kafka-consumer-patterns

Prevent duplicate messages in a Kafka consumer by implementing idempotent processing and deduplication. This approach handles duplicate deliveries by ensuring atomic side effects, processing each message exactly once without data loss or redundant operations.

Kafka delivery semantics differ by guarantee level: at-most-once allows potential loss, at-least-once permits duplicates, and exactly-once ensures single processing. Selecting the right semantic depends on your administrative workflow's tolerance for data loss versus duplication.

Use manual offset commits in Kafka instead of auto-commit when you need strict reliability. Manual commits ensure offsets are only saved after successful message processing, preventing data loss during consumer failures or unexpected restarts.

Handle Kafka consumer group rebalancing without losing messages by managing liveness configurations and rebalancing protocols. Proper rebalance handling ensures continuous message delivery and maintains consumer group stability during administrative workflow scaling.

The best way to ensure Kafka consumer reliability is by combining idempotent processing, manual offset commits, and proper delivery semantic selection. This guarantees reliable message processing without duplicates or data loss in administrative workflows.