Observability Integration

Correlating Chaos Experiments with Prometheus/Grafana Metrics

Chaos engineering experiments with Gremlin can be effectively monitored and analyzed using Prometheus and Grafana. By integrating these tools, you can visualize real-time metrics during chaos events, identify anomalies, and validate system resilience. This section explains how to set up observability pipelines, create dashboards, and correlate chaos experiments with metric changes.

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1. Prerequisites and Tooling Setup

Before correlating chaos with observability tools, ensure:
- Prometheus is installed and configured to scrape metrics from your Kubernetes cluster (e.g., kube-state-metrics, node exporter, or custom exporters).
- Grafana is set up to connect to Prometheus as a data source.
- Gremlin is deployed and configured to inject chaos experiments.

Example: Install kube-state-metrics

kubectl apply -f https://github.com/kubernetes/kube-state-metrics/releases/download/v2.10.0/kube-state-metrics.yaml

Example: Configure Prometheus to scrape metrics

# prometheus.yml
scrape_configs:
  - job_name: 'kube-state-metrics'
    static_configs:
      - targets: ['kube-state-metrics.default.svc.cluster.local:8080']

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2. Running Chaos Experiments and Monitoring Metrics

When a Gremlin experiment is active, use Grafana dashboards to track metric changes. For example:
- CPU/Memory Usage: Monitor resource exhaustion during pod termination.
- Latency/Errors: Track API response times or error rates during network chaos.
- Service Availability: Check endpoint availability after latency injection.

Example: Gremlin Experiment to Inject Latency

gremlin experiment create --type latency --target "http://my-service:8080" --duration 60 --latency 500

Example: Grafana Dashboard Query for Latency

avg_over_time(http_request_duration_seconds_bucket{job="http-server"}[1m])

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3. Correlating Chaos with Metrics Using Labels

Gremlin experiments can be tagged with labels (e.g., experiment_name, target) to filter metrics in Prometheus. For example:
- Use experiment_name="latency-test" to isolate metrics from a specific chaos experiment.
- Combine with job="http-server" to focus on relevant endpoints.

Example: Prometheus Query with Labels

sum(rate(http_request_duration_seconds_count{job="http-server", experiment_name="latency-test"}[1m]))

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4. Visualizing Chaos Impact in Grafana

Create Grafana dashboards to visualize metric changes during chaos events:
1. Real-Time Metrics: Use panels to show CPU, memory, and latency trends.
2. Alerts: Configure alerts for thresholds (e.g., error rate > 5%).
3. Correlation: Overlay chaos experiment timestamps with metric spikes using Grafana’s timeline feature.

Example: Grafana Dashboard JSON (Simplified)

{
  "panels": [
    {
      "type": "graph",
      "title": "Latency During Chaos",
      "gridPos": { "h": 1, "w": 12, "x": 0, "y": 0 },
      "targets": [
        {
          "expr": "avg(http_request_duration_seconds{job=\"http-server\"})",
          "refId": "A"
        }
      ]
    }
  ]
}

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5. Post-Experiment Analysis and Incident Management

After a chaos experiment, use Grafana to:
- Compare pre- and post-experiment metrics.
- Identify root causes of failures (e.g., resource contention, cascading failures).
- Update SLOs or error budgets based on observed behavior.

Example: Grafana Timeline View
- Use the Timeline panel to align chaos experiment timestamps with metric anomalies.

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Key takeaways

• Integrate Prometheus to scrape metrics from your Kubernetes cluster and Gremlin experiments.
• Use Grafana dashboards to visualize real-time metrics and correlate chaos events with system behavior.
• Leverage labels in Prometheus to isolate metrics by experiment type or target.
• Combine chaos experiments with observability to validate resilience and refine SLOs.