Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
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Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
Concrete Example: Pod Failure Experiment (Litmus Chaos)
The following shows a complete experiment — from hypothesis to rollback — using Litmus Chaos on Kubernetes.
Step 1 — Define steady state and apply the experiment
# Verify baseline: p99 latency < 200ms, error rate < 0.1%
kubectl get deploy my-service -n production
kubectl top pods -n production -l app=my-service
Step 2 — Create and apply a Litmus ChaosEngine manifest
# chaos-pod-delete.yamlapiVersion:litmuschaos.io/v1alpha1kind:ChaosEnginemetadata:name:my-service-pod-deletenamespace:productionspec:appinfo:appns:productionapplabel:"app=my-service"appkind:deployment# Limit blast radius: only 1 replica at a timeengineState:activechaosServiceAccount:litmus-adminexperiments:-name:pod-deletespec:components:env:-name:TOTAL_CHAOS_DURATIONvalue:"60"# seconds-name:CHAOS_INTERVALvalue:"20"# delete one pod every 20s-name:FORCEvalue:"false"-name:PODS_AFFECTED_PERCvalue:"33"# max 33% of replicas affected
# Apply the experiment
kubectl apply -f chaos-pod-delete.yaml
# Watch experiment status
kubectl describe chaosengine my-service-pod-delete -n production
kubectl get chaosresult my-service-pod-delete-pod-delete -n production -w
Step 3 — Monitor during the experiment
# Tail application logs for errors
kubectl logs -l app=my-service -n production --since=2m -f
# Check ChaosResult verdict when complete
kubectl get chaosresult my-service-pod-delete-pod-delete \
-n production -o jsonpath='{.status.experimentStatus.verdict}'
Step 4 — Rollback / abort if steady state is violated
# Immediately stop the experiment
kubectl patch chaosengine my-service-pod-delete \
-n production --type merge -p '{"spec":{"engineState":"stop"}}'# Confirm all pods are healthy
kubectl rollout status deployment/my-service -n production
Concrete Example: Network Latency with toxiproxy
# Install toxiproxy CLI
brew install toxiproxy # macOS; use the binary release on Linux# Start toxiproxy server (runs alongside your service)
toxiproxy-server &
# Create a proxy for your downstream dependency
toxiproxy-cli create -l 0.0.0.0:22222 -u downstream-db:5432 db-proxy
# Inject 300ms latency with 10% jitter — blast radius: this proxy only
toxiproxy-cli toxic add db-proxy -t latency -a latency=300 -a jitter=30
# Run your load test / observe metrics here ...# Remove the toxic to restore normal behaviour
toxiproxy-cli toxic remove db-proxy -n latency_downstream
# chaos-monkey-config.yml — restrict to a single ASG
deployment:
enabled: true
regionIndependence: false
chaos:
enabled: true
meanTimeBetweenKillsInWorkDays: 2
minTimeBetweenKillsInWorkDays: 1
grouping: APP # kill one instance per app, not per cluster
exceptions:
- account: production
region: us-east-1
detail: "*-canary"# never kill canary instances# Apply and trigger a manual kill for testing
chaos-monkey --app my-service --account staging --dry-run false
