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

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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

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

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

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

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

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

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

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

Fetch limits control data size from Kafka, while poll records limit processed records.

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

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

Kafka uses partitions to allow multiple consumers to process messages in parallel.

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