taken into account for scheduling decisions include individual and collective resource requirements, hardware / software / policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, and so on.

Node selection in kube-scheduler

kube-scheduler selects a node for the pod in a 2-step operation:

Filtering
Scoring

The filtering step finds the set of Nodes where it's feasible to schedule the Pod. For example, the PodFitsResources filter checks whether a candidate Node has enough available resource to meet a Pod's specific resource requests. After this step, the node list contains any suitable Nodes; often, there will be more than one. If the list is empty, that Pod isn't (yet) schedulable.

In the scoring step, the scheduler ranks the remaining nodes to choose the most suitable Pod placement. The scheduler assigns a score to each Node that survived filtering, basing this score on the active scoring rules.

Finally, kube-scheduler assigns the Pod to the Node with the highest ranking. If there is more than one node with equal scores, kube-scheduler selects one of these at random.

There are two supported ways to configure the filtering and scoring behavior of the scheduler:

Scheduling Policies allow you to configure Predicates for filtering and Priorities for scoring.
Scheduling Profiles allow you to configure Plugins that implement different scheduling stages, including: QueueSort, Filter, Score, Bind, Reserve, Permit, and others. You can also configure the kube-scheduler to run different profiles.

What's next

Read about scheduler performance tuning
Read about Pod topology spread constraints
Read the reference documentation for kube-scheduler
Read the kube-scheduler config (v1beta3) reference
Learn about configuring multiple schedulers
Learn about topology management policies
Learn about Pod Overhead
Learn about scheduling of Pods that use volumes in:

11.2 - Assigning Pods to Nodes

You can constrain a Pod so that it is restricted to run on particular node(s), or to prefer to run on particular nodes. There are several ways to do this and the recommended approaches all use label selectors to facilitate the selection. Often, you do not need to set any such constraints; the scheduler will automatically do a reasonable placement (for example, spreading your Pods across nodes so as not place Pods on a node with insufficient free resources). However, there are some circumstances where you may want to control which node the Pod deploys to, for example, to ensure that a Pod ends up on a node with an SSD attached to it, or to co-locate Pods from two different services that communicate a lot into the same availability zone.

You can use any of the following methods to choose where Kubernetes schedules specific Pods:

nodeSelector field matching against node labels
Affinity and anti-affinity
nodeName field
Pod topology spread constraints

Node labels

Like many other Kubernetes objects, nodes have labels. You can attach labels manually. Kubernetes also populates a standard set of labels on all nodes in a cluster. See Well-Known Labels, Annotations and Taints for a list of common node labels.

Note: The value of these labels is cloud provider specific and is not guaranteed to be reliable. For example, the value of kubernetes.io/hostname may be the same as the node name in some environments and a different value in other environments.

Node isolation/restriction

Adding labels to nodes allows you to target Pods for scheduling on specific nodes or groups of nodes. You can use this functionality to ensure that specific Pods only run on nodes with certain isolation, security, or regulatory properties.

If you use labels for node isolation, choose label keys that the kubelet cannot modify. This prevents a compromised node from setting those labels on itself so that the scheduler schedules workloads onto the compromised node.

The NodeRestriction admission plugin prevents the kubelet from setting or modifying labels with a node-restriction.kubernetes.io/ prefix.

To make use of that label prefix for node isolation:

Ensure you are using the Node authorizer and have enabled the NodeRestriction admission plugin.
Add labels with the node-restriction.kubernetes.io/ prefix to your nodes, and use those labels in your node selectors. For example, example.com.node-restriction.kubernetes.io/fips=true or example.com.node-restriction.kubernetes.io/pci-dss=true.

nodeSelector

nodeSelector is the simplest recommended form of node selection constraint. You can add the nodeSelector field to your Pod specification and specify the node labels you want the target node to have. Kubernetes only schedules the Pod onto nodes that have each of the labels you specify.

See Assign Pods to Nodes for more information.

Affinity and anti-affinity

nodeSelector is the simplest way to constrain Pods to nodes with specific labels. Affinity and anti-affinity expands the types of constraints you can define. Some of the benefits of affinity and anti-affinity include:

The affinity/anti-affinity language is more expressive. nodeSelector only selects nodes with all the specified labels. Affinity/anti-affinity gives you more control over the selection logic.
You can indicate that a rule is soft or preferred, so that the scheduler still schedules the Pod even if it can't find a matching node.
You can constrain a Pod using labels on other Pods running on the node (or other topological domain), instead of just node labels, which allows you to define rules for which Pods can be co-located on a node.

The affinity feature consists of two types of affinity:

Node affinity functions like the nodeSelector field but is more expressive and allows you to specify soft rules.
Inter-pod affinity/anti-affinity allows you to constrain Pods against labels on other Pods.

Node affinity

Node affinity is conceptually similar to nodeSelector, allowing you to constrain which nodes your Pod can be scheduled on based on node labels. There are two types of node affinity:

requiredDuringSchedulingIgnoredDuringExecution: The scheduler can't schedule the Pod unless the rule is met. This functions like nodeSelector, but with a more expressive syntax.
preferredDuringSchedulingIgnoredDuringExecution: The scheduler tries to find a node that meets the rule. If a matching node is not available, the scheduler still schedules the Pod.

Note: In the preceding types, IgnoredDuringExecution means that if the node labels change after Kubernetes schedules the Pod, the Pod continues to run.

You can specify node affinities using the .spec.affinity.nodeAffinity field in your Pod spec.

For example, consider the following Pod spec:

pods/pod-with-node-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: with-node-affinity
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: topology.kubernetes.io/zone
            operator: In
            values:
            - antarctica-east1
            - antarctica-west1
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: another-node-label-key
            operator: In
            values:
            - another-node-label-value
  containers:
  - name: with-node-affinity
    image: registry.k8s.io/pause:2.0

In this example, the following rules apply:

The node must have a label with the key topology.kubernetes.io/zone and the value of that label must be either antarctica-east1 or antarctica-west1.
The node preferably has a label with the key another-node-label-key and the value another-node-label-value.

You can use the operator field to specify a logical operator for Kubernetes to use when interpreting the rules. You can use In, NotIn, Exists, DoesNotExist, Gt and Lt.

NotIn and DoesNotExist allow you to define node anti-affinity behavior. Alternatively, you can use node taints to repel Pods from specific nodes.

Note:

If you specify both nodeSelector and nodeAffinity, both must be satisfied for the Pod to be scheduled onto a node.

If you specify multiple nodeSelectorTerms associated with nodeAffinity types, then the Pod can be scheduled onto a node if one of the specified nodeSelectorTerms can be satisfied.

If you specify multiple matchExpressions associated with a single nodeSelectorTerms, then the Pod can be scheduled onto a node only if all the matchExpressions are satisfied.

See Assign Pods to Nodes using Node Affinity for more information.

Node affinity weight

You can specify a weight between 1 and 100 for each instance of the preferredDuringSchedulingIgnoredDuringExecution affinity type. When the scheduler finds nodes that meet all the other scheduling requirements of the Pod, the scheduler iterates through every preferred rule that the node satisfies and adds the value of the weight for that expression to a sum.

The final sum is added to the score of other priority functions for the node. Nodes with the highest total score are prioritized when the scheduler makes a scheduling decision for the Pod.

For example, consider the following Pod spec:

pods/pod-with-affinity-anti-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: with-affinity-anti-affinity
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: kubernetes.io/os
            operator: In
            values:
            - linux
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: label-1
            operator: In
            values:
            - key-1
      - weight: 50
        preference:
          matchExpressions:
          - key: label-2
            operator: In
            values:
            - key-2
  containers:
  - name: with-node-affinity
    image: registry.k8s.io/pause:2.0

If there are two possible nodes that match the preferredDuringSchedulingIgnoredDuringExecution rule, one with the label-1:key-1 label and another with the label-2:key-2 label, the scheduler considers the weight of each node and adds the weight to the other scores for that node, and schedules the Pod onto the node with the highest final score.

Note: If you want Kubernetes to successfully schedule the Pods in this example, you must have existing nodes with the kubernetes.io/os=linux label.

Node affinity per scheduling profile

FEATURE STATE: Kubernetes v1.20 [beta]

When configuring multiple scheduling profiles, you can associate a profile with a node affinity, which is useful if a profile only applies to a specific set of nodes. To do so, add an addedAffinity to the args field of the NodeAffinity plugin in the scheduler configuration. For example:

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
  - schedulerName: foo-scheduler
    pluginConfig:
      - name: NodeAffinity
        args:
          addedAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: scheduler-profile
                  operator: In
                  values:
                  - foo

The addedAffinity is applied to all Pods that set .spec.schedulerName to foo-scheduler, in addition to the NodeAffinity specified in the PodSpec. That is, in order to match the Pod, nodes need to satisfy addedAffinity and the Pod's .spec.NodeAffinity.

Since the addedAffinity is not visible to end users, its behavior might be unexpected to them. Use node labels that have a clear correlation to the scheduler profile name.

Note: The DaemonSet controller, which creates Pods for DaemonSets, does not support scheduling profiles. When the DaemonSet controller creates Pods, the default Kubernetes scheduler places those Pods and honors any nodeAffinity rules in the DaemonSet controller.

Inter-pod affinity and anti-affinity

Inter-pod affinity and anti-affinity allow you to constrain which nodes your Pods can be scheduled on based on the labels of Pods already running on that node, instead of the node labels.

Inter-pod affinity and anti-affinity rules take the form "this Pod should (or, in the case of anti-affinity, should not) run in an X if that X is already running one or more Pods that meet rule Y", where X is a topology domain like node, rack, cloud provider zone or region, or similar and Y is the rule Kubernetes tries to satisfy.

You express these rules (Y) as label selectors with an optional associated list of namespaces. Pods are namespaced objects in Kubernetes, so Pod labels also implicitly have namespaces. Any label selectors for Pod labels should specify the namespaces in which Kubernetes should look for those labels.

You express the topology domain (X) using a topologyKey, which is the key for the node label that the system uses to denote the domain. For examples, see Well-Known Labels, Annotations and Taints.

Note: Inter-pod affinity and anti-affinity require substantial amount of processing which can slow down scheduling in large clusters significantly. We do not recommend using them in clusters larger than several hundred nodes.

Note: Pod anti-affinity requires nodes to be consistently labelled, in other words, every node in the cluster must have an appropriate label matching topologyKey. If some or all nodes are missing the specified topologyKey label, it can lead to unintended behavior.

Types of inter-pod affinity and anti-affinity

Similar to node affinity are two types of Pod affinity and anti-affinity as follows:

requiredDuringSchedulingIgnoredDuringExecution
preferredDuringSchedulingIgnoredDuringExecution

For example, you could use requiredDuringSchedulingIgnoredDuringExecution affinity to tell the scheduler to co-locate Pods of two services in the same cloud provider zone because they communicate with each other a lot. Similarly, you could use preferredDuringSchedulingIgnoredDuringExecution anti-affinity to spread Pods from a service across multiple cloud provider zones.

To use inter-pod affinity, use the affinity.podAffinity field in the Pod spec. For inter-pod anti-affinity, use the affinity.podAntiAffinity field in the Pod spec.

Pod affinity example

Consider the following Pod spec:

pods/pod-with-pod-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: with-pod-affinity
spec:
  affinity:
    podAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchExpressions:
          - key: security
            operator: In
            values:
            - S1
        topologyKey: topology.kubernetes.io/zone
    podAntiAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 100
        podAffinityTerm:
          labelSelector:
            matchExpressions:
            - key: security
              operator: In
              values:
              - S2
          topologyKey: topology.kubernetes.io/zone
  containers:
  - name: with-pod-affinity
    image: registry.k8s.io/pause:2.0

This example defines one Pod affinity rule and one Pod anti-affinity rule. The Pod affinity rule uses the "hard" requiredDuringSchedulingIgnoredDuringExecution, while the anti-affinity rule uses the "soft" preferredDuringSchedulingIgnoredDuringExecution.

The affinity rule says that the scheduler can only schedule a Pod onto a node if the node is in the same zone as one or more existing Pods with the label security=S1. More precisely, the scheduler must place the Pod on a node that has the topology.kubernetes.io/zone=V label, as long as there is at least one node in that zone that currently has one or more Pods with the Pod label security=S1.

The anti-affinity rule says that the scheduler should try to avoid scheduling the Pod onto a node that is in the same zone as one or more Pods with the label security=S2. More precisely, the scheduler should try to avoid placing the Pod on a node that has the topology.kubernetes.io/zone=R label if there are other nodes in the same zone currently running Pods with the Security=S2 Pod label.

To get yourself more familiar with the examples of Pod affinity and anti-affinity, refer to the design proposal.

You can use the In, NotIn, Exists and DoesNotExist values in the operator field for Pod affinity and anti-affinity.

In principle, the topologyKey can be any allowed label key with the following exceptions for performance and security reasons:

For Pod affinity and anti-affinity, an empty topologyKey field is not allowed in both requiredDuringSchedulingIgnoredDuringExecution and preferredDuringSchedulingIgnoredDuringExecution.
For requiredDuringSchedulingIgnoredDuringExecution Pod anti-affinity rules, the admission controller LimitPodHardAntiAffinityTopology limits topologyKey to kubernetes.io/hostname. You can modify or disable the admission controller if you want to allow custom topologies.

In addition to labelSelector and topologyKey, you can optionally specify a list of namespaces which the labelSelector should match against using the namespaces field at the same level as labelSelector and topologyKey. If omitted or empty, namespaces defaults to the namespace of the Pod where the affinity/anti-affinity definition appears.

Namespace selector

FEATURE STATE: Kubernetes v1.24 [stable]

You can also select matching namespaces using namespaceSelector, which is a label query over the set of namespaces. The affinity term is applied to namespaces selected by both namespaceSelector and the namespaces field. Note that an empty namespaceSelector ({}) matches all namespaces, while a null or empty namespaces list and null namespaceSelector matches the namespace of the Pod where the rule is defined.

More practical use-cases

Inter-pod affinity and anti-affinity can be even more useful when they are used with higher level collections such as ReplicaSets, StatefulSets, Deployments, etc. These rules allow you to configure that a set of workloads should be co-located in the same defined topology; for example, preferring to place two related Pods onto the same node.

For example: imagine a three-node cluster. You use the cluster to run a web application and also an in-memory cache (such as Redis). For this example, also assume that latency between the web application and the memory cache should be as low as is practical. You could use inter-pod affinity and anti-affinity to co-locate the web servers with the cache as much as possible.

In the following example Deployment for the Redis cache, the replicas get the label app=store. The podAntiAffinity rule tells the scheduler to avoid placing multiple replicas with the app=store label on a single node. This creates each cache in a separate node.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-cache
spec:
  selector:
    matchLabels:
      app: store
  replicas: 3
  template:
    metadata:
      labels:
        app: store
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - store
            topologyKey: "kubernetes.io/hostname"
      containers:
      - name: redis-server
        image: redis:3.2-alpine

The following example Deployment for the web servers creates replicas with the label app=web-store. The Pod affinity rule tells the scheduler to place each replica on a node that has a Pod with the label app=store. The Pod anti-affinity rule tells the scheduler never to place multiple app=web-store servers on a single node.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-server
spec:
  selector:
    matchLabels:
      app: web-store
  replicas: 3
  template:
    metadata:
      labels:
        app: web-store
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - web-store
            topologyKey: "kubernetes.io/hostname"
        podAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - store
            topologyKey: "kubernetes.io/hostname"
      containers:
      - name: web-app
        image: nginx:1.16-alpine

Creating the two preceding Deployments results in the following cluster layout, where each web server is co-located with a cache, on three separate nodes.

node-1	node-2	node-3
webserver-1	webserver-2	webserver-3
cache-1	cache-2	cache-3

The overall effect is that each cache instance is likely to be accessed by a single client, that is running on the same node. This approach aims to minimize both skew (imbalanced load) and latency.

You might have other reasons to use Pod anti-affinity. See the ZooKeeper tutorial for an example of a StatefulSet configured with anti-affinity for high availability, using the same technique as this example.

nodeName

nodeName is a more direct form of node selection than affinity or nodeSelector. nodeName is a field in the Pod spec. If the nodeName field is not empty, the scheduler ignores the Pod and the kubelet on the named node tries to place the Pod on that node. Using nodeName overrules using nodeSelector or affinity and anti-affinity rules.

Some of the limitations of using nodeName to select nodes are:

If the named node does not exist, the Pod will not run, and in some cases may be automatically deleted.
If the named node does not have the resources to accommodate the Pod, the Pod will fail and its reason will indicate why, for example OutOfmemory or OutOfcpu.
Node names in cloud environments are not always predictable or stable.

Here is an example of a Pod spec using the nodeName field:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  containers:
  - name: nginx
    image: nginx
  nodeName: kube-01

The above Pod will only run on the node kube-01.

Pod topology spread constraints

You can use topology spread constraints to control how Pods are spread across your cluster among failure-domains such as regions, zones, nodes, or among any other topology domains that you define. You might do this to improve performance, expected availability, or overall utilization.

Read Pod topology spread constraints to learn more about how these work.

What's next

Read more about taints and tolerations .
Read the design docs for node affinity and for inter-pod affinity/anti-affinity.
Learn about how the topology manager takes part in node-level resource allocation decisions.
Learn how to use nodeSelector.
Learn how to use affinity and anti-affinity.

11.3 - Pod Overhead

FEATURE STATE: Kubernetes v1.24 [stable]

When you run a Pod on a Node, the Pod itself takes an amount of system resources. These resources are additional to the resources needed to run the container(s) inside the Pod. In Kubernetes, Pod Overhead is a way to account for the resources consumed by the Pod infrastructure on top of the container requests & limits.

In Kubernetes, the Pod's overhead is set at admission time according to the overhead associated with the Pod's RuntimeClass.

A pod's overhead is considered in addition to the sum of container resource requests when scheduling a Pod. Similarly, the kubelet will include the Pod overhead when sizing the Pod cgroup, and when carrying out Pod eviction ranking.

Configuring Pod overhead

You need to make sure a RuntimeClass is utilized which defines the overhead field.

Usage example

To work with Pod overhead, you need a RuntimeClass that defines the overhead field. As an example, you could use the following RuntimeClass definition with a virtualization container runtime that uses around 120MiB per Pod for the virtual machine and the guest OS:

apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: kata-fc
handler: kata-fc
overhead:
  podFixed:
    memory: "120Mi"
    cpu: "250m"

Workloads which are created which specify the kata-fc RuntimeClass handler will take the memory and cpu overheads into account for resource quota calculations, node scheduling, as well as Pod cgroup sizing.

Consider running the given example workload, test-pod:

apiVersion: v1
kind: Pod
metadata:
  name: test-pod
spec:
  runtimeClassName: kata-fc
  containers:
  - name: busybox-ctr
    image: busybox:1.28
    stdin: true
    tty: true
    resources:
      limits:
        cpu: 500m
        memory: 100Mi
  - name: nginx-ctr
    image: nginx
    resources:
      limits:
        cpu: 1500m
        memory: 100Mi

At admission time the RuntimeClass admission controller updates the workload's PodSpec to include the overhead as described in the RuntimeClass. If the PodSpec already has this field defined, the Pod will be rejected. In the given example, since only the RuntimeClass name is specified, the admission controller mutates the Pod to include an overhead.

After the RuntimeClass admission controller has made modifications, you can check the updated Pod overhead value:

kubectl get pod test-pod -o jsonpath='{.spec.overhead}'

The output is:

map[cpu:250m memory:120Mi]

If a ResourceQuota is defined, the sum of container requests as well as the overhead field are counted.

When the kube-scheduler is deciding which node should run a new Pod, the scheduler considers that Pod's overhead as well as the sum of container requests for that Pod. For this example, the scheduler adds the requests and the overhead, then looks for a node that has 2.25 CPU and 320 MiB of memory available.

Once a Pod is scheduled to a node, the kubelet on that node creates a new cgroup for the Pod. It is within this pod that the underlying container runtime will create containers.

If the resource has a limit defined for each container (Guaranteed QoS or Burstable QoS with limits defined), the kubelet will set an upper limit for the pod cgroup associated with that resource (cpu.cfs_quota_us for CPU and memory.limit_in_bytes memory). This upper limit is based on the sum of the container limits plus the overhead defined in the PodSpec.

For CPU, if the Pod is Guaranteed or Burstable QoS, the kubelet will set cpu.shares based on the sum of container requests plus the overhead defined in the PodSpec.

Looking at our example, verify the container requests for the workload:

kubectl get pod test-pod -o jsonpath='{.spec.containers[*].resources.limits}'

The total container requests are 2000m CPU and 200MiB of memory:

map[cpu: 500m memory:100Mi] map[cpu:1500m memory:100Mi]

Check this against what is observed by the node:

kubectl describe node | grep test-pod -B2

The output shows requests for 2250m CPU, and for 320MiB of memory. The requests include Pod overhead:

  Namespace    Name       CPU Requests  CPU Limits   Memory Requests  Memory Limits  AGE
  ---------    ----       ------------  ----------   ---------------  -------------  ---
  default      test-pod   2250m (56%)   2250m (56%)  320Mi (1%)       320Mi (1%)     36m

Verify Pod cgroup limits

Check the Pod's memory cgroups on the node where the workload is running. In the following example, crictl is used on the node, which provides a CLI for CRI-compatible container runtimes. This is an advanced example to show Pod overhead behavior, and it is not expected that users should need to check cgroups directly on the node.

First, on the particular node, determine the Pod identifier:

# Run this on the node where the Pod is scheduled
POD_ID="$(sudo crictl pods --name test-pod -q)"

From this, you can determine the cgroup path for the Pod:

# Run this on the node where the Pod is scheduled
sudo crictl inspectp -o=json $POD_ID | grep cgroupsPath

The resulting cgroup path includes the Pod's pause container. The Pod level cgroup is one directory above.

  "cgroupsPath": "/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/7ccf55aee35dd16aca4189c952d83487297f3cd760f1bbf09620e206e7d0c27a"

In this specific case, the pod cgroup path is kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2. Verify the Pod level cgroup setting for memory:

# Run this on the node where the Pod is scheduled.
# Also, change the name of the cgroup to match the cgroup allocated for your pod.
 cat /sys/fs/cgroup/memory/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/memory.limit_in_bytes

This is 320 MiB, as expected:

335544320

Observability

Some kube_pod_overhead_* metrics are available in kube-state-metrics to help identify when Pod overhead is being utilized and to help observe stability of workloads running with a defined overhead.

What's next

Learn more about RuntimeClass
Read the PodOverhead Design enhancement proposal for extra context

11.4 - Pod Topology Spread Constraints

You can use topology spread constraints to control how Pods are spread across your cluster among failure-domains such as regions, zones, nodes, and other user-defined topology domains. This can help to achieve high availability as well as efficient resource utilization.

You can set cluster-level constraints as a default, or configure topology spread constraints for individual workloads.

Motivation

Imagine that you have a cluster of up to twenty nodes, and you want to run a workload that automatically scales how many replicas it uses. There could be as few as two Pods or as many as fifteen. When there are only two Pods, you'd prefer not to have both of those Pods run on the same node: you would run the risk that a single node failure takes your workload offline.

In addition to this basic usage, there are some advanced usage examples that enable your workloads to benefit on high availability and cluster utilization.

As you scale up and run more Pods, a different concern becomes important. Imagine that you have three nodes running five Pods each. The nodes have enough capacity to run that many replicas; however, the clients that interact with this workload are split across three different datacenters (or infrastructure zones). Now you have less concern about a single node failure, but you notice that latency is higher than you'd like, and you are paying for network costs associated with sending network traffic between the different zones.

You decide that under normal operation you'd prefer to have a similar number of replicas scheduled into each infrastructure zone, and you'd like the cluster to self-heal in the case that there is a problem.

Pod topology spread constraints offer you a declarative way to configure that.

`topologySpreadConstraints` field

The Pod API includes a field, spec.topologySpreadConstraints. The usage of this field looks like the following:

---
apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  # Configure a topology spread constraint
  topologySpreadConstraints:
    - maxSkew: <integer>
      minDomains: <integer> # optional; beta since v1.25
      topologyKey: <string>
      whenUnsatisfiable: <string>
      labelSelector: <object>
      matchLabelKeys: <list> # optional; alpha since v1.25
      nodeAffinityPolicy: [Honor|Ignore] # optional; alpha since v1.25
      nodeTaintsPolicy: [Honor|Ignore] # optional; alpha since v1.25
  ### other Pod fields go here

You can read more about this field by running kubectl explain Pod.spec.topologySpreadConstraints or refer to scheduling section of the API reference for Pod.

Spread constraint definition

You can define one or multiple topologySpreadConstraints entries to instruct the kube-scheduler how to place each incoming Pod in relation to the existing Pods across your cluster. Those fields are:

maxSkew describes the degree to which Pods may be unevenly distributed. You must specify this field and the number must be greater than zero. Its semantics differ according to the value of whenUnsatisfiable:
- if you select whenUnsatisfiable: DoNotSchedule, then maxSkew defines the maximum permitted difference between the number of matching pods in the target topology and the global minimum (the minimum number of matching pods in an eligible domain or zero if the number of eligible domains is less than MinDomains). For example, if you have 3 zones with 2, 2 and 1 matching pods respectively, MaxSkew is set to 1 then the global minimum is 1.
- if you select whenUnsatisfiable: ScheduleAnyway, the scheduler gives higher precedence to topologies that would help reduce the skew.
minDomains indicates a minimum number of eligible domains. This field is optional. A domain is a particular instance of a topology. An eligible domain is a domain whose nodes match the node selector.

Note: The minDomains field is a beta field and enabled by default in 1.25. You can disable it by disabling the MinDomainsInPodToplogySpread feature gate.
- The value of minDomains must be greater than 0, when specified. You can only specify minDomains in conjunction with whenUnsatisfiable: DoNotSchedule.
- When the number of eligible domains with match topology keys is less than minDomains, Pod topology spread treats global minimum as 0, and then the calculation of skew is performed. The global minimum is the minimum number of matching Pods in an eligible domain, or zero if the number of eligible domains is less than minDomains.
- When the number of eligible domains with matching topology keys equals or is greater than minDomains, this value has no effect on scheduling.
- If you do not specify minDomains, the constraint behaves as if minDomains is 1.
topologyKey is the key of node labels. Nodes that have a label with this key and identical values are considered to be in the same topology. We call each instance of a topology (in other words, a <key, value> pair) a domain. The scheduler will try to put a balanced number of pods into each domain. Also, we define an eligible domain as a domain whose nodes meet the requirements of nodeAffinityPolicy and nodeTaintsPolicy.
whenUnsatisfiable indicates how to deal with a Pod if it doesn't satisfy the spread constraint:
- DoNotSchedule (default) tells the scheduler not to schedule it.
- ScheduleAnyway tells the scheduler to still schedule it while prioritizing nodes that minimize the skew.
labelSelector is used to find matching Pods. Pods that match this label selector are counted to determine the number of Pods in their corresponding topology domain. See Label Selectors for more details.
matchLabelKeys is a list of pod label keys to select the pods over which spreading will be calculated. The keys are used to lookup values from the pod labels, those key-value labels are ANDed with labelSelector to select the group of existing pods over which spreading will be calculated for the incoming pod. Keys that don't exist in the pod labels will be ignored. A null or empty list means only match against the labelSelector.

