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134 results

Cloud Native Nov 16, 2021 Baidu Intelligent Testing

Service Governance and etcd: Concepts, Raft & BoltDB Implementation, and Large‑Scale Practices at Baidu

This article introduces service governance fundamentals, explains how etcd’s Raft‑based consensus and BoltDB storage work, compares etcd with ZooKeeper and Consul, and describes Baidu’s large‑scale, high‑availability, high‑performance service‑governance platform built on these technologies.

cloud-nativemicroservicesservice governanceRaftetcdBoltDB

Backend Development Oct 27, 2021 Tencent Cloud Developer

Understanding and Implementing the Raft Consensus Algorithm in Go

This guide walks readers through building a complete Raft consensus system in Go, detailing leader election, log replication, state persistence, snapshotting, and committed‑entry application, while explaining key structures, RPCs, golden rules, and offering advice on using mature libraries versus custom implementations.

GopersistencesnapshotRaftdistributed consensuslog replication

Fundamentals Jun 30, 2021 Sohu Tech Products

Raft Log Replication: Format, Process, and Consistency Guarantees

This article explains the Raft consensus algorithm’s log format, the step‑by‑step log replication process, how leaders ensure consistency through forced overwrites, and details the AppendEntries RPC parameters, illustrated with diagrams and examples.

Distributed SystemsConsistencyRaftConsensusLog Replication

Backend Development Jun 16, 2021 Ops Development Stories

How Raft Achieves Consensus: Leader Election, Log Replication, and State Machine Explained

This article explains the core mechanisms of the Raft consensus algorithm—including leader election, log replication, safety guarantees, message structures, state transitions, and key Go implementations in etcd-raft—providing code examples and detailed analysis of functions such as becomeLeader, tickElection, and appendEntry.

Distributed SystemsGoRaftConsensusetcd

Fundamentals Jun 15, 2021 Tencent Cloud Developer

Understanding the Raft Consensus Algorithm: Principles, Workflow, and Comparison with Paxos

Raft is a leader-based consensus algorithm designed to be easier to understand and implement than Paxos, decomposing consensus into leader election, log replication, and safety rules; it ensures consistent, gap‑free logs, comparable performance, and simpler deployment for distributed systems.

Distributed SystemsRaftConsensus AlgorithmLeader ElectionLog ReplicationPaxos Comparison

Databases May 29, 2021 Full-Stack Internet Architecture

Understanding Redis Cluster Architecture and Strong Consistency with Raft

This article explains Redis Cluster's decentralized sharding design, the role of master‑slave replication for high availability, and how strong consistency is achieved using consensus protocols such as Raft, highlighting key concepts like global log indexes and commit pointers.

Distributed SystemsDatabaseRedisClusterConsistencyRaft

Fundamentals Apr 14, 2021 vivo Internet Technology

An Overview of the Raft Distributed Consensus Algorithm

Raft is a fault‑tolerant distributed consensus protocol that simplifies Paxos by electing a single leader each term to coordinate client requests, replicate logs to a majority of servers, ensure safety through up‑to‑date voting, handle failures with randomized timeouts, resolve log conflicts, and compress logs via snapshots.

fault toleranceRaftleader electiondistributed consensuslog replication

Fundamentals Feb 26, 2021 Tencent Cloud Developer

Distributed Consistency Algorithms: CAP, BASE, Paxos, and Raft

From CAP and BASE trade‑offs to the rigorous Paxos consensus and the more approachable Raft protocol, this article explains how modern distributed systems achieve consistency despite partitions, failures, and latency, detailing roles, phases, and safety guarantees that underpin reliable micro‑service architectures.

distributed systemsBASE theoryCAP theoremconsistencyRaftPaxos

Fundamentals Feb 21, 2021 Top Architect

Understanding the Raft Consensus Algorithm: States, Leader Election, Consistency, and Split‑Brain Handling

This article provides a comprehensive overview of the Raft consensus algorithm, detailing node roles (follower, candidate, leader), the leader election process, mechanisms ensuring cluster consistency, handling of node failures, and strategies for resolving split‑brain scenarios in distributed systems.

Distributed SystemsConsistencyRaftConsensusLeader ElectionSplit-Brain

Fundamentals Jan 26, 2021 Wukong Talks Architecture

Explaining the Raft Consensus Algorithm with Animated Diagrams

This article uses animated diagrams to explain the Raft consensus algorithm, covering its overview, roles, single‑node and multi‑node scenarios, leader election process, term handling, election rules, heartbeat timeouts, and leader failure recovery, helping readers grasp distributed consistency fundamentals.

Distributed SystemsFault ToleranceRaftConsensusLeader Election

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