Platform Engineers

Monitoring and Troubleshooting Performance Issues in Kubernetes Clusters

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Platform Engineers
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3 min read

Maintaining optimal performance in Kubernetes clusters is crucial for ensuring the smooth operation of containerized applications. However, the dynamic nature of Kubernetes environments, with pods scaling and workloads shifting, can introduce performance bottlenecks that require efficient monitoring and troubleshooting strategies.

This blog delves into key metrics and tools for identifying performance issues in Kubernetes clusters, empowering platform engineering teams to proactively address them.

Metrics for Performance Monitoring

  • CPU Usage: Monitor CPU utilization across pods and nodes to identify potential resource saturation. Metrics like container/cpu_usage_seconds_total and node/cpu/usage_seconds_total provide insights into CPU consumption.
# Prometheus Query for Average CPU Usage per Pod
avg(container_cpu_usage_seconds_total{container!="POD_INFRA"}[5m]) by (pod)
  • Memory Usage: Track memory consumption by pods and nodes using metrics like container/memory_usage_bytes and node/memory/MemTotalBytes. High memory usage might indicate pod resource exhaustion or memory leaks.
# Prometheus Query for Memory Usage as a Percentage of Available Memory
(container_memory_usage_bytes{container!="POD_INFRA"} / node_memory_MemTotalBytes) * 100 by (node)

  • Pod Startup Time: Analyze pod startup time using metrics like container_startup_time_seconds to identify slow pod initialization issues. Investigating slow startups can reveal container image size inefficiencies or complex initialization processes.

  • Latency: Monitor request latency for services and pods to pinpoint performance bottlenecks affecting user experience. Metrics like http_request_duration_microseconds and apiserver_latency_microseconds offer valuable insights.

# Prometheus Query for Average API Server Latency
avg(apiserver_latency_microseconds[5m])
  • Resource Requests and Limits: Ensure pods have appropriate resource requests and limits configured. Insufficient resource requests can lead to pod starvation, while exceeding limits might impact other workloads. Tools like kubectl describe pod <pod_name> can display resource requests and limits.
# Resource Requests and Limits from kubectl describe pod
resources:
  limits:
    cpu: 1000m
    memory: 2Gi
  requests:
    cpu: 500m
    memory: 1Gi
  • Node Resource Utilization: Monitor overall node resource utilization (CPU, memory, network) to identify overloaded nodes impacting pod performance. Metrics like node/cpu/usage_seconds_total and node/memory/MemTotalBytes are crucial.

Troubleshooting Techniques

  • Horizontal Pod Autoscaler (HPA): Utilize HPAs to automatically scale pods based on resource utilization. This helps prevent resource exhaustion and maintain application performance during traffic spikes.

  • Resource Profiling: Leverage tools like kube-cpuprof or heapster to profile container resource usage and identify resource bottlenecks within the application code. Analyzing CPU and memory usage patterns within containers can reveal inefficiencies requiring code optimization.

  • Cluster Logging Analysis: Inspect Kubernetes cluster logs (application and system logs) for errors, warnings, or resource exhaustion messages. Logs often provide clues about specific application issues impacting performance. Tools like Loki or Elasticsearch can facilitate centralized log collection and analysis.

  • Liveness and Readiness Probes: Implement liveness and readiness probes for pods to ensure healthy application functionality. Liveness probes monitor application health and restart unhealthy pods, while readiness probes determine if pods are ready to receive traffic.

  • Network Performance Monitoring: Monitor network metrics like bandwidth utilization, packet loss, and latency to identify network-related performance issues. Tools like netstat or network monitoring platforms can be employed.

  • Kubernetes Events: Examine Kubernetes events for pod eviction notices or errors related to resource scheduling. Events can indicate resource contention or configuration issues hindering pod placement.

# Get recent Kubernetes Events
kubectl get events

Advanced Monitoring Tools

  • Prometheus and Grafana: Utilize Prometheus as a monitoring server to collect metrics from various sources (e.g., kubelet, kube-api-server) and visualize them with Grafana dashboards. Prometheus offers a rich query language for creating customized dashboards to monitor specific metrics.

  • Kubernetes Dashboard: The Kubernetes dashboard provides a visual interface to monitor cluster health, including resource utilization, pod status, and events. While primarily for basic monitoring, it offers a good starting point for quick overviews.

  • Infrastructure Monitoring Tools: Integrate platform-specific infrastructure monitoring tools (e.g., AWS CloudWatch, Azure Monitor) with Kubernetes monitoring to gain a holistic view of resource utilization across the entire infrastructure stack.

Platform Engineering