With matchLabelKeys, users don't need to update the pod.spec between different revisions. The controller/operator just needs to set different values to the same label key for different revisions. The scheduler will assume the values automatically based on matchLabelKeys. For example, if users use Deployment, they can use the label keyed with pod-template-hash, which is added automatically by the Deployment controller, to distinguish between different revisions in a single Deployment.
```
    topologySpreadConstraints:
        - maxSkew: 1
          topologyKey: kubernetes.io/hostname
          whenUnsatisfiable: DoNotSchedule
          matchLabelKeys:
            - app
            - pod-template-hash
```
Note: The matchLabelKeys field is an alpha field added in 1.25. You have to enable the MatchLabelKeysInPodTopologySpread feature gate in order to use it.
nodeAffinityPolicy indicates how we will treat Pod's nodeAffinity/nodeSelector when calculating pod topology spread skew. Options are:
- Honor: only nodes matching nodeAffinity/nodeSelector are included in the calculations.
- Ignore: nodeAffinity/nodeSelector are ignored. All nodes are included in the calculations.
If this value is null, the behavior is equivalent to the Honor policy.

Note: The nodeAffinityPolicy is an alpha-level field added in 1.25. You have to enable the NodeInclusionPolicyInPodTopologySpread feature gate in order to use it.
nodeTaintsPolicy indicates how we will treat node taints when calculating pod topology spread skew. Options are:
- Honor: nodes without taints, along with tainted nodes for which the incoming pod has a toleration, are included.
- Ignore: node taints are ignored. All nodes are included.
If this value is null, the behavior is equivalent to the Ignore policy.

Note: The nodeTaintsPolicy is an alpha-level field added in 1.25. You have to enable the NodeInclusionPolicyInPodTopologySpread feature gate in order to use it.

When a Pod defines more than one topologySpreadConstraint, those constraints are combined using a logical AND operation: the kube-scheduler looks for a node for the incoming Pod that satisfies all the configured constraints.

Node labels

Topology spread constraints rely on node labels to identify the topology domain(s) that each node is in. For example, a node might have labels:

  region: us-east-1
  zone: us-east-1a

Note:

For brevity, this example doesn't use the well-known label keys topology.kubernetes.io/zone and topology.kubernetes.io/region. However, those registered label keys are nonetheless recommended rather than the private (unqualified) label keys region and zone that are used here.

You can't make a reliable assumption about the meaning of a private label key between different contexts.

Suppose you have a 4-node cluster with the following labels:

NAME    STATUS   ROLES    AGE     VERSION   LABELS
node1   Ready    <none>   4m26s   v1.16.0   node=node1,zone=zoneA
node2   Ready    <none>   3m58s   v1.16.0   node=node2,zone=zoneA
node3   Ready    <none>   3m17s   v1.16.0   node=node3,zone=zoneB
node4   Ready    <none>   2m43s   v1.16.0   node=node4,zone=zoneB

Then the cluster is logically viewed as below:

graph TB subgraph "zoneB" n3(Node3) n4(Node4) end subgraph "zoneA" n1(Node1) n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4 k8s; class zoneA,zoneB cluster;

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Consistency

You should set the same Pod topology spread constraints on all pods in a group.

Usually, if you are using a workload controller such as a Deployment, the pod template takes care of this for you. If you mix different spread constraints then Kubernetes follows the API definition of the field; however, the behavior is more likely to become confusing and troubleshooting is less straightforward.

You need a mechanism to ensure that all the nodes in a topology domain (such as a cloud provider region) are labelled consistently. To avoid you needing to manually label nodes, most clusters automatically populate well-known labels such as topology.kubernetes.io/hostname. Check whether your cluster supports this.

Topology spread constraint examples

Example: one topology spread constraint

Suppose you have a 4-node cluster where 3 Pods labelled foo: bar are located in node1, node2 and node3 respectively:

graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class zoneA,zoneB cluster;

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If you want an incoming Pod to be evenly spread with existing Pods across zones, you can use a manifest similar to:

pods/topology-spread-constraints/one-constraint.yaml

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  containers:
  - name: pause
    image: registry.k8s.io/pause:3.1

From that manifest, topologyKey: zone implies the even distribution will only be applied to nodes that are labelled zone: <any value> (nodes that don't have a zone label are skipped). The field whenUnsatisfiable: DoNotSchedule tells the scheduler to let the incoming Pod stay pending if the scheduler can't find a way to satisfy the constraint.

If the scheduler placed this incoming Pod into zone A, the distribution of Pods would become [3, 1]. That means the actual skew is then 2 (calculated as 3 - 1), which violates maxSkew: 1. To satisfy the constraints and context for this example, the incoming Pod can only be placed onto a node in zone B:

graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) p4(mypod) --> n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster;

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graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) p4(mypod) --> n3 n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster;

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You can tweak the Pod spec to meet various kinds of requirements:

Change maxSkew to a bigger value - such as 2 - so that the incoming Pod can be placed into zone A as well.
Change topologyKey to node so as to distribute the Pods evenly across nodes instead of zones. In the above example, if maxSkew remains 1, the incoming Pod can only be placed onto the node node4.
Change whenUnsatisfiable: DoNotSchedule to whenUnsatisfiable: ScheduleAnyway to ensure the incoming Pod to be always schedulable (suppose other scheduling APIs are satisfied). However, it's preferred to be placed into the topology domain which has fewer matching Pods. (Be aware that this preference is jointly normalized with other internal scheduling priorities such as resource usage ratio).

Example: multiple topology spread constraints

This builds upon the previous example. Suppose you have a 4-node cluster where 3 existing Pods labeled foo: bar are located on node1, node2 and node3 respectively:

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You can combine two topology spread constraints to control the spread of Pods both by node and by zone:

pods/topology-spread-constraints/two-constraints.yaml

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  - maxSkew: 1
    topologyKey: node
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  containers:
  - name: pause
    image: registry.k8s.io/pause:3.1

In this case, to match the first constraint, the incoming Pod can only be placed onto nodes in zone B; while in terms of the second constraint, the incoming Pod can only be scheduled to the node node4. The scheduler only considers options that satisfy all defined constraints, so the only valid placement is onto node node4.

Example: conflicting topology spread constraints

Multiple constraints can lead to conflicts. Suppose you have a 3-node cluster across 2 zones:

graph BT subgraph "zoneB" p4(Pod) --> n3(Node3) p5(Pod) --> n3 end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n1 p3(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3,p4,p5 k8s; class zoneA,zoneB cluster;

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If you were to apply two-constraints.yaml (the manifest from the previous example) to this cluster, you would see that the Pod mypod stays in the Pending state. This happens because: to satisfy the first constraint, the Pod mypod can only be placed into zone B; while in terms of the second constraint, the Pod mypod can only schedule to node node2. The intersection of the two constraints returns an empty set, and the scheduler cannot place the Pod.

To overcome this situation, you can either increase the value of maxSkew or modify one of the constraints to use whenUnsatisfiable: ScheduleAnyway. Depending on circumstances, you might also decide to delete an existing Pod manually - for example, if you are troubleshooting why a bug-fix rollout is not making progress.

Interaction with node affinity and node selectors

The scheduler will skip the non-matching nodes from the skew calculations if the incoming Pod has spec.nodeSelector or spec.affinity.nodeAffinity defined.

Example: topology spread constraints with node affinity

Suppose you have a 5-node cluster ranging across zones A to C:

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graph BT subgraph "zoneC" n5(Node5) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n5 k8s; class zoneC cluster;

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and you know that zone C must be excluded. In this case, you can compose a manifest as below, so that Pod mypod will be placed into zone B instead of zone C. Similarly, Kubernetes also respects spec.nodeSelector.

pods/topology-spread-constraints/one-constraint-with-nodeaffinity.yaml

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: zone
            operator: NotIn
            values:
            - zoneC
  containers:
  - name: pause
    image: registry.k8s.io/pause:3.1

Implicit conventions

There are some implicit conventions worth noting here:

Only the Pods holding the same namespace as the incoming Pod can be matching candidates.
The scheduler bypasses any nodes that don't have any topologySpreadConstraints[*].topologyKey present. This implies that:
1. any Pods located on those bypassed nodes do not impact maxSkew calculation - in the above example, suppose the node node1 does not have a label "zone", then the 2 Pods will be disregarded, hence the incoming Pod will be scheduled into zone A.
2. the incoming Pod has no chances to be scheduled onto this kind of nodes - in the above example, suppose a node node5 has the mistyped label zone-typo: zoneC (and no zone label set). After node node5 joins the cluster, it will be bypassed and Pods for this workload aren't scheduled there.
Be aware of what will happen if the incoming Pod's topologySpreadConstraints[*].labelSelector doesn't match its own labels. In the above example, if you remove the incoming Pod's labels, it can still be placed onto nodes in zone B, since the constraints are still satisfied. However, after that placement, the degree of imbalance of the cluster remains unchanged - it's still zone A having 2 Pods labelled as foo: bar, and zone B having 1 Pod labelled as foo: bar. If this is not what you expect, update the workload's topologySpreadConstraints[*].labelSelector to match the labels in the pod template.

Cluster-level default constraints

It is possible to set default topology spread constraints for a cluster. Default topology spread constraints are applied to a Pod if, and only if:

It doesn't define any constraints in its .spec.topologySpreadConstraints.
It belongs to a Service, ReplicaSet, StatefulSet or ReplicationController.

Default constraints can be set as part of the PodTopologySpread plugin arguments in a scheduling profile. The constraints are specified with the same API above, except that labelSelector must be empty. The selectors are calculated from the Services, ReplicaSets, StatefulSets or ReplicationControllers that the Pod belongs to.

An example configuration might look like follows:

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints:
            - maxSkew: 1
              topologyKey: topology.kubernetes.io/zone
              whenUnsatisfiable: ScheduleAnyway
          defaultingType: List

Note: The SelectorSpread plugin is disabled by default. The Kubernetes project recommends using PodTopologySpread to achieve similar behavior.

Built-in default constraints

FEATURE STATE: Kubernetes v1.24 [stable]

If you don't configure any cluster-level default constraints for pod topology spreading, then kube-scheduler acts as if you specified the following default topology constraints:

defaultConstraints:
  - maxSkew: 3
    topologyKey: "kubernetes.io/hostname"
    whenUnsatisfiable: ScheduleAnyway
  - maxSkew: 5
    topologyKey: "topology.kubernetes.io/zone"
    whenUnsatisfiable: ScheduleAnyway

Also, the legacy SelectorSpread plugin, which provides an equivalent behavior, is disabled by default.

Note:

The PodTopologySpread plugin does not score the nodes that don't have the topology keys specified in the spreading constraints. This might result in a different default behavior compared to the legacy SelectorSpread plugin when using the default topology constraints.

If your nodes are not expected to have both kubernetes.io/hostname and topology.kubernetes.io/zone labels set, define your own constraints instead of using the Kubernetes defaults.

If you don't want to use the default Pod spreading constraints for your cluster, you can disable those defaults by setting defaultingType to List and leaving empty defaultConstraints in the PodTopologySpread plugin configuration:

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints: []
          defaultingType: List

Comparison with podAffinity and podAntiAffinity

In Kubernetes, inter-Pod affinity and anti-affinity control how Pods are scheduled in relation to one another - either more packed or more scattered.

podAffinity: attracts Pods; you can try to pack any number of Pods into qualifying topology domain(s) podAntiAffinity; repels Pods. If you set this to requiredDuringSchedulingIgnoredDuringExecution mode then only a single Pod can be scheduled into a single topology domain; if you choose preferredDuringSchedulingIgnoredDuringExecution then you lose the ability to enforce the constraint.

For finer control, you can specify topology spread constraints to distribute Pods across different topology domains - to achieve either high availability or cost-saving. This can also help on rolling update workloads and scaling out replicas smoothly.

For more context, see the Motivation section of the enhancement proposal about Pod topology spread constraints.

Known limitations

There's no guarantee that the constraints remain satisfied when Pods are removed. For example, scaling down a Deployment may result in imbalanced Pods distribution.

You can use a tool such as the Descheduler to rebalance the Pods distribution.
Pods matched on tainted nodes are respected. See Issue 80921.
The scheduler doesn't have prior knowledge of all the zones or other topology domains that a cluster has. They are determined from the existing nodes in the cluster. This could lead to a problem in autoscaled clusters, when a node pool (or node group) is scaled to zero nodes, and you're expecting the cluster to scale up, because, in this case, those topology domains won't be considered until there is at least one node in them.

You can work around this by using an cluster autoscaling tool that is aware of Pod topology spread constraints and is also aware of the overall set of topology domains.

What's next

The blog article Introducing PodTopologySpread explains maxSkew in some detail, as well as covering some advanced usage examples.
Read the scheduling section of the API reference for Pod.

11.5 - Taints and Tolerations

Node affinity is a property of Pods that attracts them to a set of nodes (either as a preference or a hard requirement). Taints are the opposite -- they allow a node to repel a set of pods.

Tolerations are applied to pods. Tolerations allow the scheduler to schedule pods with matching taints. Tolerations allow scheduling but don't guarantee scheduling: the scheduler also evaluates other parameters as part of its function.

Taints and tolerations work together to ensure that pods are not scheduled onto inappropriate nodes. One or more taints are applied to a node; this marks that the node should not accept any pods that do not tolerate the taints.

Concepts

You add a taint to a node using kubectl taint. For example,

kubectl taint nodes node1 key1=value1:NoSchedule

places a taint on node node1. The taint has key key1, value value1, and taint effect NoSchedule. This means that no pod will be able to schedule onto node1 unless it has a matching toleration.

To remove the taint added by the command above, you can run:

kubectl taint nodes node1 key1=value1:NoSchedule-

You specify a toleration for a pod in the PodSpec. Both of the following tolerations "match" the taint created by the kubectl taint line above, and thus a pod with either toleration would be able to schedule onto node1:

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoSchedule"

tolerations:
- key: "key1"
  operator: "Exists"
  effect: "NoSchedule"

Here's an example of a pod that uses tolerations:

pods/pod-with-toleration.yaml

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  tolerations:
  - key: "example-key"
    operator: "Exists"
    effect: "NoSchedule"

The default value for operator is Equal.

A toleration "matches" a taint if the keys are the same and the effects are the same, and:

the operator is Exists (in which case no value should be specified), or
the operator is Equal and the values are equal.

Note:

There are two special cases:

An empty key with operator Exists matches all keys, values and effects which means this will tolerate everything.

An empty effect matches all effects with key key1.

The above example used effect of NoSchedule. Alternatively, you can use effect of PreferNoSchedule. This is a "preference" or "soft" version of NoSchedule -- the system will try to avoid placing a pod that does not tolerate the taint on the node, but it is not required. The third kind of effect is NoExecute, described later.

You can put multiple taints on the same node and multiple tolerations on the same pod. The way Kubernetes processes multiple taints and tolerations is like a filter: start with all of a node's taints, then ignore the ones for which the pod has a matching toleration; the remaining un-ignored taints have the indicated effects on the pod. In particular,

if there is at least one un-ignored taint with effect NoSchedule then Kubernetes will not schedule the pod onto that node
if there is no un-ignored taint with effect NoSchedule but there is at least one un-ignored taint with effect PreferNoSchedule then Kubernetes will try to not schedule the pod onto the node
if there is at least one un-ignored taint with effect NoExecute then the pod will be evicted from the node (if it is already running on the node), and will not be scheduled onto the node (if it is not yet running on the node).

For example, imagine you taint a node like this

kubectl taint nodes node1 key1=value1:NoSchedule
kubectl taint nodes node1 key1=value1:NoExecute
kubectl taint nodes node1 key2=value2:NoSchedule

And a pod has two tolerations:

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoSchedule"
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoExecute"

In this case, the pod will not be able to schedule onto the node, because there is no toleration matching the third taint. But it will be able to continue running if it is already running on the node when the taint is added, because the third taint is the only one of the three that is not tolerated by the pod.

Normally, if a taint with effect NoExecute is added to a node, then any pods that do not tolerate the taint will be evicted immediately, and pods that do tolerate the taint will never be evicted. However, a toleration with NoExecute effect can specify an optional tolerationSeconds field that dictates how long the pod will stay bound to the node after the taint is added. For example,

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoExecute"
  tolerationSeconds: 3600

means that if this pod is running and a matching taint is added to the node, then the pod will stay bound to the node for 3600 seconds, and then be evicted. If the taint is removed before that time, the pod will not be evicted.

Example Use Cases

Taints and tolerations are a flexible way to steer pods away from nodes or evict pods that shouldn't be running. A few of the use cases are

Dedicated Nodes: If you want to dedicate a set of nodes for exclusive use by a particular set of users, you can add a taint to those nodes (say, kubectl taint nodes nodename dedicated=groupName:NoSchedule) and then add a corresponding toleration to their pods (this would be done most easily by writing a custom admission controller). The pods with the tolerations will then be allowed to use the tainted (dedicated) nodes as well as any other nodes in the cluster. If you want to dedicate the nodes to them and ensure they only use the dedicated nodes, then you should additionally add a label similar to the taint to the same set of nodes (e.g. dedicated=groupName), and the admission controller should additionally add a node affinity to require that the pods can only schedule onto nodes labeled with dedicated=groupName.
Nodes with Special Hardware: In a cluster where a small subset of nodes have specialized hardware (for example GPUs), it is desirable to keep pods that don't need the specialized hardware off of those nodes, thus leaving room for later-arriving pods that do need the specialized hardware. This can be done by tainting the nodes that have the specialized hardware (e.g. kubectl taint nodes nodename special=true:NoSchedule or kubectl taint nodes nodename special=true:PreferNoSchedule) and adding a corresponding toleration to pods that use the special hardware. As in the dedicated nodes use case, it is probably easiest to apply the tolerations using a custom admission controller. For example, it is recommended to use Extended Resources to represent the special hardware, taint your special hardware nodes with the extended resource name and run the ExtendedResourceToleration admission controller. Now, because the nodes are tainted, no pods without the toleration will schedule on them. But when you submit a pod that requests the extended resource, the ExtendedResourceToleration admission controller will automatically add the correct toleration to the pod and that pod will schedule on the special hardware nodes. This will make sure that these special hardware nodes are dedicated for pods requesting such hardware and you don't have to manually add tolerations to your pods.
Taint based Evictions: A per-pod-configurable eviction behavior when there are node problems, which is described in the next section.

Taint based Evictions

FEATURE STATE: Kubernetes v1.18 [stable]

The NoExecute taint effect, mentioned above, affects pods that are already running on the node as follows

pods that do not tolerate the taint are evicted immediately
pods that tolerate the taint without specifying tolerationSeconds in their toleration specification remain bound forever
pods that tolerate the taint with a specified tolerationSeconds remain bound for the specified amount of time

The node controller automatically taints a Node when certain conditions are true. The following taints are built in:

node.kubernetes.io/not-ready: Node is not ready. This corresponds to the NodeCondition Ready being "False".
node.kubernetes.io/unreachable: Node is unreachable from the node controller. This corresponds to the NodeCondition Ready being "Unknown".
node.kubernetes.io/memory-pressure: Node has memory pressure.
node.kubernetes.io/disk-pressure: Node has disk pressure.
node.kubernetes.io/pid-pressure: Node has PID pressure.
node.kubernetes.io/network-unavailable: Node's network is unavailable.
node.kubernetes.io/unschedulable: Node is unschedulable.
node.cloudprovider.kubernetes.io/uninitialized: When the kubelet is started with "external" cloud provider, this taint is set on a node to mark it as unusable. After a controller from the cloud-controller-manager initializes this node, the kubelet removes this taint.

In case a node is to be evicted, the node controller or the kubelet adds relevant taints with NoExecute effect. If the fault condition returns to normal the kubelet or node controller can remove the relevant taint(s).

Note: The control plane limits the rate of adding node new taints to nodes. This rate limiting manages the number of evictions that are triggered when many nodes become unreachable at once (for example: if there is a network disruption).

You can specify tolerationSeconds for a Pod to define how long that Pod stays bound to a failing or unresponsive Node.

For example, you might want to keep an application with a lot of local state bound to node for a long time in the event of network partition, hoping that the partition will recover and thus the pod eviction can be avoided. The toleration you set for that Pod might look like:

tolerations:
- key: "node.kubernetes.io/unreachable"
  operator: "Exists"
  effect: "NoExecute"
  tolerationSeconds: 6000

Note:

Kubernetes automatically adds a toleration for node.kubernetes.io/not-ready and node.kubernetes.io/unreachable with tolerationSeconds=300, unless you, or a controller, set those tolerations explicitly.

These automatically-added tolerations mean that Pods remain bound to Nodes for 5 minutes after one of these problems is detected.

DaemonSet pods are created with NoExecute tolerations for the following taints with no tolerationSeconds:

node.kubernetes.io/unreachable
node.kubernetes.io/not-ready

This ensures that DaemonSet pods are never evicted due to these problems.

Taint Nodes by Condition

The control plane, using the node controller, automatically creates taints with a NoSchedule effect for node conditions.

The scheduler checks taints, not node conditions, when it makes scheduling decisions. This ensures that node conditions don't directly affect scheduling. For example, if the DiskPressure node condition is active, the control plane adds the node.kubernetes.io/disk-pressure taint and does not schedule new pods onto the affected node. If the MemoryPressure node condition is active, the control plane adds the node.kubernetes.io/memory-pressure taint.

You can ignore node conditions for newly created pods by adding the corresponding Pod tolerations. The control plane also adds the node.kubernetes.io/memory-pressure toleration on pods that have a QoS class other than BestEffort. This is because Kubernetes treats pods in the Guaranteed or Burstable QoS classes (even pods with no memory request set) as if they are able to cope with memory pressure, while new BestEffort pods are not scheduled onto the affected node.

The DaemonSet controller automatically adds the following NoSchedule tolerations to all daemons, to prevent DaemonSets from breaking.

node.kubernetes.io/memory-pressure
node.kubernetes.io/disk-pressure
node.kubernetes.io/pid-pressure (1.14 or later)
node.kubernetes.io/unschedulable (1.10 or later)
node.kubernetes.io/network-unavailable (host network only)

Adding these tolerations ensures backward compatibility. You can also add arbitrary tolerations to DaemonSets.

What's next

Read about Node-pressure Eviction and how you can configure it
Read about Pod Priority

11.6 - Pod Priority and Preemption

FEATURE STATE: Kubernetes v1.14 [stable]

Pods can have priority. Priority indicates the importance of a Pod relative to other Pods. If a Pod cannot be scheduled, the scheduler tries to preempt (evict) lower priority Pods to make scheduling of the pending Pod possible.

Warning:

In a cluster where not all users are trusted, a malicious user could create Pods at the highest possible priorities, causing other Pods to be evicted/not get scheduled. An administrator can use ResourceQuota to prevent users from creating pods at high priorities.

See limit Priority Class consumption by default for details.

How to use priority and preemption

To use priority and preemption:

Add one or more PriorityClasses.
Create Pods withpriorityClassName set to one of the added PriorityClasses. Of course you do not need to create the Pods directly; normally you would add priorityClassName to the Pod template of a collection object like a Deployment.

Keep reading for more information about these steps.

Note: Kubernetes already ships with two PriorityClasses: system-cluster-critical and system-node-critical. These are common classes and are used to ensure that critical components are always scheduled first.

PriorityClass

A PriorityClass is a non-namespaced object that defines a mapping from a priority class name to the integer value of the priority. The name is specified in the name field of the PriorityClass object's metadata. The value is specified in the required value field. The higher the value, the higher the priority. The name of a PriorityClass object must be a valid DNS subdomain name, and it cannot be prefixed with system-.

A PriorityClass object can have any 32-bit integer value smaller than or equal to 1 billion. Larger numbers are reserved for critical system Pods that should not normally be preempted or evicted. A cluster admin should create one PriorityClass object for each such mapping that they want.

PriorityClass also has two optional fields: globalDefault and description. The globalDefault field indicates that the value of this PriorityClass should be used for Pods without a priorityClassName. Only one PriorityClass with globalDefault set to true can exist in the system. If there is no PriorityClass with globalDefault set, the priority of Pods with no priorityClassName is zero.

The description field is an arbitrary string. It is meant to tell users of the cluster when they should use this PriorityClass.

Notes about PodPriority and existing clusters

If you upgrade an existing cluster without this feature, the priority of your existing Pods is effectively zero.
Addition of a PriorityClass with globalDefault set to true does not change the priorities of existing Pods. The value of such a PriorityClass is used only for Pods created after the PriorityClass is added.
If you delete a PriorityClass, existing Pods that use the name of the deleted PriorityClass remain unchanged, but you cannot create more Pods that use the name of the deleted PriorityClass.

Example PriorityClass

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority
value: 1000000
globalDefault: false
description: "This priority class should be used for XYZ service pods only."

Non-preempting PriorityClass

FEATURE STATE: Kubernetes v1.24 [stable]

Pods with preemptionPolicy: Never will be placed in the scheduling queue ahead of lower-priority pods, but they cannot preempt other pods. A non-preempting pod waiting to be scheduled will stay in the scheduling queue, until sufficient resources are free, and it can be scheduled. Non-preempting pods, like other pods, are subject to scheduler back-off. This means that if the scheduler tries these pods and they cannot be scheduled, they will be retried with lower frequency, allowing other pods with lower priority to be scheduled before them.

Non-preempting pods may still be preempted by other, high-priority pods.

preemptionPolicy defaults to PreemptLowerPriority, which will allow pods of that PriorityClass to preempt lower-priority pods (as is existing default behavior). If preemptionPolicy is set to Never, pods in that PriorityClass will be non-preempting.

An example use case is for data science workloads. A user may submit a job that they want to be prioritized above other workloads, but do not wish to discard existing work by preempting running pods. The high priority job with preemptionPolicy: Never will be scheduled ahead of other queued pods, as soon as sufficient cluster resources "naturally" become free.

Example Non-preempting PriorityClass

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority-nonpreempting
value: 1000000
preemptionPolicy: Never
globalDefault: false
description: "This priority class will not cause other pods to be preempted."

Pod priority

After you have one or more PriorityClasses, you can create Pods that specify one of those PriorityClass names in their specifications. The priority admission controller uses the priorityClassName field and populates the integer value of the priority. If the priority class is not found, the Pod is rejected.

The following YAML is an example of a Pod configuration that uses the PriorityClass created in the preceding example. The priority admission controller checks the specification and resolves the priority of the Pod to 1000000.

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  priorityClassName: high-priority

Effect of Pod priority on scheduling order

When Pod priority is enabled, the scheduler orders pending Pods by their priority and a pending Pod is placed ahead of other pending Pods with lower priority in the scheduling queue. As a result, the higher priority Pod may be scheduled sooner than Pods with lower priority if its scheduling requirements are met. If such Pod cannot be scheduled, scheduler will continue and tries to schedule other lower priority Pods.

Preemption

When Pods are created, they go to a queue and wait to be scheduled. The scheduler picks a Pod from the queue and tries to schedule it on a Node. If no Node is found that satisfies all the specified requirements of the Pod, preemption logic is triggered for the pending Pod. Let's call the pending Pod P. Preemption logic tries to find a Node where removal of one or more Pods with lower priority than P would enable P to be scheduled on that Node. If such a Node is found, one or more lower priority Pods get evicted from the Node. After the Pods are gone, P can be scheduled on the Node.

User exposed information

When Pod P preempts one or more Pods on Node N, nominatedNodeName field of Pod P's status is set to the name of Node N. This field helps scheduler track resources reserved for Pod P and also gives users information about preemptions in their clusters.

