MODULE 03 // DATA STREAM INTEGRATION
MODULE 06 // RESILIENCE PATTERNS

Error Recovery.

Streams fail. Networks drop. Services timeout. Build error handling that maintains data integrity and ensures no behavioral events are lost.

ERROR RECOVERY PATTERNS

Stream failures are inevitable: network partitions, ABIS maintenance windows, transformation bugs, schema mismatches. Your pipeline must handle failures gracefully—retry transient errors, quarantine poison messages, and never lose behavioral data.

The Dead Letter Queue (DLQ) pattern is essential: events that fail processing after retries go to a separate queue for investigation. This prevents poison messages from blocking the pipeline while preserving them for later reprocessing or analysis.

Idempotency ensures that retrying an event doesn't corrupt analysis. Each event should have a unique ID (UUID). ABIS deduplicates based on this ID, so sending the same event twice (due to retry) produces the same result as sending it once.

RETRY

Transient Failures

Network timeouts, rate limits, temporary unavailability. Retry with exponential backoff (1s, 2s, 4s, 8s). Max 3-5 attempts.

DLQ

Persistent Failures

Schema errors, validation failures, unrecoverable errors. Route to Dead Letter Queue for investigation. Alert on DLQ growth.

SUCCESS

Confirmed Delivery

ABIS acknowledged receipt. Remove from local buffer. Update processing metrics. Event analysis in progress.

KNOWLEDGE CHECK // Q06
What is the purpose of a Dead Letter Queue in stream processing, and when should events be routed there?