Consensus in Distributed Systems with the RAFT Algorithm
Achieving consensus among multiple nodes has long been a challenge in distributed systems. RAFT, the Reliable, Replicated, Redundant, And Fault-Tolerant algorithm, is one way of achieving distributed consensus. Somewhere down the line RAFT came through as a more comprehensible alternative to its predecessor, the Paxos algorithm.
At its core, RAFT is a combination of three key movements: leader election, log replication, and safety. These principles work in coordination to ensure that a distributed system can maintain consistency and continue functioning even in the face of network partitions or node failures. In this post I’ll go through each of these components to understand how RAFT achieves it’s distributed consensus.
Leader Election
In RAFT, nodes in a cluster can be in one of three states: follower, candidate, or leader. The system initializes with all nodes as followers. If a follower doesn’t receive communication from a leader, it becomes a candidate and starts an election.
The election process begins with the candidate requesting votes from other nodes. Nodes will vote for a candidate if they haven’t voted for another candidate in this term. The candidate becomes the leader if it receives votes from a majority of nodes. If no candidate receives a majority, a new election term starts.
Leaders send periodic heartbeats to all followers to maintain their authority. If a follower doesn’t receive a heartbeat within a set time frame, it assumes the leader has failed and initiates a new election.
Log Replication
Once a leader is elected, it manages all client requests and log replication. When a client sends a command to the leader, the leader first appends the command to its log. It then sends AppendEntries RPCs to all followers. Once the majority of followers acknowledge the entry, the leader commits it. The leader then applies the entry to its state machine and returns the result to the client. In subsequent AppendEntries RPCs, the leader notifies followers that the entry is committed.
Followers append entries to their logs if the entries are from the current term and have the correct previous log index and term. This process ensures that all nodes in the cluster eventually have the same log entries in the same order.
Safety
RAFT ensures safety through several mechanisms. The election safety property guarantees that at most one leader can be elected in a given term. Leaders never overwrite or delete entries in their logs, adhering to the leader append-only principle.
The log matching property states that if two logs contain an entry with the same index and term, the logs are identical up to that point. This ensures consistency across the cluster. The leader completeness property guarantees that if a log entry is committed, it will be present in the logs of all future leaders.
Finally, the state machine safety property ensures that if a server has applied a log entry to its state machine, no other server will apply a different entry for the same log index. These safety mechanisms work together to maintain the integrity and consistency of the distributed system.
Example Applications
RAFT has been implemented in various distributed systems:
- etcd: A distributed key-value store used in Kubernetes, implements RAFT for maintaining consistency across nodes.
- CockroachDB: A distributed SQL database that uses RAFT to ensure data consistency and fault tolerance across geographically distributed nodes.
- Consul: HashiCorp’s service mesh solution uses RAFT for leader election and maintaining a consistent view of the network topology.
RAFT simplifies the implementation of distributed consensus by breaking it down into more manageable subproblems: leader election, log replication, and safety. This approach makes it easier for developers to understand and implement reliable distributed systems.