Please note that Pod P is not necessarily scheduled to the "nominated Node". The scheduler always tries the "nominated Node" before iterating over any other nodes. After victim Pods are preempted, they get their graceful termination period. If another node becomes available while scheduler is waiting for the victim Pods to terminate, scheduler may use the other node to schedule Pod P. As a result nominatedNodeName and nodeName of Pod spec are not always the same. Also, if scheduler preempts Pods on Node N, but then a higher priority Pod than Pod P arrives, scheduler may give Node N to the new higher priority Pod. In such a case, scheduler clears nominatedNodeName of Pod P. By doing this, scheduler makes Pod P eligible to preempt Pods on another Node.

Limitations of preemption

Graceful termination of preemption victims

When Pods are preempted, the victims get their graceful termination period. They have that much time to finish their work and exit. If they don't, they are killed. This graceful termination period creates a time gap between the point that the scheduler preempts Pods and the time when the pending Pod (P) can be scheduled on the Node (N). In the meantime, the scheduler keeps scheduling other pending Pods. As victims exit or get terminated, the scheduler tries to schedule Pods in the pending queue. Therefore, there is usually a time gap between the point that scheduler preempts victims and the time that Pod P is scheduled. In order to minimize this gap, one can set graceful termination period of lower priority Pods to zero or a small number.

PodDisruptionBudget is supported, but not guaranteed

A PodDisruptionBudget (PDB) allows application owners to limit the number of Pods of a replicated application that are down simultaneously from voluntary disruptions. Kubernetes supports PDB when preempting Pods, but respecting PDB is best effort. The scheduler tries to find victims whose PDB are not violated by preemption, but if no such victims are found, preemption will still happen, and lower priority Pods will be removed despite their PDBs being violated.

Inter-Pod affinity on lower-priority Pods

A Node is considered for preemption only when the answer to this question is yes: "If all the Pods with lower priority than the pending Pod are removed from the Node, can the pending Pod be scheduled on the Node?"

Note: Preemption does not necessarily remove all lower-priority Pods. If the pending Pod can be scheduled by removing fewer than all lower-priority Pods, then only a portion of the lower-priority Pods are removed. Even so, the answer to the preceding question must be yes. If the answer is no, the Node is not considered for preemption.

If a pending Pod has inter-pod affinity to one or more of the lower-priority Pods on the Node, the inter-Pod affinity rule cannot be satisfied in the absence of those lower-priority Pods. In this case, the scheduler does not preempt any Pods on the Node. Instead, it looks for another Node. The scheduler might find a suitable Node or it might not. There is no guarantee that the pending Pod can be scheduled.

Our recommended solution for this problem is to create inter-Pod affinity only towards equal or higher priority Pods.

Cross node preemption

Suppose a Node N is being considered for preemption so that a pending Pod P can be scheduled on N. P might become feasible on N only if a Pod on another Node is preempted. Here's an example:

Pod P is being considered for Node N.
Pod Q is running on another Node in the same Zone as Node N.
Pod P has Zone-wide anti-affinity with Pod Q (topologyKey: topology.kubernetes.io/zone).
There are no other cases of anti-affinity between Pod P and other Pods in the Zone.
In order to schedule Pod P on Node N, Pod Q can be preempted, but scheduler does not perform cross-node preemption. So, Pod P will be deemed unschedulable on Node N.

If Pod Q were removed from its Node, the Pod anti-affinity violation would be gone, and Pod P could possibly be scheduled on Node N.

We may consider adding cross Node preemption in future versions if there is enough demand and if we find an algorithm with reasonable performance.

Troubleshooting

Pod priority and pre-emption can have unwanted side effects. Here are some examples of potential problems and ways to deal with them.

Pods are preempted unnecessarily

Preemption removes existing Pods from a cluster under resource pressure to make room for higher priority pending Pods. If you give high priorities to certain Pods by mistake, these unintentionally high priority Pods may cause preemption in your cluster. Pod priority is specified by setting the priorityClassName field in the Pod's specification. The integer value for priority is then resolved and populated to the priority field of podSpec.

To address the problem, you can change the priorityClassName for those Pods to use lower priority classes, or leave that field empty. An empty priorityClassName is resolved to zero by default.

When a Pod is preempted, there will be events recorded for the preempted Pod. Preemption should happen only when a cluster does not have enough resources for a Pod. In such cases, preemption happens only when the priority of the pending Pod (preemptor) is higher than the victim Pods. Preemption must not happen when there is no pending Pod, or when the pending Pods have equal or lower priority than the victims. If preemption happens in such scenarios, please file an issue.

Pods are preempted, but the preemptor is not scheduled

When pods are preempted, they receive their requested graceful termination period, which is by default 30 seconds. If the victim Pods do not terminate within this period, they are forcibly terminated. Once all the victims go away, the preemptor Pod can be scheduled.

While the preemptor Pod is waiting for the victims to go away, a higher priority Pod may be created that fits on the same Node. In this case, the scheduler will schedule the higher priority Pod instead of the preemptor.

This is expected behavior: the Pod with the higher priority should take the place of a Pod with a lower priority.

Higher priority Pods are preempted before lower priority pods

The scheduler tries to find nodes that can run a pending Pod. If no node is found, the scheduler tries to remove Pods with lower priority from an arbitrary node in order to make room for the pending pod. If a node with low priority Pods is not feasible to run the pending Pod, the scheduler may choose another node with higher priority Pods (compared to the Pods on the other node) for preemption. The victims must still have lower priority than the preemptor Pod.

When there are multiple nodes available for preemption, the scheduler tries to choose the node with a set of Pods with lowest priority. However, if such Pods have PodDisruptionBudget that would be violated if they are preempted then the scheduler may choose another node with higher priority Pods.

When multiple nodes exist for preemption and none of the above scenarios apply, the scheduler chooses a node with the lowest priority.

Interactions between Pod priority and quality of service

Pod priority and QoS class are two orthogonal features with few interactions and no default restrictions on setting the priority of a Pod based on its QoS classes. The scheduler's preemption logic does not consider QoS when choosing preemption targets. Preemption considers Pod priority and attempts to choose a set of targets with the lowest priority. Higher-priority Pods are considered for preemption only if the removal of the lowest priority Pods is not sufficient to allow the scheduler to schedule the preemptor Pod, or if the lowest priority Pods are protected by PodDisruptionBudget.

The kubelet uses Priority to determine pod order for node-pressure eviction. You can use the QoS class to estimate the order in which pods are most likely to get evicted. The kubelet ranks pods for eviction based on the following factors:

Whether the starved resource usage exceeds requests
Pod Priority
Amount of resource usage relative to requests

See Pod selection for kubelet eviction for more details.

kubelet node-pressure eviction does not evict Pods when their usage does not exceed their requests. If a Pod with lower priority is not exceeding its requests, it won't be evicted. Another Pod with higher priority that exceeds its requests may be evicted.

What's next

Read about using ResourceQuotas in connection with PriorityClasses: limit Priority Class consumption by default
Learn about Pod Disruption
Learn about API-initiated Eviction
Learn about Node-pressure Eviction

11.7 - Node-pressure Eviction

Node-pressure eviction is the process by which the kubelet proactively terminates pods to reclaim resources on nodes.

The kubelet monitors resources like memory, disk space, and filesystem inodes on your cluster's nodes. When one or more of these resources reach specific consumption levels, the kubelet can proactively fail one or more pods on the node to reclaim resources and prevent starvation.

During a node-pressure eviction, the kubelet sets the PodPhase for the selected pods to Failed. This terminates the pods.

Node-pressure eviction is not the same as API-initiated eviction.

The kubelet does not respect your configured PodDisruptionBudget or the pod's terminationGracePeriodSeconds. If you use soft eviction thresholds, the kubelet respects your configured eviction-max-pod-grace-period. If you use hard eviction thresholds, it uses a 0s grace period for termination.

If the pods are managed by a workload resource (such as StatefulSet or Deployment) that replaces failed pods, the control plane or kube-controller-manager creates new pods in place of the evicted pods.

Note: The kubelet attempts to reclaim node-level resources before it terminates end-user pods. For example, it removes unused container images when disk resources are starved.

The kubelet uses various parameters to make eviction decisions, like the following:

Eviction signals
Eviction thresholds
Monitoring intervals

Eviction signals

Eviction signals are the current state of a particular resource at a specific point in time. Kubelet uses eviction signals to make eviction decisions by comparing the signals to eviction thresholds, which are the minimum amount of the resource that should be available on the node.

Kubelet uses the following eviction signals:

Eviction Signal	Description
`memory.available`	`memory.available` := `node.status.capacity[memory]` - `node.stats.memory.workingSet`
`nodefs.available`	`nodefs.available` := `node.stats.fs.available`
`nodefs.inodesFree`	`nodefs.inodesFree` := `node.stats.fs.inodesFree`
`imagefs.available`	`imagefs.available` := `node.stats.runtime.imagefs.available`
`imagefs.inodesFree`	`imagefs.inodesFree` := `node.stats.runtime.imagefs.inodesFree`
`pid.available`	`pid.available` := `node.stats.rlimit.maxpid` - `node.stats.rlimit.curproc`

In this table, the Description column shows how kubelet gets the value of the signal. Each signal supports either a percentage or a literal value. Kubelet calculates the percentage value relative to the total capacity associated with the signal.

The value for memory.available is derived from the cgroupfs instead of tools like free -m. This is important because free -m does not work in a container, and if users use the node allocatable feature, out of resource decisions are made local to the end user Pod part of the cgroup hierarchy as well as the root node. This script reproduces the same set of steps that the kubelet performs to calculate memory.available. The kubelet excludes inactive_file (i.e. # of bytes of file-backed memory on inactive LRU list) from its calculation as it assumes that memory is reclaimable under pressure.

The kubelet supports the following filesystem partitions:

nodefs: The node's main filesystem, used for local disk volumes, emptyDir, log storage, and more. For example, nodefs contains /var/lib/kubelet/.
imagefs: An optional filesystem that container runtimes use to store container images and container writable layers.

Kubelet auto-discovers these filesystems and ignores other filesystems. Kubelet does not support other configurations.

Some kubelet garbage collection features are deprecated in favor of eviction:

Existing Flag	New Flag	Rationale
`--image-gc-high-threshold`	`--eviction-hard` or `--eviction-soft`	existing eviction signals can trigger image garbage collection
`--image-gc-low-threshold`	`--eviction-minimum-reclaim`	eviction reclaims achieve the same behavior
`--maximum-dead-containers`	-	deprecated once old logs are stored outside of container's context
`--maximum-dead-containers-per-container`	-	deprecated once old logs are stored outside of container's context
`--minimum-container-ttl-duration`	-	deprecated once old logs are stored outside of container's context

Eviction thresholds

You can specify custom eviction thresholds for the kubelet to use when it makes eviction decisions.

Eviction thresholds have the form [eviction-signal][operator][quantity], where:

eviction-signal is the eviction signal to use.
operator is the relational operator you want, such as < (less than).
quantity is the eviction threshold amount, such as 1Gi. The value of quantity must match the quantity representation used by Kubernetes. You can use either literal values or percentages (%).

For example, if a node has 10Gi of total memory and you want trigger eviction if the available memory falls below 1Gi, you can define the eviction threshold as either memory.available<10% or memory.available<1Gi. You cannot use both.

You can configure soft and hard eviction thresholds.

Soft eviction thresholds

A soft eviction threshold pairs an eviction threshold with a required administrator-specified grace period. The kubelet does not evict pods until the grace period is exceeded. The kubelet returns an error on startup if there is no specified grace period.

You can specify both a soft eviction threshold grace period and a maximum allowed pod termination grace period for kubelet to use during evictions. If you specify a maximum allowed grace period and the soft eviction threshold is met, the kubelet uses the lesser of the two grace periods. If you do not specify a maximum allowed grace period, the kubelet kills evicted pods immediately without graceful termination.

You can use the following flags to configure soft eviction thresholds:

eviction-soft: A set of eviction thresholds like memory.available<1.5Gi that can trigger pod eviction if held over the specified grace period.
eviction-soft-grace-period: A set of eviction grace periods like memory.available=1m30s that define how long a soft eviction threshold must hold before triggering a Pod eviction.
eviction-max-pod-grace-period: The maximum allowed grace period (in seconds) to use when terminating pods in response to a soft eviction threshold being met.

Hard eviction thresholds

A hard eviction threshold has no grace period. When a hard eviction threshold is met, the kubelet kills pods immediately without graceful termination to reclaim the starved resource.

You can use the eviction-hard flag to configure a set of hard eviction thresholds like memory.available<1Gi.

The kubelet has the following default hard eviction thresholds:

memory.available<100Mi
nodefs.available<10%
imagefs.available<15%
nodefs.inodesFree<5% (Linux nodes)

These default values of hard eviction thresholds will only be set if none of the parameters is changed. If you changed the value of any parameter, then the values of other parameters will not be inherited as the default values and will be set to zero. In order to provide custom values, you should provide all the thresholds respectively.

Eviction monitoring interval

The kubelet evaluates eviction thresholds based on its configured housekeeping-interval which defaults to 10s.

Node conditions

The kubelet reports node conditions to reflect that the node is under pressure because hard or soft eviction threshold is met, independent of configured grace periods.

The kubelet maps eviction signals to node conditions as follows:

Node Condition	Eviction Signal	Description
`MemoryPressure`	`memory.available`	Available memory on the node has satisfied an eviction threshold
`DiskPressure`	`nodefs.available`, `nodefs.inodesFree`, `imagefs.available`, or `imagefs.inodesFree`	Available disk space and inodes on either the node's root filesystem or image filesystem has satisfied an eviction threshold
`PIDPressure`	`pid.available`	Available processes identifiers on the (Linux) node has fallen below an eviction threshold

The kubelet updates the node conditions based on the configured --node-status-update-frequency, which defaults to 10s.

Node condition oscillation

In some cases, nodes oscillate above and below soft eviction thresholds without holding for the defined grace periods. This causes the reported node condition to constantly switch between true and false, leading to bad eviction decisions.

To protect against oscillation, you can use the eviction-pressure-transition-period flag, which controls how long the kubelet must wait before transitioning a node condition to a different state. The transition period has a default value of 5m.

Reclaiming node level resources

The kubelet tries to reclaim node-level resources before it evicts end-user pods.

When a DiskPressure node condition is reported, the kubelet reclaims node-level resources based on the filesystems on the node.

With `imagefs`

If the node has a dedicated imagefs filesystem for container runtimes to use, the kubelet does the following:

If the nodefs filesystem meets the eviction thresholds, the kubelet garbage collects dead pods and containers.
If the imagefs filesystem meets the eviction thresholds, the kubelet deletes all unused images.

Without `imagefs`

If the node only has a nodefs filesystem that meets eviction thresholds, the kubelet frees up disk space in the following order:

Garbage collect dead pods and containers
Delete unused images

Pod selection for kubelet eviction

If the kubelet's attempts to reclaim node-level resources don't bring the eviction signal below the threshold, the kubelet begins to evict end-user pods.

The kubelet uses the following parameters to determine the pod eviction order:

Whether the pod's resource usage exceeds requests
Pod Priority
The pod's resource usage relative to requests

As a result, kubelet ranks and evicts pods in the following order:

BestEffort or Burstable pods where the usage exceeds requests. These pods are evicted based on their Priority and then by how much their usage level exceeds the request.
Guaranteed pods and Burstable pods where the usage is less than requests are evicted last, based on their Priority.

Note: The kubelet does not use the pod's QoS class to determine the eviction order. You can use the QoS class to estimate the most likely pod eviction order when reclaiming resources like memory. QoS does not apply to EphemeralStorage requests, so the above scenario will not apply if the node is, for example, under DiskPressure.

Guaranteed pods are guaranteed only when requests and limits are specified for all the containers and they are equal. These pods will never be evicted because of another pod's resource consumption. If a system daemon (such as kubelet and journald) is consuming more resources than were reserved via system-reserved or kube-reserved allocations, and the node only has Guaranteed or Burstable pods using less resources than requests left on it, then the kubelet must choose to evict one of these pods to preserve node stability and to limit the impact of resource starvation on other pods. In this case, it will choose to evict pods of lowest Priority first.

When the kubelet evicts pods in response to inode or PID starvation, it uses the Priority to determine the eviction order, because inodes and PIDs have no requests.

The kubelet sorts pods differently based on whether the node has a dedicated imagefs filesystem:

With `imagefs`

If nodefs is triggering evictions, the kubelet sorts pods based on nodefs usage (local volumes + logs of all containers).

If imagefs is triggering evictions, the kubelet sorts pods based on the writable layer usage of all containers.

Without `imagefs`

If nodefs is triggering evictions, the kubelet sorts pods based on their total disk usage (local volumes + logs & writable layer of all containers)

Minimum eviction reclaim

In some cases, pod eviction only reclaims a small amount of the starved resource. This can lead to the kubelet repeatedly hitting the configured eviction thresholds and triggering multiple evictions.

You can use the --eviction-minimum-reclaim flag or a kubelet config file to configure a minimum reclaim amount for each resource. When the kubelet notices that a resource is starved, it continues to reclaim that resource until it reclaims the quantity you specify.

For example, the following configuration sets minimum reclaim amounts:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
evictionHard:
  memory.available: "500Mi"
  nodefs.available: "1Gi"
  imagefs.available: "100Gi"
evictionMinimumReclaim:
  memory.available: "0Mi"
  nodefs.available: "500Mi"
  imagefs.available: "2Gi"

In this example, if the nodefs.available signal meets the eviction threshold, the kubelet reclaims the resource until the signal reaches the threshold of 1Gi, and then continues to reclaim the minimum amount of 500Mi it until the signal reaches 1.5Gi.

Similarly, the kubelet reclaims the imagefs resource until the imagefs.available signal reaches 102Gi.

The default eviction-minimum-reclaim is 0 for all resources.

Node out of memory behavior

If the node experiences an out of memory (OOM) event prior to the kubelet being able to reclaim memory, the node depends on the oom_killer to respond.

The kubelet sets an oom_score_adj value for each container based on the QoS for the pod.

Quality of Service	oom_score_adj
`Guaranteed`	-997
`BestEffort`	1000
`Burstable`	min(max(2, 1000 - (1000 * memoryRequestBytes) / machineMemoryCapacityBytes), 999)

Note: The kubelet also sets an oom_score_adj value of -997 for containers in Pods that have system-node-critical .

If the kubelet can't reclaim memory before a node experiences OOM, the oom_killer calculates an oom_score based on the percentage of memory it's using on the node, and then adds the oom_score_adj to get an effective oom_score for each container. It then kills the container with the highest score.

This means that containers in low QoS pods that consume a large amount of memory relative to their scheduling requests are killed first.

Unlike pod eviction, if a container is OOM killed, the kubelet can restart it based on its RestartPolicy.

Best practices

The following sections describe best practices for eviction configuration.

Schedulable resources and eviction policies

When you configure the kubelet with an eviction policy, you should make sure that the scheduler will not schedule pods if they will trigger eviction because they immediately induce memory pressure.

Consider the following scenario:

Node memory capacity: 10Gi
Operator wants to reserve 10% of memory capacity for system daemons (kernel, kubelet, etc.)
Operator wants to evict Pods at 95% memory utilization to reduce incidence of system OOM.

For this to work, the kubelet is launched as follows:

--eviction-hard=memory.available<500Mi
--system-reserved=memory=1.5Gi

In this configuration, the --system-reserved flag reserves 1.5Gi of memory for the system, which is 10% of the total memory + the eviction threshold amount.

The node can reach the eviction threshold if a pod is using more than its request, or if the system is using more than 1Gi of memory, which makes the memory.available signal fall below 500Mi and triggers the threshold.

DaemonSet

Pod Priority is a major factor in making eviction decisions. If you do not want the kubelet to evict pods that belong to a DaemonSet, give those pods a high enough priorityClass in the pod spec. You can also use a lower priorityClass or the default to only allow DaemonSet pods to run when there are enough resources.

Known issues

The following sections describe known issues related to out of resource handling.

kubelet may not observe memory pressure right away

By default, the kubelet polls cAdvisor to collect memory usage stats at a regular interval. If memory usage increases within that window rapidly, the kubelet may not observe MemoryPressure fast enough, and the OOMKiller will still be invoked.

You can use the --kernel-memcg-notification flag to enable the memcg notification API on the kubelet to get notified immediately when a threshold is crossed.

If you are not trying to achieve extreme utilization, but a sensible measure of overcommit, a viable workaround for this issue is to use the --kube-reserved and --system-reserved flags to allocate memory for the system.

active_file memory is not considered as available memory

On Linux, the kernel tracks the number of bytes of file-backed memory on active LRU list as the active_file statistic. The kubelet treats active_file memory areas as not reclaimable. For workloads that make intensive use of block-backed local storage, including ephemeral local storage, kernel-level caches of file and block data means that many recently accessed cache pages are likely to be counted as active_file. If enough of these kernel block buffers are on the active LRU list, the kubelet is liable to observe this as high resource use and taint the node as experiencing memory pressure - triggering pod eviction.

For more details, see https://github.com/kubernetes/kubernetes/issues/43916

You can work around that behavior by setting the memory limit and memory request the same for containers likely to perform intensive I/O activity. You will need to estimate or measure an optimal memory limit value for that container.

What's next

Learn about API-initiated Eviction
Learn about Pod Priority and Preemption
Learn about PodDisruptionBudgets
Learn about Quality of Service (QoS)
Check out the Eviction API

11.8 - API-initiated Eviction

API-initiated eviction is the process by which you use the Eviction API to create an Eviction object that triggers graceful pod termination.

You can request eviction by calling the Eviction API directly, or programmatically using a client of the API server, like the kubectl drain command. This creates an Eviction object, which causes the API server to terminate the Pod.

API-initiated evictions respect your configured PodDisruptionBudgets and terminationGracePeriodSeconds.

Using the API to create an Eviction object for a Pod is like performing a policy-controlled DELETE operation on the Pod.

Calling the Eviction API

You can use a Kubernetes language client to access the Kubernetes API and create an Eviction object. To do this, you POST the attempted operation, similar to the following example:

policy/v1
policy/v1beta1

Note: policy/v1 Eviction is available in v1.22+. Use policy/v1beta1 with prior releases.

{
  "apiVersion": "policy/v1",
  "kind": "Eviction",
  "metadata": {
    "name": "quux",
    "namespace": "default"
  }
}

Note: Deprecated in v1.22 in favor of policy/v1

{
  "apiVersion": "policy/v1beta1",
  "kind": "Eviction",
  "metadata": {
    "name": "quux",
    "namespace": "default"
  }
}

Alternatively, you can attempt an eviction operation by accessing the API using curl or wget, similar to the following example:

curl -v -H 'Content-type: application/json' https://your-cluster-api-endpoint.example/api/v1/namespaces/default/pods/quux/eviction -d @eviction.json

How API-initiated eviction works

When you request an eviction using the API, the API server performs admission checks and responds in one of the following ways:

200 OK: the eviction is allowed, the Eviction subresource is created, and the Pod is deleted, similar to sending a DELETE request to the Pod URL.
429 Too Many Requests: the eviction is not currently allowed because of the configured PodDisruptionBudget. You may be able to attempt the eviction again later. You might also see this response because of API rate limiting.
500 Internal Server Error: the eviction is not allowed because there is a misconfiguration, like if multiple PodDisruptionBudgets reference the same Pod.

If the Pod you want to evict isn't part of a workload that has a PodDisruptionBudget, the API server always returns 200 OK and allows the eviction.

If the API server allows the eviction, the Pod is deleted as follows:

The Pod resource in the API server is updated with a deletion timestamp, after which the API server considers the Pod resource to be terminated. The Pod resource is also marked with the configured grace period.
The kubelet on the node where the local Pod is running notices that the Pod resource is marked for termination and starts to gracefully shut down the local Pod.
While the kubelet is shutting the Pod down, the control plane removes the Pod from Endpoint and EndpointSlice objects. As a result, controllers no longer consider the Pod as a valid object.
After the grace period for the Pod expires, the kubelet forcefully terminates the local Pod.
The kubelet tells the API server to remove the Pod resource.
The API server deletes the Pod resource.

Troubleshooting stuck evictions

In some cases, your applications may enter a broken state, where the Eviction API will only return 429 or 500 responses until you intervene. This can happen if, for example, a ReplicaSet creates pods for your application but new pods do not enter a Ready state. You may also notice this behavior in cases where the last evicted Pod had a long termination grace period.

If you notice stuck evictions, try one of the following solutions:

Abort or pause the automated operation causing the issue. Investigate the stuck application before you restart the operation.
Wait a while, then directly delete the Pod from your cluster control plane instead of using the Eviction API.

What's next

Learn how to protect your applications with a Pod Disruption Budget.
Learn about Node-pressure Eviction.
Learn about Pod Priority and Preemption.

11.9 - Resource Bin Packing

In the scheduling-plugin NodeResourcesFit of kube-scheduler, there are two scoring strategies that support the bin packing of resources: MostAllocated and RequestedToCapacityRatio.

Enabling bin packing using MostAllocated strategy

The MostAllocated strategy scores the nodes based on the utilization of resources, favoring the ones with higher allocation. For each resource type, you can set a weight to modify its influence in the node score.

To set the MostAllocated strategy for the NodeResourcesFit plugin, use a scheduler configuration similar to the following:

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration
profiles:
- pluginConfig:
  - args:
      scoringStrategy:
        resources:
        - name: cpu
          weight: 1
        - name: memory
          weight: 1
        - name: intel.com/foo
          weight: 3
        - name: intel.com/bar
          weight: 3
        type: MostAllocated
    name: NodeResourcesFit

To learn more about other parameters and their default configuration, see the API documentation for NodeResourcesFitArgs.

Enabling bin packing using RequestedToCapacityRatio

The RequestedToCapacityRatio strategy allows the users to specify the resources along with weights for each resource to score nodes based on the request to capacity ratio. This allows users to bin pack extended resources by using appropriate parameters to improve the utilization of scarce resources in large clusters. It favors nodes according to a configured function of the allocated resources. The behavior of the RequestedToCapacityRatio in the NodeResourcesFit score function can be controlled by the scoringStrategy field. Within the scoringStrategy field, you can configure two parameters: requestedToCapacityRatio and resources. The shape in the requestedToCapacityRatio parameter allows the user to tune the function as least requested or most requested based on utilization and score values. The resources parameter consists of name of the resource to be considered during scoring and weight specify the weight of each resource.

Below is an example configuration that sets the bin packing behavior for extended resources intel.com/foo and intel.com/bar using the requestedToCapacityRatio field.

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration
profiles:
- pluginConfig:
  - args:
      scoringStrategy:
        resources:
        - name: intel.com/foo
          weight: 3
        - name: intel.com/bar
          weight: 3
        requestedToCapacityRatio:
          shape:
          - utilization: 0
            score: 0
          - utilization: 100
            score: 10
        type: RequestedToCapacityRatio
    name: NodeResourcesFit

Referencing the KubeSchedulerConfiguration file with the kube-scheduler flag --config=/path/to/config/file will pass the configuration to the scheduler.

To learn more about other parameters and their default configuration, see the API documentation for NodeResourcesFitArgs.

