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Pod Overview

This page provides an overview of Pod, the smallest deployable object in the Kubernetes object model.

Understanding Pods

A Pod is the basic execution unit of a Kubernetes application--the smallest and simplest unit in the Kubernetes object model that you create or deploy. A Pod represents processes running on your clusterA set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node. .

A Pod encapsulates an application's container (or, in some cases, multiple containers), storage resources, a unique network identity (IP address), as well as options that govern how the container(s) should run. A Pod represents a unit of deployment: a single instance of an application in Kubernetes, which might consist of either a single containerA lightweight and portable executable image that contains software and all of its dependencies. or a small number of containers that are tightly coupled and that share resources.

Docker is the most common container runtime used in a Kubernetes Pod, but Pods support other container runtimes as well.

Pods in a Kubernetes cluster can be 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, and Kubernetes manages the Pods rather than the containers directly.
  • Pods that run multiple containers that need to work together. A Pod might encapsulate an application composed of multiple co-located containers that are tightly coupled and need to share resources. These co-located containers might form a single cohesive unit of service--one container serving files from a shared volume to the public, while a separate "sidecar" container refreshes or updates those files. The Pod wraps these containers and storage resources together as a single manageable entity.

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_A control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. . See Pods and controllers for more information on how Kubernetes uses controllers to implement workload scaling and 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.

Note that 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. 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:

example pod diagram

Some Pods have init containersOne or more initialization containers that must run to completion before any app containers run. as well as app containersA container used to run part of a workload. Compare with init container. . Init containers run and complete before the app containers are started.

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


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. Containers inside a 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).


A Pod can specify a set of shared storage volumesA directory containing data, accessible to the containers in a pod. . 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 Volumes for more information on how Kubernetes implements shared storage in a Pod.

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_A control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. ), it is scheduled to run on a NodeA node is a worker machine in Kubernetes. in your cluster. The Pod remains on that node until the process is terminated, 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 a container. A Pod persists until it is deleted.

Pods do not, by themselves, self-heal. If a Pod is scheduled to a Node that fails, or if the scheduling operation itself 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. Thus, while it is possible to use Pod directly, it's far more common in Kubernetes to manage your pods using a controller.

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 workloadA workload is an application running on Kubernetes. 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
  name: hello
    # This is the pod template
      - name: hello
        image: busybox
        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 effect on the Pods that already exist. Pods do not receive template updates directly; instead, a new Pod is created to match the revised pod template.

For example, a Deployment controller ensures that the running Pods match the current pod template. If the template is updated, the controller has to remove the existing Pods and create new Pods based on the updated template. Each workload controller implements its own rules for handling changes to the Pod template.

On Nodes, the kubeletAn agent that runs on each node in the cluster. It makes sure that containers are running in a pod. 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.

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