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Classifying failures and using controlled retries in Kafka prevents outages.

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

You can replay Kafka events safely by separating live and replay processes.

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

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

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

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

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

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