Tuning the score function

shape is used to specify the behavior of the RequestedToCapacityRatio function.

shape:
 - utilization: 0
   score: 0
 - utilization: 100
   score: 10

The above arguments give the node a score of 0 if utilization is 0% and 10 for utilization 100%, thus enabling bin packing behavior. To enable least requested the score value must be reversed as follows.

shape:
  - utilization: 0
    score: 10
  - utilization: 100
    score: 0

resources is an optional parameter which defaults to:

resources:
  - name: cpu
    weight: 1
  - name: memory
    weight: 1

It can be used to add extended resources as follows:

resources:
  - name: intel.com/foo
    weight: 5
  - name: cpu
    weight: 3
  - name: memory
    weight: 1

The weight parameter is optional and is set to 1 if not specified. Also, the weight cannot be set to a negative value.

Node scoring for capacity allocation

This section is intended for those who want to understand the internal details of this feature. Below is an example of how the node score is calculated for a given set of values.

Requested resources:

intel.com/foo : 2
memory: 256MB
cpu: 2

Resource weights:

intel.com/foo : 5
memory: 1
cpu: 3

FunctionShapePoint {{0, 0}, {100, 10}}

Node 1 spec:

Available:
  intel.com/foo: 4
  memory: 1 GB
  cpu: 8

Used:
  intel.com/foo: 1
  memory: 256MB
  cpu: 1

Node score:

intel.com/foo  = resourceScoringFunction((2+1),4)
               = (100 - ((4-3)*100/4)
               = (100 - 25)
               = 75                       # requested + used = 75% * available
               = rawScoringFunction(75) 
               = 7                        # floor(75/10) 

memory         = resourceScoringFunction((256+256),1024)
               = (100 -((1024-512)*100/1024))
               = 50                       # requested + used = 50% * available
               = rawScoringFunction(50)
               = 5                        # floor(50/10)

cpu            = resourceScoringFunction((2+1),8)
               = (100 -((8-3)*100/8))
               = 37.5                     # requested + used = 37.5% * available
               = rawScoringFunction(37.5)
               = 3                        # floor(37.5/10)

NodeScore   =  (7 * 5) + (5 * 1) + (3 * 3) / (5 + 1 + 3)
            =  5

Node 2 spec:

Available:
  intel.com/foo: 8
  memory: 1GB
  cpu: 8
Used:
  intel.com/foo: 2
  memory: 512MB
  cpu: 6

Node score:

intel.com/foo  = resourceScoringFunction((2+2),8)
               =  (100 - ((8-4)*100/8)
               =  (100 - 50)
               =  50
               =  rawScoringFunction(50)
               = 5

memory         = resourceScoringFunction((256+512),1024)
               = (100 -((1024-768)*100/1024))
               = 75
               = rawScoringFunction(75)
               = 7

cpu            = resourceScoringFunction((2+6),8)
               = (100 -((8-8)*100/8))
               = 100
               = rawScoringFunction(100)
               = 10

NodeScore   =  (5 * 5) + (7 * 1) + (10 * 3) / (5 + 1 + 3)
            =  7

What's next

Read more about the scheduling framework
Read more about scheduler configuration

11.10 - Scheduling Framework

FEATURE STATE: Kubernetes v1.19 [stable]

The scheduling framework is a pluggable architecture for the Kubernetes scheduler. It adds a new set of "plugin" APIs to the existing scheduler. Plugins are compiled into the scheduler. The APIs allow most scheduling features to be implemented as plugins, while keeping the scheduling "core" lightweight and maintainable. Refer to the design proposal of the scheduling framework for more technical information on the design of the framework.

Framework workflow

The Scheduling Framework defines a few extension points. Scheduler plugins register to be invoked at one or more extension points. Some of these plugins can change the scheduling decisions and some are informational only.

Each attempt to schedule one Pod is split into two phases, the scheduling cycle and the binding cycle.

Scheduling Cycle & Binding Cycle

The scheduling cycle selects a node for the Pod, and the binding cycle applies that decision to the cluster. Together, a scheduling cycle and binding cycle are referred to as a "scheduling context".

Scheduling cycles are run serially, while binding cycles may run concurrently.

A scheduling or binding cycle can be aborted if the Pod is determined to be unschedulable or if there is an internal error. The Pod will be returned to the queue and retried.

Extension points

The following picture shows the scheduling context of a Pod and the extension points that the scheduling framework exposes. In this picture "Filter" is equivalent to "Predicate" and "Scoring" is equivalent to "Priority function".

One plugin may register at multiple extension points to perform more complex or stateful tasks.

QueueSort

These plugins are used to sort Pods in the scheduling queue. A queue sort plugin essentially provides a Less(Pod1, Pod2) function. Only one queue sort plugin may be enabled at a time.

PreFilter

These plugins are used to pre-process info about the Pod, or to check certain conditions that the cluster or the Pod must meet. If a PreFilter plugin returns an error, the scheduling cycle is aborted.

Filter

These plugins are used to filter out nodes that cannot run the Pod. For each node, the scheduler will call filter plugins in their configured order. If any filter plugin marks the node as infeasible, the remaining plugins will not be called for that node. Nodes may be evaluated concurrently.

PostFilter

These plugins are called after Filter phase, but only when no feasible nodes were found for the pod. Plugins are called in their configured order. If any postFilter plugin marks the node as Schedulable, the remaining plugins will not be called. A typical PostFilter implementation is preemption, which tries to make the pod schedulable by preempting other Pods.

PreScore

These plugins are used to perform "pre-scoring" work, which generates a sharable state for Score plugins to use. If a PreScore plugin returns an error, the scheduling cycle is aborted.

Score

These plugins are used to rank nodes that have passed the filtering phase. The scheduler will call each scoring plugin for each node. There will be a well defined range of integers representing the minimum and maximum scores. After the NormalizeScore phase, the scheduler will combine node scores from all plugins according to the configured plugin weights.

NormalizeScore

These plugins are used to modify scores before the scheduler computes a final ranking of Nodes. A plugin that registers for this extension point will be called with the Score results from the same plugin. This is called once per plugin per scheduling cycle.

For example, suppose a plugin BlinkingLightScorer ranks Nodes based on how many blinking lights they have.

func ScoreNode(_ *v1.pod, n *v1.Node) (int, error) {
    return getBlinkingLightCount(n)
}

However, the maximum count of blinking lights may be small compared to NodeScoreMax. To fix this, BlinkingLightScorer should also register for this extension point.

func NormalizeScores(scores map[string]int) {
    highest := 0
    for _, score := range scores {
        highest = max(highest, score)
    }
    for node, score := range scores {
        scores[node] = score*NodeScoreMax/highest
    }
}

If any NormalizeScore plugin returns an error, the scheduling cycle is aborted.

Note: Plugins wishing to perform "pre-reserve" work should use the NormalizeScore extension point.

Reserve

A plugin that implements the Reserve extension has two methods, namely Reserve and Unreserve, that back two informational scheduling phases called Reserve and Unreserve, respectively. Plugins which maintain runtime state (aka "stateful plugins") should use these phases to be notified by the scheduler when resources on a node are being reserved and unreserved for a given Pod.

The Reserve phase happens before the scheduler actually binds a Pod to its designated node. It exists to prevent race conditions while the scheduler waits for the bind to succeed. The Reserve method of each Reserve plugin may succeed or fail; if one Reserve method call fails, subsequent plugins are not executed and the Reserve phase is considered to have failed. If the Reserve method of all plugins succeed, the Reserve phase is considered to be successful and the rest of the scheduling cycle and the binding cycle are executed.

The Unreserve phase is triggered if the Reserve phase or a later phase fails. When this happens, the Unreserve method of all Reserve plugins will be executed in the reverse order of Reserve method calls. This phase exists to clean up the state associated with the reserved Pod.

Caution: The implementation of the Unreserve method in Reserve plugins must be idempotent and may not fail.

Permit

Permit plugins are invoked at the end of the scheduling cycle for each Pod, to prevent or delay the binding to the candidate node. A permit plugin can do one of the three things:

approve
Once all Permit plugins approve a Pod, it is sent for binding.
deny
If any Permit plugin denies a Pod, it is returned to the scheduling queue. This will trigger the Unreserve phase in Reserve plugins.
wait (with a timeout)
If a Permit plugin returns "wait", then the Pod is kept in an internal "waiting" Pods list, and the binding cycle of this Pod starts but directly blocks until it gets approved. If a timeout occurs, wait becomes deny and the Pod is returned to the scheduling queue, triggering the Unreserve phase in Reserve plugins.

Note: While any plugin can access the list of "waiting" Pods and approve them (see FrameworkHandle), we expect only the permit plugins to approve binding of reserved Pods that are in "waiting" state. Once a Pod is approved, it is sent to the PreBind phase.

PreBind

These plugins are used to perform any work required before a Pod is bound. For example, a pre-bind plugin may provision a network volume and mount it on the target node before allowing the Pod to run there.

If any PreBind plugin returns an error, the Pod is rejected and returned to the scheduling queue.

Bind

These plugins are used to bind a Pod to a Node. Bind plugins will not be called until all PreBind plugins have completed. Each bind plugin is called in the configured order. A bind plugin may choose whether or not to handle the given Pod. If a bind plugin chooses to handle a Pod, the remaining bind plugins are skipped.

PostBind

This is an informational extension point. Post-bind plugins are called after a Pod is successfully bound. This is the end of a binding cycle, and can be used to clean up associated resources.

Plugin API

There are two steps to the plugin API. First, plugins must register and get configured, then they use the extension point interfaces. Extension point interfaces have the following form.

type Plugin interface {
    Name() string
}

type QueueSortPlugin interface {
    Plugin
    Less(*v1.pod, *v1.pod) bool
}

type PreFilterPlugin interface {
    Plugin
    PreFilter(context.Context, *framework.CycleState, *v1.pod) error
}

// ...

Plugin configuration

You can enable or disable plugins in the scheduler configuration. If you are using Kubernetes v1.18 or later, most scheduling plugins are in use and enabled by default.

In addition to default plugins, you can also implement your own scheduling plugins and get them configured along with default plugins. You can visit scheduler-plugins for more details.

If you are using Kubernetes v1.18 or later, you can configure a set of plugins as a scheduler profile and then define multiple profiles to fit various kinds of workload. Learn more at multiple profiles.

11.11 - Scheduler Performance Tuning

FEATURE STATE: Kubernetes v1.14 [beta]

kube-scheduler is the Kubernetes default scheduler. It is responsible for placement of Pods on Nodes in a cluster.

Nodes in a cluster that meet the scheduling requirements of a Pod are called feasible Nodes for the Pod. The scheduler finds feasible Nodes for a Pod and then runs a set of functions to score the feasible Nodes, picking a Node with the highest score among the feasible ones to run the Pod. The scheduler then notifies the API server about this decision in a process called Binding.

This page explains performance tuning optimizations that are relevant for large Kubernetes clusters.

In large clusters, you can tune the scheduler's behaviour balancing scheduling outcomes between latency (new Pods are placed quickly) and accuracy (the scheduler rarely makes poor placement decisions).

You configure this tuning setting via kube-scheduler setting percentageOfNodesToScore. This KubeSchedulerConfiguration setting determines a threshold for scheduling nodes in your cluster.

Setting the threshold

The percentageOfNodesToScore option accepts whole numeric values between 0 and 100. The value 0 is a special number which indicates that the kube-scheduler should use its compiled-in default. If you set percentageOfNodesToScore above 100, kube-scheduler acts as if you had set a value of 100.

To change the value, edit the kube-scheduler configuration file and then restart the scheduler. In many cases, the configuration file can be found at /etc/kubernetes/config/kube-scheduler.yaml.

After you have made this change, you can run

kubectl get pods -n kube-system | grep kube-scheduler

to verify that the kube-scheduler component is healthy.

Node scoring threshold

To improve scheduling performance, the kube-scheduler can stop looking for feasible nodes once it has found enough of them. In large clusters, this saves time compared to a naive approach that would consider every node.

You specify a threshold for how many nodes are enough, as a whole number percentage of all the nodes in your cluster. The kube-scheduler converts this into an integer number of nodes. During scheduling, if the kube-scheduler has identified enough feasible nodes to exceed the configured percentage, the kube-scheduler stops searching for more feasible nodes and moves on to the scoring phase.

How the scheduler iterates over Nodes describes the process in detail.

Default threshold

If you don't specify a threshold, Kubernetes calculates a figure using a linear formula that yields 50% for a 100-node cluster and yields 10% for a 5000-node cluster. The lower bound for the automatic value is 5%.

This means that, the kube-scheduler always scores at least 5% of your cluster no matter how large the cluster is, unless you have explicitly set percentageOfNodesToScore to be smaller than 5.

If you want the scheduler to score all nodes in your cluster, set percentageOfNodesToScore to 100.

Example

Below is an example configuration that sets percentageOfNodesToScore to 50%.

apiVersion: kubescheduler.config.k8s.io/v1alpha1
kind: KubeSchedulerConfiguration
algorithmSource:
  provider: DefaultProvider

...

percentageOfNodesToScore: 50

Tuning percentageOfNodesToScore

percentageOfNodesToScore must be a value between 1 and 100 with the default value being calculated based on the cluster size. There is also a hardcoded minimum value of 50 nodes.

Note:

In clusters with less than 50 feasible nodes, the scheduler still checks all the nodes because there are not enough feasible nodes to stop the scheduler's search early.

In a small cluster, if you set a low value for percentageOfNodesToScore, your change will have no or little effect, for a similar reason.

If your cluster has several hundred Nodes or fewer, leave this configuration option at its default value. Making changes is unlikely to improve the scheduler's performance significantly.

An important detail to consider when setting this value is that when a smaller number of nodes in a cluster are checked for feasibility, some nodes are not sent to be scored for a given Pod. As a result, a Node which could possibly score a higher value for running the given Pod might not even be passed to the scoring phase. This would result in a less than ideal placement of the Pod.

You should avoid setting percentageOfNodesToScore very low so that kube-scheduler does not make frequent, poor Pod placement decisions. Avoid setting the percentage to anything below 10%, unless the scheduler's throughput is critical for your application and the score of nodes is not important. In other words, you prefer to run the Pod on any Node as long as it is feasible.

How the scheduler iterates over Nodes

This section is intended for those who want to understand the internal details of this feature.

In order to give all the Nodes in a cluster a fair chance of being considered for running Pods, the scheduler iterates over the nodes in a round robin fashion. You can imagine that Nodes are in an array. The scheduler starts from the start of the array and checks feasibility of the nodes until it finds enough Nodes as specified by percentageOfNodesToScore. For the next Pod, the scheduler continues from the point in the Node array that it stopped at when checking feasibility of Nodes for the previous Pod.

If Nodes are in multiple zones, the scheduler iterates over Nodes in various zones to ensure that Nodes from different zones are considered in the feasibility checks. As an example, consider six nodes in two zones:

Zone 1: Node 1, Node 2, Node 3, Node 4
Zone 2: Node 5, Node 6

The Scheduler evaluates feasibility of the nodes in this order:

Node 1, Node 5, Node 2, Node 6, Node 3, Node 4

After going over all the Nodes, it goes back to Node 1.

What's next

Check the kube-scheduler configuration reference (v1beta3)

12 - Cluster Administration

Lower-level detail relevant to creating or administering a Kubernetes cluster.

The cluster administration overview is for anyone creating or administering a Kubernetes cluster. It assumes some familiarity with core Kubernetes concepts.

Planning a cluster

See the guides in Setup for examples of how to plan, set up, and configure Kubernetes clusters. The solutions listed in this article are called distros.

Note: Not all distros are actively maintained. Choose distros which have been tested with a recent version of Kubernetes.

Before choosing a guide, here are some considerations:

Do you want to try out Kubernetes on your computer, or do you want to build a high-availability, multi-node cluster? Choose distros best suited for your needs.
Will you be using a hosted Kubernetes cluster, such as Google Kubernetes Engine, or hosting your own cluster?
Will your cluster be on-premises, or in the cloud (IaaS)? Kubernetes does not directly support hybrid clusters. Instead, you can set up multiple clusters.
If you are configuring Kubernetes on-premises, consider which networking model fits best.
Will you be running Kubernetes on "bare metal" hardware or on virtual machines (VMs)?
Do you want to run a cluster, or do you expect to do active development of Kubernetes project code? If the latter, choose an actively-developed distro. Some distros only use binary releases, but offer a greater variety of choices.
Familiarize yourself with the components needed to run a cluster.

Managing a cluster

Learn how to manage nodes.
Learn how to set up and manage the resource quota for shared clusters.

Securing a cluster

Generate Certificates describes the steps to generate certificates using different tool chains.
Kubernetes Container Environment describes the environment for Kubelet managed containers on a Kubernetes node.
Controlling Access to the Kubernetes API describes how Kubernetes implements access control for its own API.
Authenticating explains authentication in Kubernetes, including the various authentication options.
Authorization is separate from authentication, and controls how HTTP calls are handled.
Using Admission Controllers explains plug-ins which intercepts requests to the Kubernetes API server after authentication and authorization.
Using Sysctls in a Kubernetes Cluster describes to an administrator how to use the sysctl command-line tool to set kernel parameters .
Auditing describes how to interact with Kubernetes' audit logs.

Securing the kubelet

Optional Cluster Services

DNS Integration describes how to resolve a DNS name directly to a Kubernetes service.
Logging and Monitoring Cluster Activity explains how logging in Kubernetes works and how to implement it.

12.1 - Certificates

To learn how to generate certificates for your cluster, see Certificates.

12.2 - Managing Resources

You've deployed your application and exposed it via a service. Now what? Kubernetes provides a number of tools to help you manage your application deployment, including scaling and updating. Among the features that we will discuss in more depth are configuration files and labels.

Organizing resource configurations

Many applications require multiple resources to be created, such as a Deployment and a Service. Management of multiple resources can be simplified by grouping them together in the same file (separated by --- in YAML). For example:

application/nginx-app.yaml

apiVersion: v1
kind: Service
metadata:
  name: my-nginx-svc
  labels:
    app: nginx
spec:
  type: LoadBalancer
  ports:
  - port: 80
  selector:
    app: nginx
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Multiple resources can be created the same way as a single resource:

kubectl apply -f https://k8s.io/examples/application/nginx-app.yaml

service/my-nginx-svc created
deployment.apps/my-nginx created

The resources will be created in the order they appear in the file. Therefore, it's best to specify the service first, since that will ensure the scheduler can spread the pods associated with the service as they are created by the controller(s), such as Deployment.

kubectl apply also accepts multiple -f arguments:

kubectl apply -f https://k8s.io/examples/application/nginx/nginx-svc.yaml -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml

And a directory can be specified rather than or in addition to individual files:

kubectl apply -f https://k8s.io/examples/application/nginx/

kubectl will read any files with suffixes .yaml, .yml, or .json.

It is a recommended practice to put resources related to the same microservice or application tier into the same file, and to group all of the files associated with your application in the same directory. If the tiers of your application bind to each other using DNS, you can deploy all of the components of your stack together.

A URL can also be specified as a configuration source, which is handy for deploying directly from configuration files checked into GitHub:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/website/main/content/en/examples/application/nginx/nginx-deployment.yaml

deployment.apps/my-nginx created

Bulk operations in kubectl

Resource creation isn't the only operation that kubectl can perform in bulk. It can also extract resource names from configuration files in order to perform other operations, in particular to delete the same resources you created:

kubectl delete -f https://k8s.io/examples/application/nginx-app.yaml

deployment.apps "my-nginx" deleted
service "my-nginx-svc" deleted

In the case of two resources, you can specify both resources on the command line using the resource/name syntax:

kubectl delete deployments/my-nginx services/my-nginx-svc

For larger numbers of resources, you'll find it easier to specify the selector (label query) specified using -l or --selector, to filter resources by their labels:

kubectl delete deployment,services -l app=nginx

deployment.apps "my-nginx" deleted
service "my-nginx-svc" deleted

Because kubectl outputs resource names in the same syntax it accepts, you can chain operations using $() or xargs:

kubectl get $(kubectl create -f docs/concepts/cluster-administration/nginx/ -o name | grep service)
kubectl create -f docs/concepts/cluster-administration/nginx/ -o name | grep service | xargs -i kubectl get {}

NAME           TYPE           CLUSTER-IP   EXTERNAL-IP   PORT(S)      AGE
my-nginx-svc   LoadBalancer   10.0.0.208   <pending>     80/TCP       0s

With the above commands, we first create resources under examples/application/nginx/ and print the resources created with -o name output format (print each resource as resource/name). Then we grep only the "service", and then print it with kubectl get.

If you happen to organize your resources across several subdirectories within a particular directory, you can recursively perform the operations on the subdirectories also, by specifying --recursive or -R alongside the --filename,-f flag.

For instance, assume there is a directory project/k8s/development that holds all of the manifests needed for the development environment, organized by resource type:

project/k8s/development
├── configmap
│   └── my-configmap.yaml
├── deployment
│   └── my-deployment.yaml
└── pvc
    └── my-pvc.yaml

By default, performing a bulk operation on project/k8s/development will stop at the first level of the directory, not processing any subdirectories. If we had tried to create the resources in this directory using the following command, we would have encountered an error:

kubectl apply -f project/k8s/development

error: you must provide one or more resources by argument or filename (.json|.yaml|.yml|stdin)

Instead, specify the --recursive or -R flag with the --filename,-f flag as such:

kubectl apply -f project/k8s/development --recursive

configmap/my-config created
deployment.apps/my-deployment created
persistentvolumeclaim/my-pvc created

The --recursive flag works with any operation that accepts the --filename,-f flag such as: kubectl {create,get,delete,describe,rollout} etc.

The --recursive flag also works when multiple -f arguments are provided:

kubectl apply -f project/k8s/namespaces -f project/k8s/development --recursive

namespace/development created
namespace/staging created
configmap/my-config created
deployment.apps/my-deployment created
persistentvolumeclaim/my-pvc created

If you're interested in learning more about kubectl, go ahead and read Command line tool (kubectl).

Using labels effectively

The examples we've used so far apply at most a single label to any resource. There are many scenarios where multiple labels should be used to distinguish sets from one another.

For instance, different applications would use different values for the app label, but a multi-tier application, such as the guestbook example, would additionally need to distinguish each tier. The frontend could carry the following labels:

     labels:
        app: guestbook
        tier: frontend

while the Redis master and slave would have different tier labels, and perhaps even an additional role label:

     labels:
        app: guestbook
        tier: backend
        role: master

and

     labels:
        app: guestbook
        tier: backend
        role: slave

The labels allow us to slice and dice our resources along any dimension specified by a label:

kubectl apply -f examples/guestbook/all-in-one/guestbook-all-in-one.yaml
kubectl get pods -Lapp -Ltier -Lrole

NAME                           READY     STATUS    RESTARTS   AGE       APP         TIER       ROLE
guestbook-fe-4nlpb             1/1       Running   0          1m        guestbook   frontend   <none>
guestbook-fe-ght6d             1/1       Running   0          1m        guestbook   frontend   <none>
guestbook-fe-jpy62             1/1       Running   0          1m        guestbook   frontend   <none>
guestbook-redis-master-5pg3b   1/1       Running   0          1m        guestbook   backend    master
guestbook-redis-slave-2q2yf    1/1       Running   0          1m        guestbook   backend    slave
guestbook-redis-slave-qgazl    1/1       Running   0          1m        guestbook   backend    slave
my-nginx-divi2                 1/1       Running   0          29m       nginx       <none>     <none>
my-nginx-o0ef1                 1/1       Running   0          29m       nginx       <none>     <none>

kubectl get pods -lapp=guestbook,role=slave

NAME                          READY     STATUS    RESTARTS   AGE
guestbook-redis-slave-2q2yf   1/1       Running   0          3m
guestbook-redis-slave-qgazl   1/1       Running   0          3m

Canary deployments

Another scenario where multiple labels are needed is to distinguish deployments of different releases or configurations of the same component. It is common practice to deploy a canary of a new application release (specified via image tag in the pod template) side by side with the previous release so that the new release can receive live production traffic before fully rolling it out.

For instance, you can use a track label to differentiate different releases.

The primary, stable release would have a track label with value as stable:

     name: frontend
     replicas: 3
     ...
     labels:
        app: guestbook
        tier: frontend
        track: stable
     ...
     image: gb-frontend:v3

and then you can create a new release of the guestbook frontend that carries the track label with different value (i.e. canary), so that two sets of pods would not overlap:

     name: frontend-canary
     replicas: 1
     ...
     labels:
        app: guestbook
        tier: frontend
        track: canary
     ...
     image: gb-frontend:v4

The frontend service would span both sets of replicas by selecting the common subset of their labels (i.e. omitting the track label), so that the traffic will be redirected to both applications:

  selector:
     app: guestbook
     tier: frontend

You can tweak the number of replicas of the stable and canary releases to determine the ratio of each release that will receive live production traffic (in this case, 3:1). Once you're confident, you can update the stable track to the new application release and remove the canary one.

For a more concrete example, check the tutorial of deploying Ghost.

Updating labels

Sometimes existing pods and other resources need to be relabeled before creating new resources. This can be done with kubectl label. For example, if you want to label all your nginx pods as frontend tier, run:

kubectl label pods -l app=nginx tier=fe

pod/my-nginx-2035384211-j5fhi labeled
pod/my-nginx-2035384211-u2c7e labeled
pod/my-nginx-2035384211-u3t6x labeled

This first filters all pods with the label "app=nginx", and then labels them with the "tier=fe". To see the pods you labeled, run:

kubectl get pods -l app=nginx -L tier

NAME                        READY     STATUS    RESTARTS   AGE       TIER
my-nginx-2035384211-j5fhi   1/1       Running   0          23m       fe
my-nginx-2035384211-u2c7e   1/1       Running   0          23m       fe
my-nginx-2035384211-u3t6x   1/1       Running   0          23m       fe

This outputs all "app=nginx" pods, with an additional label column of pods' tier (specified with -L or --label-columns).

For more information, please see labels and kubectl label.

Updating annotations

Sometimes you would want to attach annotations to resources. Annotations are arbitrary non-identifying metadata for retrieval by API clients such as tools, libraries, etc. This can be done with kubectl annotate. For example:

kubectl annotate pods my-nginx-v4-9gw19 description='my frontend running nginx'
kubectl get pods my-nginx-v4-9gw19 -o yaml

apiVersion: v1
kind: pod
metadata:
  annotations:
    description: my frontend running nginx
...

For more information, please see annotations and kubectl annotate document.

Scaling your application

When load on your application grows or shrinks, use kubectl to scale your application. For instance, to decrease the number of nginx replicas from 3 to 1, do:

kubectl scale deployment/my-nginx --replicas=1

deployment.apps/my-nginx scaled

Now you only have one pod managed by the deployment.

kubectl get pods -l app=nginx

NAME                        READY     STATUS    RESTARTS   AGE
my-nginx-2035384211-j5fhi   1/1       Running   0          30m

To have the system automatically choose the number of nginx replicas as needed, ranging from 1 to 3, do:

kubectl autoscale deployment/my-nginx --min=1 --max=3

horizontalpodautoscaler.autoscaling/my-nginx autoscaled

Now your nginx replicas will be scaled up and down as needed, automatically.

For more information, please see kubectl scale, kubectl autoscale and horizontal pod autoscaler document.

In-place updates of resources

Sometimes it's necessary to make narrow, non-disruptive updates to resources you've created.

kubectl apply

It is suggested to maintain a set of configuration files in source control (see configuration as code), so that they can be maintained and versioned along with the code for the resources they configure. Then, you can use kubectl apply to push your configuration changes to the cluster.

