Raft (computer science)

Raft (computer science)
source
Name	Raft
Developers	Diego Ongaro, John Ousterhout
First appeared	2014
Paradigm	Distributed consensus algorithm
Influenced by	Paxos
License	BSD-style (original paper)

Raft (computer science) is a distributed consensus algorithm designed to manage a replicated log across a cluster of servers to provide fault-tolerant state machine replication. Developed by Diego Ongaro and John Ousterhout, Raft was introduced to provide a more understandable alternative to Leslie Lamport's Paxos (computer science), aiming for clarity without sacrificing performance or safety. Raft’s design separates consensus into modular components and has influenced multiple open-source systems and commercial products.

Overview

Raft formalizes consensus in a cluster of servers via a leader-based approach, using terms, elections, and replicated logs to ensure consistency among replicas. The original Raft paper and follow-up implementations emphasize understandability through decomposed concepts: leader election, log replication, safety, and membership changes. Raft is deployed in projects connected to Google, CoreOS, HashiCorp, and research from universities such as Stanford University, where many evaluations compared Raft to Paxos-based systems. Raft’s model maps to state machine replication used in systems like ZooKeeper and etcd and informed designs in distributed databases and coordination services.

Algorithm Design and Components

Raft divides the consensus problem into distinct components: leader election, log replication, safety rules, and membership changes (configuration). The protocol operates on terms (monotonically increasing epochs) and uses persistent state across crashes to maintain safety; these ideas relate to earlier work by Barbara Liskov and Raymond Chen on reliable systems. Each server maintains a log of commands and applies committed entries to a replicated state machine; commitment relies on majority quorums analogous to concepts in Byzantine Generals Problem-inspired literature, while the election mechanism uses randomized timers to avoid split votes. Raft specifies RPCs such as RequestVote and AppendEntries with clearly defined preconditions, enabling formal reasoning and model checking by researchers from institutions like MIT and University of California, Berkeley.

Leader Election and Log Replication

Leader election in Raft elects a single leader per term using RequestVote RPCs; leaders are responsible for client request ordering and AppendEntries RPCs to followers. Elections use randomized election timeouts to reduce collision; this approach has been studied alongside leader election techniques in systems like Elections in Distributed Systems and implemented in products from Red Hat and Amazon Web Services. Log replication proceeds with the leader sending AppendEntries containing new log entries and heartbeat messages; followers persist entries and respond, enabling the leader to advance commit indices once a majority acknowledges. Raft handles leader crashes and network partitions by allowing followers to become candidates and solicit votes; comparisons of election latency and throughput have been performed by teams at Microsoft Research and Carnegie Mellon University.

Safety and Correctness Properties

Raft provides safety properties including election safety (at most one leader per term), leader completeness (a leader contains all committed entries), and state machine safety (servers apply the same sequence of commands). These guarantees depend on persistent storage across reboots and majority quorums similar to Lamport's Paxos guarantees; formal proofs and mechanized verifications were produced by groups at Princeton University and INRIA. Raft’s log matching property ensures that if two logs contain an entry with the same index and term, then the logs are identical up to that index, which prevents conflicting histories. Liveness requires eventual stability of leader election under reliable communication, a property examined in the context of systems designed by Facebook and Netflix for highly available services.

Performance and Implementation Considerations

Performance of Raft is shaped by leader-centric throughput, network topology, disk I/O, and batching strategies; implementations balance latency and throughput through batching, pipelining, and snapshotting. Snapshotting and log compaction reduce state size for recovery, paralleling techniques used in LevelDB and RocksDB integrations within Raft-based storage engines. Implementers must manage heartbeats, election timeouts, and leader stickiness to tune responsiveness under churn; production-grade deployments by CoreOS (etcd) and HashiCorp (Consul) illustrate practical trade-offs. Benchmarks from research labs compare Raft variants with multi-leader and Paxos variants, evaluating metrics such as write latency, recovery time, and commit rate on clusters used in projects by Google Research and Amazon.

Variants, Extensions, and Applications

Raft has spawned variants and extensions addressing membership changes (joint consensus), dynamic cluster reconfiguration, log compaction, and optimizations for geo-distribution. Extensions include approaches for sharding and multi-raft deployments used in CockroachDB and TiKV, and integrations with consensus layers in systems from Red Hat and VMware. Research extensions explore Byzantine fault tolerance, hybrid leaderless modes, and speculative execution, with academic contributions from ETH Zurich and University of Cambridge. Applications of Raft include configuration services, distributed key-value stores, replicated databases, and orchestration components in projects by Kubernetes contributors and companies such as DigitalOcean and IBM.

Category:Distributed algorithms