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

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka rebalances consumer groups to manage partition assignments effectively.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

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

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

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

Kafka's parallel processing depends on partitions, not just consumers or threads.

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

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

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

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

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