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This article, the second in a series on Raft, explains the election mechanism, server states, timers, RPC handling, and partition scenarios while providing complete Go code examples for the consensus module and its interactions.
This article provides a thorough introduction to the Raft consensus algorithm, explaining its purpose, core components such as state machine replication, log and consensus module, leader‑follower model, client interaction, fault‑tolerance considerations, the CAP trade‑off, and why Go is a suitable implementation language.
This article introduces the Raft distributed consensus algorithm, explains its core concepts such as replicated state machines, leader‑follower roles, client interaction, fault tolerance, the CAP trade‑off, and why Go is a suitable language for implementing Raft.
This article explains linearizability in distributed systems, compares Raft read strategies, and describes how CB‑SQL uses range leases and node leases to achieve strong consistency while reducing overhead, illustrating the trade‑offs and evolutionary design choices behind its consensus mechanisms.
This article explains the fundamentals of distributed consistency by introducing the Raft consensus algorithm, covering its roles, leader election, log replication, handling of split votes, random timeouts, and various failure scenarios such as leader crashes and network partitions.
This article explains distributed data consistency concepts, the CAP theorem, various consistency levels, and provides detailed overviews of three major consensus algorithms—Paxos, ZAB, and Raft—including their mechanisms, roles, and practical applications such as in CB‑SQL.
This article explains the fundamentals of distributed consistency, introduces key terminology, and provides an in‑depth look at Raft's leader election, term handling, log replication, failure scenarios, and comparisons with Paxos, ZAB, PacificA, and Viewstamped Replication.
The article explains how the CAP theorem forces trade‑offs between consistency, availability and partition tolerance, then surveys distributed commit protocols (2PC, 3PC) and consensus algorithms (Paxos, Raft, Zookeeper’s ZAB), and shows their practical use in systems such as ZanKV that combine Raft with RocksDB for strongly consistent, fault‑tolerant key‑value storage.
The article compares Raft, a crash‑only consensus used in private blockchains with leader election and O(N) communication, to PBFT, a Byzantine‑tolerant protocol for consortium chains that requires 3f+1 nodes, uses three‑phase messaging, O(N²) complexity, and different fault‑tolerance limits.
This article explains the fundamentals of Raft and PBFT consensus algorithms, compares their processes, fault tolerance limits, and suitability for private and consortium blockchains, and highlights key differences in leader election, message handling, and fault models.