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

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

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 rebalances consumer groups to manage partition assignments effectively.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

Kafka Rebalance Listeners help manage state during partition changes in consumer groups.

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

Kafka's assign() method allows manual control of partition assignments for consumers.

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