This article explains Redisson's various distributed lock mechanisms—including watchdog, reentrant lock, multi‑lock, read‑write lock, semaphore, RedLock, and CountDownLatch—detailing their principles, usage patterns, code examples, and best‑practice recommendations for robust backend concurrency control.
This article provides a comprehensive technical walkthrough of Redis, covering its core concepts, data structures, performance tricks, caching pitfalls, persistence options, high‑availability architectures, distributed locking mechanisms, and consistency strategies, all illustrated with concrete examples and code snippets.
This article provides a comprehensive overview of Redis, covering basic concepts, pipeline optimization, publish/subscribe messaging, key expiration strategies, transaction behavior, persistence mechanisms (RDB, AOF, hybrid), distributed locking techniques, Redisson and Redlock algorithms, as well as high‑availability setups using replication, Sentinel and Cluster modes.
This comprehensive guide covers Redis fundamentals, pipeline, pub/sub, expiration strategies, transactions, persistence mechanisms, distributed lock implementations, and the three clustering modes, while also addressing common cache problems and practical deployment steps.
This comprehensive guide explains what Redis is, its core data structures, typical scenarios such as caching, counters, message queues and leaderboards, installation steps on Linux, configuration options, advanced features like pipelines, Lua scripting, persistence mechanisms, replication, caching strategies, consistency challenges, and distributed locking techniques.
This article presents a collection of interview questions covering backend fundamentals such as linked‑list sorting, encryption differences, TCP reliability, IO models, Hystrix, delayed tasks, HTTPS flow, transaction isolation, index pitfalls, virtual memory, Redis leaderboards, distributed locks, zero‑copy techniques, Java synchronized, and Snowflake ID generation.
This article explains what data consistency means for MySQL and Redis, analyzes inconsistency cases in both single‑threaded and concurrent environments, and proposes practical strategies—including read‑only and read‑write cache handling, message‑queue retries, binlog subscription, delayed double‑delete, and distributed locking—to achieve eventual or strong consistency.
Apache Zookeeper is an open‑source coordination service that provides reliable distributed synchronization, configuration management, and naming for big‑data components such as Hadoop, HBase, and Kafka, offering features like hierarchical znode structures, watches, master election, and distributed locks to maintain cluster health.
This article details SuNing Financial’s red‑envelope platform architecture and evolution, covering high‑concurrency challenges, traffic shaping, asynchronous processing, multi‑level caching, Redis‑based distributed locks, task scheduling, payment‑chain isolation, high‑availability deployment, multi‑layer flow control, monitoring, and future scalability directions.
This article introduces ZooKeeper’s fundamental architecture, explains its key concepts such as cluster roles, sessions, ZNodes, watches, and ACLs, and then details how it powers essential distributed coordination tasks—including configuration management, naming services, master election, and distributed locks—in large‑scale Hadoop and HBase ecosystems.
This article revisits the Redlock debate by examining Antirez's rebuttal to Martin Kleppmann, analyzing Hacker News discussions, comparing Redis, ZooKeeper, and Chubby lock implementations, and exploring timing assumptions, clock skew, and fencing token mechanisms to assess distributed lock safety.
This guide offers a complete collection of practical examples that demonstrate Curator's usage for Apache ZooKeeper, covering leader election, distributed locks, barriers, counters, caches, queues, and transaction handling, with each chapter independent and source code available on GitHub.
