Abstract

Raft is a consensus algorithm for managing a replicated log. It produces a result equivalent to (multi-)Paxos, and it is as efficient as Paxos, but its structure is different from Paxos; this makes Raft more understandable than Paxos and also provides a better foundation for building practical systems. In order to enhance understandability, Raft separates the key elements of consensus, such as leader election, log replication, and safety, and it enforces a stronger degree of coherency to reduce the number of states that must be considered. Results from a user study demonstrate that Raft is easier for students to learn than Paxos. Raft also includes a new mechanism for changing the cluster membership, which uses overlapping majorities to guarantee safety.

Key Concepts

Consensus Problem

Consensus algorithms allow a collection of machines to work as a coherent group that can survive the failures of some of its members. They play a key role in building reliable large-scale software systems.

Raft's Design Principles

Understandability First

Raft was designed with understandability as the primary goal. The algorithm separates key elements of consensus and reduces the degree of nondeterminism compared to Paxos.

Strong Leadership

Raft uses a stronger form of leadership than other consensus algorithms. Log entries only flow from the leader to other servers, simplifying the management of the replicated log.

Randomized Leader Election

Raft uses randomized timers to elect leaders, adding only a small amount of mechanism to the heartbeats already required for any consensus algorithm while resolving conflicts simply and rapidly.

Membership Changes

Raft's mechanism for changing the set of servers in the cluster uses a new joint consensus approach where the majorities of two different configurations overlap during transitions.

Core Components

Leader Election

• Election Process: When a follower doesn't receive heartbeat from leader, it becomes candidate and starts election
• Voting: Candidates request votes from other servers
• Term Numbers: Each election has a unique term number
• Randomized Timeouts: Prevents split votes in most cases

Log Replication

• Log Entries: Each entry contains command, term number, and index
• AppendEntries RPC: Leader sends log entries to followers
• Consistency Check: Followers verify log consistency before accepting entries
• Commitment: Entries are committed when replicated to majority

Safety Properties

• Election Safety: At most one leader per term
• Leader Append-Only: Leader never overwrites or deletes entries
• Log Matching: If two logs contain entry with same index and term, they are identical
• Leader Completeness: Leader contains all committed entries from previous terms

Algorithm States

Follower

• Receives RPCs from leader and candidates
• Responds to AppendEntries and RequestVote RPCs
• Becomes candidate if election timeout elapses

Candidate

• Starts new election term
• Votes for self
• Requests votes from other servers
• Becomes leader if receives majority votes
• Returns to follower if discovers current leader

Leader

• Sends AppendEntries heartbeats to all followers
• Receives client requests and appends to log
• Commits entries when replicated to majority
• Handles client requests

Membership Changes

Joint Consensus

• Uses overlapping majorities during configuration changes
• Old configuration: Cold
• New configuration: Cnew
• Joint configuration: Cold,new (majority of both required)

Safety Guarantees

• Prevents split-brain scenarios during membership changes
• Ensures only one leader exists at any time
• Maintains log consistency across configuration changes

Real-World Applications

Systems Using Raft

• etcd: Distributed key-value store used by Kubernetes
• Consul: Service discovery and configuration management
• CockroachDB: Distributed SQL database
• TiKV: Distributed transactional key-value database
• LogCabin: Distributed storage system

Comparison with Paxos

Advantages of Raft

• Understandability: Easier to learn and implement
• Strong Leadership: Simplifies log management
• Better Documentation: Clear separation of concerns
• Practical Implementation: Designed for real systems

Paxos Limitations

• Complexity: Difficult to understand and implement correctly
• Multiple Roles: Proposers, acceptors, learners
• Optimization Challenges: Requires complex optimizations for practical use

Implementation Considerations

Performance Optimizations

• Batching: Group multiple log entries in single RPC
• Pipelining: Send multiple AppendEntries without waiting for responses
• Read Optimization: Serve reads without going through log replication

Fault Tolerance

• Network Partitions: Handles split-brain scenarios
• Node Failures: Continues operating with majority of nodes
• Timing Independence: Doesn't rely on synchronized clocks

