Understanding the RAFT Consensus Algorithm

Posted on by Jim O'Connell Posted in distributed systems, raft algorithm
Reading Time: 3 minutes

A distributed system needs a reliable mechanism for reaching consensus across multiple nodes. The RAFT algorithm is one such consensus algorithm.  It was designed to be easier to understand than Paxos (an earlier, more complex protocol for providing consensus in distributed networks,) while maintaining strong fault tolerance.

1. What is RAFT?

RAFT is widely used in distributed systems that require strong consistency. In a distributed environment, multiple nodes work together to store and process data reliably. However, ensuring that all nodes agree on the same state at any given time is a challenge. RAFT helps solve this problem by ensuring that commands are consistently applied across all nodes, preventing conflicts and maintaining data integrity. Unlike traditional databases that rely on a single primary instance for writes, distributed systems using RAFT elect a leader that coordinates operations, ensuring that updates are applied in the same order across all nodes. This makes RAFT a crucial component for building fault-tolerant, highly available distributed applications.

RAFT (Reliable, Replicated, and Fault-Tolerant) is a consensus algorithm used to manage a replicated log in distributed systems. It ensures that multiple nodes in a distributed system agree on the same sequence of commands, maintaining consistency even in the face of network failures.

(For comparison, another well-known consensus algorithm is Paxos, which is more complex but serves a similar purpose. You can read more about it here.)

Key Goals of RAFT:

  • Leader Election – Ensuring that one node acts as the leader at any given time.
  • Log Replication – Maintaining a consistent log across all nodes.
  • Safety & Fault Tolerance – Ensuring that committed entries are never lost, even if some nodes fail.

RAFT is used in distributed databases, coordination services, and in-memory data grids like Hazelcast to ensure consistency.

2. RAFT’s Three Key Roles

RAFT divides nodes into three roles:

  • Leader – The central authority that handles client requests and replicates logs.
  • Followers – Passive nodes that accept updates from the leader.
  • Candidates – Nodes that attempt to become the leader during elections.

At any given time, there is at most one leader, while all other nodes function as followers.

3. Leader Election Process

When a RAFT cluster starts, or if the leader fails, an election process takes place:

  1. A follower node times out and transitions to a candidate state.
  2. The candidate requests votes from other nodes.
  3. If a majority of nodes vote for the candidate, it becomes the leader.
  4. The leader then starts sending heartbeat messages to followers, maintaining authority.

Example: Leader Election

Node A (Follower) -> Times out -> Becomes Candidate
Node A requests votes from Nodes B & C
Nodes B & C vote for A (majority wins)
Node A becomes the Leader

This mechanism ensures a stable leadership structure even during network failures.

4. Log Replication

Once a leader is established, it manages log replication:

  1. The leader receives a client request.
  2. The request is appended to the leader’s log.
  3. The leader sends the log entry to followers.
  4. Once a majority acknowledges the entry, it is committed.
  5. Followers apply the committed log to their state machine.

What is a Log in RAFT?

A log in RAFT is an append-only structure that stores client requests (commands). The leader ensures that all followers maintain an identical sequence of logs so that they reach the same state.

Example: Log Replication

Client -> Sends "Write X=10" to Leader
Leader -> Appends "Write X=10" to log
Leader -> Sends entry to Followers
Majority acknowledges -> Entry is committed

This ensures all nodes eventually apply the same commands in order.

Log Consistency Rules in RAFT

  • Leader Enforces Order: Followers must accept logs from the leader that match their current state.
  • Log Matching Property: If two logs share the same index and term, they must contain the same command.
  • Commit Rule: A log entry is committed when a majority of nodes replicate it.

Example of Log Entries

Log Index Term Command
1 1 Write X = 5
2 2 Write Y = 20
3 2 Write X = 10
  • The Log Index ensures the correct order.
  • The Term tracks the leader’s election cycle.
  • The Command is the action that changes the system state.

Logs serve as the foundation for fault tolerance in RAFT, ensuring that even if a node fails, the system can recover and maintain a consistent state.

5. Handling Failures

RAFT ensures that failures do not cause inconsistencies by following strict rules:

  • Election Timeout: If a leader crashes, a new election starts after a timeout.
  • Log Matching Property: Followers accept only consistent log updates from the leader.
  • Commit Consistency: Entries are only committed when a majority acknowledges them.

This design prevents split-brain scenarios and guarantees system integrity.

6. RAFT in Practice

Many distributed systems, including Hazelcast, leverage RAFT for high availability and fault tolerance. Hazelcast’s CP Subsystem implements RAFT to ensure data consistency in distributed environments.

7. Further Reading

For a deeper dive into RAFT, check out:

 

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