Understanding and scaling Raft

If you have worked with distributed systems, you must have come across this famous quote: In a distributed system, the only thing two nodes can agree on is that they can’t agree on anything. This quote has stuck with me because it perfectly captures the complexity of building distributed systems.

What is Raft?

Raft is a consensus algorithm designed to be more understandable than Paxos. It was introduced in a paper by Diego Ongaro and John Ousterhout in 2014 and has since become one of the most widely used consensus algorithms in distributed systems.

The key features of Raft include:

• Leader election
• Log replication
• Safety guarantees

Leader Election in Raft

The Raft consensus algorithm uses a leader-based approach. At any given time, one server is the leader, and all other servers are followers. The leader handles all client requests, and if followers receive client requests, they forward them to the leader.

func (rf *Raft) startElection() {
    rf.currentTerm += 1
    rf.votedFor = rf.me
    rf.state = Candidate
    
    // Send RequestVote RPCs to all other servers
    votesReceived := 1  // Vote for self
    
    // If we receive votes from majority of servers, become leader
    if votesReceived > len(rf.peers)/2 {
        rf.state = Leader
        // Initialize leader state
    }
}

Scaling Challenges

As we scale Raft to more nodes, several challenges emerge:

1. Increased communication overhead: With more nodes, the leader needs to communicate with more followers
2. Slower consensus: More nodes means more round-trips for consensus
3. Higher likelihood of leader failures: With more components, failures become more common

Solutions for Scaling

Hierarchical Raft

One approach is to implement a hierarchical Raft where nodes are organized in a tree structure. This reduces the communication overhead and allows for better scalability.

Raft with Observers

Another approach is to use non-voting members (observers) that receive log entries but don’t participate in voting. This allows for read scalability without affecting write performance.

Conclusion

Understanding Raft is crucial for building reliable distributed systems. While it solves the consensus problem elegantly, scaling it requires careful consideration of the trade-offs involved.

In future posts, I’ll dive deeper into specific implementation details and optimization techniques for Raft in production environments.