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Consensus ensures that distributed systems can agree on decisions even during failures.

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.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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

Consistency models determine what data reads in distributed systems can see.

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.

Vector clocks help track the order and concurrency of events in distributed systems.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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.

Choosing the right distributed lock depends on your system's needs and existing tools.

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.