Understanding Raft: A Beginner’s Guide to Distributed Consensus in Go

This article is the first in a series that introduces the Raft distributed consensus algorithm and its complete implementation in Go.

1. Replicated State Machine

Distributed consensus solves the problem of replicating a deterministic state machine across multiple servers. A state machine represents any service—databases, file servers, lock servers, etc.—and its behavior can be captured by inputs and state transitions.

Clients send requests to the cluster; each server runs a replica of the state machine. If a server crashes, the service becomes unavailable, so replication across multiple servers forms a cluster that continues operating despite individual failures.

All replicas must communicate to keep their state synchronized.

2. Consensus Module and Raft Log

The Raft algorithm ensures that client commands are reliably recorded in a persistent log and applied to the state machine only after they have been replicated to a majority of servers.

State machine: same as described above.

Log: stores all client commands; it is durable and can be used to replay the state machine after a crash.

Consensus module: core of Raft; receives commands, replicates them across the cluster, and commits them to the state machine once safety is guaranteed.

3. Leader and Followers

Raft uses a strong leader model: one replica acts as the leader, the others as followers. The leader handles client requests, replicates log entries to followers, and returns responses.

Followers simply copy the leader’s log. If the leader fails or a network partition occurs, a follower can take over, keeping the service available.

4. Client Interaction

Clients know the network addresses of all cluster replicas. They initially contact any replica; if it is the leader, the request is processed immediately. If it is a follower, the client is redirected to the leader, possibly after several attempts.

5. Fault Tolerance and the CAP Principle

Raft tolerates two main failure types: server crashes and network partitions. The algorithm requires a majority of servers to be reachable for progress, allowing it to survive up to N failures in a 2N+1 cluster.

According to the CAP theorem, Raft chooses consistency over availability during partitions.

6. Why Go?

The implementation is written in Go because the language offers strong concurrency support, a powerful standard library for networking, and simplicity that helps avoid unnecessary complexity in distributed systems.

7. Next Steps

The next article will begin the actual implementation of Raft in Go.