The Raft consensus algorithm is a protocol designed to manage a replicated log in a distributed system. It ensures that multiple servers (or nodes) agree on a consistent state, even in the presence of failures. Raft is often used to implement fault-tolerant services. This is an incomplete attempt at implementing Raft from scratch in Python to develop a deeper understanding about Networking in and fault tolerance in Python.
Raft divides time into terms, and in each term, a leader is elected. The leader handles all client interactions and log replication. If a leader fails, a new leader is elected.
The leader receives log entries from clients and replicates them to follower nodes. Once a majority of followers acknowledge the entries, they are considered committed.
Raft ensures that committed entries are durable and consistent across all nodes. Even if some nodes fail and recover, they will eventually reach the same state as the leader.
- Leader: Handles client requests and log replication.
- Followers: Replicate the leader's log entries.
- Candidates: Nodes that are trying to become the leader.
A term is a logical time unit in Raft. Each term starts with an election. Terms are identified by monotonically increasing numbers.
Log entries are the commands that need to be replicated across the nodes. Each entry has an index and a term number.
- A follower becomes a candidate if it doesn't hear from the leader.
- The candidate requests votes from other nodes.
- If a candidate receives a majority of votes, it becomes the leader.
- The leader appends the client's command to its log.
- The leader sends AppendEntries RPCs to followers.
- Followers append the entries to their logs and send acknowledgments.
- Once a majority of followers acknowledge, the entry is committed.
- Election Safety: At most one leader can be elected in a given term.
- Leader Append-Only: A leader never overwrites or deletes its log entries.
- Log Matching: If two logs contain an entry with the same index and term, they are identical up to that point.
- Leader Completeness: If a log entry is committed in a term, that entry will be present in the logs of the leaders for all higher-numbered terms.
- Simplicity: Raft is designed to be easy to understand.
- Strong Leader: The leader handles all client interactions, simplifying the system.
- Consistency: Raft ensures that all nodes have a consistent view of the log.
The Raft consensus algorithm provides a robust and easy-to-understand method for achieving consensus in distributed systems. Its clear leader-based approach and strong safety guarantees make it a popular choice for building reliable, fault-tolerant services.
For more detailed information, refer to the Raft paper.