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Raft AlgorithmEdit

Raft is a consensus algorithm for state machine replication that helps a cluster of machines stay in sync even in the presence of failures. Introduced by Diego Ongaro and John Ousterhout in 2014, Raft was designed to be easier to understand and implement than many alternatives, notably Paxos. The core idea is to provide a safe and predictable way for multiple servers to agree on a sequence of operations, so that every non-faulty machine applies the same commands in the same order. The algorithm achieves this through a clean separation of concerns: leader election, log replication, and safety guarantees.

Raft operates in terms, with a leader elected to coordinate activity for the current term. Each node can be in one of three roles: follower, candidate, or leader. The system tolerates up to floor((N-1)/2) faulty nodes in a cluster of N nodes, meaning that a majority of servers must agree to commit actions. Clients interact with the cluster through the current leader, which handles requests and drives the replication of commands to followers. The replicated commands are applied to a local state machine on each server, ensuring the cluster remains consistent. The design emphasizes readability and practical deployment, and it is widely used in production systems such as etcd and Consul to achieve fault-tolerant service behavior.

Overview

  • Raft is a consensus algorithm for state machine replication that aims to be understandable while delivering robust fault tolerance. It contrasts with other approaches such as Paxos by offering a clearer structure around leadership and log management.
  • The architecture relies on a majority quorum. With N servers, the system tolerates up to floor((N-1)/2) faults; progress requires a majority to agree on entries and on leadership.
  • The log in Raft is the source of truth for commands that modify the state machine. Clients submit commands to the leader, which appends them to its log and replicates them to followers via AppendEntries RPCs.
  • A key safety property is that committed entries are durable and will appear in the logs of all future leaders. This guarantees consistency across restarts and reconfigurations.
  • Raft supports log compaction and snapshots to prevent unbounded growth, and it provides a mechanism for handling configuration changes (cluster membership) without sacrificing safety.

How Raft works

Leader election

  • When a cluster starts, all servers begin as followers. If a follower does not hear from a current leader within a randomized timeout, it becomes a candidate and starts a new term, incrementing the term number.
  • The candidate requests votes from the other servers. A server votes for a candidate if it has not voted in the current term and if the candidate’s log is at least as up-to-date as its own. The up-to-date condition is based on a comparison of the term and index of the last log entry.
  • If a candidate collects a majority of votes, it becomes the leader for that term and starts sending periodic heartbeats to followers to maintain authority. If it fails to win, another election occurs in a new term.
  • The election mechanism relies on a randomized timeout to reduce the odds of split votes and to promote timely leadership.

Log replication and state machine updates

  • The leader handles client requests and appends them to its own log. It then sends AppendEntries RPCs to followers to replicate the new entries.
  • Followers append the new entries to their logs and respond to the leader. Once an entry has been replicated on a majority of servers, the leader marks it as committed.
  • Committed entries are applied in order to the local state machines on all servers, ensuring that every non-faulty server progresses through the same sequence of state transitions.
  • The leader maintains two important per-follower indices: nextIndex (the next log index to send to that follower) and matchIndex (the highest log index known to be replicated on that follower). These indices help drive efficient replication and recovery from lagging followers.
  • If a follower falls behind, the leader can bring it up to date by sending a sequence of AppendEntries RPCs that contain the missing entries.

Safety and correctness

  • Raft enforces safety by ensuring that a log entry is only considered committed when it is known to exist on a majority of servers. Additionally, a leader can only commit entries from its current term, a rule that helps guarantee that the committed log never contradicts future leaders.
  • If a leader fails, a new term begins through elections, and the new leader must have a log that is at least as up-to-date as any other server, ensuring that leadership cannot introduce conflicting histories.
  • The combination of leader-driven replication, majority decision-making, and careful commit rules provides a robust guarantee that all non-faulty servers converge on the same log and state machine through time.

Configuration changes and reconfiguration

  • Changing the cluster composition (adding or removing servers) is handled through special log entries known as config changes. Raft uses a reconfiguration mechanism that preserves safety during membership transitions, often described in terms of a phased or joint consensus approach to ensure that the system remains available and safe as the set of participants changes.

Variants and practical considerations

  • Log growth and maintenance: Real deployments use snapshotting and log compaction to prevent unbounded growth of the log, while still retaining safety guarantees.
  • Read operations: For linearizable reads, Raft typically routes reads through the leader to preserve strong consistency, though optimizations exist for certain workloads.
  • Performance trade-offs: A single leader can become a bottleneck for write-heavy workloads, particularly in large clusters. Various practical deployments address this with tuning, multi-Raft instances, or careful workload design.
  • Real-world deployments: Raft has become popular in cloud-native systems and distributed databases due to its clarity and predictable behavior. Notable implementations and adopters include etcd, Consul (HashiCorp), and other distributed systems that require replicated state machines.

Controversies and debates

  • Simplicity versus scalability: Proponents of Raft emphasize its clarity and maintainability, which can reduce development and maintenance risk. Critics sometimes argue that the centralized leadership model can bottleneck write throughput on large clusters, motivating research into alternative approaches or optimized Raft variants.
  • Raft versus Paxos: The Raft authors positioned their design as more understandable and accessible than traditional Paxos formulations. The ongoing debate in the field centers on whether Raft’s leadership-based approach truly delivers superior practicality in all settings, or whether Paxos-inspired variants (and multi-Paxos) offer different performance characteristics suitable for particular workloads.
  • Reconfiguration costs: Handling dynamic membership without compromising safety can be tricky. Some debates focus on the best strategies for adding or removing servers in long-running systems without causing long pauses or risk to safety, though Raft’s design includes explicit mechanisms for safe reconfigurations.
  • Real-world tuning: In practice, many teams tailor Raft deployments to their workloads, which can lead to a spectrum of configurations from small, fast clusters to larger, more fault-tolerant deployments. Critics sometimes point out that the “one-size-fits-all” narrative is too optimistic, while supporters argue that the core guarantees remain intact across well-chosen configurations.

See also