This article explores the challenges of distributed transactions in microservice architectures, explains consistency theories like CAP and BASE, compares classic 2PC with eBay's event‑queue approach, TCC compensation, and cache‑based eventual consistency, and offers practical guidance for choosing the right solution.
This article explains the challenges of consistency in distributed systems, introduces the CAP theorem and ACID properties, describes common distributed transaction techniques such as local message tables, transactional message middleware like RocketMQ, and details the two‑phase and three‑phase commit protocols with their advantages and drawbacks.
This article explains distributed system fundamentals—CAP theorem, ACID properties, consistency models, and how 2PC and 3PC protocols, along with message‑middleware solutions like RocketMQ, address the challenges of achieving reliable complex distributed transactions.
This article explains the core concepts of distributed system consistency—including the CAP theorem, ACID properties, various distributed transaction techniques, and the two‑phase and three‑phase commit protocols—while illustrating practical implementations with message queues and local message tables.
This article explores core distributed system design principles, detailing the CAP theorem and its implications, the BASE extension, shared‑nothing architecture, various load‑balancing algorithms and deployment modes, as well as high‑availability strategies such as active‑standby, active‑active, and clustering to eliminate single points of failure.
This article examines the challenges of consistency in large‑scale distributed service systems, presents real‑world case studies such as payment transfers and order processing, and outlines practical patterns—including ACID/BASE theory, two‑phase and three‑phase commit, TCC, query, compensation, periodic reconciliation, and reliable messaging—to help engineers design robust, eventually consistent architectures.
This article explains the theory and practical techniques behind synchronizing state across data centers using a two‑site three‑center model, covering distributed protocols like Paxos and Raft, CAP trade‑offs, latency challenges, and high‑availability designs for robust database replication.
This article explores core system architecture concepts—including the CAP theorem and its BASE extension, the shared‑nothing design, various load‑balancing algorithms and deployment modes, and high‑availability patterns such as active‑standby, active‑active and clustering—providing practical guidance for building scalable, reliable distributed applications.
This article introduces core distributed‑system concepts—including the definition of consensus, the two‑phase and three‑phase commit protocols, the CAP theorem and its engineering implications, and logical‑clock mechanisms such as Lamport timestamps, vector clocks, and version vectors—explaining their models, challenges, and practical trade‑offs.
This article explains the fundamentals of transactions, the ACID properties, isolation levels, distributed transactions, the CAP theorem, and the BASE model, illustrating how they shape consistency, availability, and reliability in modern database and distributed system design.
This article examines the fundamental challenges of achieving consistency in distributed systems, explains the CAP theorem, compares two‑phase and three‑phase commit protocols, explores XA transactions, and presents practical compensation patterns such as local message tables, non‑transactional and transactional MQ designs, highlighting their trade‑offs and applicability.
This article explains the importance of data storage, describes single‑node storage engines and data models, outlines transaction and concurrency control, and covers distributed storage principles, CAP and FLP theorems, 2PC and Paxos protocols, as well as redundancy, backup, and failover mechanisms.
An in‑depth overview of NoSQL databases explains the limitations of traditional relational systems, details ACID properties, introduces the CAP theorem and BASE model, compares RDBMS with NoSQL, outlines advantages, disadvantages, history, classifications, and real‑world usage examples.
This article explores the core difficulties of distributed systems—including network latency, failures, the CAP theorem, consistency models, and common techniques such as consistent hashing, quorum, vector clocks, lease mechanisms, gossip protocols, and distributed transaction protocols—providing practical insights and references for building robust scalable architectures.
This article explains the CAP theorem—its three core properties, why a distributed system can satisfy at most two of them, the formal proof, practical strategies for handling partitions, and how modern databases and architectures attempt to work around its constraints.
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.