This command will compare the version of the configuration that you're pushing with the previous version and apply the changes you've made, without overwriting any automated changes to properties you haven't specified.

kubectl apply -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml
deployment.apps/my-nginx configured

Note that kubectl apply attaches an annotation to the resource in order to determine the changes to the configuration since the previous invocation. When it's invoked, kubectl apply does a three-way diff between the previous configuration, the provided input and the current configuration of the resource, in order to determine how to modify the resource.

Currently, resources are created without this annotation, so the first invocation of kubectl apply will fall back to a two-way diff between the provided input and the current configuration of the resource. During this first invocation, it cannot detect the deletion of properties set when the resource was created. For this reason, it will not remove them.

All subsequent calls to kubectl apply, and other commands that modify the configuration, such as kubectl replace and kubectl edit, will update the annotation, allowing subsequent calls to kubectl apply to detect and perform deletions using a three-way diff.

kubectl edit

Alternatively, you may also update resources with kubectl edit:

kubectl edit deployment/my-nginx

This is equivalent to first get the resource, edit it in text editor, and then apply the resource with the updated version:

kubectl get deployment my-nginx -o yaml > /tmp/nginx.yaml
vi /tmp/nginx.yaml
# do some edit, and then save the file

kubectl apply -f /tmp/nginx.yaml
deployment.apps/my-nginx configured

rm /tmp/nginx.yaml

This allows you to do more significant changes more easily. Note that you can specify the editor with your EDITOR or KUBE_EDITOR environment variables.

For more information, please see kubectl edit document.

kubectl patch

You can use kubectl patch to update API objects in place. This command supports JSON patch, JSON merge patch, and strategic merge patch. See Update API Objects in Place Using kubectl patch and kubectl patch.

Disruptive updates

In some cases, you may need to update resource fields that cannot be updated once initialized, or you may want to make a recursive change immediately, such as to fix broken pods created by a Deployment. To change such fields, use replace --force, which deletes and re-creates the resource. In this case, you can modify your original configuration file:

kubectl replace -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml --force

deployment.apps/my-nginx deleted
deployment.apps/my-nginx replaced

Updating your application without a service outage

At some point, you'll eventually need to update your deployed application, typically by specifying a new image or image tag, as in the canary deployment scenario above. kubectl supports several update operations, each of which is applicable to different scenarios.

We'll guide you through how to create and update applications with Deployments.

Let's say you were running version 1.14.2 of nginx:

kubectl create deployment my-nginx --image=nginx:1.14.2

deployment.apps/my-nginx created

with 3 replicas (so the old and new revisions can coexist):

kubectl scale deployment my-nginx --current-replicas=1 --replicas=3

deployment.apps/my-nginx scaled

To update to version 1.16.1, change .spec.template.spec.containers[0].image from nginx:1.14.2 to nginx:1.16.1 using the previous kubectl commands.

kubectl edit deployment/my-nginx

That's it! The Deployment will declaratively update the deployed nginx application progressively behind the scene. It ensures that only a certain number of old replicas may be down while they are being updated, and only a certain number of new replicas may be created above the desired number of pods. To learn more details about it, visit Deployment page.

What's next

Learn about how to use kubectl for application introspection and debugging.
See Configuration Best Practices and Tips.

12.3 - Cluster Networking

Networking is a central part of Kubernetes, but it can be challenging to understand exactly how it is expected to work. There are 4 distinct networking problems to address:

Highly-coupled container-to-container communications: this is solved by Pods and localhost communications.
Pod-to-Pod communications: this is the primary focus of this document.
Pod-to-Service communications: this is covered by Services.
External-to-Service communications: this is also covered by Services.

Kubernetes is all about sharing machines between applications. Typically, sharing machines requires ensuring that two applications do not try to use the same ports. Coordinating ports across multiple developers is very difficult to do at scale and exposes users to cluster-level issues outside of their control.

Dynamic port allocation brings a lot of complications to the system - every application has to take ports as flags, the API servers have to know how to insert dynamic port numbers into configuration blocks, services have to know how to find each other, etc. Rather than deal with this, Kubernetes takes a different approach.

To learn about the Kubernetes networking model, see here.

How to implement the Kubernetes network model

The network model is implemented by the container runtime on each node. The most common container runtimes use Container Network Interface (CNI) plugins to manage their network and security capabilities. Many different CNI plugins exist from many different vendors. Some of these provide only basic features of adding and removing network interfaces, while others provide more sophisticated solutions, such as integration with other container orchestration systems, running multiple CNI plugins, advanced IPAM features etc.

See this page for a non-exhaustive list of networking addons supported by Kubernetes.

What's next

The early design of the networking model and its rationale, and some future plans are described in more detail in the networking design document.

12.4 - Logging Architecture

Application logs can help you understand what is happening inside your application. The logs are particularly useful for debugging problems and monitoring cluster activity. Most modern applications have some kind of logging mechanism. Likewise, container engines are designed to support logging. The easiest and most adopted logging method for containerized applications is writing to standard output and standard error streams.

However, the native functionality provided by a container engine or runtime is usually not enough for a complete logging solution.

For example, you may want to access your application's logs if a container crashes, a pod gets evicted, or a node dies.

In a cluster, logs should have a separate storage and lifecycle independent of nodes, pods, or containers. This concept is called cluster-level logging.

Cluster-level logging architectures require a separate backend to store, analyze, and query logs. Kubernetes does not provide a native storage solution for log data. Instead, there are many logging solutions that integrate with Kubernetes. The following sections describe how to handle and store logs on nodes.

Pod and container logs

Kubernetes captures logs from each container in a running Pod.

This example uses a manifest for a Pod with a container that writes text to the standard output stream, once per second.

debug/counter-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; i=$((i+1)); sleep 1; done']

To run this pod, use the following command:

kubectl apply -f https://k8s.io/examples/debug/counter-pod.yaml

The output is:

pod/counter created

To fetch the logs, use the kubectl logs command, as follows:

kubectl logs counter

The output is similar to:

0: Fri Apr  1 11:42:23 UTC 2022
1: Fri Apr  1 11:42:24 UTC 2022
2: Fri Apr  1 11:42:25 UTC 2022

You can use kubectl logs --previous to retrieve logs from a previous instantiation of a container. If your pod has multiple containers, specify which container's logs you want to access by appending a container name to the command, with a -c flag, like so:

kubectl logs counter -c count

See the kubectl logs documentation for more details.

How nodes handle container logs

Node level logging

A container runtime handles and redirects any output generated to a containerized application's stdout and stderr streams. Different container runtimes implement this in different ways; however, the integration with the kubelet is standardized as the CRI logging format.

By default, if a container restarts, the kubelet keeps one terminated container with its logs. If a pod is evicted from the node, all corresponding containers are also evicted, along with their logs.

The kubelet makes logs available to clients via a special feature of the Kubernetes API. The usual way to access this is by running kubectl logs.

Log rotation

FEATURE STATE: Kubernetes v1.21 [stable]

You can configure the kubelet to rotate logs automatically.

If you configure rotation, the kubelet is responsible for rotating container logs and managing the logging directory structure. The kubelet sends this information to the container runtime (using CRI), and the runtime writes the container logs to the given location.

You can configure two kubelet configuration settings, containerLogMaxSize and containerLogMaxFiles, using the kubelet configuration file. These settings let you configure the maximum size for each log file and the maximum number of files allowed for each container respectively.

When you run kubectl logs as in the basic logging example, the kubelet on the node handles the request and reads directly from the log file. The kubelet returns the content of the log file.

Note:

Only the contents of the latest log file are available through kubectl logs.

For example, if a Pod writes 40 MiB of logs and the kubelet rotates logs after 10 MiB, running kubectl logs returns at most 10MiB of data.

System component logs

There are two types of system components: those that typically run in a container, and those components directly involved in running containers. For example:

The kubelet and container runtime do not run in containers. The kubelet runs your containers (grouped together in pods)
The Kubernetes scheduler, controller manager, and API server run within pods (usually static Pods). The etcd component runs in the control plane, and most commonly also as a static pod. If your cluster uses kube-proxy, you typically run this as a DaemonSet.

Log locations

The way that the kubelet and container runtime write logs depends on the operating system that the node uses:

Linux
Windows

On Linux nodes that use systemd, the kubelet and container runtime write to journald by default. You use journalctl to read the systemd journal; for example: journalctl -u kubelet.

If systemd is not present, the kubelet and container runtime write to .log files in the /var/log directory. If you want to have logs written elsewhere, you can indirectly run the kubelet via a helper tool, kube-log-runner, and use that tool to redirect kubelet logs to a directory that you choose.

You can also set a logging directory using the deprecated kubelet command line argument --log-dir. However, the kubelet always directs your container runtime to write logs into directories within /var/log/pods.

For more information on kube-log-runner, read System Logs.

By default, the kubelet writes logs to files within the directory C:\var\logs (notice that this is not C:\var\log).

Although C:\var\log is the Kubernetes default location for these logs, several cluster deployment tools set up Windows nodes to log to C:\var\log\kubelet instead.

If you want to have logs written elsewhere, you can indirectly run the kubelet via a helper tool, kube-log-runner, and use that tool to redirect kubelet logs to a directory that you choose.

However, the kubelet always directs your container runtime to write logs within the directory C:\var\log\pods.

For more information on kube-log-runner, read System Logs.

For Kubernetes cluster components that run in pods, these write to files inside the /var/log directory, bypassing the default logging mechanism (the components do not write to the systemd journal). You can use Kubernetes' storage mechanisms to map persistent storage into the container that runs the component.

For details about etcd and its logs, view the etcd documentation. Again, you can use Kubernetes' storage mechanisms to map persistent storage into the container that runs the component.

Note:

If you deploy Kubernetes cluster components (such as the scheduler) to log to a volume shared from the parent node, you need to consider and ensure that those logs are rotated. Kubernetes does not manage that log rotation.

Your operating system may automatically implement some log rotation - for example, if you share the directory /var/log into a static Pod for a component, node-level log rotation treats a file in that directory the same as a file written by any component outside Kubernetes.

Some deploy tools account for that log rotation and automate it; others leave this as your responsibility.

Cluster-level logging architectures

While Kubernetes does not provide a native solution for cluster-level logging, there are several common approaches you can consider. Here are some options:

Use a node-level logging agent that runs on every node.
Include a dedicated sidecar container for logging in an application pod.
Push logs directly to a backend from within an application.

Using a node logging agent

Using a node level logging agent

You can implement cluster-level logging by including a node-level logging agent on each node. The logging agent is a dedicated tool that exposes logs or pushes logs to a backend. Commonly, the logging agent is a container that has access to a directory with log files from all of the application containers on that node.

Because the logging agent must run on every node, it is recommended to run the agent as a DaemonSet.

Node-level logging creates only one agent per node and doesn't require any changes to the applications running on the node.

Containers write to stdout and stderr, but with no agreed format. A node-level agent collects these logs and forwards them for aggregation.

Using a sidecar container with the logging agent

You can use a sidecar container in one of the following ways:

The sidecar container streams application logs to its own stdout.
The sidecar container runs a logging agent, which is configured to pick up logs from an application container.

Streaming sidecar container

Sidecar container with a streaming container

By having your sidecar containers write to their own stdout and stderr streams, you can take advantage of the kubelet and the logging agent that already run on each node. The sidecar containers read logs from a file, a socket, or journald. Each sidecar container prints a log to its own stdout or stderr stream.

This approach allows you to separate several log streams from different parts of your application, some of which can lack support for writing to stdout or stderr. The logic behind redirecting logs is minimal, so it's not a significant overhead. Additionally, because stdout and stderr are handled by the kubelet, you can use built-in tools like kubectl logs.

For example, a pod runs a single container, and the container writes to two different log files using two different formats. Here's a manifest for the Pod:

admin/logging/two-files-counter-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done      
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

It is not recommended to write log entries with different formats to the same log stream, even if you managed to redirect both components to the stdout stream of the container. Instead, you can create two sidecar containers. Each sidecar container could tail a particular log file from a shared volume and then redirect the logs to its own stdout stream.

Here's a manifest for a pod that has two sidecar containers:

admin/logging/two-files-counter-pod-streaming-sidecar.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done      
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-1
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/1.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-2
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/2.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

Now when you run this pod, you can access each log stream separately by running the following commands:

kubectl logs counter count-log-1

The output is similar to:

0: Fri Apr  1 11:42:26 UTC 2022
1: Fri Apr  1 11:42:27 UTC 2022
2: Fri Apr  1 11:42:28 UTC 2022
...

kubectl logs counter count-log-2

The output is similar to:

Fri Apr  1 11:42:29 UTC 2022 INFO 0
Fri Apr  1 11:42:30 UTC 2022 INFO 0
Fri Apr  1 11:42:31 UTC 2022 INFO 0
...

If you installed a node-level agent in your cluster, that agent picks up those log streams automatically without any further configuration. If you like, you can configure the agent to parse log lines depending on the source container.

Even for Pods that only have low CPU and memory usage (order of a couple of millicores for cpu and order of several megabytes for memory), writing logs to a file and then streaming them to stdout can double how much storage you need on the node. If you have an application that writes to a single file, it's recommended to set /dev/stdout as the destination rather than implement the streaming sidecar container approach.

Sidecar containers can also be used to rotate log files that cannot be rotated by the application itself. An example of this approach is a small container running logrotate periodically. However, it's more straightforward to use stdout and stderr directly, and leave rotation and retention policies to the kubelet.

Sidecar container with a logging agent

If the node-level logging agent is not flexible enough for your situation, you can create a sidecar container with a separate logging agent that you have configured specifically to run with your application.

Note: Using a logging agent in a sidecar container can lead to significant resource consumption. Moreover, you won't be able to access those logs using kubectl logs because they are not controlled by the kubelet.

Here are two example manifests that you can use to implement a sidecar container with a logging agent. The first manifest contains a ConfigMap to configure fluentd.

admin/logging/fluentd-sidecar-config.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: fluentd-config
data:
  fluentd.conf: |
    <source>
      type tail
      format none
      path /var/log/1.log
      pos_file /var/log/1.log.pos
      tag count.format1
    </source>

    <source>
      type tail
      format none
      path /var/log/2.log
      pos_file /var/log/2.log.pos
      tag count.format2
    </source>

    <match **>
      type google_cloud
    </match>

Note: In the sample configurations, you can replace fluentd with any logging agent, reading from any source inside an application container.

The second manifest describes a pod that has a sidecar container running fluentd. The pod mounts a volume where fluentd can pick up its configuration data.

admin/logging/two-files-counter-pod-agent-sidecar.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done      
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-agent
    image: registry.k8s.io/fluentd-gcp:1.30
    env:
    - name: FLUENTD_ARGS
      value: -c /etc/fluentd-config/fluentd.conf
    volumeMounts:
    - name: varlog
      mountPath: /var/log
    - name: config-volume
      mountPath: /etc/fluentd-config
  volumes:
  - name: varlog
    emptyDir: {}
  - name: config-volume
    configMap:
      name: fluentd-config

Exposing logs directly from the application

Cluster-logging that exposes or pushes logs directly from every application is outside the scope of Kubernetes.

What's next

Read about Kubernetes system logs
Learn about Traces For Kubernetes System Components
Learn how to customise the termination message that Kubernetes records when a Pod fails

12.5 - Metrics For Kubernetes System Components

System component metrics can give a better look into what is happening inside them. Metrics are particularly useful for building dashboards and alerts.

Kubernetes components emit metrics in Prometheus format. This format is structured plain text, designed so that people and machines can both read it.

Metrics in Kubernetes

In most cases metrics are available on /metrics endpoint of the HTTP server. For components that doesn't expose endpoint by default it can be enabled using --bind-address flag.

Examples of those components:

In a production environment you may want to configure Prometheus Server or some other metrics scraper to periodically gather these metrics and make them available in some kind of time series database.

Note that kubelet also exposes metrics in /metrics/cadvisor, /metrics/resource and /metrics/probes endpoints. Those metrics do not have same lifecycle.

If your cluster uses RBAC, reading metrics requires authorization via a user, group or ServiceAccount with a ClusterRole that allows accessing /metrics. For example:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
  - nonResourceURLs:
      - "/metrics"
    verbs:
      - get

Metric lifecycle

Alpha metric → Stable metric → Deprecated metric → Hidden metric → Deleted metric

Alpha metrics have no stability guarantees. These metrics can be modified or deleted at any time.

Stable metrics are guaranteed to not change. This means:

A stable metric without a deprecated signature will not be deleted or renamed
A stable metric's type will not be modified

Deprecated metrics are slated for deletion, but are still available for use. These metrics include an annotation about the version in which they became deprecated.

For example:

Before deprecation

# HELP some_counter this counts things
# TYPE some_counter counter
some_counter 0

After deprecation

# HELP some_counter (Deprecated since 1.15.0) this counts things
# TYPE some_counter counter
some_counter 0

Hidden metrics are no longer published for scraping, but are still available for use. To use a hidden metric, please refer to the Show hidden metrics section.

Deleted metrics are no longer published and cannot be used.

Show hidden metrics

As described above, admins can enable hidden metrics through a command-line flag on a specific binary. This intends to be used as an escape hatch for admins if they missed the migration of the metrics deprecated in the last release.

The flag show-hidden-metrics-for-version takes a version for which you want to show metrics deprecated in that release. The version is expressed as x.y, where x is the major version, y is the minor version. The patch version is not needed even though a metrics can be deprecated in a patch release, the reason for that is the metrics deprecation policy runs against the minor release.

The flag can only take the previous minor version as it's value. All metrics hidden in previous will be emitted if admins set the previous version to show-hidden-metrics-for-version. The too old version is not allowed because this violates the metrics deprecated policy.

Take metric A as an example, here assumed that A is deprecated in 1.n. According to metrics deprecated policy, we can reach the following conclusion:

In release 1.n, the metric is deprecated, and it can be emitted by default.
In release 1.n+1, the metric is hidden by default and it can be emitted by command line show-hidden-metrics-for-version=1.n.
In release 1.n+2, the metric should be removed from the codebase. No escape hatch anymore.

If you're upgrading from release 1.12 to 1.13, but still depend on a metric A deprecated in 1.12, you should set hidden metrics via command line: --show-hidden-metrics=1.12 and remember to remove this metric dependency before upgrading to 1.14

Disable accelerator metrics

The kubelet collects accelerator metrics through cAdvisor. To collect these metrics, for accelerators like NVIDIA GPUs, kubelet held an open handle on the driver. This meant that in order to perform infrastructure changes (for example, updating the driver), a cluster administrator needed to stop the kubelet agent.

The responsibility for collecting accelerator metrics now belongs to the vendor rather than the kubelet. Vendors must provide a container that collects metrics and exposes them to the metrics service (for example, Prometheus).

The DisableAcceleratorUsageMetrics feature gate disables metrics collected by the kubelet, with a timeline for enabling this feature by default.

Component metrics

kube-controller-manager metrics

Controller manager metrics provide important insight into the performance and health of the controller manager. These metrics include common Go language runtime metrics such as go_routine count and controller specific metrics such as etcd request latencies or Cloudprovider (AWS, GCE, OpenStack) API latencies that can be used to gauge the health of a cluster.

Starting from Kubernetes 1.7, detailed Cloudprovider metrics are available for storage operations for GCE, AWS, Vsphere and OpenStack. These metrics can be used to monitor health of persistent volume operations.

For example, for GCE these metrics are called:

cloudprovider_gce_api_request_duration_seconds { request = "instance_list"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_insert"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_delete"}
cloudprovider_gce_api_request_duration_seconds { request = "attach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "detach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "list_disk"}

kube-scheduler metrics

FEATURE STATE: Kubernetes v1.21 [beta]

The scheduler exposes optional metrics that reports the requested resources and the desired limits of all running pods. These metrics can be used to build capacity planning dashboards, assess current or historical scheduling limits, quickly identify workloads that cannot schedule due to lack of resources, and compare actual usage to the pod's request.

The kube-scheduler identifies the resource requests and limits configured for each Pod; when either a request or limit is non-zero, the kube-scheduler reports a metrics timeseries. The time series is labelled by:

namespace
pod name
the node where the pod is scheduled or an empty string if not yet scheduled
priority
the assigned scheduler for that pod
the name of the resource (for example, cpu)
the unit of the resource if known (for example, cores)

Once a pod reaches completion (has a restartPolicy of Never or OnFailure and is in the Succeeded or Failed pod phase, or has been deleted and all containers have a terminated state) the series is no longer reported since the scheduler is now free to schedule other pods to run. The two metrics are called kube_pod_resource_request and kube_pod_resource_limit.

The metrics are exposed at the HTTP endpoint /metrics/resources and require the same authorization as the /metrics endpoint on the scheduler. You must use the --show-hidden-metrics-for-version=1.20 flag to expose these alpha stability metrics.

Disabling metrics

You can explicitly turn off metrics via command line flag --disabled-metrics. This may be desired if, for example, a metric is causing a performance problem. The input is a list of disabled metrics (i.e. --disabled-metrics=metric1,metric2).

Metric cardinality enforcement

Metrics with unbounded dimensions could cause memory issues in the components they instrument. To limit resource use, you can use the --allow-label-value command line option to dynamically configure an allow-list of label values for a metric.

In alpha stage, the flag can only take in a series of mappings as metric label allow-list. Each mapping is of the format <metric_name>,<label_name>=<allowed_labels> where <allowed_labels> is a comma-separated list of acceptable label names.

The overall format looks like: --allow-label-value <metric_name>,<label_name>='<allow_value1>, <allow_value2>...', <metric_name2>,<label_name>='<allow_value1>, <allow_value2>...', ....

Here is an example: --allow-label-value number_count_metric,odd_number='1,3,5', number_count_metric,even_number='2,4,6', date_gauge_metric,weekend='Saturday,Sunday'

What's next

Read about the Prometheus text format for metrics
See the list of stable Kubernetes metrics
Read about the Kubernetes deprecation policy

12.6 - System Logs

System component logs record events happening in cluster, which can be very useful for debugging. You can configure log verbosity to see more or less detail. Logs can be as coarse-grained as showing errors within a component, or as fine-grained as showing step-by-step traces of events (like HTTP access logs, pod state changes, controller actions, or scheduler decisions).

Klog

klog is the Kubernetes logging library. klog generates log messages for the Kubernetes system components.

For more information about klog configuration, see the Command line tool reference.

Kubernetes is in the process of simplifying logging in its components. The following klog command line flags are deprecated starting with Kubernetes 1.23 and will be removed in a future release:

--add-dir-header
--alsologtostderr
--log-backtrace-at
--log-dir
--log-file
--log-file-max-size
--logtostderr
--one-output
--skip-headers
--skip-log-headers
--stderrthreshold

Output will always be written to stderr, regardless of the output format. Output redirection is expected to be handled by the component which invokes a Kubernetes component. This can be a POSIX shell or a tool like systemd.

In some cases, for example a distroless container or a Windows system service, those options are not available. Then the kube-log-runner binary can be used as wrapper around a Kubernetes component to redirect output. A prebuilt binary is included in several Kubernetes base images under its traditional name as /go-runner and as kube-log-runner in server and node release archives.

This table shows how kube-log-runner invocations correspond to shell redirection:

Usage	POSIX shell (such as bash)	`kube-log-runner <options> <cmd>`
Merge stderr and stdout, write to stdout	`2>&1`	`kube-log-runner` (default behavior)
Redirect both into log file	`1>>/tmp/log 2>&1`	`kube-log-runner -log-file=/tmp/log`
Copy into log file and to stdout	`2>&1 \| tee -a /tmp/log`	`kube-log-runner -log-file=/tmp/log -also-stdout`
Redirect only stdout into log file	`>/tmp/log`	`kube-log-runner -log-file=/tmp/log -redirect-stderr=false`

Klog output

An example of the traditional klog native format:

I1025 00:15:15.525108       1 httplog.go:79] GET /api/v1/namespaces/kube-system/pods/metrics-server-v0.3.1-57c75779f-9p8wg: (1.512ms) 200 [pod_nanny/v0.0.0 (linux/amd64) kubernetes/$Format 10.56.1.19:51756]

The message string may contain line breaks:

I1025 00:15:15.525108       1 example.go:79] This is a message
which has a line break.

Structured Logging

FEATURE STATE: Kubernetes v1.23 [beta]

Warning:

Migration to structured log messages is an ongoing process. Not all log messages are structured in this version. When parsing log files, you must also handle unstructured log messages.

Log formatting and value serialization are subject to change.

Structured logging introduces a uniform structure in log messages allowing for programmatic extraction of information. You can store and process structured logs with less effort and cost. The code which generates a log message determines whether it uses the traditional unstructured klog output or structured logging.

The default formatting of structured log messages is as text, with a format that is backward compatible with traditional klog:

<klog header> "<message>" <key1>="<value1>" <key2>="<value2>" ...

Example:

I1025 00:15:15.525108       1 controller_utils.go:116] "Pod status updated" pod="kube-system/kubedns" status="ready"

Strings are quoted. Other values are formatted with %+v, which may cause log messages to continue on the next line depending on the data.

I1025 00:15:15.525108       1 example.go:116] "Example" data="This is text with a line break\nand \"quotation marks\"." someInt=1 someFloat=0.1 someStruct={StringField: First line,
second line.}

Contextual Logging

FEATURE STATE: Kubernetes v1.24 [alpha]

Contextual logging builds on top of structured logging. It is primarily about how developers use logging calls: code based on that concept is more flexible and supports additional use cases as described in the Contextual Logging KEP.

If developers use additional functions like WithValues or WithName in their components, then log entries contain additional information that gets passed into functions by their caller.

Currently this is gated behind the StructuredLogging feature gate and disabled by default. The infrastructure for this was added in 1.24 without modifying components. The component-base/logs/example command demonstrates how to use the new logging calls and how a component behaves that supports contextual logging.

$ cd $GOPATH/src/k8s.io/kubernetes/staging/src/k8s.io/component-base/logs/example/cmd/
$ go run . --help
...
      --feature-gates mapStringBool  A set of key=value pairs that describe feature gates for alpha/experimental features. Options are:
                                     AllAlpha=true|false (ALPHA - default=false)
                                     AllBeta=true|false (BETA - default=false)
                                     ContextualLogging=true|false (ALPHA - default=false)
$ go run . --feature-gates ContextualLogging=true
...
I0404 18:00:02.916429  451895 logger.go:94] "example/myname: runtime" foo="bar" duration="1m0s"
I0404 18:00:02.916447  451895 logger.go:95] "example: another runtime" foo="bar" duration="1m0s"

The example prefix and foo="bar" were added by the caller of the function which logs the runtime message and duration="1m0s" value, without having to modify that function.

With contextual logging disable, WithValues and WithName do nothing and log calls go through the global klog logger. Therefore this additional information is not in the log output anymore:

$ go run . --feature-gates ContextualLogging=false
...
I0404 18:03:31.171945  452150 logger.go:94] "runtime" duration="1m0s"
I0404 18:03:31.171962  452150 logger.go:95] "another runtime" duration="1m0s"

JSON log format

FEATURE STATE: Kubernetes v1.19 [alpha]

Warning:

JSON output does not support many standard klog flags. For list of unsupported klog flags, see the Command line tool reference.

Not all logs are guaranteed to be written in JSON format (for example, during process start). If you intend to parse logs, make sure you can handle log lines that are not JSON as well.

