This article explains the Byzantine Generals problem, its fault‑tolerant challenges in distributed systems, and how the Raft consensus algorithm addresses these issues by using leader election, log replication, and safety mechanisms, while also comparing Raft with other algorithms such as Paxos, ZAB and PBFT.
Understanding Paxos and other consensus algorithms, this article explains how crash‑fault‑tolerant protocols like Paxos, Raft, and ZAB achieve sequential consistency through two‑phase voting, contrasts them with Byzantine‑fault‑tolerant methods, and details Multi‑Paxos optimizations, leader election, and practical trade‑offs for distributed systems.
This article uses the popular Chinese card game Three Kingdoms to illustrate the Byzantine Generals Problem, explaining its relevance to distributed consensus, outlining four foundational theories and eight protocols, and demonstrating how voting, message tampering, and signature mechanisms affect agreement among nodes.
This article explains the CAP theorem, illustrates the Byzantine Generals problem, and provides detailed overviews of PBFT, Paxos (including Multi‑Paxos), and Raft consensus algorithms, highlighting their phases, roles, and practical considerations for achieving consistency in distributed systems.
Distributed systems rely on consensus to ensure consistent results, but achieving it faces fundamental challenges such as network unreliability, node failures, and trade‑offs captured by the CAP theorem, FLP impossibility, and various algorithms like Paxos, Raft, and Byzantine Fault Tolerance, each balancing consistency, availability, and safety.
