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A Deep Dive into Kubernetes Pod Lifecycle

Table of Contents

Understanding Kubernetes Pod Lifecycle

Kubernetes Pods go through several phases between their creation and deletion. Each phase represents a broader lifecycle state of the Pod and provides insights into what’s happening under the hood. Knowing these phases in detail helps DevOps engineers and SREs efficiently troubleshoot and optimize deployments.

Phase 1: Pending

Definition: A Pod is in the Pending phase when it has been accepted by the Kubernetes API server, but one or more of its containers has not yet been scheduled onto a Node.

Example Scenario: You create a new deployment with resource limits that are too high (resources.requests.memory: 64Gi), and your cluster doesn’t have enough capacity. The pod remains in Pending as it can’t be scheduled anywhere.

Pod Creation Failures

An invalid manifest may prevent the Pod from even being created.

Fix:

  • Run kubectl apply --dry-run=client -f pod.yaml
  • Use tools like kubeval, kubectl explain, or kube-linter

Scheduling Failures

Examples:

  • nodeSelector: disktype: ssd but no nodes match
  • Pod requesting 2 CPUs, but nodes only have 1 available

Fix:

  • Ensure labels are accurate and nodes match selection
  • Scale up or optimize resource usage

Phase 2: Running

Definition: A Pod enters the Running phase once it has been scheduled to a node, and all containers have been created. At least one container is still running or is starting.

Example Scenario: You deploy a web application that uses an init container to copy static assets, followed by a main app container that starts the web server. If the init container runs successfully and the app starts, the pod will remain in Running.

Init Container Failures

Failures here delay app startup.

Example:

  • Init container tries to access a secret volume that doesn’t exist.

Fix:

  • Check logs: kubectl logs <pod> -c <init-container>

Application Container Startup Issues

Examples:

  • Misspelled image tag causes ImagePullBackOff
  • App crashes due to missing environment variable (CrashLoopBackOff)
  • Liveness probe set to port 8080, but app listens on 3000

Fix:

  • Adjust image references, container command, and probe configs

Phase 3: Succeeded

Definition: A Pod is in the Succeeded phase when all containers in the Pod have terminated successfully (i.e., exited with status code 0) and will not be restarted.

Example Scenario: You run a Kubernetes Job to back up a database. Once the backup completes and the container exits normally, the Pod status becomes Succeeded.

No remediation is needed unless the termination was unintentional (e.g., incorrect loop condition that led to early exit).

Phase 4: Failed

Definition: A Pod is in the Failed phase when all containers in the Pod have terminated, and at least one container terminated with a non-zero exit code or was terminated by the system.

Example Scenario:

  • A container tries to run a script that fails due to a missing file error
  • Memory-intensive workload exceeds the defined memory limit (OOMKilled)

Fix:

  • Use kubectl logs <pod> to inspect failures
  • Adjust memory limits and optimize code behavior

Phase 5: Unknown

Definition: A Pod is in the Unknown phase when the state of the Pod cannot be obtained, usually due to a communication error between the node and the control plane.

Example Scenario: A node crashes or becomes unreachable due to network issues. The kubelet on that node can’t report the pod status, so Kubernetes marks the pod as Unknown.

Fix:

  • Run kubectl get nodes to check node health
  • Use cloud provider metrics or logs to debug node connectivity
  • Evict or reschedule affected pods if necessary

Visual Representation

Kubernetes Pod Lifecycle

This visual outlines the five main pod phases along with detailed failure points and suggested solutions.

Additional Information and Sources

To deepen your understanding of Kubernetes Pod lifecycle and related troubleshooting, consider exploring the following resources:

If you’re running Kubernetes on a managed service like GKE, AKS, or EKS, refer to their respective docs for platform-specific behaviors around scheduling, taints, and node health checks.

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