The Raft consensus algorithm provides a more understandable alternative to previous protocols like Paxos while maintaining strong consistency guarantees in distributed systems. By breaking consensus into three distinct components—leader election, log replication, and safety—Raft creates a clear mental model for developers. Its widespread adoption spans distributed databases, configuration management, container orchestration, microservices infrastructure, and blockchain systems. Despite inherent challenges, including leader bottlenecks and brief unavailability during leader changes, Raft offers significant benefits through its straightforward design. Current innovations address these limitations through performance optimizations, multi-Raft architectures, formal verification, edge computing adaptations, and educational tools, ensuring the algorithm’s continued relevance as distributed computing evolves.
Keywords: Algorithm, consensus, distributed, fault-tolerance, replication
