AI Capture: Distributed Consensus and Raft Fundamentals

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Summary of Raft State Machine

Raft decomposes consensus into three independent subproblems:

1. Leader Election: A leader must be elected when an existing leader fails.
2. Log Replication: The leader accepts log entries from clients and replicates them across followers.
3. Safety: If any server has applied a particular log entry to its state machine, no other server may apply a different log entry for the same log index.

Server States

At any time, each server is in one of three states:

• Leader: Handles all client requests and coordinates replication.
• Follower: Completely passive; responds to incoming RPCs.
• Candidate: Used during leader election to collect votes.

       [Start]
          |
          v
    +------------+    Times out, starts election    +-------------+
    |  Follower  | -------------------------------> |  Candidate  |
    +------------+                                  +-------------+
          ^                                                |
          |  Discovers current leader                      |  Receives votes from
          |  or higher term                                |  majority of servers
          |                                                v
          |           Discovers server with higher term   +-------------+
          +---------------------------------------------- |   Leader    |
                                                          +-------------+

Provenance & Follow-up Actions

• Source: Generated via AI query on consensus protocols.
• Related garden notes: preventing-goroutine-leaks (Go concurrency in distributed nodes).
• Distillation target: Extract practical Go implementation notes into a public seedling note once tested.