This article explains the principles of distributed storage, the CAP theorem and consistency models, compares popular distributed file systems, and provides a detailed overview of MogileFS architecture, features, and operation workflow.
This article explains the challenges of maintaining data consistency across multiple services in distributed e‑commerce architectures and presents six practical solutions—including business integration, the eBay BASE pattern, Qunar's approach, Mogujie's design, Alipay's DTS, and Nongxin's scheme—highlighting their advantages, drawbacks, and implementation details.
The article explains how multi‑datacenter architectures prevent total service loss, improve latency by placing services near users, and balance the CAP trade‑offs through models like AC, CP, and AP, while outlining practical design, sharding, monitoring, and failover strategies for large‑scale backend systems.
The article explains why consistency is essential in distributed systems, describes the CAP theorem, outlines various consistency models such as strong, weak, and eventual consistency, and discusses the trade‑offs between data correctness and system performance.
The article revisits the CAP theorem, explains why the classic "two‑of‑three" interpretation is misleading, compares ACID, BASE and CAP, discusses the role of latency, and presents practical strategies for detecting, handling, and recovering from network partitions in distributed systems.
The article examines the challenges of scaling a single‑server data service, compares data partitioning and replication, explains consistency models, and surveys distributed transaction protocols such as 2PC, 3PC, Paxos, and Dynamo's NWR model, highlighting their trade‑offs in availability, consistency, and performance.
