Run Chaos Experiment

Inject controlled failures to test and improve system resilience.

When to Use

• Before major product launches (load testing)
• After architecture changes (validate resilience)
• During GameDays or disaster recovery drills
• To validate assumptions about failure modes
• As part of SRE maturity program

Inputs

• Required: Kubernetes cluster (for Litmus or Chaos Mesh)
• Required: Steady-state definition (what "normal" looks like)
• Required: Hypothesis to test (e.g., "API stays available if one pod crashes")
• Optional: Observability stack (Prometheus, Grafana) to measure impact
• Optional: Rollback plan

Procedure

Step 1: Define Steady State and Hypothesis

Document normal system behavior:

## Steady State Definition

### Service: API Gateway
- **Availability**: 99.9% (< 0.1% error rate)
- **Latency**: p95 < 200ms
- **Throughput**: 1000 req/s
- **Dependencies**: Database (Postgres), Cache (Redis), Auth Service

### Metrics
- `rate(http_requests_total{job="api"}[5m])`
- `histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))`
- `rate(http_requests_total{status=~"5.."}[5m])`

## Hypothesis
**"If one API pod is killed, the remaining pods will handle the load with <5s
disruption and no increase in error rate."**

### Validation Criteria
- Error rate remains <1%
- p95 latency stays <300ms (50ms grace)
- Service recovers within 5 seconds
- No cascading failures to downstream services

Expected: Clear, measurable definition of normal behavior and success criteria.

On failure: If you can't define steady state, observability is insufficient. Add metrics first.

Step 2: Set Blast Radius Limits

Scope the experiment to minimize risk:

# chaos-config.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: chaos-testing

---
# Label pods participating in chaos experiments
apiVersion: v1
kind: Pod
metadata:
  labels:
    chaos-enabled: "true"
    environment: staging  # NEVER production for first run

Set safeguards:

## Blast Radius Controls

### Environment
- **Scope**: Staging only (first 5 runs)
- **Production**: Only after 5 successful staging runs
- **Timing**: Business hours (09:00-17:00 local), never weekends/holidays

### Target Selection
- **Limit**: Max 1 pod per service
- **Percentage**: Max 25% of replicas
- **Exclusions**: Database, payment service, auth service (critical path)

### Auto-Abort Conditions
- Error rate >10% for >30 seconds
- Customer-facing alerts fire
- Manual abort signal from on-call engineer

### Rollback Plan
- Kubernetes will auto-restart killed pods
- Manual rollback: `kubectl rollout undo deployment/api`
- Incident declared if recovery takes >5 minutes

Expected: Experiment has clear boundaries, won't take down entire system.

On failure: If blast radius is too large, narrow scope. Start with one non-critical service.

Step 3: Install Chaos Mesh

Deploy Chaos Mesh (Kubernetes-native):

# Add Chaos Mesh Helm repo
helm repo add chaos-mesh https://charts.chaos-mesh.org
helm repo update

# Install Chaos Mesh in isolated namespace
helm install chaos-mesh chaos-mesh/chaos-mesh \
  --namespace chaos-mesh \
  --create-namespace \
  --set dashboard.create=true \
  --set controllerManager.replicaCount=1

# Verify installation
kubectl get pods -n chaos-mesh

# Access dashboard
kubectl port-forward -n chaos-mesh svc/chaos-dashboard 2333:2333
# Open http://localhost:2333

Alternative: Litmus (vendor-neutral):

# Install Litmus
kubectl apply -f https://litmuschaos.github.io/litmus/litmus-operator-v2.14.0.yaml

# Wait for Litmus pods
kubectl get pods -n litmus

# Install Litmus CRDs
kubectl apply -f https://hub.litmuschaos.io/api/chaos/master?file=charts/generic/experiments.yaml

Expected: Chaos Mesh or Litmus running, dashboard accessible.

On failure: Check RBAC permissions. Chaos tools need cluster-wide access.

