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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.
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 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.
This article explains the evolution from centralized to distributed architectures, the role of Zookeeper in solving consistency problems, the mechanics and drawbacks of 2‑phase and 3‑phase commits, and key consensus algorithms such as Paxos, Raft, ZAB, as well as CAP and BASE theories that guide practical system design.
This article provides a detailed summary of database concepts including data models (relational, document, graph), storage engine architectures (page‑oriented B‑tree, log‑structured LSM), transaction mechanisms, isolation levels, distributed transaction protocols, partitioning strategies, indexing, consistency models, and consensus algorithms such as Lamport timestamps and Raft.
This article explains why distributed theory and algorithms are crucial for architects, outlines the four foundational theories and eight key protocols, discusses their four evaluation dimensions, and provides a step‑by‑step learning roadmap illustrated with stories and practical examples.
This article explains the Byzantine Generals Problem, maps its concepts to distributed consensus, distinguishes consensus from consistency, outlines oral‑message and signed‑message solutions, and analyzes their applicability and limitations in fault‑tolerant distributed systems.
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.
This article explains the core challenges of achieving consistency in distributed systems, describes consensus algorithms such as Paxos and Raft, discusses theoretical limits like the FLP impossibility and CAP theorem, and shows how trade‑offs among consistency, availability, and partition tolerance shape practical system design.