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

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

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Kafka uses brokers for data handling and KRaft controllers for metadata management.

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

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

Kafka rebalances consumer groups to manage partition assignments effectively.

Raft handles leader failure by allowing followers to elect a new leader through voting.

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

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

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

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

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

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