Step 4: Create and Execute Experiment

Example: Pod Kill Experiment (Chaos Mesh):

# pod-kill-experiment.yaml
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
  name: api-pod-kill-test
  namespace: chaos-testing
spec:
  action: pod-kill
  mode: one  # Kill one pod only
  selector:
    namespaces:
      - production
    labelSelectors:
      app: api-gateway
      chaos-enabled: "true"
  duration: "30s"
  scheduler:
    cron: "@every 5m"  # Repeat every 5 minutes (for sustained testing)

Apply the experiment:

# Apply experiment
kubectl apply -f pod-kill-experiment.yaml

# Watch experiment status
kubectl get podchaos -n chaos-testing -w

# View detailed status
kubectl describe podchaos api-pod-kill-test -n chaos-testing

# Check which pods were affected
kubectl get events -n production --sort-by=.metadata.creationTimestamp | grep api-gateway

Monitor impact in Grafana:

# Error rate during experiment
rate(http_requests_total{status=~"5..", job="api"}[1m])

# Latency spike
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket{job="api"}[1m]))

# Pod restarts
rate(kube_pod_container_status_restarts_total{pod=~"api-.*"}[5m])

Expected: Pod is killed, Kubernetes restarts it, service continues with minor blip.

On failure: If error rate spikes or service degrades significantly, abort experiment and investigate.

Step 5: Analyze Results and Iterate

Create experiment report:

# Chaos Experiment Report: API Pod Kill

**Date**: 2025-02-09
**Hypothesis**: API stays available if one pod crashes
**Tool**: Chaos Mesh
**Environment**: Staging
**Duration**: 30 seconds (pod kill + recovery)

## Results

### Metrics During Experiment
- **Error Rate**: Increased from 0.1% to 2.3% (spike lasted 8 seconds)
- **p95 Latency**: Increased from 180ms to 450ms (spike lasted 12 seconds)
- **Recovery Time**: 8 seconds (pod restart + load balancer update)

### Hypothesis Outcome
**FAILED**: Error rate exceeded 1% threshold, latency spike >300ms

## Root Cause Analysis
- Load balancer continued routing to killed pod for 8 seconds (stale endpoint)
- Readiness probe set to 10s interval (too slow)
- No pre-stop hook to drain connections gracefully

## Improvements Made
1. **Reduced readiness probe interval**: 10s → 2s
2. **Added pre-stop hook**: 5-second sleep for connection draining
3. **Tuned load balancer**: Enabled faster endpoint updates

## Follow-Up Experiment
- Re-run with same parameters in 1 week
- Expected: Error rate <1%, recovery <5s

Track experiments in a log:

# chaos-experiment-log.csv
date,experiment,environment,status,error_rate_peak,recovery_time_s,outcome
2025-02-09,pod-kill-api,staging,complete,2.3%,8,failed
2025-02-16,pod-kill-api,staging,complete,0.8%,4,passed
2025-02-23,network-delay-db,staging,aborted,15%,N/A,failed

Expected: Learnings captured, fixes implemented, follow-up scheduled.

On failure: If no action is taken post-experiment, chaos engineering becomes theater. Prioritize fixes.

Step 6: Graduate to Production (Carefully)

Once staging experiments pass consistently:

# Production pod-kill experiment (more conservative)
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
  name: api-pod-kill-prod
  namespace: chaos-testing
spec:
  action: pod-kill
  mode: one
  selector:
    namespaces:
      - production
    labelSelectors:
      app: api-gateway
      chaos-enabled: "true"
  duration: "10s"  # Shorter than staging
  scheduler:
    cron: "0 10 * * 2"  # Tuesdays at 10 AM only (predictable, low-risk time)

Production safeguards:

# Create a kill switch for production chaos
kubectl create configmap chaos-killswitch \
  -n chaos-testing \
  --from-literal=enabled=true

# Update experiments to check kill switch
# (implementation depends on chaos tool)

Expected: Production experiments run during low-risk windows, with kill switch ready.

On failure: If production experiment causes incident, disable immediately and post-mortem.

Validation

• Steady state and hypothesis clearly defined
• Blast radius limited (environment, scope, timing)
• Chaos tool (Chaos Mesh or Litmus) installed and tested
• Experiment runs successfully in staging
• Results documented with metrics and analysis
• Improvements implemented based on findings
• Follow-up experiment validates fixes
• Production experiments run only after 5+ staging successes

Common Pitfalls

• No hypothesis: Running chaos "to see what happens" wastes time. Always have a hypothesis.
• Too broad scope: Killing all pods at once tests disaster recovery, not resilience. Start small.
• Production-first: Never run first experiment in production. Staging first, always.
• Ignoring results: Chaos without action is theater. Fix what you learn.
• Alert fatigue: Chaos experiments trigger alerts. Annotate Grafana or silence expected alerts.
• No abort plan: If experiment goes wrong, you need a kill switch. Have it ready.

Related Skills

• setup-prometheus-monitoring - metrics to measure experiment impact
• configure-alerting-rules - alerts that fire during chaos (expected)
• define-slo-sli-sla - steady state tied to SLOs