Kubernetes Sudden CPU or Memory Spike Troubleshooting

Why this matters

Unexpected CPU or memory spikes can degrade latency, trigger throttling, or cause OOM kills. On shared clusters, a single noisy workload can starve other services and cause cascading failures.

Symptoms

• Monitoring shows a sharp increase in CPU or memory usage for a workload, namespace, or node.
• Pods are being OOMKilled in container status.
• Requests are being throttled (CPU throttling, higher latency).
• HPA scales pods up aggressively or cannot scale further due to node limits.

Common root causes

• Recent code change introducing heavier computation or inefficient queries.
• Increased traffic or new background jobs enabled.
• Resource requests/limits set too low or too high relative to real usage.
• Memory leaks or unbounded caches in the application.
• Noisy neighbor effect from another workload on the same node.

How KubeGraf helps

• Surfaces per-workload and per-node resource usage in one place.
• Topology/resource views help you see which namespaces and workloads are generating load.
• Links pods to their nodes so you can spot noisy neighbors.
• Incident timeline can show correlation between deploys, config changes, and resource spikes.

Step-by-step using KubeGraf UI

1. Identify the scope of the spike

• Start from your external monitoring alert (service, namespace, or node).
• Open KubeGraf and select the relevant cluster.
• Use the Topology or Resource Map view to find the namespace and workload with elevated CPU/memory.

2. Inspect workload-level metrics

• Select the suspect Deployment/StatefulSet.
• Check resource usage panels (if available): requests vs actual usage, memory vs OOM events.
• Note whether HPA is scaling as expected.

3. Drill into pods and nodes

• From the workload, list pods and their nodes.
• Look for pods with Reason=OOMKilled or high CPU utilization.
• If a single node is hot, inspect other workloads on that node for noisy neighbors.

4. Correlate with recent changes

• Open the Incident Timeline for the workload/namespace.
• Look for events near the start of the spike: new Deployment rollout, config changes, HPA/limit updates.

5. Decide on immediate mitigation

• Short-term actions might include:

• Temporarily scaling replicas horizontally if capacity exists.
• Raising memory limits slightly to stop OOM churn (only if you have node headroom).
• Reducing concurrency or disabling heavy background jobs via config.

6. Plan and apply a proper fix

• If usage jump aligns with a new release, work with devs to profile and fix the regression.
• If limits are unrealistic, adjust requests/limits based on observed usage.
• If a noisy neighbor is the issue, consider rebalancing workloads or adjusting node pools.

What to check next

• Are any other workloads on the same node under pressure?
• Is the cluster close to overall capacity (node CPU/memory pressure)?
• Are HPA policies and target metrics configured sanely for this workload?

Common mistakes

• Only raising limits without understanding the root cause (masking a memory leak).
• Ignoring node-level saturation and focusing only on one Deployment.
• Changing limits directly in the cluster rather than updating the declarative spec.
• Treating a one-off spike as normal and not checking if it repeats.

Related issues

• Deployment rollout stuck / ProgressDeadlineExceeded
• CrashLoopBackOff Kubernetes debugging
• "It was working yesterday" incidents

Expected outcome

After following this playbook you should:

• Identify which workload(s) are responsible for the spike and on which nodes.
• Have an immediate mitigation (scaling or config change) and a plan for a longer-term fix.
• See resource usage return to a stable baseline that matches limits and capacity.

[ TODO: screenshot showing KubeGraf resource map with a hot workload/node highlighted. ]