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

Consistent hashing helps distribute data across servers with minimal movement.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Crash failures stop a service, while network failures disrupt communication.

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

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

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

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

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

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