⚡ Streaming Resilience Playbook
Design fault-tolerant streaming pipelines with cross-region replication, comprehensive observability, and automated disaster recovery. This playbook covers patterns for achieving 99.99% availability in mission-critical streaming workloads.
Overview
The Streaming Resilience Playbook provides battle-tested patterns for building streaming systems that withstand failures, scale under pressure, and recover automatically. Based on learnings from customers processing billions of events daily, this guide helps you design for the real world.
What's Included
- Resilience Patterns — Proven architectures for fault tolerance and graceful degradation
- Replication Strategies — Cross-region and cross-cluster data synchronization approaches
- Observability Framework — Metrics, logs, and traces for streaming workloads
- Disaster Recovery Runbooks — Step-by-step procedures for incident response
- Chaos Engineering Tests — Failure injection scenarios for validation
Resilience Fundamentals
1. Exactly-Once Processing
Ensure data integrity even during failures and retries.
- Idempotent producers with sequence numbers
- Transactional consumers with offset management
- Deduplication strategies for at-least-once sources
- End-to-end exactly-once with Flink checkpoints
2. Backpressure Management
Handle traffic spikes without data loss or cascading failures.
- Adaptive rate limiting at ingestion
- Buffer sizing and overflow policies
- Consumer lag-based autoscaling
- Circuit breakers for downstream protection
3. State Management
Maintain processing state reliably across restarts and failures.
- Incremental checkpointing strategies
- State backend selection (RocksDB vs. heap)
- State migration during schema evolution
- Savepoint best practices for deployments
Cross-Region Replication
Active-Passive
Maintain a standby region for disaster recovery with RPO < 1 minute.
- Asynchronous replication with MirrorMaker 2
- Topic and consumer group synchronization
- Automated failover with DNS switching
- Failback procedures and data reconciliation
Active-Active
Process data in multiple regions simultaneously for low latency and high availability.
- Conflict resolution strategies
- Global ordering vs. regional ordering trade-offs
- Cross-region consumer coordination
- Aggregate synchronization patterns
Observability Stack
Key Metrics
- Consumer lag — Messages behind per partition
- Throughput — Messages and bytes per second
- Latency — End-to-end processing time percentiles
- Error rates — Failed messages and retry counts
- Resource utilization — CPU, memory, network, disk
Alerting Strategy
- Symptom-based alerts (lag, latency) over cause-based
- Multi-window alerting for noise reduction
- Severity levels with escalation policies
- Runbook links in alert payloads
Distributed Tracing
- Context propagation through message headers
- Trace sampling strategies for high-volume streams
- Correlation with downstream service traces
- Integration with Jaeger, Zipkin, and cloud-native APM
Disaster Recovery Procedures
Failure Scenarios Covered
- Broker failures and partition leader elections
- Consumer group rebalancing
- Network partitions and split-brain
- Regional outages and failover
- Data corruption and recovery
Recovery Time Objectives
| Scenario | Target RTO | Target RPO |
|---|---|---|
| Single broker failure | < 30 seconds | 0 (no data loss) |
| Availability zone failure | < 2 minutes | 0 (no data loss) |
| Regional outage | < 15 minutes | < 1 minute |
Chaos Engineering
Validate resilience with controlled failure injection:
- Broker termination and partition migration
- Network latency injection between components
- Consumer crash and rebalance simulation
- Disk fill and resource exhaustion tests
Webinar Recording
Watch our 60-minute deep dive into streaming resilience patterns, featuring live demonstrations and Q&A with Datorth engineering leads.
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