Understanding the Raft Consensus Algorithm

Consensus algorithms are the backbone of distributed systems. They let a cluster of machines agree on a single value even when some nodes crash or messages get dropped.

Raft was designed to be understandable — unlike Paxos, which is notoriously difficult to reason about. Here's the core insight that makes Raft tractable.

The Three Roles

Every Raft node is in one of three states at any time:

• Follower — passive, just receives log entries
• Candidate — campaigning for leadership
• Leader — the one source of truth; handles all writes

Leader Election

If a follower doesn't hear from a leader within a random election timeout (150–300ms), it becomes a Candidate and starts an election. It votes for itself and broadcasts a RequestVote RPC.

A node grants its vote if:

1. It hasn't voted yet this term
2. The candidate's log is at least as up-to-date as its own

Once a candidate wins a majority, it becomes leader.

Log Replication

The leader accepts client requests and appends them to its log. It then replicates the entry to followers via AppendEntries RPCs. Once a majority have acknowledged the entry, it's committed and applied to the state machine.

This is the key safety property: a committed entry will never be overwritten.

Why Raft is Easier to Understand

Raft decomposes consensus into three relatively independent subproblems:

• Leader election
• Log replication
• Safety

Each has a clear invariant you can reason about independently. If you want to implement it yourself, the extended Raft paper is the place to start — and then MIT 6.5840 is where you build it.