Cadvisor consume large cpu: How to reduce it

General
1

If you’re experiencing high CPU usage with Cadvisor, there are several steps you can take to diagnose and resolve
the issue:

  1. Identify Specific Containers or Pods:

    • Determine which specific containers or pods within your Kubernetes cluster have unusually high resource
      consumption.

  2. Review Resource Limits:

    • Check if any of these resources exceed their defined CPU limits.
    # Example Pod definition with limited CPU usage:
apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  containers:
  - name: my-container
    image: myimage:v1.0.0
    resources:
      limits:
        cpu: "200m"

  3. Analyze CPU Metrics Using Prometheus and Grafana (with Cadvisor):

    • Create custom dashboards in Grafana to visualize the resource usage.
    • Identify trends or spikes that might be causing high consumption.

  4. Optimize Application Code/Containers:

    • Review your application code for any inefficiencies, such as unnecessary loops, excessive logging at runtime,
      etc.
    • Update containers and dependencies if there are known performance improvements available in newer versions of
      the software you’re using within those containers (e.g., updating Docker images).

  5. Increase Resource Quotas or Limits:

    • If certain applications genuinely require more resources to function correctly without impacting others
      negatively:
      # Example definition with increased CPU limits for a critical application:
apiVersion: v1
kind: Pod
metadata:
  name: high-performance-pod
spec:
  containers:
  - name: my-container
    image: myimage:v2.0.0
    resources:
      requests:
        cpu: "500m"

  6. Horizontal Scaling and Load Balancing (Kubernetes):

    • Use Horizontal Pod Autoscaler to automatically scale the number of pods based on CPU usage metrics.
      # Example HPA configuration for automatic scaling:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: example-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-deployment
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 80% # Target CPU usage percentage for scaling up.

  7. Investigate and Debug Container Processes:

    • Use tools like kubectl top to see the real-time or historical data on container resources consumed by each
      pod/container.

  8. Use Kubernetes Logs and Events (for troubleshooting):

    kubectl describe pods <pod-name>

  9. Monitor for Resource Contention:

    • Identify if other containers are competing with high-priority services, which might be leading to resource
      contention issues.

  10. Consult Documentation/Support from Vendor/Product Maintainers (if applicable):

    • Sometimes specific applications or container images have known performance characteristics that can guide
      you towards optimizations.

By following these steps systematically and analyzing the collected data carefully through Grafana dashboards
powered by Prometheus metrics, Cadvisor will be able to provide insights into why certain containers are consuming
high CPU resources. Adjust your resource limits/requests based on what is found in such analysis while ensuring
overall system stability remains intact.