Understanding CAP, Byzantine Fault Tolerance, PBFT, Paxos, and Raft Consensus Algorithms
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.
In distributed systems the CAP theorem states that consistency, availability and partition tolerance cannot be simultaneously guaranteed, prompting the need for consistency mechanisms.
The classic Byzantine Generals problem illustrates the difficulty of achieving agreement when some nodes may act maliciously, leading to Byzantine Fault Tolerance (BFT) solutions.
Practical Byzantine Fault Tolerance (PBFT) is a state‑machine replication protocol with O(N^2) complexity that operates in three phases— Pre-Prepare, Prepare and Commit —using primary and backup replicas within a rotating view, where the primary is selected by p = v mod |R|.
Non‑Byzantine consensus algorithms such as Paxos achieve agreement in asynchronous crash‑fault environments; Paxos uses Proposer and Acceptor roles, a two‑phase PrepareRequest / PrepareResponse and AcceptRequest / AcceptResponse process, and can be extended to Multi‑Paxos for continuous log replication.
Raft simplifies consensus by decomposing it into leader election, log replication, safety, log compaction and membership changes, using Leader, Follower and Candidate roles, and is widely adopted in production systems.
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Big Data Technology & Architecture
Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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