Implementing Raft: A Deep Dive into Distributed Consensus

This is the first post in a series detailing the Raft distributed consensus algorithm and its Go implementation. Raft solves the problem of replicating a deterministic state machine across multiple servers, ensuring service availability even with server failures. The post introduces core Raft components: the state machine, log, consensus module, leader/follower roles, and client interaction. It discusses Raft's fault tolerance, the CAP theorem, and the choice of Go as the implementation language. Subsequent posts will delve into the algorithm's implementation.