Deep Dive: Raft Explained

Consensus Made Understandable

Raft is a consensus algorithm designed as an alternative to Paxos, with a primary goal of being easier to understand and implement. Developed by Diego Ongaro and John Ousterhout at Stanford University, Raft aims to provide the same fault tolerance and correctness guarantees as Paxos but through a structure that is more intuitive for developers and students. It achieves this by decomposing the consensus problem into three relatively independent subproblems: Leader Election, Log Replication, and Safety.

Key Goals of Raft

• Understandability: The design prioritizes clarity. The paper itself is structured to aid comprehension.
• Ease of Implementation: By clearly defining states and RPCs, Raft reduces ambiguity for implementers.
• Full Specification: Raft specifies enough detail to build practical systems.
• Efficiency: While understandability is key, Raft is also designed to be efficient.

Raft's Core Components

1. Leader Election

Raft operates with a strong leader. All client requests (commands to be replicated) go through the leader. If a leader fails or becomes disconnected, a new leader must be elected.

• Server States: Servers are in one of three states: Follower, Candidate, or Leader.
• Terms: Time is divided into "terms," each starting with an election. A term has at most one leader. If an election fails (e.g., split vote), the term ends, and a new one begins quickly.
• Election Process:
  1. A follower times out (election timeout) if it hasn't heard from the leader.
  2. It increments its current term, transitions to Candidate state, and votes for itself.
  3. It sends RequestVote RPCs to all other servers.
  4. A candidate wins if it receives votes from a majority of servers in the current term. It then becomes Leader.
  5. If it receives an AppendEntries RPC from a new leader (with same or higher term), it reverts to Follower.
  6. If the election timeout elapses again (split vote), it starts a new election.

This election mechanism is vital. The stability and performance of distributed systems often hinge on reliable leadership, a principle echoed in managing complex IT infrastructure, as discussed in Foundations of Site Reliability Engineering.

2. Log Replication

Once a leader is elected, it services client requests. Each request contains a command to be executed by the replicated state machines. The leader appends the command to its log as a new entry, then issues AppendEntries RPCs in parallel to each of the followers to replicate the entry.

• Log Matching Property: Raft maintains the invariant that if two logs contain an entry with the same index and term, then the logs are identical in all preceding entries.
• Committing Entries: When an entry has been replicated on a majority of servers, the leader considers it committed. The leader then applies the command to its state machine and returns the result to the client. Followers learn about committed entries via subsequent AppendEntries RPCs (which include the leader's commit index).
• Follower Consistency: Leaders force followers' logs to match their own. If a follower's log is inconsistent, the leader will find the last point of agreement and send entries to overwrite the inconsistent part of the follower's log.

Managing logs effectively is key to data consistency, a challenge also present in handling large datasets, where understanding tools for Real-time Data Processing with Apache Kafka becomes relevant.

3. Safety

Raft includes several safety mechanisms to ensure correctness despite failures, particularly that only one leader can exist per term and that committed log entries are durable and eventually executed by all state machines.

• Election Safety: A candidate cannot win an election unless its log is at least as up-to-date as any other log in the majority that voted for it. This prevents a server with an outdated log from becoming leader and overwriting committed entries.
• Leader Completeness: A leader must have all committed entries from previous terms in its log.
• State Machine Safety: If a server has applied a log entry at a given index to its state machine, no other server will ever apply a different log entry for the same index.
• Commitment Rules: An entry from a leader's current term is committed once it is stored on a majority of servers. Entries from previous terms are committed if the leader sees they are stored on a majority and at least one new entry from the leader's current term is also stored on a majority.

Raft vs. Paxos

While both solve consensus, Raft differs from Paxos primarily in its structure and emphasis:

• Understandability: Raft's separation of concerns (leader election, log replication, safety) and stronger leader role generally make it easier to grasp.
• Leader-centric: Raft heavily relies on a leader, simplifying normal operation. Paxos is more symmetric, which can lead to more complex interactions.
• Practicality: Raft was designed with implementation in mind, providing more concrete details for building systems.

Raft has seen widespread adoption in systems like etcd (used by Kubernetes), Consul, TiKV, and CockroachDB. Its understandability has been a key factor in its success. For individuals managing personal investments or exploring financial markets, understanding the reliability of underlying data systems, often built on principles like those in Raft, can be reassuring, although Pomegra itself focuses on higher-level AI analytics rather than consensus protocols directly.

After understanding Raft, you might be interested in the challenges of Byzantine Fault Tolerance (BFT), which deals with more malicious types of failures.