🍠Frontline tech director and chief architect at top-tier companies 🥝 Years of deep experience in internet, e‑commerce, social, and finance sectors 🌾 Committed to publishing high‑quality articles covering core technologies of leading internet firms, application architecture, and AI breakthroughs.
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 explores the key challenges of building a large‑scale instant‑messaging service—including message reliability, ordering, read‑sync, data security, avalanche effects, and weak‑network handling—and presents practical architectural and algorithmic solutions for each problem.
This article explains the default repeatable‑read isolation level in InnoDB, details the three main lock types—record, gap, and next‑key—and shows how SELECT … FOR UPDATE applies different locking rules depending on primary key, unique, ordinary, range, or table‑scan queries, with practical SQL examples.
Redis Sentinel provides automatic monitoring, fault detection, and failover for Redis master‑slave clusters, enabling high availability by electing a new master when the original fails, using sdown/odown states, quorum voting, and pub/sub communication to keep services running with minimal downtime.
Microservices have evolved from early SOA to container‑driven, cloud‑native architectures, offering fine‑grained, loosely coupled services with benefits like scalability, independent deployment, and fault isolation, while also presenting challenges such as distributed complexity, testing, and operational overhead, and are best adopted when traffic, team size, or rapid iteration demand it.
This article explores the challenges of maintaining data consistency under high concurrency in distributed systems, reviewing common consistency issues, distributed lock implementations, optimistic and pessimistic strategies, CAS and ABA problems, and practical solutions such as Redis locks, Zookeeper, and transaction protocols.
This article explains Redis master‑slave replication, covering read‑write separation, configuration steps for master and slave nodes, the PSYNC synchronization protocol, and how these mechanisms together provide high availability and consistent data across distributed Redis deployments.
This article explains the evolution of distributed systems, outlines their benefits and challenges, and details key consistency mechanisms such as the CAP theorem, XA two‑phase and three‑phase commit, MQ‑based transactions, and the TCC pattern, with real‑world application scenarios.
This article examines the challenges of handling millions of simultaneous requests to hot data, explains why cache misses can cause database overload, and presents a comprehensive set of Redis‑based techniques—including pre‑warming, staggered expiration, request filtering, queueing, distributed locking, rate limiting, and fallback strategies—to ensure stability and prevent cache avalanche, penetration, and breakdown.
This article enumerates the most frequent situations that cause MySQL indexes to become ineffective—such as violating the left‑most rule, using functions or expressions on indexed columns, type conversions, improper LIKE patterns, OR/IN/NOT IN usage, and inequality operators—and demonstrates how to reproduce and verify each case with sample data and SQL scripts.