Field names and JSON serialization are subject to change.

The --logging-format=json flag changes the format of logs from klog native format to JSON format. Example of JSON log format (pretty printed):

{
   "ts": 1580306777.04728,
   "v": 4,
   "msg": "Pod status updated",
   "pod":{
      "name": "nginx-1",
      "namespace": "default"
   },
   "status": "ready"
}

Keys with special meaning:

ts - timestamp as Unix time (required, float)
v - verbosity (only for info and not for error messages, int)
err - error string (optional, string)
msg - message (required, string)

List of components currently supporting JSON format:

Log verbosity level

The -v flag controls log verbosity. Increasing the value increases the number of logged events. Decreasing the value decreases the number of logged events. Increasing verbosity settings logs increasingly less severe events. A verbosity setting of 0 logs only critical events.

Log location

There are two types of system components: those that run in a container and those that do not run in a container. For example:

The Kubernetes scheduler and kube-proxy run in a container.
The kubelet and container runtime do not run in containers.

On machines with systemd, the kubelet and container runtime write to journald. Otherwise, they write to .log files in the /var/log directory. System components inside containers always write to .log files in the /var/log directory, bypassing the default logging mechanism. Similar to the container logs, you should rotate system component logs in the /var/log directory. In Kubernetes clusters created by the kube-up.sh script, log rotation is configured by the logrotate tool. The logrotate tool rotates logs daily, or once the log size is greater than 100MB.

What's next

Read about the Kubernetes Logging Architecture
Read about Structured Logging
Read about Contextual Logging
Read about deprecation of klog flags
Read about the Conventions for logging severity

12.7 - Traces For Kubernetes System Components

FEATURE STATE: Kubernetes v1.22 [alpha]

System component traces record the latency of and relationships between operations in the cluster.

Kubernetes components emit traces using the OpenTelemetry Protocol with the gRPC exporter and can be collected and routed to tracing backends using an OpenTelemetry Collector.

Trace Collection

For a complete guide to collecting traces and using the collector, see Getting Started with the OpenTelemetry Collector. However, there are a few things to note that are specific to Kubernetes components.

By default, Kubernetes components export traces using the grpc exporter for OTLP on the IANA OpenTelemetry port, 4317. As an example, if the collector is running as a sidecar to a Kubernetes component, the following receiver configuration will collect spans and log them to standard output:

receivers:
  otlp:
    protocols:
      grpc:
exporters:
  # Replace this exporter with the exporter for your backend
  logging:
    logLevel: debug
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [logging]

Component traces

kube-apiserver traces

The kube-apiserver generates spans for incoming HTTP requests, and for outgoing requests to webhooks, etcd, and re-entrant requests. It propagates the W3C Trace Context with outgoing requests but does not make use of the trace context attached to incoming requests, as the kube-apiserver is often a public endpoint.

Enabling tracing in the kube-apiserver

To enable tracing, enable the APIServerTracing feature gate on the kube-apiserver. Also, provide the kube-apiserver with a tracing configuration file with --tracing-config-file=<path-to-config>. This is an example config that records spans for 1 in 10000 requests, and uses the default OpenTelemetry endpoint:

apiVersion: apiserver.config.k8s.io/v1alpha1
kind: TracingConfiguration
# default value
#endpoint: localhost:4317
samplingRatePerMillion: 100

For more information about the TracingConfiguration struct, see API server config API (v1alpha1).

kubelet traces

FEATURE STATE: Kubernetes v1.25 [alpha]

The kubelet CRI interface and authenticated http servers are instrumented to generate trace spans. As with the apiserver, the endpoint and sampling rate are configurable. Trace context propagation is also configured. A parent span's sampling decision is always respected. A provided tracing configuration sampling rate will apply to spans without a parent. Enabled without a configured endpoint, the default OpenTelemetry Collector reciever address of "localhost:4317" is set.

Enabling tracing in the kubelet

To enable tracing, enable the KubeletTracing feature gate on the kubelet. Also, provide the kubelet with a tracing configuration. This is an example snippet of a kubelet config that records spans for 1 in 10000 requests, and uses the default OpenTelemetry endpoint:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
featureGates:
  KubeletTracing: true
tracing:
  # default value
  #endpoint: localhost:4317
  samplingRatePerMillion: 100

Stability

Tracing instrumentation is still under active development, and may change in a variety of ways. This includes span names, attached attributes, instrumented endpoints, etc. Until this feature graduates to stable, there are no guarantees of backwards compatibility for tracing instrumentation.

What's next

Read about Getting Started with the OpenTelemetry Collector

12.8 - Proxies in Kubernetes

This page explains proxies used with Kubernetes.

Proxies

There are several different proxies you may encounter when using Kubernetes:

The kubectl proxy:
- runs on a user's desktop or in a pod
- proxies from a localhost address to the Kubernetes apiserver
- client to proxy uses HTTP
- proxy to apiserver uses HTTPS
- locates apiserver
- adds authentication headers
The apiserver proxy:
- is a bastion built into the apiserver
- connects a user outside of the cluster to cluster IPs which otherwise might not be reachable
- runs in the apiserver processes
- client to proxy uses HTTPS (or http if apiserver so configured)
- proxy to target may use HTTP or HTTPS as chosen by proxy using available information
- can be used to reach a Node, Pod, or Service
- does load balancing when used to reach a Service
The kube proxy:
- runs on each node
- proxies UDP, TCP and SCTP
- does not understand HTTP
- provides load balancing
- is only used to reach services
A Proxy/Load-balancer in front of apiserver(s):
- existence and implementation varies from cluster to cluster (e.g. nginx)
- sits between all clients and one or more apiservers
- acts as load balancer if there are several apiservers.
Cloud Load Balancers on external services:
- are provided by some cloud providers (e.g. AWS ELB, Google Cloud Load Balancer)
- are created automatically when the Kubernetes service has type LoadBalancer
- usually supports UDP/TCP only
- SCTP support is up to the load balancer implementation of the cloud provider
- implementation varies by cloud provider.

Kubernetes users will typically not need to worry about anything other than the first two types. The cluster admin will typically ensure that the latter types are set up correctly.

Requesting redirects

Proxies have replaced redirect capabilities. Redirects have been deprecated.

12.9 - API Priority and Fairness

FEATURE STATE: Kubernetes v1.20 [beta]

Controlling the behavior of the Kubernetes API server in an overload situation is a key task for cluster administrators. The kube-apiserver has some controls available (i.e. the --max-requests-inflight and --max-mutating-requests-inflight command-line flags) to limit the amount of outstanding work that will be accepted, preventing a flood of inbound requests from overloading and potentially crashing the API server, but these flags are not enough to ensure that the most important requests get through in a period of high traffic.

The API Priority and Fairness feature (APF) is an alternative that improves upon aforementioned max-inflight limitations. APF classifies and isolates requests in a more fine-grained way. It also introduces a limited amount of queuing, so that no requests are rejected in cases of very brief bursts. Requests are dispatched from queues using a fair queuing technique so that, for example, a poorly-behaved controller need not starve others (even at the same priority level).

This feature is designed to work well with standard controllers, which use informers and react to failures of API requests with exponential back-off, and other clients that also work this way.

Caution: Some requests classified as "long-running"—such as remote command execution or log tailing—are not subject to the API Priority and Fairness filter. This is also true for the --max-requests-inflight flag without the API Priority and Fairness feature enabled. API Priority and Fairness does apply to watch requests. When API Priority and Fairness is disabled, watch requests are not subject to the --max-requests-inflight limit.

Enabling/Disabling API Priority and Fairness

The API Priority and Fairness feature is controlled by a feature gate and is enabled by default. See Feature Gates for a general explanation of feature gates and how to enable and disable them. The name of the feature gate for APF is "APIPriorityAndFairness". This feature also involves an API Group with: (a) a v1alpha1 version, disabled by default, and (b) v1beta1 and v1beta2 versions, enabled by default. You can disable the feature gate and API group beta versions by adding the following command-line flags to your kube-apiserver invocation:

kube-apiserver \
--feature-gates=APIPriorityAndFairness=false \
--runtime-config=flowcontrol.apiserver.k8s.io/v1beta1=false,flowcontrol.apiserver.k8s.io/v1beta2=false \
 # …and other flags as usual

Alternatively, you can enable the v1alpha1 version of the API group with --runtime-config=flowcontrol.apiserver.k8s.io/v1alpha1=true.

The command-line flag --enable-priority-and-fairness=false will disable the API Priority and Fairness feature, even if other flags have enabled it.

Concepts

There are several distinct features involved in the API Priority and Fairness feature. Incoming requests are classified by attributes of the request using FlowSchemas, and assigned to priority levels. Priority levels add a degree of isolation by maintaining separate concurrency limits, so that requests assigned to different priority levels cannot starve each other. Within a priority level, a fair-queuing algorithm prevents requests from different flows from starving each other, and allows for requests to be queued to prevent bursty traffic from causing failed requests when the average load is acceptably low.

Priority Levels

Without APF enabled, overall concurrency in the API server is limited by the kube-apiserver flags --max-requests-inflight and --max-mutating-requests-inflight. With APF enabled, the concurrency limits defined by these flags are summed and then the sum is divided up among a configurable set of priority levels. Each incoming request is assigned to a single priority level, and each priority level will only dispatch as many concurrent requests as its configuration allows.

The default configuration, for example, includes separate priority levels for leader-election requests, requests from built-in controllers, and requests from Pods. This means that an ill-behaved Pod that floods the API server with requests cannot prevent leader election or actions by the built-in controllers from succeeding.

Seats Occupied by a Request

The above description of concurrency management is the baseline story. In it, requests have different durations but are counted equally at any given moment when comparing against a priority level's concurrency limit. In the baseline story, each request occupies one unit of concurrency. The word "seat" is used to mean one unit of concurrency, inspired by the way each passenger on a train or aircraft takes up one of the fixed supply of seats.

But some requests take up more than one seat. Some of these are list requests that the server estimates will return a large number of objects. These have been found to put an exceptionally heavy burden on the server, among requests that take a similar amount of time to run. For this reason, the server estimates the number of objects that will be returned and considers the request to take a number of seats that is proportional to that estimated number.

Execution time tweaks for watch requests

API Priority and Fairness manages watch requests, but this involves a couple more excursions from the baseline behavior. The first concerns how long a watch request is considered to occupy its seat. Depending on request parameters, the response to a watch request may or may not begin with create notifications for all the relevant pre-existing objects. API Priority and Fairness considers a watch request to be done with its seat once that initial burst of notifications, if any, is over.

The normal notifications are sent in a concurrent burst to all relevant watch response streams whenever the server is notified of an object create/update/delete. To account for this work, API Priority and Fairness considers every write request to spend some additional time occupying seats after the actual writing is done. The server estimates the number of notifications to be sent and adjusts the write request's number of seats and seat occupancy time to include this extra work.

Queuing

Even within a priority level there may be a large number of distinct sources of traffic. In an overload situation, it is valuable to prevent one stream of requests from starving others (in particular, in the relatively common case of a single buggy client flooding the kube-apiserver with requests, that buggy client would ideally not have much measurable impact on other clients at all). This is handled by use of a fair-queuing algorithm to process requests that are assigned the same priority level. Each request is assigned to a flow, identified by the name of the matching FlowSchema plus a flow distinguisher — which is either the requesting user, the target resource's namespace, or nothing — and the system attempts to give approximately equal weight to requests in different flows of the same priority level. To enable distinct handling of distinct instances, controllers that have many instances should authenticate with distinct usernames

After classifying a request into a flow, the API Priority and Fairness feature then may assign the request to a queue. This assignment uses a technique known as shuffle sharding, which makes relatively efficient use of queues to insulate low-intensity flows from high-intensity flows.

The details of the queuing algorithm are tunable for each priority level, and allow administrators to trade off memory use, fairness (the property that independent flows will all make progress when total traffic exceeds capacity), tolerance for bursty traffic, and the added latency induced by queuing.

Exempt requests

Some requests are considered sufficiently important that they are not subject to any of the limitations imposed by this feature. These exemptions prevent an improperly-configured flow control configuration from totally disabling an API server.

Resources

The flow control API involves two kinds of resources. PriorityLevelConfigurations define the available priority levels, the share of the available concurrency budget that each can handle, and allow for fine-tuning queuing behavior. FlowSchemas are used to classify individual inbound requests, matching each to a single PriorityLevelConfiguration. There is also a v1alpha1 version of the same API group, and it has the same Kinds with the same syntax and semantics.

PriorityLevelConfiguration

A PriorityLevelConfiguration represents a single priority level. Each PriorityLevelConfiguration has an independent limit on the number of outstanding requests, and limitations on the number of queued requests.

Concurrency limits for PriorityLevelConfigurations are not specified in absolute number of requests, but rather in "concurrency shares." The total concurrency limit for the API Server is distributed among the existing PriorityLevelConfigurations in proportion with these shares. This allows a cluster administrator to scale up or down the total amount of traffic to a server by restarting kube-apiserver with a different value for --max-requests-inflight (or --max-mutating-requests-inflight), and all PriorityLevelConfigurations will see their maximum allowed concurrency go up (or down) by the same fraction.

Caution: With the Priority and Fairness feature enabled, the total concurrency limit for the server is set to the sum of --max-requests-inflight and --max-mutating-requests-inflight. There is no longer any distinction made between mutating and non-mutating requests; if you want to treat them separately for a given resource, make separate FlowSchemas that match the mutating and non-mutating verbs respectively.

When the volume of inbound requests assigned to a single PriorityLevelConfiguration is more than its permitted concurrency level, the type field of its specification determines what will happen to extra requests. A type of Reject means that excess traffic will immediately be rejected with an HTTP 429 (Too Many Requests) error. A type of Queue means that requests above the threshold will be queued, with the shuffle sharding and fair queuing techniques used to balance progress between request flows.

The queuing configuration allows tuning the fair queuing algorithm for a priority level. Details of the algorithm can be read in the enhancement proposal, but in short:

Increasing queues reduces the rate of collisions between different flows, at the cost of increased memory usage. A value of 1 here effectively disables the fair-queuing logic, but still allows requests to be queued.
Increasing queueLengthLimit allows larger bursts of traffic to be sustained without dropping any requests, at the cost of increased latency and memory usage.
Changing handSize allows you to adjust the probability of collisions between different flows and the overall concurrency available to a single flow in an overload situation.

Note: A larger handSize makes it less likely for two individual flows to collide (and therefore for one to be able to starve the other), but more likely that a small number of flows can dominate the apiserver. A larger handSize also potentially increases the amount of latency that a single high-traffic flow can cause. The maximum number of queued requests possible from a single flow is handSize * queueLengthLimit.

Following is a table showing an interesting collection of shuffle sharding configurations, showing for each the probability that a given mouse (low-intensity flow) is squished by the elephants (high-intensity flows) for an illustrative collection of numbers of elephants. See https://play.golang.org/p/Gi0PLgVHiUg , which computes this table.

Example Shuffle Sharding Configurations
HandSize	Queues	1 elephant	4 elephants	16 elephants
12	32	4.428838398950118e-09	0.11431348830099144	0.9935089607656024
10	32	1.550093439632541e-08	0.0626479840223545	0.9753101519027554
10	64	6.601827268370426e-12	0.00045571320990370776	0.49999929150089345
9	64	3.6310049976037345e-11	0.00045501212304112273	0.4282314876454858
8	64	2.25929199850899e-10	0.0004886697053040446	0.35935114681123076
8	128	6.994461389026097e-13	3.4055790161620863e-06	0.02746173137155063
7	128	1.0579122850901972e-11	6.960839379258192e-06	0.02406157386340147
7	256	7.597695465552631e-14	6.728547142019406e-08	0.0006709661542533682
6	256	2.7134626662687968e-12	2.9516464018476436e-07	0.0008895654642000348
6	512	4.116062922897309e-14	4.982983350480894e-09	2.26025764343413e-05
6	1024	6.337324016514285e-16	8.09060164312957e-11	4.517408062903668e-07

FlowSchema

A FlowSchema matches some inbound requests and assigns them to a priority level. Every inbound request is tested against every FlowSchema in turn, starting with those with numerically lowest --- which we take to be the logically highest --- matchingPrecedence and working onward. The first match wins.

Caution: Only the first matching FlowSchema for a given request matters. If multiple FlowSchemas match a single inbound request, it will be assigned based on the one with the highest matchingPrecedence. If multiple FlowSchemas with equal matchingPrecedence match the same request, the one with lexicographically smaller name will win, but it's better not to rely on this, and instead to ensure that no two FlowSchemas have the same matchingPrecedence.

A FlowSchema matches a given request if at least one of its rules matches. A rule matches if at least one of its subjects and at least one of its resourceRules or nonResourceRules (depending on whether the incoming request is for a resource or non-resource URL) matches the request.

For the name field in subjects, and the verbs, apiGroups, resources, namespaces, and nonResourceURLs fields of resource and non-resource rules, the wildcard * may be specified to match all values for the given field, effectively removing it from consideration.

A FlowSchema's distinguisherMethod.type determines how requests matching that schema will be separated into flows. It may be either ByUser, in which case one requesting user will not be able to starve other users of capacity, or ByNamespace, in which case requests for resources in one namespace will not be able to starve requests for resources in other namespaces of capacity, or it may be blank (or distinguisherMethod may be omitted entirely), in which case all requests matched by this FlowSchema will be considered part of a single flow. The correct choice for a given FlowSchema depends on the resource and your particular environment.

Defaults

Each kube-apiserver maintains two sorts of APF configuration objects: mandatory and suggested.

Mandatory Configuration Objects

The four mandatory configuration objects reflect fixed built-in guardrail behavior. This is behavior that the servers have before those objects exist, and when those objects exist their specs reflect this behavior. The four mandatory objects are as follows.

The mandatory exempt priority level is used for requests that are not subject to flow control at all: they will always be dispatched immediately. The mandatory exempt FlowSchema classifies all requests from the system:masters group into this priority level. You may define other FlowSchemas that direct other requests to this priority level, if appropriate.
The mandatory catch-all priority level is used in combination with the mandatory catch-all FlowSchema to make sure that every request gets some kind of classification. Typically you should not rely on this catch-all configuration, and should create your own catch-all FlowSchema and PriorityLevelConfiguration (or use the suggested global-default priority level that is installed by default) as appropriate. Because it is not expected to be used normally, the mandatory catch-all priority level has a very small concurrency share and does not queue requests.

Suggested Configuration Objects

The suggested FlowSchemas and PriorityLevelConfigurations constitute a reasonable default configuration. You can modify these and/or create additional configuration objects if you want. If your cluster is likely to experience heavy load then you should consider what configuration will work best.

The suggested configuration groups requests into six priority levels:

The node-high priority level is for health updates from nodes.
The system priority level is for non-health requests from the system:nodes group, i.e. Kubelets, which must be able to contact the API server in order for workloads to be able to schedule on them.
The leader-election priority level is for leader election requests from built-in controllers (in particular, requests for endpoints, configmaps, or leases coming from the system:kube-controller-manager or system:kube-scheduler users and service accounts in the kube-system namespace). These are important to isolate from other traffic because failures in leader election cause their controllers to fail and restart, which in turn causes more expensive traffic as the new controllers sync their informers.
The workload-high priority level is for other requests from built-in controllers.
The workload-low priority level is for requests from any other service account, which will typically include all requests from controllers running in Pods.
The global-default priority level handles all other traffic, e.g. interactive kubectl commands run by nonprivileged users.

The suggested FlowSchemas serve to steer requests into the above priority levels, and are not enumerated here.

Maintenance of the Mandatory and Suggested Configuration Objects

Each kube-apiserver independently maintains the mandatory and suggested configuration objects, using initial and periodic behavior. Thus, in a situation with a mixture of servers of different versions there may be thrashing as long as different servers have different opinions of the proper content of these objects.

Each kube-apiserver makes an initial maintenance pass over the mandatory and suggested configuration objects, and after that does periodic maintenance (once per minute) of those objects.

For the mandatory configuration objects, maintenance consists of ensuring that the object exists and, if it does, has the proper spec. The server refuses to allow a creation or update with a spec that is inconsistent with the server's guardrail behavior.

Maintenance of suggested configuration objects is designed to allow their specs to be overridden. Deletion, on the other hand, is not respected: maintenance will restore the object. If you do not want a suggested configuration object then you need to keep it around but set its spec to have minimal consequences. Maintenance of suggested objects is also designed to support automatic migration when a new version of the kube-apiserver is rolled out, albeit potentially with thrashing while there is a mixed population of servers.

Maintenance of a suggested configuration object consists of creating it --- with the server's suggested spec --- if the object does not exist. OTOH, if the object already exists, maintenance behavior depends on whether the kube-apiservers or the users control the object. In the former case, the server ensures that the object's spec is what the server suggests; in the latter case, the spec is left alone.

The question of who controls the object is answered by first looking for an annotation with key apf.kubernetes.io/autoupdate-spec. If there is such an annotation and its value is true then the kube-apiservers control the object. If there is such an annotation and its value is false then the users control the object. If neither of those condtions holds then the metadata.generation of the object is consulted. If that is 1 then the kube-apiservers control the object. Otherwise the users control the object. These rules were introduced in release 1.22 and their consideration of metadata.generation is for the sake of migration from the simpler earlier behavior. Users who wish to control a suggested configuration object should set its apf.kubernetes.io/autoupdate-spec annotation to false.

Maintenance of a mandatory or suggested configuration object also includes ensuring that it has an apf.kubernetes.io/autoupdate-spec annotation that accurately reflects whether the kube-apiservers control the object.

Maintenance also includes deleting objects that are neither mandatory nor suggested but are annotated apf.kubernetes.io/autoupdate-spec=true.

Health check concurrency exemption

The suggested configuration gives no special treatment to the health check requests on kube-apiservers from their local kubelets --- which tend to use the secured port but supply no credentials. With the suggested config, these requests get assigned to the global-default FlowSchema and the corresponding global-default priority level, where other traffic can crowd them out.

If you add the following additional FlowSchema, this exempts those requests from rate limiting.

Caution: Making this change also allows any hostile party to then send health-check requests that match this FlowSchema, at any volume they like. If you have a web traffic filter or similar external security mechanism to protect your cluster's API server from general internet traffic, you can configure rules to block any health check requests that originate from outside your cluster.

priority-and-fairness/health-for-strangers.yaml

apiVersion: flowcontrol.apiserver.k8s.io/v1beta2
kind: FlowSchema
metadata:
  name: health-for-strangers
spec:
  matchingPrecedence: 1000
  priorityLevelConfiguration:
    name: exempt
  rules:
  - nonResourceRules:
    - nonResourceURLs:
      - "/healthz"
      - "/livez"
      - "/readyz"
      verbs:
      - "*"
    subjects:
    - kind: Group
      group:
        name: system:unauthenticated

Diagnostics

Every HTTP response from an API server with the priority and fairness feature enabled has two extra headers: X-Kubernetes-PF-FlowSchema-UID and X-Kubernetes-PF-PriorityLevel-UID, noting the flow schema that matched the request and the priority level to which it was assigned, respectively. The API objects' names are not included in these headers in case the requesting user does not have permission to view them, so when debugging you can use a command like

kubectl get flowschemas -o custom-columns="uid:{metadata.uid},name:{metadata.name}"
kubectl get prioritylevelconfigurations -o custom-columns="uid:{metadata.uid},name:{metadata.name}"

to get a mapping of UIDs to names for both FlowSchemas and PriorityLevelConfigurations.

Observability

Metrics

Note: In versions of Kubernetes before v1.20, the labels flow_schema and priority_level were inconsistently named flowSchema and priorityLevel, respectively. If you're running Kubernetes versions v1.19 and earlier, you should refer to the documentation for your version.

When you enable the API Priority and Fairness feature, the kube-apiserver exports additional metrics. Monitoring these can help you determine whether your configuration is inappropriately throttling important traffic, or find poorly-behaved workloads that may be harming system health.

apiserver_flowcontrol_rejected_requests_total is a counter vector (cumulative since server start) of requests that were rejected, broken down by the labels flow_schema (indicating the one that matched the request), priority_level (indicating the one to which the request was assigned), and reason. The reason label will be have one of the following values:
- queue-full, indicating that too many requests were already queued,
- concurrency-limit, indicating that the PriorityLevelConfiguration is configured to reject rather than queue excess requests, or
- time-out, indicating that the request was still in the queue when its queuing time limit expired.
apiserver_flowcontrol_dispatched_requests_total is a counter vector (cumulative since server start) of requests that began executing, broken down by the labels flow_schema (indicating the one that matched the request) and priority_level (indicating the one to which the request was assigned).
apiserver_current_inqueue_requests is a gauge vector of recent high water marks of the number of queued requests, grouped by a label named request_kind whose value is mutating or readOnly. These high water marks describe the largest number seen in the one second window most recently completed. These complement the older apiserver_current_inflight_requests gauge vector that holds the last window's high water mark of number of requests actively being served.
apiserver_flowcontrol_read_vs_write_request_count_samples is a histogram vector of observations of the then-current number of requests, broken down by the labels phase (which takes on the values waiting and executing) and request_kind (which takes on the values mutating and readOnly). The observations are made periodically at a high rate. Each observed value is a ratio, between 0 and 1, of a number of requests divided by the corresponding limit on the number of requests (queue length limit for waiting and concurrency limit for executing).
apiserver_flowcontrol_read_vs_write_request_count_watermarks is a histogram vector of high or low water marks of the number of requests (divided by the corresponding limit to get a ratio in the range 0 to 1) broken down by the labels phase (which takes on the values waiting and executing) and request_kind (which takes on the values mutating and readOnly); the label mark takes on values high and low. The water marks are accumulated over windows bounded by the times when an observation was added to apiserver_flowcontrol_read_vs_write_request_count_samples. These water marks show the range of values that occurred between samples.
apiserver_flowcontrol_current_inqueue_requests is a gauge vector holding the instantaneous number of queued (not executing) requests, broken down by the labels priority_level and flow_schema.
apiserver_flowcontrol_current_executing_requests is a gauge vector holding the instantaneous number of executing (not waiting in a queue) requests, broken down by the labels priority_level and flow_schema.
apiserver_flowcontrol_request_concurrency_in_use is a gauge vector holding the instantaneous number of occupied seats, broken down by the labels priority_level and flow_schema.
apiserver_flowcontrol_priority_level_request_count_samples is a histogram vector of observations of the then-current number of requests broken down by the labels phase (which takes on the values waiting and executing) and priority_level. Each histogram gets observations taken periodically, up through the last activity of the relevant sort. The observations are made at a high rate. Each observed value is a ratio, between 0 and 1, of a number of requests divided by the corresponding limit on the number of requests (queue length limit for waiting and concurrency limit for executing).
apiserver_flowcontrol_priority_level_request_count_watermarks is a histogram vector of high or low water marks of the number of requests (divided by the corresponding limit to get a ratio in the range 0 to 1) broken down by the labels phase (which takes on the values waiting and executing) and priority_level; the label mark takes on values high and low. The water marks are accumulated over windows bounded by the times when an observation was added to apiserver_flowcontrol_priority_level_request_count_samples. These water marks show the range of values that occurred between samples.
apiserver_flowcontrol_priority_level_seat_count_samples is a histogram vector of observations of the utilization of a priority level's concurrency limit, broken down by priority_level. This utilization is the fraction (number of seats occupied) / (concurrency limit). This metric considers all stages of execution (both normal and the extra delay at the end of a write to cover for the corresponding notification work) of all requests except WATCHes; for those it considers only the initial stage that delivers notifications of pre-existing objects. Each histogram in the vector is also labeled with phase: executing (there is no seat limit for the waiting phase). Each histogram gets observations taken periodically, up through the last activity of the relevant sort. The observations are made at a high rate.
apiserver_flowcontrol_priority_level_seat_count_watermarks is a histogram vector of high or low water marks of the utilization of a priority level's concurrency limit, broken down by priority_level and mark (which takes on values high and low). Each histogram in the vector is also labeled with phase: executing (there is no seat limit for the waiting phase). The water marks are accumulated over windows bounded by the times when an observation was added to apiserver_flowcontrol_priority_level_seat_count_samples. These water marks show the range of values that occurred between samples.
apiserver_flowcontrol_request_queue_length_after_enqueue is a histogram vector of queue lengths for the queues, broken down by the labels priority_level and flow_schema, as sampled by the enqueued requests. Each request that gets queued contributes one sample to its histogram, reporting the length of the queue immediately after the request was added. Note that this produces different statistics than an unbiased survey would.

