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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

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

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

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

Leader election helps distributed systems select one coordinator from many nodes.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.