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

Raft and Paxos are protocols that help computers agree on values in distributed systems.

Paxos is a protocol that helps distributed systems agree on one value despite failures.

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

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

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

Quorum and majority help distributed systems maintain data consistency and availability.

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

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

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

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

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

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