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

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

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

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

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Consistent hashing helps distribute data across servers with minimal movement.

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

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

Kafka rebalances consumer groups to manage partition assignments effectively.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

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

Kafka maintains message order within partitions using keys for routing.