Note: An outlier value in a histogram here means it is likely that a single flow (i.e., requests by one user or for one namespace, depending on configuration) is flooding the API server, and being throttled. By contrast, if one priority level's histogram shows that all queues for that priority level are longer than those for other priority levels, it may be appropriate to increase that PriorityLevelConfiguration's concurrency shares.
apiserver_flowcontrol_request_concurrency_limit is a gauge vector holding the computed concurrency limit (based on the API server's total concurrency limit and PriorityLevelConfigurations' concurrency shares), broken down by the label priority_level.
apiserver_flowcontrol_request_wait_duration_seconds is a histogram vector of how long requests spent queued, broken down by the labels flow_schema (indicating which one matched the request), priority_level (indicating the one to which the request was assigned), and execute (indicating whether the request started executing).

Note: Since each FlowSchema always assigns requests to a single PriorityLevelConfiguration, you can add the histograms for all the FlowSchemas for one priority level to get the effective histogram for requests assigned to that priority level.
apiserver_flowcontrol_request_execution_seconds is a histogram vector of how long requests took to actually execute, broken down by the labels flow_schema (indicating which one matched the request) and priority_level (indicating the one to which the request was assigned).
apiserver_flowcontrol_watch_count_samples is a histogram vector of the number of active WATCH requests relevant to a given write, broken down by flow_schema and priority_level.
apiserver_flowcontrol_work_estimated_seats is a histogram vector of the number of estimated seats (maximum of initial and final stage of execution) associated with requests, broken down by flow_schema and priority_level.
apiserver_flowcontrol_request_dispatch_no_accommodation_total is a counter vec of the number of events that in principle could have led to a request being dispatched but did not, due to lack of available concurrency, broken down by flow_schema and priority_level. The relevant sorts of events are arrival of a request and completion of a request.

Debug endpoints

When you enable the API Priority and Fairness feature, the kube-apiserver serves the following additional paths at its HTTP[S] ports.

/debug/api_priority_and_fairness/dump_priority_levels - a listing of all the priority levels and the current state of each. You can fetch like this:

kubectl get --raw /debug/api_priority_and_fairness/dump_priority_levels

The output is similar to this:

PriorityLevelName, ActiveQueues, IsIdle, IsQuiescing, WaitingRequests, ExecutingRequests,
workload-low,      0,            true,   false,       0,               0,
global-default,    0,            true,   false,       0,               0,
exempt,            <none>,       <none>, <none>,      <none>,          <none>,
catch-all,         0,            true,   false,       0,               0,
system,            0,            true,   false,       0,               0,
leader-election,   0,            true,   false,       0,               0,
workload-high,     0,            true,   false,       0,               0,

/debug/api_priority_and_fairness/dump_queues - a listing of all the queues and their current state. You can fetch like this:

kubectl get --raw /debug/api_priority_and_fairness/dump_queues

The output is similar to this:

PriorityLevelName, Index,  PendingRequests, ExecutingRequests, VirtualStart,
workload-high,     0,      0,               0,                 0.0000,
workload-high,     1,      0,               0,                 0.0000,
workload-high,     2,      0,               0,                 0.0000,
...
leader-election,   14,     0,               0,                 0.0000,
leader-election,   15,     0,               0,                 0.0000,

/debug/api_priority_and_fairness/dump_requests - a listing of all the requests that are currently waiting in a queue. You can fetch like this:

kubectl get --raw /debug/api_priority_and_fairness/dump_requests

The output is similar to this:

PriorityLevelName, FlowSchemaName, QueueIndex, RequestIndexInQueue, FlowDistingsher,       ArriveTime,
exempt,            <none>,         <none>,     <none>,              <none>,                <none>,
system,            system-nodes,   12,         0,                   system:node:127.0.0.1, 2020-07-23T15:26:57.179170694Z,

In addition to the queued requests, the output includes one phantom line for each priority level that is exempt from limitation.

You can get a more detailed listing with a command like this:

kubectl get --raw '/debug/api_priority_and_fairness/dump_requests?includeRequestDetails=1'

The output is similar to this:

PriorityLevelName, FlowSchemaName, QueueIndex, RequestIndexInQueue, FlowDistingsher,       ArriveTime,                     UserName,              Verb,   APIPath,                                                     Namespace, Name,   APIVersion, Resource, SubResource,
system,            system-nodes,   12,         0,                   system:node:127.0.0.1, 2020-07-23T15:31:03.583823404Z, system:node:127.0.0.1, create, /api/v1/namespaces/scaletest/configmaps,
system,            system-nodes,   12,         1,                   system:node:127.0.0.1, 2020-07-23T15:31:03.594555947Z, system:node:127.0.0.1, create, /api/v1/namespaces/scaletest/configmaps,

What's next

For background information on design details for API priority and fairness, see the enhancement proposal. You can make suggestions and feature requests via SIG API Machinery or the feature's slack channel.

12.10 - Installing Addons

Add-ons extend the functionality of Kubernetes.

This page lists some of the available add-ons and links to their respective installation instructions. The list does not try to be exhaustive.

Networking and Network Policy

ACI provides integrated container networking and network security with Cisco ACI.
Antrea operates at Layer 3/4 to provide networking and security services for Kubernetes, leveraging Open vSwitch as the networking data plane.
Calico is a networking and network policy provider. Calico supports a flexible set of networking options so you can choose the most efficient option for your situation, including non-overlay and overlay networks, with or without BGP. Calico uses the same engine to enforce network policy for hosts, pods, and (if using Istio & Envoy) applications at the service mesh layer.
Canal unites Flannel and Calico, providing networking and network policy.
Cilium is a networking, observability, and security solution with an eBPF-based data plane. Cilium provides a simple flat Layer 3 network with the ability to span multiple clusters in either a native routing or overlay/encapsulation mode, and can enforce network policies on L3-L7 using an identity-based security model that is decoupled from network addressing. Cilium can act as a replacement for kube-proxy; it also offers additional, opt-in observability and security features.
CNI-Genie enables Kubernetes to seamlessly connect to a choice of CNI plugins, such as Calico, Canal, Flannel, or Weave.
Contiv provides configurable networking (native L3 using BGP, overlay using vxlan, classic L2, and Cisco-SDN/ACI) for various use cases and a rich policy framework. Contiv project is fully open sourced. The installer provides both kubeadm and non-kubeadm based installation options.
Contrail, based on Tungsten Fabric, is an open source, multi-cloud network virtualization and policy management platform. Contrail and Tungsten Fabric are integrated with orchestration systems such as Kubernetes, OpenShift, OpenStack and Mesos, and provide isolation modes for virtual machines, containers/pods and bare metal workloads.
Flannel is an overlay network provider that can be used with Kubernetes.
Knitter is a plugin to support multiple network interfaces in a Kubernetes pod.
Multus is a Multi plugin for multiple network support in Kubernetes to support all CNI plugins (e.g. Calico, Cilium, Contiv, Flannel), in addition to SRIOV, DPDK, OVS-DPDK and VPP based workloads in Kubernetes.
OVN-Kubernetes is a networking provider for Kubernetes based on OVN (Open Virtual Network), a virtual networking implementation that came out of the Open vSwitch (OVS) project. OVN-Kubernetes provides an overlay based networking implementation for Kubernetes, including an OVS based implementation of load balancing and network policy.
Nodus is an OVN based CNI controller plugin to provide cloud native based Service function chaining(SFC).
NSX-T Container Plug-in (NCP) provides integration between VMware NSX-T and container orchestrators such as Kubernetes, as well as integration between NSX-T and container-based CaaS/PaaS platforms such as Pivotal Container Service (PKS) and OpenShift.
Nuage is an SDN platform that provides policy-based networking between Kubernetes Pods and non-Kubernetes environments with visibility and security monitoring.
Romana is a Layer 3 networking solution for pod networks that also supports the NetworkPolicy API.
Weave Net provides networking and network policy, will carry on working on both sides of a network partition, and does not require an external database.

Service Discovery

CoreDNS is a flexible, extensible DNS server which can be installed as the in-cluster DNS for pods.

Visualization & Control

Dashboard is a dashboard web interface for Kubernetes.
Weave Scope is a tool for graphically visualizing your containers, pods, services etc. Use it in conjunction with a Weave Cloud account or host the UI yourself.

Infrastructure

KubeVirt is an add-on to run virtual machines on Kubernetes. Usually run on bare-metal clusters.
The node problem detector runs on Linux nodes and reports system issues as either Events or Node conditions.

Legacy Add-ons

There are several other add-ons documented in the deprecated cluster/addons directory.

Well-maintained ones should be linked to here. PRs welcome!

13 - Extending Kubernetes

Different ways to change the behavior of your Kubernetes cluster.

Kubernetes is highly configurable and extensible. As a result, there is rarely a need to fork or submit patches to the Kubernetes project code.

This guide describes the options for customizing a Kubernetes cluster. It is aimed at cluster operators who want to understand how to adapt their Kubernetes cluster to the needs of their work environment. Developers who are prospective Platform Developers or Kubernetes Project Contributors will also find it useful as an introduction to what extension points and patterns exist, and their trade-offs and limitations.

Customization approaches can be broadly divided into configuration, which only involves changing command line arguments, local configuration files, or API resources; and extensions, which involve running additional programs, additional network services, or both. This document is primarily about extensions.

Configuration

Configuration files and command arguments are documented in the Reference section of the online documentation, with a page for each binary:

Command arguments and configuration files may not always be changeable in a hosted Kubernetes service or a distribution with managed installation. When they are changeable, they are usually only changeable by the cluster operator. Also, they are subject to change in future Kubernetes versions, and setting them may require restarting processes. For those reasons, they should be used only when there are no other options.

Built-in policy APIs, such as ResourceQuota, NetworkPolicy and Role-based Access Control (RBAC), are built-in Kubernetes APIs that provide declaratively configured policy settings. APIs are typically usable even with hosted Kubernetes services and with managed Kubernetes installations. The built-in policy APIs follow the same conventions as other Kubernetes resources such as Pods. When you use a policy APIs that is stable, you benefit from a defined support policy like other Kubernetes APIs. For these reasons, policy APIs are recommended over configuration files and command arguments where suitable.

Extensions

Extensions are software components that extend and deeply integrate with Kubernetes. They adapt it to support new types and new kinds of hardware.

Many cluster administrators use a hosted or distribution instance of Kubernetes. These clusters come with extensions pre-installed. As a result, most Kubernetes users will not need to install extensions and even fewer users will need to author new ones.

Extension patterns

Kubernetes is designed to be automated by writing client programs. Any program that reads and/or writes to the Kubernetes API can provide useful automation. Automation can run on the cluster or off it. By following the guidance in this doc you can write highly available and robust automation. Automation generally works with any Kubernetes cluster, including hosted clusters and managed installations.

There is a specific pattern for writing client programs that work well with Kubernetes called the controller pattern. Controllers typically read an object's .spec, possibly do things, and then update the object's .status.

A controller is a client of the Kubernetes API. When Kubernetes is the client and calls out to a remote service, Kubernetes calls this a webhook. The remote service is called a webhook backend. As with custom controllers, webhooks do add a point of failure.

Note: Outside of Kubernetes, the term “webhook” typically refers to a mechanism for asynchronous notifications, where the webhook call serves as a one-way notification to another system or component. In the Kubernetes ecosystem, even synchronous HTTP callouts are often described as “webhooks”.

In the webhook model, Kubernetes makes a network request to a remote service. With the alternative binary Plugin model, Kubernetes executes a binary (program). Binary plugins are used by the kubelet (for example, CSI storage plugins and CNI network plugins), and by kubectl (see Extend kubectl with plugins).

Extension points

This diagram shows the extension points in a Kubernetes cluster and the clients that access it.

Key to the figure

Users often interact with the Kubernetes API using kubectl. Plugins customise the behaviour of clients. There are generic extensions that can apply to different clients, as well as specific ways to extend kubectl.
The API server handles all requests. Several types of extension points in the API server allow authenticating requests, or blocking them based on their content, editing content, and handling deletion. These are described in the API Access Extensions section.
The API server serves various kinds of resources. Built-in resource kinds, such as pods, are defined by the Kubernetes project and can't be changed. Read API extensions to learn about extending the Kubernetes API.
The Kubernetes scheduler decides which nodes to place pods on. There are several ways to extend scheduling, which are described in the Scheduling extensions section.
Much of the behavior of Kubernetes is implemented by programs called controllers, that are clients of the API server. Controllers are often used in conjunction with custom resources. Read combining new APIs with automation and Changing built-in resources to learn more.
The kubelet runs on servers (nodes), and helps pods appear like virtual servers with their own IPs on the cluster network. Network Plugins allow for different implementations of pod networking.
You can use Device Plugins to integrate custom hardware or other special node-local facilities, and make these available to Pods running in your cluster. The kubelet includes support for working with device plugins.

The kubelet also mounts and unmounts volume for pods and their containers. You can use Storage Plugins to add support for new kinds of storage and other volume types.

Extension point choice flowchart

If you are unsure where to start, this flowchart can help. Note that some solutions may involve several types of extensions.

Flowchart with questions about use cases and guidance for implementers. Green circles indicate yes; red circles indicate no. — Flowchart guide to select an extension approach

Client extensions

Plugins for kubectl are separate binaries that add or replace the behavior of specific subcommands. The kubectl tool can also integrate with credential plugins These extensions only affect a individual user's local environment, and so cannot enforce site-wide policies.

If you want to extend the kubectl tool, read Extend kubectl with plugins.

API extensions

Custom resource definitions

Consider adding a Custom Resource to Kubernetes if you want to define new controllers, application configuration objects or other declarative APIs, and to manage them using Kubernetes tools, such as kubectl.

For more about Custom Resources, see the Custom Resources concept guide.

API aggregation layer

You can use Kubernetes' API Aggregation Layer to integrate the Kubernetes API with additional services such as for metrics.

Combining new APIs with automation

A combination of a custom resource API and a control loop is called the controllers pattern. If your controller takes the place of a human operator deploying infrastructure based on a desired state, then the controller may also be following the operator pattern. The Operator pattern is used to manage specific applications; usually, these are applications that maintain state and require care in how they are managed.

You can also make your own custom APIs and control loops that manage other resources, such as storage, or to define policies (such as an access control restriction).

Changing built-in resources

When you extend the Kubernetes API by adding custom resources, the added resources always fall into a new API Groups. You cannot replace or change existing API groups. Adding an API does not directly let you affect the behavior of existing APIs (such as Pods), whereas API Access Extensions do.

API access extensions

When a request reaches the Kubernetes API Server, it is first authenticated, then authorized, and is then subject to various types of admission control (some requests are in fact not authenticated, and get special treatment). See Controlling Access to the Kubernetes API for more on this flow.

Each of the steps in the Kubernetes authentication / authorization flow offers extension points.

Authentication

Authentication maps headers or certificates in all requests to a username for the client making the request.

Kubernetes has several built-in authentication methods that it supports. It can also sit behind an authenticating proxy, and it can send a token from an Authorization: header to a remote service for verification (an authentication webhook) if those don't meet your needs.

Authorization

Authorization determines whether specific users can read, write, and do other operations on API resources. It works at the level of whole resources -- it doesn't discriminate based on arbitrary object fields.

If the built-in authorization options don't meet your needs, an authorization webhook allows calling out to custom code that makes an authorization decision.

Dynamic admission control

After a request is authorized, if it is a write operation, it also goes through Admission Control steps. In addition to the built-in steps, there are several extensions:

The Image Policy webhook restricts what images can be run in containers.
To make arbitrary admission control decisions, a general Admission webhook can be used. Admission webhooks can reject creations or updates. Some admission webhooks modify the incoming request data before it is handled further by Kubernetes.

Infrastructure extensions

Device plugins

Device plugins allow a node to discover new Node resources (in addition to the builtin ones like cpu and memory) via a Device Plugin.

Storage plugins

Container Storage Interface (CSI) plugins provide a way to extend Kubernetes with supports for new kinds of volumes. The volumes can be backed by durable external storage, or provide ephemeral storage, or they might offer a read-only interface to information using a filesystem paradigm.

Kubernetes also includes support for FlexVolume plugins, which are deprecated since Kubernetes v1.23 (in favour of CSI).

FlexVolume plugins allow users to mount volume types that aren't natively supported by Kubernetes. When you run a Pod that relies on FlexVolume storage, the kubelet calls a binary plugin to mount the volume. The archived FlexVolume design proposal has more detail on this approach.

The Kubernetes Volume Plugin FAQ for Storage Vendors includes general information on storage plugins.

Network plugins

Your Kubernetes cluster needs a network plugin in order to have a working Pod network and to support other aspects of the Kubernetes network model.

Network Plugins allow Kubernetes to work with different networking topologies and technologies.

Scheduling extensions

The scheduler is a special type of controller that watches pods, and assigns pods to nodes. The default scheduler can be replaced entirely, while continuing to use other Kubernetes components, or multiple schedulers can run at the same time.

This is a significant undertaking, and almost all Kubernetes users find they do not need to modify the scheduler.

You can control which scheduling plugins are active, or associate sets of plugins with different named scheduler profiles. You can also write your own plugin that integrates with one or more of the kube-scheduler's extension points.

Finally, the built in kube-scheduler component supports a webhook that permits a remote HTTP backend (scheduler extension) to filter and / or prioritize the nodes that the kube-scheduler chooses for a pod.

Note: You can only affect node filtering and node prioritization with a scheduler extender webhook; other extension points are not available through the webhook integration.

What's next

Learn more about infrastructure extensions
- Device Plugins
- Network Plugins
- CSI storage plugins
Learn about kubectl plugins
Learn more about Custom Resources
Learn more about Extension API Servers
Learn about Dynamic admission control
Learn about the Operator pattern

13.1 - Extending the Kubernetes API

13.1.1 - Custom Resources

Custom resources are extensions of the Kubernetes API. This page discusses when to add a custom resource to your Kubernetes cluster and when to use a standalone service. It describes the two methods for adding custom resources and how to choose between them.

Custom resources

A resource is an endpoint in the Kubernetes API that stores a collection of API objects of a certain kind; for example, the built-in pods resource contains a collection of Pod objects.

A custom resource is an extension of the Kubernetes API that is not necessarily available in a default Kubernetes installation. It represents a customization of a particular Kubernetes installation. However, many core Kubernetes functions are now built using custom resources, making Kubernetes more modular.

Custom resources can appear and disappear in a running cluster through dynamic registration, and cluster admins can update custom resources independently of the cluster itself. Once a custom resource is installed, users can create and access its objects using kubectl, just as they do for built-in resources like Pods.

Custom controllers

On their own, custom resources let you store and retrieve structured data. When you combine a custom resource with a custom controller, custom resources provide a true declarative API.

The Kubernetes declarative API enforces a separation of responsibilities. You declare the desired state of your resource. The Kubernetes controller keeps the current state of Kubernetes objects in sync with your declared desired state. This is in contrast to an imperative API, where you instruct a server what to do.

You can deploy and update a custom controller on a running cluster, independently of the cluster's lifecycle. Custom controllers can work with any kind of resource, but they are especially effective when combined with custom resources. The Operator pattern combines custom resources and custom controllers. You can use custom controllers to encode domain knowledge for specific applications into an extension of the Kubernetes API.

Should I add a custom resource to my Kubernetes cluster?

When creating a new API, consider whether to aggregate your API with the Kubernetes cluster APIs or let your API stand alone.

Consider API aggregation if:	Prefer a stand-alone API if:
Your API is Declarative.	Your API does not fit the Declarative model.
You want your new types to be readable and writable using `kubectl`.	`kubectl` support is not required
You want to view your new types in a Kubernetes UI, such as dashboard, alongside built-in types.	Kubernetes UI support is not required.
You are developing a new API.	You already have a program that serves your API and works well.
You are willing to accept the format restriction that Kubernetes puts on REST resource paths, such as API Groups and Namespaces. (See the API Overview.)	You need to have specific REST paths to be compatible with an already defined REST API.
Your resources are naturally scoped to a cluster or namespaces of a cluster.	Cluster or namespace scoped resources are a poor fit; you need control over the specifics of resource paths.
You want to reuse Kubernetes API support features.	You don't need those features.

Declarative APIs

In a Declarative API, typically:

Your API consists of a relatively small number of relatively small objects (resources).
The objects define configuration of applications or infrastructure.
The objects are updated relatively infrequently.
Humans often need to read and write the objects.
The main operations on the objects are CRUD-y (creating, reading, updating and deleting).
Transactions across objects are not required: the API represents a desired state, not an exact state.

Imperative APIs are not declarative. Signs that your API might not be declarative include:

The client says "do this", and then gets a synchronous response back when it is done.
The client says "do this", and then gets an operation ID back, and has to check a separate Operation object to determine completion of the request.
You talk about Remote Procedure Calls (RPCs).
Directly storing large amounts of data; for example, > a few kB per object, or > 1000s of objects.
High bandwidth access (10s of requests per second sustained) needed.
Store end-user data (such as images, PII, etc.) or other large-scale data processed by applications.
The natural operations on the objects are not CRUD-y.
The API is not easily modeled as objects.
You chose to represent pending operations with an operation ID or an operation object.

Should I use a ConfigMap or a custom resource?

Use a ConfigMap if any of the following apply:

There is an existing, well-documented configuration file format, such as a mysql.cnf or pom.xml.
You want to put the entire configuration into one key of a ConfigMap.
The main use of the configuration file is for a program running in a Pod on your cluster to consume the file to configure itself.
Consumers of the file prefer to consume via file in a Pod or environment variable in a pod, rather than the Kubernetes API.
You want to perform rolling updates via Deployment, etc., when the file is updated.

Note: Use a for sensitive data, which is similar to a ConfigMap but more secure.

Use a custom resource (CRD or Aggregated API) if most of the following apply:

You want to use Kubernetes client libraries and CLIs to create and update the new resource.
You want top-level support from kubectl; for example, kubectl get my-object object-name.
You want to build new automation that watches for updates on the new object, and then CRUD other objects, or vice versa.
You want to write automation that handles updates to the object.
You want to use Kubernetes API conventions like .spec, .status, and .metadata.
You want the object to be an abstraction over a collection of controlled resources, or a summarization of other resources.

Adding custom resources

Kubernetes provides two ways to add custom resources to your cluster:

CRDs are simple and can be created without any programming.
API Aggregation requires programming, but allows more control over API behaviors like how data is stored and conversion between API versions.

Kubernetes provides these two options to meet the needs of different users, so that neither ease of use nor flexibility is compromised.

Aggregated APIs are subordinate API servers that sit behind the primary API server, which acts as a proxy. This arrangement is called API Aggregation (AA). To users, the Kubernetes API appears extended.

CRDs allow users to create new types of resources without adding another API server. You do not need to understand API Aggregation to use CRDs.

Regardless of how they are installed, the new resources are referred to as Custom Resources to distinguish them from built-in Kubernetes resources (like pods).

Note:

Avoid using a Custom Resource as data storage for application, end user, or monitoring data: architecture designs that store application data within the Kubernetes API typically represent a design that is too closely coupled.

Architecturally, cloud native application architectures favor loose coupling between components. If part of your workload requires a backing service for its routine operation, run that backing service as a component or consume it as an external service. This way, your workload does not rely on the Kubernetes API for its normal operation.

CustomResourceDefinitions

The CustomResourceDefinition API resource allows you to define custom resources. Defining a CRD object creates a new custom resource with a name and schema that you specify. The Kubernetes API serves and handles the storage of your custom resource. The name of a CRD object must be a valid DNS subdomain name.

This frees you from writing your own API server to handle the custom resource, but the generic nature of the implementation means you have less flexibility than with API server aggregation.

Refer to the custom controller example for an example of how to register a new custom resource, work with instances of your new resource type, and use a controller to handle events.

API server aggregation

Usually, each resource in the Kubernetes API requires code that handles REST requests and manages persistent storage of objects. The main Kubernetes API server handles built-in resources like pods and services, and can also generically handle custom resources through CRDs.

The aggregation layer allows you to provide specialized implementations for your custom resources by writing and deploying your own API server. The main API server delegates requests to your API server for the custom resources that you handle, making them available to all of its clients.

Choosing a method for adding custom resources

CRDs are easier to use. Aggregated APIs are more flexible. Choose the method that best meets your needs.

Typically, CRDs are a good fit if:

You have a handful of fields
You are using the resource within your company, or as part of a small open-source project (as opposed to a commercial product)

Comparing ease of use

CRDs are easier to create than Aggregated APIs.

CRDs	Aggregated API
Do not require programming. Users can choose any language for a CRD controller.	Requires programming and building binary and image.
No additional service to run; CRDs are handled by API server.	An additional service to create and that could fail.
No ongoing support once the CRD is created. Any bug fixes are picked up as part of normal Kubernetes Master upgrades.	May need to periodically pickup bug fixes from upstream and rebuild and update the Aggregated API server.
No need to handle multiple versions of your API; for example, when you control the client for this resource, you can upgrade it in sync with the API.	You need to handle multiple versions of your API; for example, when developing an extension to share with the world.

Advanced features and flexibility

Aggregated APIs offer more advanced API features and customization of other features; for example, the storage layer.

