Understanding and Implementing the Raft Consensus Algorithm in Go

This article provides a comprehensive, step‑by‑step guide to implementing the Raft distributed consensus algorithm in Go. It starts with an overview of Raft’s four major modules—leader election, log replication, persistence, and snapshotting—explaining the purpose of each and how they interact to achieve fault‑tolerant state machine replication.

1. Leader Election

The system defines three node states (Follower, Candidate, Leader). A node becomes a Candidate when it does not receive heartbeats within a randomized election timeout, increments its term, votes for itself, and sends RequestVote RPCs to other peers. The election succeeds when a candidate receives votes from a majority of nodes. The article details the Go structures ( PeerState , Raft ) and the functions becomeCandidate , becomeLeader , and ticker that drive this process, as well as the “golden rule” that a node must step down immediately when it discovers a higher term.

2. Log Replication

Once a leader is elected, it periodically sends AppendEntries RPCs (heartbeats when the entry list is empty) to all followers. The RPC carries the previous log index/term, new entries, and the leader’s commit index. Followers validate the term, update their leaderId , and either append new entries or truncate conflicting entries. The leader maintains nextIndex and matchIndex arrays to track each follower’s progress and uses a majority rule to advance its own commitIndex . The article includes the full Go implementation of AppendEntries , sendAppendEntriesToPeer , and the supporting log data structure ( LogEntry , rLog ).

3. Persistence

Raft persists three critical pieces of state— currentTerm , votedFor , and the log—using Gob encoding. The functions persist and readPersist are called immediately after any change to these fields, ensuring that a crash‑recovery can restore the node to a consistent state. The article shows where persist is invoked in the election, log replication, and snapshot code paths.

4. Snapshotting

To prevent unbounded log growth, Raft supports compacting the log into a snapshot. A snapshot records the last included index and term, plus an opaque state blob supplied by the application. The article explains the Snapshot and CondInstallSnapshot APIs, the InstallSnapshot RPC, and how the leader decides between sending log entries or a snapshot (based on nextIndex vs. LastIncludedIndex ). It also describes the necessary adjustments to the log data structure ( LastIncludedIndex , LastIncludedTerm ) and the handling of conflicts using ConflictTerm and ConflictIndex fields in the RPC reply.

5. Applying Committed Entries

A separate goroutine runs applyLog , which periodically checks whether commitIndex exceeds lastApplied . When it does, the node packages each newly committed entry into an ApplyMsg and sends it over applyCh to the upper‑layer state machine. The same channel is also used for delivering snapshots.

6. Summary of Key Concepts

Three node states and term‑based leadership.

Two perpetual loops: ticker (election timeout) and ping (log replication/heartbeat).

Three RPCs: RequestVote , AppendEntries , InstallSnapshot .

Two “golden rules”: step down on higher term; persist any change to term, vote, or log immediately.

Snapshot mechanism with LastIncludedIndex to keep log indices aligned.

The article concludes by recommending the use of mature Raft libraries (e.g., hashicorp/raft ) for production systems, while emphasizing that building Raft from scratch offers deep insight into distributed consensus.