distributed consensus

**Distributed Consensus** — algorithms that ensure multiple nodes in a distributed system agree on a single value or sequence of values, despite network failures and delays, forming the foundation of reliable distributed systems. **The Consensus Problem** - N nodes must agree on a value (e.g., "who is the leader?" or "what is the next log entry?") - Must handle: Network partitions, message delays, node crashes - Impossible to solve with guaranteed termination in asynchronous systems (FLP impossibility) — practical algorithms use timeouts **Raft (Most Popular Today)** - Designed for understandability (vs Paxos complexity) - **Leader Election**: Nodes start as followers. If no heartbeat received → become candidate → request votes → majority wins → become leader - **Log Replication**: Leader receives client requests, appends to log, replicates to followers. Committed when majority acknowledge - **Safety**: Only nodes with up-to-date logs can become leader **Paxos** - Original consensus algorithm (Lamport, 1989) - Proposer, Acceptor, Learner roles - Correct but famously difficult to understand and implement - Multi-Paxos: Extension for sequence of values (replicated log) **Real-World Usage** - **etcd**: Raft-based key-value store (used by Kubernetes) - **ZooKeeper**: ZAB protocol (Paxos variant). Coordination service - **CockroachDB, TiKV**: Raft for distributed transactions - **Google Spanner**: Paxos for global consensus **Distributed consensus** is the hardest problem in distributed systems — but it's what makes databases, orchestrators, and cloud infrastructure reliable.