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

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

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

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

matchIndex tracks what each follower has, while commitIndex shows what is committed.

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

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

Poison messages in Kafka can cause infinite retries, risking system stability.

Consistent hashing helps distribute data across servers with minimal movement.

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

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

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

RPC allows one service to call a function in another service over the network.

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

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

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