How I Solved Kubernetes Pods Stuck in "Pending" State

Introduction:

Kubernetes has become the go-to platform for managing containerized applications. It automates the deployment, scaling, and management of containerized applications across a cluster of machines. However, even with Kubernetes' powerful features, things don’t always go smoothly.

In this post, I’ll walk you through a scenario where some of my Kubernetes pods were stuck in the “Pending” state and wouldn’t get scheduled to any nodes in the cluster. This problem can be frustrating, especially when you need your application to be up and running. Through some troubleshooting and adjustments, I was able to identify and resolve the issue. I’ll share the steps I took to get the pods scheduled and deployed successfully.

Issue I Faced:

While working with a Kubernetes cluster, I noticed that some of my pods were stuck in the “Pending” state, and no matter how long I waited, they wouldn’t transition to the "Running" state. This was problematic, as these pods were critical to the application’s deployment.

The issue wasn’t immediately clear. Kubernetes provides a lot of information in its pod descriptions, but nothing jumped out as an error. I could see that the pods weren’t being scheduled to any nodes, but it wasn’t obvious why they were stuck in the Pending state.

What Wasn’t Obvious:

At first, I checked the status of the pods and found they were stuck in the Pending state with no specific error messages. The default assumption was that there might be a node issue or that Kubernetes was unable to find an appropriate node for the pod. However, the root cause wasn’t immediately clear. I suspected it could be related to resource allocation or node configuration, but there were no clear indications from the logs.

Troubleshooting Process:

1. Checked Pod Descriptions with kubectl describe:

   The first thing I did was check the pod details using the kubectl describe pod command. This command provides in-depth information about the pod, including events and resource requests.

    kubectl describe pod <pod-name>
   

   In the pod description, I noticed that the “Events” section showed a message indicating that there were insufficient resources in the cluster to schedule the pod. Kubernetes wasn’t able to find a node that had the required CPU and memory resources available to run the pod.

2. Analyzed Cluster Resources:

   After realizing that the issue was related to resource availability, I checked the resources in the cluster using the following command:

    kubectl get nodes
   

   This command provided an overview of the nodes in the cluster, along with their available CPU and memory resources. I discovered that some of the nodes were already running other high-demand workloads, leaving them with insufficient resources to accommodate the new pods.

3. Scaled the Cluster by Adding More Nodes:

   Since resource availability was the key issue, the next step was to scale the cluster by adding more nodes. By increasing the number of nodes in the cluster, I was able to provide more capacity for new pods. After scaling the cluster, I checked the available resources again and found that the new nodes had enough resources to run the pending pods.

4. Adjusted Pod Specifications to Use Node Affinity:

   I also noticed that the pod specifications were too generic, and Kubernetes wasn’t able to place the pods on the most suitable nodes. To improve pod scheduling, I used node affinity, which allows you to constrain which nodes your pods are eligible to be scheduled based on labels assigned to the nodes.

   I added node affinity to the pod specification to ensure that the pods would be scheduled to nodes with the required resources and specific labels:

    apiVersion: v1
    kind: Pod
    metadata:
      name: my-app-pod
    spec:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: "kubernetes.io/hostname"
                operator: In
                values:
                - node-1
                - node-2
      containers:
      - name: my-container
        image: my-image
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1"
   

   With node affinity, the pods were specifically scheduled on the nodes I designated, ensuring they would run on the most appropriate machines with enough resources.

5. Optimized Resource Requests:

   Another step I took was to review and adjust the resource requests and limits for the pods. Kubernetes uses resource requests to determine if a node has enough capacity to schedule a pod. If the pod requests too many resources, it may be left in the Pending state if there aren’t enough available resources in the cluster.

   I optimized the resource requests by aligning them with the actual capacity available on the nodes:

    resources:
      requests:
        cpu: "250m"
        memory: "256Mi"
      limits:
        cpu: "500m"
        memory: "512Mi"
   

   By adjusting the requests and limits to more realistic values, Kubernetes had a better chance of scheduling the pods on available nodes.

Resolution:

1. Scaled the Cluster and Added More Nodes:

   After adding additional nodes to the cluster, I was able to provide more resources for the pending pods, which helped resolve the issue of insufficient resources.

2. Applied Node Affinity:

   By adjusting the pod specifications and adding node affinity, I ensured that the pods would be scheduled on nodes that were better suited for their resource needs, improving the overall efficiency of the pod scheduling process.

3. Optimized Resource Requests and Limits:

   Finally, optimizing the resource requests and limits allowed Kubernetes to schedule the pods more efficiently. By fine-tuning the resource requirements, I ensured that the pods wouldn’t be left pending due to unrealistic resource requests.

Key Takeaways:

Here are the main lessons I learned from troubleshooting this issue:

• Ensure Sufficient Resources in the Cluster: Always check the available resources across your cluster before deploying new pods. If your cluster doesn’t have enough resources, consider scaling it by adding more nodes.

• Use Node Affinity for Better Scheduling: Node affinity allows you to control where your pods are scheduled based on node labels. This is particularly useful for ensuring that pods are scheduled on nodes with specific resources or characteristics.

• Adjust Resource Requests and Limits: Be mindful of the resource requests and limits you set for your pods. Setting requests too high can prevent pods from being scheduled, while setting limits too low can lead to resource starvation.

• Monitor Cluster Health Regularly: Continuously monitor your cluster’s resource utilization to avoid running into scheduling issues like this in the future. Tools like kubectl top nodes and kubectl top pods can give you valuable insights.

Conclusion:

Kubernetes is a powerful tool for managing containerized applications, but issues like pods getting stuck in the Pending state can still occur if resources aren’t properly managed. By scaling the cluster, using node affinity, and optimizing resource requests and limits, I was able to get the pods scheduled successfully and continue with the deployment.

If you've faced similar issues with Kubernetes scheduling or have any tips for managing resources in your clusters, feel free to share your experiences in the comments below. Let's continue learning and improving our Kubernetes workflows together!