Introduction to Raft: A Comprehensive Overview of the Distributed Consensus Algorithm

Raft is a relatively new (2014) distributed consensus algorithm that has seen extensive adoption in industry, most notably by Kubernetes through etcd. This first article in a series offers a complete overview of Raft to lay the groundwork for a full Go implementation.

Distributed consensus can be viewed as the problem of replicating a deterministic state machine across multiple servers. A state machine represents any service—databases, file servers, lock servers—so replicating it ensures consistent responses even when individual servers fail.

The article introduces key terminology: a service (the logical task), a server or replica (an instance running Raft), and a cluster (a group of Raft servers, typically 3 or 5 nodes).

Raft’s core consists of three modules: the state machine (the service logic), the log (a persistent record of client commands that are applied only after being safely replicated), and the consensus module (which receives commands, replicates them across the cluster, and commits them to the state machine).

Raft employs a strong leader‑follower model: one replica acts as the leader, handling client requests, replicating commands to followers, and returning responses. Followers simply copy the leader’s log; if the leader fails, a follower can take over, preserving availability.

Clients interact with the entire cluster rather than a single server. They initially contact any replica; if it is the leader, the request proceeds, otherwise the replica redirects the client to the current leader. This design simplifies client logic and enables rapid recovery after failures.

Fault tolerance is addressed by tolerating server crashes and network partitions. Raft requires a majority of servers to be operational for progress, allowing it to survive up to N failures in a 2N+1 node cluster. In the presence of partitions, Raft chooses consistency over availability, adhering to the CAP theorem.

The implementation is written in Go because the language offers strong concurrency primitives, a powerful standard library (including net/rpc for inter‑replica communication), and simplicity that helps avoid unnecessary complexity in distributed systems.

The series will continue with detailed code walkthroughs, exploring Raft’s election process, log replication, safety guarantees, and handling of edge cases.