Feature	Description	CRDs	Aggregated API
Validation	Help users prevent errors and allow you to evolve your API independently of your clients. These features are most useful when there are many clients who can't all update at the same time.	Yes. Most validation can be specified in the CRD using OpenAPI v3.0 validation. Any other validations supported by addition of a Validating Webhook.	Yes, arbitrary validation checks
Defaulting	See above	Yes, either via OpenAPI v3.0 validation `default` keyword (GA in 1.17), or via a Mutating Webhook (though this will not be run when reading from etcd for old objects).	Yes
Multi-versioning	Allows serving the same object through two API versions. Can help ease API changes like renaming fields. Less important if you control your client versions.	Yes	Yes
Custom Storage	If you need storage with a different performance mode (for example, a time-series database instead of key-value store) or isolation for security (for example, encryption of sensitive information, etc.)	No	Yes
Custom Business Logic	Perform arbitrary checks or actions when creating, reading, updating or deleting an object	Yes, using Webhooks.	Yes
Scale Subresource	Allows systems like HorizontalPodAutoscaler and PodDisruptionBudget interact with your new resource	Yes	Yes
Status Subresource	Allows fine-grained access control where user writes the spec section and the controller writes the status section. Allows incrementing object Generation on custom resource data mutation (requires separate spec and status sections in the resource)	Yes	Yes
Other Subresources	Add operations other than CRUD, such as "logs" or "exec".	No	Yes
strategic-merge-patch	The new endpoints support PATCH with `Content-Type: application/strategic-merge-patch+json`. Useful for updating objects that may be modified both locally, and by the server. For more information, see "Update API Objects in Place Using kubectl patch"	No	Yes
Protocol Buffers	The new resource supports clients that want to use Protocol Buffers	No	Yes
OpenAPI Schema	Is there an OpenAPI (swagger) schema for the types that can be dynamically fetched from the server? Is the user protected from misspelling field names by ensuring only allowed fields are set? Are types enforced (in other words, don't put an `int` in a `string` field?)	Yes, based on the OpenAPI v3.0 validation schema (GA in 1.16).	Yes

Common Features

When you create a custom resource, either via a CRD or an AA, you get many features for your API, compared to implementing it outside the Kubernetes platform:

Feature	What it does
CRUD	The new endpoints support CRUD basic operations via HTTP and `kubectl`
Watch	The new endpoints support Kubernetes Watch operations via HTTP
Discovery	Clients like `kubectl` and dashboard automatically offer list, display, and field edit operations on your resources
json-patch	The new endpoints support PATCH with `Content-Type: application/json-patch+json`
merge-patch	The new endpoints support PATCH with `Content-Type: application/merge-patch+json`
HTTPS	The new endpoints uses HTTPS
Built-in Authentication	Access to the extension uses the core API server (aggregation layer) for authentication
Built-in Authorization	Access to the extension can reuse the authorization used by the core API server; for example, RBAC.
Finalizers	Block deletion of extension resources until external cleanup happens.
Admission Webhooks	Set default values and validate extension resources during any create/update/delete operation.
UI/CLI Display	Kubectl, dashboard can display extension resources.
Unset versus Empty	Clients can distinguish unset fields from zero-valued fields.
Client Libraries Generation	Kubernetes provides generic client libraries, as well as tools to generate type-specific client libraries.
Labels and annotations	Common metadata across objects that tools know how to edit for core and custom resources.

Preparing to install a custom resource

There are several points to be aware of before adding a custom resource to your cluster.

Third party code and new points of failure

While creating a CRD does not automatically add any new points of failure (for example, by causing third party code to run on your API server), packages (for example, Charts) or other installation bundles often include CRDs as well as a Deployment of third-party code that implements the business logic for a new custom resource.

Installing an Aggregated API server always involves running a new Deployment.

Storage

Custom resources consume storage space in the same way that ConfigMaps do. Creating too many custom resources may overload your API server's storage space.

Aggregated API servers may use the same storage as the main API server, in which case the same warning applies.

Authentication, authorization, and auditing

CRDs always use the same authentication, authorization, and audit logging as the built-in resources of your API server.

If you use RBAC for authorization, most RBAC roles will not grant access to the new resources (except the cluster-admin role or any role created with wildcard rules). You'll need to explicitly grant access to the new resources. CRDs and Aggregated APIs often come bundled with new role definitions for the types they add.

Aggregated API servers may or may not use the same authentication, authorization, and auditing as the primary API server.

Accessing a custom resource

Kubernetes client libraries can be used to access custom resources. Not all client libraries support custom resources. The Go and Python client libraries do.

When you add a custom resource, you can access it using:

kubectl
The Kubernetes dynamic client.
A REST client that you write.
A client generated using Kubernetes client generation tools (generating one is an advanced undertaking, but some projects may provide a client along with the CRD or AA).

What's next

Learn how to Extend the Kubernetes API with the aggregation layer.
Learn how to Extend the Kubernetes API with CustomResourceDefinition.

13.1.2 - Kubernetes API Aggregation Layer

The aggregation layer allows Kubernetes to be extended with additional APIs, beyond what is offered by the core Kubernetes APIs. The additional APIs can either be ready-made solutions such as a metrics server, or APIs that you develop yourself.

The aggregation layer is different from Custom Resources, which are a way to make the kube-apiserver recognise new kinds of object.

Aggregation layer

The aggregation layer runs in-process with the kube-apiserver. Until an extension resource is registered, the aggregation layer will do nothing. To register an API, you add an APIService object, which "claims" the URL path in the Kubernetes API. At that point, the aggregation layer will proxy anything sent to that API path (e.g. /apis/myextension.mycompany.io/v1/…) to the registered APIService.

The most common way to implement the APIService is to run an extension API server in Pod(s) that run in your cluster. If you're using the extension API server to manage resources in your cluster, the extension API server (also written as "extension-apiserver") is typically paired with one or more controllers. The apiserver-builder library provides a skeleton for both extension API servers and the associated controller(s).

Response latency

Extension API servers should have low latency networking to and from the kube-apiserver. Discovery requests are required to round-trip from the kube-apiserver in five seconds or less.

If your extension API server cannot achieve that latency requirement, consider making changes that let you meet it.

What's next

To get the aggregator working in your environment, configure the aggregation layer.
Then, setup an extension api-server to work with the aggregation layer.
Read about APIService in the API reference

Alternatively: learn how to extend the Kubernetes API using Custom Resource Definitions.

13.2 - Compute, Storage, and Networking Extensions

This section covers extensions to your cluster that do not come as part as Kubernetes itself. You can use these extensions to enhance the nodes in your cluster, or to provide the network fabric that links Pods together.

CSI and FlexVolume storage plugins

Container Storage Interface (CSI) plugins provide a way to extend Kubernetes with supports for new kinds of volumes. The volumes can be backed by durable external storage, or provide ephemeral storage, or they might offer a read-only interface to information using a filesystem paradigm.

Kubernetes also includes support for FlexVolume plugins, which are deprecated since Kubernetes v1.23 (in favour of CSI).

FlexVolume plugins allow users to mount volume types that aren't natively supported by Kubernetes. When you run a Pod that relies on FlexVolume storage, the kubelet calls a binary plugin to mount the volume. The archived FlexVolume design proposal has more detail on this approach.

The Kubernetes Volume Plugin FAQ for Storage Vendors includes general information on storage plugins.
Device plugins

Device plugins allow a node to discover new Node facilities (in addition to the built-in node resources such as cpu and memory), and provide these custom node-local facilities to Pods that request them.
Network plugins

A network plugin allow Kubernetes to work with different networking topologies and technologies. Your Kubernetes cluster needs a network plugin in order to have a working Pod network and to support other aspects of the Kubernetes network model.

Kubernetes 1.25 is compatible with CNI network plugins.

13.2.1 - Network Plugins

Kubernetes 1.25 supports Container Network Interface (CNI) plugins for cluster networking. You must use a CNI plugin that is compatible with your cluster and that suits your needs. Different plugins are available (both open- and closed- source) in the wider Kubernetes ecosystem.

A CNI plugin is required to implement the Kubernetes network model.

You must use a CNI plugin that is compatible with the v0.4.0 or later releases of the CNI specification. The Kubernetes project recommends using a plugin that is compatible with the v1.0.0 CNI specification (plugins can be compatible with multiple spec versions).

Installation

A Container Runtime, in the networking context, is a daemon on a node configured to provide CRI Services for kubelet. In particular, the Container Runtime must be configured to load the CNI plugins required to implement the Kubernetes network model.

Note:

Prior to Kubernetes 1.24, the CNI plugins could also be managed by the kubelet using the cni-bin-dir and network-plugin command-line parameters. These command-line parameters were removed in Kubernetes 1.24, with management of the CNI no longer in scope for kubelet.

See Troubleshooting CNI plugin-related errors if you are facing issues following the removal of dockershim.

For specific information about how a Container Runtime manages the CNI plugins, see the documentation for that Container Runtime, for example:

For specific information about how to install and manage a CNI plugin, see the documentation for that plugin or networking provider.

Network Plugin Requirements

For plugin developers and users who regularly build or deploy Kubernetes, the plugin may also need specific configuration to support kube-proxy. The iptables proxy depends on iptables, and the plugin may need to ensure that container traffic is made available to iptables. For example, if the plugin connects containers to a Linux bridge, the plugin must set the net/bridge/bridge-nf-call-iptables sysctl to 1 to ensure that the iptables proxy functions correctly. If the plugin does not use a Linux bridge, but uses something like Open vSwitch or some other mechanism instead, it should ensure container traffic is appropriately routed for the proxy.

By default, if no kubelet network plugin is specified, the noop plugin is used, which sets net/bridge/bridge-nf-call-iptables=1 to ensure simple configurations (like Docker with a bridge) work correctly with the iptables proxy.

Loopback CNI

In addition to the CNI plugin installed on the nodes for implementing the Kubernetes network model, Kubernetes also requires the container runtimes to provide a loopback interface lo, which is used for each sandbox (pod sandboxes, vm sandboxes, ...). Implementing the loopback interface can be accomplished by re-using the CNI loopback plugin. or by developing your own code to achieve this (see this example from CRI-O).

Support hostPort

The CNI networking plugin supports hostPort. You can use the official portmap plugin offered by the CNI plugin team or use your own plugin with portMapping functionality.

If you want to enable hostPort support, you must specify portMappings capability in your cni-conf-dir. For example:

{
  "name": "k8s-pod-network",
  "cniVersion": "0.4.0",
  "plugins": [
    {
      "type": "calico",
      "log_level": "info",
      "datastore_type": "kubernetes",
      "nodename": "127.0.0.1",
      "ipam": {
        "type": "host-local",
        "subnet": "usePodCidr"
      },
      "policy": {
        "type": "k8s"
      },
      "kubernetes": {
        "kubeconfig": "/etc/cni/net.d/calico-kubeconfig"
      }
    },
    {
      "type": "portmap",
      "capabilities": {"portMappings": true}
    }
  ]
}

Support traffic shaping

Experimental Feature

The CNI networking plugin also supports pod ingress and egress traffic shaping. You can use the official bandwidth plugin offered by the CNI plugin team or use your own plugin with bandwidth control functionality.

If you want to enable traffic shaping support, you must add the bandwidth plugin to your CNI configuration file (default /etc/cni/net.d) and ensure that the binary is included in your CNI bin dir (default /opt/cni/bin).

{
  "name": "k8s-pod-network",
  "cniVersion": "0.4.0",
  "plugins": [
    {
      "type": "calico",
      "log_level": "info",
      "datastore_type": "kubernetes",
      "nodename": "127.0.0.1",
      "ipam": {
        "type": "host-local",
        "subnet": "usePodCidr"
      },
      "policy": {
        "type": "k8s"
      },
      "kubernetes": {
        "kubeconfig": "/etc/cni/net.d/calico-kubeconfig"
      }
    },
    {
      "type": "bandwidth",
      "capabilities": {"bandwidth": true}
    }
  ]
}

Now you can add the kubernetes.io/ingress-bandwidth and kubernetes.io/egress-bandwidth annotations to your pod. For example:

apiVersion: v1
kind: Pod
metadata:
  annotations:
    kubernetes.io/ingress-bandwidth: 1M
    kubernetes.io/egress-bandwidth: 1M
...

What's next

13.2.2 - Device Plugins

Device plugins let you configure your cluster with support for devices or resources that require vendor-specific setup, such as GPUs, NICs, FPGAs, or non-volatile main memory.

FEATURE STATE: Kubernetes v1.10 [beta]

Kubernetes provides a device plugin framework that you can use to advertise system hardware resources to the Kubelet.

Instead of customizing the code for Kubernetes itself, vendors can implement a device plugin that you deploy either manually or as a DaemonSet. The targeted devices include GPUs, high-performance NICs, FPGAs, InfiniBand adapters, and other similar computing resources that may require vendor specific initialization and setup.

Device plugin registration

The kubelet exports a Registration gRPC service:

service Registration {
	rpc Register(RegisterRequest) returns (Empty) {}
}

A device plugin can register itself with the kubelet through this gRPC service. During the registration, the device plugin needs to send:

The name of its Unix socket.
The Device Plugin API version against which it was built.
The ResourceName it wants to advertise. Here ResourceName needs to follow the extended resource naming scheme as vendor-domain/resourcetype. (For example, an NVIDIA GPU is advertised as nvidia.com/gpu.)

Following a successful registration, the device plugin sends the kubelet the list of devices it manages, and the kubelet is then in charge of advertising those resources to the API server as part of the kubelet node status update. For example, after a device plugin registers hardware-vendor.example/foo with the kubelet and reports two healthy devices on a node, the node status is updated to advertise that the node has 2 "Foo" devices installed and available.

Then, users can request devices as part of a Pod specification (see container). Requesting extended resources is similar to how you manage requests and limits for other resources, with the following differences:

Extended resources are only supported as integer resources and cannot be overcommitted.
Devices cannot be shared between containers.

Example

Suppose a Kubernetes cluster is running a device plugin that advertises resource hardware-vendor.example/foo on certain nodes. Here is an example of a pod requesting this resource to run a demo workload:

---
apiVersion: v1
kind: Pod
metadata:
  name: demo-pod
spec:
  containers:
    - name: demo-container-1
      image: registry.k8s.io/pause:2.0
      resources:
        limits:
          hardware-vendor.example/foo: 2
#
# This Pod needs 2 of the hardware-vendor.example/foo devices
# and can only schedule onto a Node that's able to satisfy
# that need.
#
# If the Node has more than 2 of those devices available, the
# remainder would be available for other Pods to use.

Device plugin implementation

The general workflow of a device plugin includes the following steps:

Initialization. During this phase, the device plugin performs vendor specific initialization and setup to make sure the devices are in a ready state.

The plugin starts a gRPC service, with a Unix socket under host path /var/lib/kubelet/device-plugins/, that implements the following interfaces:

service DevicePlugin {
      // GetDevicePluginOptions returns options to be communicated with Device Manager.
      rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {}

      // ListAndWatch returns a stream of List of Devices
      // Whenever a Device state change or a Device disappears, ListAndWatch
      // returns the new list
      rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {}

      // Allocate is called during container creation so that the Device
      // Plugin can run device specific operations and instruct Kubelet
      // of the steps to make the Device available in the container
      rpc Allocate(AllocateRequest) returns (AllocateResponse) {}

      // GetPreferredAllocation returns a preferred set of devices to allocate
      // from a list of available ones. The resulting preferred allocation is not
      // guaranteed to be the allocation ultimately performed by the
      // devicemanager. It is only designed to help the devicemanager make a more
      // informed allocation decision when possible.
      rpc GetPreferredAllocation(PreferredAllocationRequest) returns (PreferredAllocationResponse) {}

      // PreStartContainer is called, if indicated by Device Plugin during registeration phase,
      // before each container start. Device plugin can run device specific operations
      // such as resetting the device before making devices available to the container.
      rpc PreStartContainer(PreStartContainerRequest) returns (PreStartContainerResponse) {}
}

Note: Plugins are not required to provide useful implementations for GetPreferredAllocation() or PreStartContainer(). Flags indicating which (if any) of these calls are available should be set in the DevicePluginOptions message sent back by a call to GetDevicePluginOptions(). The kubelet will always call GetDevicePluginOptions() to see which optional functions are available, before calling any of them directly.

The plugin registers itself with the kubelet through the Unix socket at host path /var/lib/kubelet/device-plugins/kubelet.sock.
After successfully registering itself, the device plugin runs in serving mode, during which it keeps monitoring device health and reports back to the kubelet upon any device state changes. It is also responsible for serving Allocate gRPC requests. During Allocate, the device plugin may do device-specific preparation; for example, GPU cleanup or QRNG initialization. If the operations succeed, the device plugin returns an AllocateResponse that contains container runtime configurations for accessing the allocated devices. The kubelet passes this information to the container runtime.

Handling kubelet restarts

A device plugin is expected to detect kubelet restarts and re-register itself with the new kubelet instance. In the current implementation, a new kubelet instance deletes all the existing Unix sockets under /var/lib/kubelet/device-plugins when it starts. A device plugin can monitor the deletion of its Unix socket and re-register itself upon such an event.

Device plugin deployment

You can deploy a device plugin as a DaemonSet, as a package for your node's operating system, or manually.

The canonical directory /var/lib/kubelet/device-plugins requires privileged access, so a device plugin must run in a privileged security context. If you're deploying a device plugin as a DaemonSet, /var/lib/kubelet/device-plugins must be mounted as a Volume in the plugin's PodSpec.

If you choose the DaemonSet approach you can rely on Kubernetes to: place the device plugin's Pod onto Nodes, to restart the daemon Pod after failure, and to help automate upgrades.

API compatibility

Kubernetes device plugin support is in beta. The API may change before stabilization, in incompatible ways. As a project, Kubernetes recommends that device plugin developers:

Watch for changes in future releases.
Support multiple versions of the device plugin API for backward/forward compatibility.

If you enable the DevicePlugins feature and run device plugins on nodes that need to be upgraded to a Kubernetes release with a newer device plugin API version, upgrade your device plugins to support both versions before upgrading these nodes. Taking that approach will ensure the continuous functioning of the device allocations during the upgrade.

Monitoring device plugin resources

FEATURE STATE: Kubernetes v1.15 [beta]

In order to monitor resources provided by device plugins, monitoring agents need to be able to discover the set of devices that are in-use on the node and obtain metadata to describe which container the metric should be associated with. Prometheus metrics exposed by device monitoring agents should follow the Kubernetes Instrumentation Guidelines, identifying containers using pod, namespace, and container prometheus labels.

The kubelet provides a gRPC service to enable discovery of in-use devices, and to provide metadata for these devices:

// PodResourcesLister is a service provided by the kubelet that provides information about the
// node resources consumed by pods and containers on the node
service PodResourcesLister {
    rpc List(ListPodResourcesRequest) returns (ListPodResourcesResponse) {}
    rpc GetAllocatableResources(AllocatableResourcesRequest) returns (AllocatableResourcesResponse) {}
}

`List` gRPC endpoint

The List endpoint provides information on resources of running pods, with details such as the id of exclusively allocated CPUs, device id as it was reported by device plugins and id of the NUMA node where these devices are allocated. Also, for NUMA-based machines, it contains the information about memory and hugepages reserved for a container.

// ListPodResourcesResponse is the response returned by List function
message ListPodResourcesResponse {
    repeated PodResources pod_resources = 1;
}

// PodResources contains information about the node resources assigned to a pod
message PodResources {
    string name = 1;
    string namespace = 2;
    repeated ContainerResources containers = 3;
}

// ContainerResources contains information about the resources assigned to a container
message ContainerResources {
    string name = 1;
    repeated ContainerDevices devices = 2;
    repeated int64 cpu_ids = 3;
    repeated ContainerMemory memory = 4;
}

// ContainerMemory contains information about memory and hugepages assigned to a container
message ContainerMemory {
    string memory_type = 1;
    uint64 size = 2;
    TopologyInfo topology = 3;
}

// Topology describes hardware topology of the resource
message TopologyInfo {
        repeated NUMANode nodes = 1;
}

// NUMA representation of NUMA node
message NUMANode {
        int64 ID = 1;
}

// ContainerDevices contains information about the devices assigned to a container
message ContainerDevices {
    string resource_name = 1;
    repeated string device_ids = 2;
    TopologyInfo topology = 3;
}

Note:

cpu_ids in the ContainerResources in the List endpoint correspond to exclusive CPUs allocated to a partilar container. If the goal is to evaluate CPUs that belong to the shared pool, the List endpoint needs to be used in conjunction with the GetAllocatableResources endpoint as explained below:

Call GetAllocatableResources to get a list of all the allocatable CPUs
Call GetCpuIds on all ContainerResources in the system
Subtract out all of the CPUs from the GetCpuIds calls from the GetAllocatableResources call

`GetAllocatableResources` gRPC endpoint

FEATURE STATE: Kubernetes v1.23 [beta]

GetAllocatableResources provides information on resources initially available on the worker node. It provides more information than kubelet exports to APIServer.

Note: GetAllocatableResources should only be used to evaluate allocatable resources on a node. If the goal is to evaluate free/unallocated resources it should be used in conjunction with the List() endpoint. The result obtained by GetAllocatableResources would remain the same unless the underlying resources exposed to kubelet change. This happens rarely but when it does (for example: hotplug/hotunplug, device health changes), client is expected to call GetAlloctableResources endpoint. However, calling GetAllocatableResources endpoint is not sufficient in case of cpu and/or memory update and Kubelet needs to be restarted to reflect the correct resource capacity and allocatable.

// AllocatableResourcesResponses contains informations about all the devices known by the kubelet
message AllocatableResourcesResponse {
    repeated ContainerDevices devices = 1;
    repeated int64 cpu_ids = 2;
    repeated ContainerMemory memory = 3;
}

Starting from Kubernetes v1.23, the GetAllocatableResources is enabled by default. You can disable it by turning off the KubeletPodResourcesGetAllocatable feature gate.

Preceding Kubernetes v1.23, to enable this feature kubelet must be started with the following flag:

--feature-gates=KubeletPodResourcesGetAllocatable=true

ContainerDevices do expose the topology information declaring to which NUMA cells the device is affine. The NUMA cells are identified using a opaque integer ID, which value is consistent to what device plugins report when they register themselves to the kubelet.

The gRPC service is served over a unix socket at /var/lib/kubelet/pod-resources/kubelet.sock. Monitoring agents for device plugin resources can be deployed as a daemon, or as a DaemonSet. The canonical directory /var/lib/kubelet/pod-resources requires privileged access, so monitoring agents must run in a privileged security context. If a device monitoring agent is running as a DaemonSet, /var/lib/kubelet/pod-resources must be mounted as a Volume in the device monitoring agent's PodSpec.

Support for the PodResourcesLister service requires KubeletPodResources feature gate to be enabled. It is enabled by default starting with Kubernetes 1.15 and is v1 since Kubernetes 1.20.

Device plugin integration with the Topology Manager

FEATURE STATE: Kubernetes v1.18 [beta]

The Topology Manager is a Kubelet component that allows resources to be co-ordinated in a Topology aligned manner. In order to do this, the Device Plugin API was extended to include a TopologyInfo struct.

message TopologyInfo {
    repeated NUMANode nodes = 1;
}

message NUMANode {
    int64 ID = 1;
}

Device Plugins that wish to leverage the Topology Manager can send back a populated TopologyInfo struct as part of the device registration, along with the device IDs and the health of the device. The device manager will then use this information to consult with the Topology Manager and make resource assignment decisions.

TopologyInfo supports setting a nodes field to either nil or a list of NUMA nodes. This allows the Device Plugin to advertise a device that spans multiple NUMA nodes.

Setting TopologyInfo to nil or providing an empty list of NUMA nodes for a given device indicates that the Device Plugin does not have a NUMA affinity preference for that device.

An example TopologyInfo struct populated for a device by a Device Plugin:

pluginapi.Device{ID: "25102017", Health: pluginapi.Healthy, Topology:&pluginapi.TopologyInfo{Nodes: []*pluginapi.NUMANode{&pluginapi.NUMANode{ID: 0,},}}}

Device plugin examples

Here are some examples of device plugin implementations:

The AMD GPU device plugin
The Intel device plugins for Intel GPU, FPGA, QAT, VPU, SGX, DSA, DLB and IAA devices
The KubeVirt device plugins for hardware-assisted virtualization
The NVIDIA GPU device plugin for Container-Optimized OS
The RDMA device plugin
The SocketCAN device plugin
The Solarflare device plugin
The SR-IOV Network device plugin
The Xilinx FPGA device plugins for Xilinx FPGA devices

What's next

Learn about scheduling GPU resources using device plugins
Learn about advertising extended resources on a node
Learn about the Topology Manager
Read about using hardware acceleration for TLS ingress with Kubernetes

13.3 - Operator pattern

Operators are software extensions to Kubernetes that make use of custom resources to manage applications and their components. Operators follow Kubernetes principles, notably the control loop.

Motivation

The operator pattern aims to capture the key aim of a human operator who is managing a service or set of services. Human operators who look after specific applications and services have deep knowledge of how the system ought to behave, how to deploy it, and how to react if there are problems.

People who run workloads on Kubernetes often like to use automation to take care of repeatable tasks. The operator pattern captures how you can write code to automate a task beyond what Kubernetes itself provides.

Operators in Kubernetes

Kubernetes is designed for automation. Out of the box, you get lots of built-in automation from the core of Kubernetes. You can use Kubernetes to automate deploying and running workloads, and you can automate how Kubernetes does that.

Kubernetes' operator pattern concept lets you extend the cluster's behaviour without modifying the code of Kubernetes itself by linking controllers to one or more custom resources. Operators are clients of the Kubernetes API that act as controllers for a Custom Resource.

An example operator

Some of the things that you can use an operator to automate include:

deploying an application on demand
taking and restoring backups of that application's state
handling upgrades of the application code alongside related changes such as database schemas or extra configuration settings
publishing a Service to applications that don't support Kubernetes APIs to discover them
simulating failure in all or part of your cluster to test its resilience
choosing a leader for a distributed application without an internal member election process

What might an operator look like in more detail? Here's an example:

A custom resource named SampleDB, that you can configure into the cluster.
A Deployment that makes sure a Pod is running that contains the controller part of the operator.
A container image of the operator code.
Controller code that queries the control plane to find out what SampleDB resources are configured.
The core of the operator is code to tell the API server how to make reality match the configured resources.
- If you add a new SampleDB, the operator sets up PersistentVolumeClaims to provide durable database storage, a StatefulSet to run SampleDB and a Job to handle initial configuration.
- If you delete it, the operator takes a snapshot, then makes sure that the StatefulSet and Volumes are also removed.
The operator also manages regular database backups. For each SampleDB resource, the operator determines when to create a Pod that can connect to the database and take backups. These Pods would rely on a ConfigMap and / or a Secret that has database connection details and credentials.
Because the operator aims to provide robust automation for the resource it manages, there would be additional supporting code. For this example, code checks to see if the database is running an old version and, if so, creates Job objects that upgrade it for you.

Deploying operators

The most common way to deploy an operator is to add the Custom Resource Definition and its associated Controller to your cluster. The Controller will normally run outside of the control plane, much as you would run any containerized application. For example, you can run the controller in your cluster as a Deployment.

Using an operator

Once you have an operator deployed, you'd use it by adding, modifying or deleting the kind of resource that the operator uses. Following the above example, you would set up a Deployment for the operator itself, and then:

kubectl get SampleDB                   # find configured databases

kubectl edit SampleDB/example-database # manually change some settings

…and that's it! The operator will take care of applying the changes as well as keeping the existing service in good shape.

Writing your own operator

If there isn't an operator in the ecosystem that implements the behavior you want, you can code your own.

You also implement an operator (that is, a Controller) using any language / runtime that can act as a client for the Kubernetes API.

Following are a few libraries and tools you can use to write your own cloud native operator.

Charmed Operator Framework
Java Operator SDK
Kopf (Kubernetes Operator Pythonic Framework)
kube-rs (Rust)
kubebuilder
KubeOps (.NET operator SDK)
KUDO (Kubernetes Universal Declarative Operator)
Metacontroller along with WebHooks that you implement yourself
Operator Framework
shell-operator

What's next

Read the CNCF Operator White Paper.
Learn more about Custom Resources
Find ready-made operators on OperatorHub.io to suit your use case
Publish your operator for other people to use
Read CoreOS' original article that introduced the operator pattern (this is an archived version of the original article).
Read an article from Google Cloud about best practices for building operators