kafka-deep

Design Kafka topics by calculating partition counts based on desired throughput and setting replication factors to three for production environments to ensure high availability and fault tolerance.

Implement reliable KafkaJS producers by enabling idempotent writes to prevent duplicates, and configure consumers with proper consumer group management and offset commits to handle failures gracefully.

Achieve exactly-once semantics in Kafka by utilizing transactional messaging, which ensures atomic writes across multiple partitions and prevents data duplication during consumer group rebalances.

Handle consumer errors in Kafka data pipelines by implementing Dead Letter Queue (DLQ) patterns, allowing failed messages to be routed separately for debugging while continuous processing continues uninterrupted.

You need at-least-once delivery guarantees in distributed streaming systems when processing events where occasional duplicates are acceptable but data loss is unacceptable, requiring robust offset management.

Consumer lag occurs when consumers process messages slower than producers generate them, and you monitor it by tracking the offset difference between the latest producer message and the processed consumer offset.