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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

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

Kafka uses replication and in-sync replicas to ensure data is always available.

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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

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