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Building a Raft Consensus Algorithm in Go

Distributed systems often require agreement among multiple nodes on a single value or a sequence of operations. This is known as distributed consensus. Implementing a robust consensus algorithm is crucial for building fault-tolerant and reliable distributed applications. This challenge will guide you through building a simplified version of the Raft consensus algorithm in Go.

Problem Description

Your task is to implement a basic Raft consensus module in Go. Raft is a distributed consensus algorithm designed to be understandable. The goal is to allow a cluster of nodes to agree on a replicated log, even in the face of failures. For this challenge, you will focus on the core leader election and log replication mechanisms.

Key Requirements:

  1. Node States: Implement the three Raft node states: Follower, Candidate, and Leader.
  2. Leader Election:
    • Followers time out and become Candidates.
    • Candidates request votes from other nodes.
    • A Candidate becomes Leader if it receives votes from a majority of servers.
    • Leaders send heartbeats to followers to maintain authority.
  3. Log Replication:
    • When a client proposes an entry to the Leader, the Leader appends it to its log and sends AppendEntries RPCs to Followers.
    • Followers append the entry if the RPC is valid (matching log index and term).
    • Once an entry is replicated to a majority, the Leader commits it.
  4. RPC Communication: You'll need to simulate or implement a simple RPC mechanism for nodes to communicate with each other (e.g., using Go's net/rpc or a custom channel-based approach).
  5. Term Management: Implement Raft's term-based logic. If a node receives an RPC with a higher term, it reverts to Follower state and updates its term.

Expected Behavior:

  • When a cluster starts, one node should eventually become the Leader.
  • The Leader should periodically send heartbeats to Followers.
  • If a Leader fails, a new Leader should be elected.
  • The Leader should be able to replicate log entries to Followers.

Edge Cases to Consider:

  • Network partitions: While a full implementation is complex, consider how your design might handle nodes being temporarily unreachable.
  • Node failures and restarts: How does a node rejoin the cluster and catch up?
  • Multiple nodes timing out simultaneously.

Examples

Due to the distributed nature of this problem, providing concrete input/output examples is challenging. Instead, we'll describe scenarios and expected outcomes.

Scenario 1: Initial Cluster Start

  • Setup: A cluster of 3 nodes (Node A, Node B, Node C) starts simultaneously. No leader is present.
  • Expected Outcome:
    • Initially, all nodes are Followers.
    • After a random timeout, one node (e.g., Node A) becomes a Candidate and starts an election.
    • Node A sends RequestVote RPCs to Node B and Node C.
    • If Node B and Node C grant their votes to Node A, Node A becomes the Leader.
    • Node A then starts sending AppendEntries (heartbeats) to Node B and Node C.
    • Node B and Node C remain Followers.

Scenario 2: Leader Failure and New Election

  • Setup: Node A is the Leader, Node B and Node C are Followers. Suddenly, Node A stops responding.
  • Expected Outcome:
    • Node B and Node C, after their election timeouts expire, become Candidates.
    • Both Node B and Node C send RequestVote RPCs to each other.
    • One of them (e.g., Node B) receives a vote from Node C (or vice versa) and a majority is reached. Node B becomes the new Leader.
    • Node B starts sending heartbeats to Node C.

Scenario 3: Log Replication

  • Setup: Node A is the Leader, Node B and Node C are Followers. The cluster has successfully elected a leader. A client sends a command to Node A to log "set x=10".
  • Expected Outcome:
    • Node A appends "set x=10" to its log.
    • Node A sends an AppendEntries RPC to Node B and Node C containing the new log entry.
    • If Node B and Node C have consistent logs with Node A up to the point of the new entry, they append it to their logs.
    • Once Node A knows the entry is replicated to a majority (e.g., Node B and C), it commits the entry and responds to the client.

Constraints

  • The cluster will consist of an odd number of nodes (e.g., 3, 5, 7).
  • The communication between nodes will be reliable (no message loss, though messages can be delayed).
  • You should implement at least the core leader election and a simplified log replication for a single log entry.
  • Focus on correctness for a small number of nodes and log entries. Performance optimization is secondary.
  • Your implementation should be in Go.

Notes

  • This challenge requires understanding concepts like timeouts, RPCs, state machines, and majority voting.
  • Consider using Go's context package for managing RPC deadlines and cancellations.
  • Think about how to represent Raft messages and how nodes will handle incoming messages based on their current state.
  • You'll likely need to simulate network communication. Using channels for intra-process communication can be a good starting point for local testing before moving to actual network RPCs.
  • A common approach is to have each node run in its own goroutine and communicate via channels or a simulated RPC layer.
  • Refer to the original Raft paper for detailed specifications of the algorithm.
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