Overview: I’d validate resilience via staged, automated chaos experiments targeting ingestion, buffering, processing, and storage, progressing from non-prod → canary prod → full prod with strict blast-radius and rollback controls.
Failures to inject (examples & targets)
- Instance crashes: kill worker nodes (Spark executors, Kafka Connect) to test auto-restart, task rebalancing, exactly-once semantics.
- Network partitions: isolate subsets (producer → broker, broker → consumer, cross-AZ partition) to validate failover and leader election.
- Increased latency: add tail latency on RPCs (ingest API, metadata db, object store) to reveal backpressure and timeouts.
- Disk full / I/O errors: simulate disk saturation on buffer nodes and local shuffle to test spill-to-cloud and pipeline backpressure.
- ZooKeeper/coordination outages and broker throttling.
Safety guardrails
- Timebox experiments with circuit breaker: kill after X minutes or on error-rate threshold.
- Pre-checks: capacity headroom, replication factor >= 3, backups/snapshots fresh, schema compatibility checks.
- Traffic steering: use canary traffic slices (5% → 20%) and tagged tenants; run during low-impact windows.
- Alerting & runbook pinned to experiment ID.
Blast-radius limits & staging
- Non-prod → integration with synthetic producers.
- Canary-prod: 1 AZ, <5% traffic, non-critical pipeline paths.
- Scoped lists: whitelist consumers and teams; blacklisted pipelines (billing, security).
Rollback & remediation
- Automated rollback playbooks: restore node pool, re-balance partitions, revert network policy.
- Rollback triggers: sustained consumer lag > threshold, data loss detected, SLA breach.
- Snapshots & replay: guarantee offsets retained and provide automated replay from durable storage.
Success criteria & metrics
- No data loss (offsets checkpointed/replayable).
- Consumer lag returns to baseline within SLA window.
- Error rate stays below defined SLO increase (e.g., <3x baseline) and recovery RTO/RPO met.
- Downstream schemas and jobs produce identical outputs (sampling & checksums).
Automation & gating into production
- Define experiments as code (Terraform/Helm + chaos scripts) and run via CI pipeline.
- Progressive rollout gate: pass automated safety tests in staging → run smoke, then canary experiment; only auto-promote if observability tests pass.
- Approvals: require on-call + owner sign-off for full-production run; automated gates check monitoring, alerts, backups before and during.
- Postmortem & automated verification: each experiment posts results, diff of metrics, and must pass verification tests before next expansion.
Example quick test: inject 30s delay on S3 writes for 5% of producers in canary → assert ingestion latency SLA breach handled via buffer spillover, no data loss, consumer lag recovers within 10 minutes.
