kafka-best-practices

Configure Kafka idempotent producers by enabling `enable.idempotence` and proper `acks` settings to prevent duplicate messages during network retries, ensuring reliable streaming delivery exactly-once.

Design Kafka topics for high-throughput streaming by applying proper partitioning strategies, retention policies, and log compaction considerations to achieve scalable and reliable message processing.

Kafka transactional processing uses transactional APIs to group producer and consumer offset commits atomically, ensuring exactly-once semantics and reliable delivery in streaming pipelines without data duplication.

Kafka consumers lose messages or read duplicates during rebalancing due to improper offset management; applying correct offset commit patterns and rebalance handling ensures reliable failure recovery.

Handling high-throughput Kafka workloads requires specific consumer configurations for offset management and failure recovery, alongside operational scaling tips and proper topic partitioning to maintain streaming reliability.

Use Kafka log compaction instead of standard retention policies when you need to maintain the latest state for individual message keys, whereas retention policies delete older data based on time or size limits.