Raft Consensus Algorithm Visualizer (Elections, Heartbeats & Split Brains)
Partition cluster nodes, trigger split elections, and watch Raft maintain quorum consensus. Simulate a 5-node distributed Raft cluster. Kill leader nodes, introduce network partitions, inspect election timeouts, and observe quorum validation (N/2 + 1).
Raft Distributed Consensus Simulator (5-Node Quorum)
Simulate leader elections, log entry replication, heartbeat sync, and split-brain partition tolerance.
Term: 1
Log Entries: 3
Term: 1
Log Entries: 3
Term: 1
Log Entries: 3
Term: 1
Log Entries: 3
Term: 1
Log Entries: 3
Committed Consensus Log Stream (Raft Log)
How It Works Under the Hood
Raft is a distributed consensus algorithm designed to be understandable while providing complete fault tolerance equivalent to Multi-Paxos. A Raft cluster decomposes consensus into three independent sub-problems: Leader Election, Log Replication, and Safety. Nodes exist in one of three states: Follower, Candidate, or Leader. Leaders send periodic heartbeat RPCs to maintain authority. If a Follower receives no heartbeats within a randomized election timeout [150ms, 300ms], it transitions to Candidate and solicits votes. A candidate wins when it secures votes from a majority (quorum) of cluster nodes (e.g., 3 out of 5 nodes).
Core Architectural Principles
- Majority Quorum: A cluster of 2F + 1 nodes can tolerate F node failures without losing availability.
- Randomized Election Timeouts: Prevents split-vote ties when multiple followers suspect leader failure simultaneously.
- Log Matching Property: If two logs contain an entry with the same index and term, they are identical up to that point.
When an interviewer asks how to avoid split-brain states during network partitions, explain that only the partition containing a strict majority (N/2 + 1) of nodes can elect a leader or commit log entries. The isolated minority partition simply queues requests without committing.
Synchronous quorum round-trips on write operations vs absolute consistency and partition tolerance (CP in CAP theorem).