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This article explains the Paxos consensus algorithm, detailing its roles (proposer, acceptor, learner), the two-phase prepare and accept process, handling of proposal numbers, and how it ensures strong consistency across distributed nodes through examples and diagrams.
This article objectively compares NewSQL distributed databases with traditional middleware‑based sharding, examining their architectural advantages, distributed transaction handling, high‑availability mechanisms, scaling and sharding strategies, storage engine differences, ecosystem maturity, and provides guidance on selecting the appropriate solution for various workloads.
This article examines high‑concurrency distributed consistency algorithms—explaining the CAP challenges, detailing Paxos, Raft, and ZAB’s core concepts, roles, and workflow, and discussing their practical applications and selection criteria for ensuring strong data consistency in critical systems.
This article explains the principles, roles, phases, and typical application scenarios of four major distributed consensus algorithms—Paxos, Raft, ZAB, and Gossip—while also offering promotional material for extensive architecture and interview resources.
Distributed leader election ensures a single node performs exclusive tasks in high‑availability systems, with common implementations like Etcd, Kafka, Elasticsearch, and Zookeeper using algorithms such as Paxos, Raft, and ZAB, while Kubernetes provides the Lease resource to manage leader election via a distributed lock.
This article explains consistency in distributed systems, distinguishing strong and eventual consistency, outlines the CAP and FLP impossibility theorems, and details common consensus mechanisms such as two‑phase commit, three‑phase commit, and the Paxos algorithm.
This article explains the concept of consistency in distributed systems, distinguishes strong and weak (eventual) consistency, outlines typical use cases and challenges, and reviews key protocols such as 2‑Phase Commit, 3‑Phase Commit, Paxos, and Raft, while referencing the FLP and CAP theorems.
This article explains the Byzantine Generals problem, its fault‑tolerance limits, and how the Raft consensus algorithm solves a simplified version of the problem through leader election, log replication, and safety mechanisms, while also comparing Raft with Paxos, ZAB, and PBFT and providing Go code examples.
The article explains how Paxos can be generalized by defining its round number (rnd) over any partially ordered set, enabling both forced and non‑forced conflict exclusion mechanisms similar to 2PC, and showing how this expands Paxos’s applicability to multi‑dimensional transaction ordering and simplifies distributed database architectures.
The article presents the architecture of WLock, a Paxos‑based distributed lock service, analyzes key isolation schemes, evaluates cluster expansion and splitting, and details a multi‑step key migration process—including forward and reverse migration, node scaling, and consistency safeguards—to achieve high‑availability and isolated lock handling in multi‑tenant environments.
The article reviews OceanBase's distributed relational database design, its integrated architecture, high‑compression LSM‑tree storage engine, Paxos‑enhanced two‑phase commit protocol, and how these innovations enabled the system to set successive world records in the TPC‑C benchmark, illustrating China's growing database capabilities.
The OceanBase 4.0 core technology presentation recounts twelve years of evolution from the early 0.1 version to a single-node distributed integrated database, highlighting breakthroughs such as sub-8-second RTO, RPO = 0, Paxos three-replica high availability, and competitive performance in TPC-C and TPC-H benchmarks.
This article provides a comprehensive introduction to the Paxos consensus algorithm, explaining its purpose, core concepts, roles of proposers, acceptors and learners, safety and liveness properties, and the step‑by‑step derivation of its two‑phase protocol for achieving fault‑tolerant distributed consistency.
This article presents abstract‑paxos, a unified framework that derives Paxos and Raft from first principles, defines information certainty, quorum, and a total order on states using commit_index, and details a two‑phase protocol, member‑change handling, and how the model maps to classic Paxos and Raft implementations.
This article explains the core principles and a range of consistency protocols—including CAP, ACID, BASE, 2PC, 3PC, Paxos, Raft, Gossip, NWR, Quorum, and Lease—detailing their roles, mechanisms, trade‑offs, and typical use cases in distributed systems.
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.
The article explains consistency concepts in distributed systems—including strong and weak (eventual) consistency, CAP and FLP theorems, and key protocols such as 2PC, 3PC, and Paxos—detailing their mechanisms, advantages, and challenges.
This article provides a comprehensive overview of distributed system fundamentals, covering system models, replicas, performance and availability metrics, data distribution strategies, replica protocols such as lease, quorum, two‑phase commit, MVCC, Paxos, and the CAP theorem, with practical engineering considerations.
This article provides a comprehensive overview of distributed system fundamentals, covering node models, replica concepts, various consistency levels, data distribution strategies, lease-based caching, quorum mechanisms, two‑phase commit, MVCC, Paxos consensus, and the CAP theorem, illustrating each with practical examples and diagrams.
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.