Event Replay & DLQ Monitoring
Event-DrivenResilient event pipeline with dead-letter queues, replay capability, and alerting
8 nodes9 connections
Use Case
Financial transaction processing, order fulfillment, notification systems with strict delivery guarantees
Stack Breakdown
KafkaDLQReplay WorkerPagerDutyDashboard
Architecture Layers
1Event Production
2Stream Processing
3Consumer Services
4Dead Letter Handling
5Alerting & Replay
Components by Category
backend
Producer ServiceConsumer AConsumer BReplay Worker
async
KafkaDLQ
frontend
Dashboard
external
PagerDuty
Why This Topology Works
Failed events land in a dedicated DLQ instead of blocking the main pipeline. Replay workers can re-process at controlled rates. PagerDuty alerts ensure no failures go unnoticed.
Scaling Notes
Kafka partitions scale consumers horizontally. DLQ is a separate topic with its own retention. Replay rate is throttled to avoid overwhelming downstream services.
Observability
Monitor consumer lag, DLQ depth, replay success rate, and time-to-recovery. Alert on DLQ growth exceeding threshold.