Raft Consensus Algorithm: A Practical Guide

Raft(Replicated and Fault Tolerant) is a consensus algorithm designed to be understandable and implementable while providing the same safety and liveness properties as classical consensus protocols. It solves the problem of replicating a log of commands across a cluster of unreliable machines so they present a single, consistent state machine to clients.

Why Raft?

Raft breaks consensus into clear subproblems — leader election, log replication, safety, and configuration changes — which makes it easier to reason about and implement reliably. It is commonly used in distributed systems that require strong consistency, such as distributed key-value stores, coordination services, and databases.

Core Concepts

Term: A monotonically increasing logical clock. New elections increment the term. Higher term numbers indicate more recent information.
Leader / Follower / Candidate: Nodes are followers by default, become candidates to start an election, and a single leader handles client requests and replicates entries.
Log Entry: Each entry records a command, a term, and an index. The leader appends entries to its log and replicates them to followers.
Commit: An entry is committed when a majority of nodes have added it to their logs and the leader marks it committed; committed entries are applied to the state machine.

Leader Election (Brief)

Followers expect periodic heartbeats from the leader. If none arrive within a randomized election timeout, a follower becomes a candidate and starts an election by incrementing its term and requesting votes.
Nodes grant a vote to the first candidate they see for a term if the candidate’s log is at least as up-to-date as their own.
A candidate that receives votes from a majority becomes leader and immediately sends heartbeats (AppendEntries) to assert leadership.
If a node observes a message with a higher term, it updates its term and reverts to follower.

Log Replication (Brief)

The leader receives client commands, appends them to its log, and sends AppendEntries RPCs to followers.
Followers append entries only if the previous log index and term match; this ensures logs stay consistent.
When a leader sees an entry replicated on a majority, it marks the entry committed and tells followers to apply it to their state machines.
If a follower’s log and leader’s log diverge, the leader finds the matching index/term and overwrites conflicting entries on the follower.

Safety Guarantees

Raft ensures that any committed entry is present in the logs of future leaders. This is enforced by voting rules that require a candidate’s log to be at least as up-to-date as voters’ logs.
The combination of terms, majority voting, and careful commit rules prevents divergent committed histories.

Log Compaction and Snapshots

Logs grow indefinitely; Raft supports snapshotting: compacting applied state into a snapshot and truncating earlier log entries.
Snapshots can be transferred to slow or new followers to bring them up to date without replaying the entire log.

Membership Changes

Safe membership change is performed using a two-phase (joint) configuration: first operate in a configuration that includes both old and new members, achieve a majority, then switch to the new configuration. This preserves majority-based safety during transitions.

Practical Tips for Implementations

Use randomized election timeouts to reduce split votes and election collisions.
Keep heartbeats frequent relative to the election timeout.
Monitor metrics: election frequency, replication lag, commit latency, and snapshot/compaction activity.
Handle persistence carefully: term, voted-for, and log must survive crashes.
Make RPCs idempotent and design retry/backoff strategies for unstable networks.

When to Choose Raft

Choose Raft when you need strong consistency and clear reasoning about correctness. For eventual consistency or very large-scale, partition-tolerant systems, other architectures may be more appropriate. Raft is a solid choice for systems where ease of implementation, maintainability, and strong correctness guarantees matter.