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Integration / Apache Kafka Interview questions

Explain the lifecycle of a Kafka consumer group rebalance?

A rebalance is the process of reassigning a topic's partitions among a consumer group's current members, triggered whenever group membership changes — a consumer joins, leaves, crashes, or the subscribed topic's partition count changes.

flowchart TD A[Trigger: consumer joins/leaves, crash, or topic metadata change] --> B[Group coordinator detects the membership change] B --> C[Coordinator asks all current members to rejoin the group] C --> D[Each member sends its subscription info to the coordinator] D --> E[One member elected as group leader computes the new partition assignment] E --> F[Assignment sent back to the coordinator, then out to all members] F --> G[Members with revoked partitions stop processing them] G --> H[Members with newly assigned partitions resume from the last committed offset] H --> I[Group returns to steady-state processing]

In the classic (eager) rebalance protocol, every member has to stop processing all its partitions during the whole rebalance, even ones it keeps — a real pause in throughput. The newer cooperative/incremental rebalancing (and the KIP-848 next-generation protocol, GA in Kafka 4.0) narrows this down so only the specific partitions actually being reassigned are paused, letting members keep processing their unaffected partitions throughout, which is a meaningful throughput improvement for large, frequently-changing groups.

What commonly triggers a Kafka consumer group rebalance?
What does cooperative/incremental rebalancing improve compared to the classic eager protocol?

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