Practical Considerations

• Snapshotting: Compress log by taking periodic snapshots
• Log Compaction: Remove old log entries safely
• Client Interaction: Handle client requests efficiently

Educational Value

Learning Outcomes

• Consensus Understanding: Clear grasp of distributed consensus
• System Design: Foundation for building reliable distributed systems
• Algorithm Analysis: Understanding of correctness and performance

Teaching Benefits

• Separation of Concerns: Clear division between election, replication, and safety
• State Machine Approach: Intuitive state transitions
• Visual Aids: Easy to visualize and explain

Research Impact

Academic Contributions

• Formal Specification: Complete TLA+ specification available
• Safety Proofs: Formal verification of correctness properties
• Performance Analysis: Comprehensive evaluation against Paxos

Industry Adoption

• Open Source: Multiple implementations available
• Production Use: Deployed in mission-critical systems
• Community Support: Active development and maintenance

Future Directions

Ongoing Research

• Performance Improvements: Optimizations for specific workloads
• Security Extensions: Byzantine fault tolerance adaptations
• Scalability Enhancements: Support for larger clusters

Practical Developments

• Tool Integration: Better debugging and monitoring tools
• Configuration Management: Improved membership change protocols
• Cross-Datacenter: Extensions for geo-distributed deployments

Interview Questions

Basic Concepts

1. What is Raft and how does it differ from Paxos?
   • Raft is a consensus algorithm designed for understandability
   • Uses strong leadership model vs Paxos's peer-to-peer approach
   • Separates leader election, log replication, and safety
2. Explain the three states in Raft
   • Follower: Receives RPCs, becomes candidate on timeout
   • Candidate: Starts election, requests votes, becomes leader with majority
   • Leader: Sends heartbeats, replicates log entries, handles client requests
3. How does Raft handle leader election?
   • Followers become candidates when election timeout elapses
   • Candidates request votes from all servers
   • Server with majority votes becomes leader
   • Randomized timeouts prevent split votes

Advanced Topics

4. What are Raft's safety properties?
   • Election safety: at most one leader per term
   • Leader append-only: leader never overwrites entries
   • Log matching: logs with same index/term are identical
   • Leader completeness: leader has all committed entries
5. How does Raft handle membership changes?
   • Uses joint consensus with overlapping majorities
   • Prevents split-brain during configuration changes
   • Ensures safety while allowing cluster reconfiguration
6. Compare Raft's performance with Paxos
   • Similar performance characteristics
   • Raft may have slightly higher latency due to strong leadership
   • Better throughput for read-heavy workloads
   • Easier to optimize and tune

Implementation Questions

7. How would you implement Raft in a distributed system?
   • Implement three states with state machine
   • Use RPCs for AppendEntries and RequestVote
   • Handle timeouts and elections
   • Implement log replication and commitment
8. What optimizations can improve Raft performance?
   • Batching multiple log entries
   • Pipelining AppendEntries RPCs
   • Optimizing read operations
   • Using snapshots for log compaction
9. How does Raft handle network partitions?
   • Continues operating with majority partition
   • Minority partition cannot elect leader
   • Automatically recovers when partition heals
   • Maintains consistency across partitions

System Design

10. When would you choose Raft over other consensus algorithms?
    • When understandability is important
    • For systems requiring strong consistency
    • When building distributed databases or key-value stores
    • For educational or research purposes

Conclusion

Raft represents a significant advancement in consensus algorithm design, prioritizing understandability without sacrificing performance or correctness. Its clear separation of concerns, strong leadership model, and comprehensive documentation make it an excellent choice for both educational purposes and practical system implementation. The algorithm's success in industry adoption and continued research development demonstrates its value as a foundation for building reliable distributed systems.

The paper's contribution extends beyond the algorithm itself, providing a framework for thinking about consensus that has influenced subsequent research and system design. Raft's emphasis on understandability has made distributed consensus more accessible to practitioners and students alike, contributing to the broader goal of building more reliable and maintainable distributed systems.