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Kafka consumer lag shows how far behind consumers are from new messages.

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

Backpressure prevents system overload by managing message processing rates in Kafka.

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

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

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

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

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

Kafka uses heartbeats and timeouts to check if consumers are alive.

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

Fetch limits control data size from Kafka, while poll records limit processed records.

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