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Kafka consumers resume processing from the last committed offset after a crash.

Kafka lag recovery time is how fast consumers can process backlogged messages.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka commit strategies determine how offsets are managed during message processing.

Kafka Streams uses local state for quick data access and changelogs for recovery.

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

Kafka rebalances consumer groups to manage partition assignments effectively.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Kafka uses state stores to remember previous events in stateful stream processing.

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

Kafka consumer offsets help track message processing to avoid loss and duplication.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Older offsets can overwrite newer ones in Kafka if not handled properly.