This article introduces a comprehensive Spring Boot 3 practical case collection and walks through building a reentrant, auto‑renewing distributed lock using Redis and Lua scripts, providing full code snippets and configuration guidance for robust backend concurrency control.
This guide explores 16 practical Redis use cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tags, filtering, follow models, and ranking—providing code snippets and implementation tips for each pattern.
The article explains how Java's synchronized keyword works only within a single JVM, why it fails to provide mutual exclusion across multiple processes in distributed systems, and presents alternative distributed locking mechanisms such as database locks and Redis-based locks with code examples.
This article explains the concept of distributed locks and compares three common implementation approaches—using a database, Redis, and ZooKeeper—detailing their mechanisms, advantages, drawbacks, and suitable scenarios for ensuring consistent access to shared resources in distributed systems.
This article explains how to use Redis key expiration notifications combined with a strategy pattern to create real‑time overdue reminders for workflow tasks, covering configuration, listener implementation, distributed locking, message handling, and practical usage in a SpringBoot project.
This article reviews Java Spring code for a coupon claim and approval workflow, identifies concurrency and transaction issues such as missing locks and inconsistent update order, and proposes optimizations including user‑level locking, simplifying lock management, and aligning transactional update sequences to prevent deadlocks.
This article introduces Lua scripting in Redis, explains its fundamentals and advantages, and provides step‑by‑step instructions for integrating and executing Lua scripts within a Spring Boot application, including code examples, performance optimization, error handling, security considerations, and best practices.
This article explains how Redisson leverages Redis, Lua scripts, and Pub/Sub to implement a robust distributed lock in Java, covering lock characteristics, client creation, acquisition, renewal, and release mechanisms with code examples.
This article explains how to protect time‑consuming business operations and critical data by designing a Redis‑based distributed lock using custom annotations, Spring AOP, and a ScheduledExecutorService that automatically extends the lock’s expiration to avoid premature release.
This article explains the challenges of thread synchronization in distributed systems, introduces the concept of distributed locks, compares common implementations such as Redis, Zookeeper, and databases, and provides robust Java and Lua solutions to ensure atomicity, avoid deadlocks, and support lock renewal.
The Nacos 3.0‑Alpha release introduces finer‑grained API classification, default security per API type, a revised default namespace handling, native xDS support, a redesigned Admin API with flexible console/engine deployment, experimental distributed‑lock capability, and plans to upgrade to Spring Boot 3 and JDK 17, all outlined in the community roadmap.
This article compiles sixteen practical Redis use‑cases—from caching and distributed locks to rate limiting, bitmaps, shopping carts, timelines, queues, likes, tags, filtering, follow/fan relationships, and leaderboards—providing code snippets and command examples to help backend developers design scalable systems.
Distributed locks synchronize multiple services across nodes, and can be implemented using Redis’s fast, AP‑oriented SET‑NX with automatic TTL renewal or Zookeeper’s CP‑oriented ephemeral sequential nodes, each offering distinct trade‑offs in performance, consistency, and suitability for various workload requirements.
This article explains how to implement request debouncing and distributed locking in a Java backend using Redis and Redisson, covering the definition of debounce, identifying suitable APIs, generating unique keys, and providing complete code examples for both cache‑based and Redisson‑based lock mechanisms.
This article explains why API debouncing is crucial for backend Java services, outlines which endpoints need protection, describes how to identify duplicate requests, and provides step‑by‑step implementations using Redis shared‑cache and Redisson distributed locks with concrete code examples and test results.
The article examines how frequent inventory adjustments in JD retail supply‑chain can suffer from concurrency issues, explains why traditional locking may fail, analyzes a real‑world case of lock misuse, and presents code‑level, database‑level, and architectural solutions to guarantee data consistency.
This article explains the Redisson IllegalMonitorStateException caused by unlocking a lock held by another thread, analyzes the underlying node‑id and thread‑id concepts, and presents several practical solutions to correctly manage distributed locks in Java applications.
This article walks through a zero‑to‑one charging‑station system built with Spring Cloud microservices, Vue front‑end, and a suite of enterprise features such as distributed locks, idempotency, multi‑level caching, and custom starters, providing concrete design documents and module breakdowns to help candidates showcase real project experience in interviews.
This article explains the concept of request debouncing, identifies which API endpoints need it, and demonstrates two distributed lock solutions—shared Redis cache and Redisson—by providing complete Java code examples, configuration steps, and test results to prevent duplicate submissions in backend services.
This article introduces tldb's distributed lock, explains its lock, trylock, and unlock methods, and provides step‑by‑step Go and Java client examples—including Maven configuration and code snippets—for acquiring and releasing locks across languages in a distributed environment.
This article explains how to implement distributed locks in Redis using the SETNX command, atomic Lua scripts, the Redisson Java client, and the RedLock algorithm, providing code examples and discussing their advantages and limitations in distributed backend systems.
This tutorial explains how to implement IP‑and‑URL rate limiting in a Spring Boot application by creating a custom HandlerInterceptor that tracks request counts in Redis, uses a distributed lock to enforce limits, and registers the interceptor to protect services from malicious or excessive traffic.
Lock4j is a Java distributed lock library supporting Spring AOP, RedisTemplate, Redisson, and Zookeeper, offering simple usage, configurable properties, annotation-driven locking, custom executors, key generators, and failure strategies, with detailed setup, code examples, and advanced customization instructions.
This article explains why API debounce is essential, identifies the types of endpoints that need it, and provides two distributed solutions—shared Redis cache and Redisson lock—along with complete Java code examples and deployment tips for preventing duplicate submissions in a Spring Boot backend.
This article explains the four common Redis cache pitfalls—cache penetration, avalanche, breakdown, and data inconsistency—demonstrates how they can crash high‑traffic systems, and provides practical Java/Spring Boot solutions such as empty‑object caching, Bloom filters, distributed locks, and delayed double‑delete strategies.
This article explains Redis persistence mechanisms (RDB and AOF), common caching challenges such as avalanche and penetration with mitigation techniques, outlines Redis data types and their use cases, compares cluster solutions, and details replication, transactions, and distributed locking strategies.
This article explains ZooKeeper's hierarchical data model, node types, session mechanism, cluster roles and election process, ZAB protocol, watch mechanism, ACL permissions, and common distributed lock implementations, providing a comprehensive overview of its core concepts and practical usage.
This article explains how to use Redis for distributed locking, discusses the shortcomings of naive implementations, and presents robust solutions including identifier verification, Lua scripting for atomicity, the RedLock algorithm, and Redisson's advanced features such as re‑entrancy and watchdog mechanisms.
This article introduces Lock4j, a Java distributed lock library supporting RedisTemplate, Redisson, and Zookeeper, walks through its key features, dependency setup, configuration, annotation attributes, simple and advanced usage examples, and demonstrates custom executors, key builders, and failure strategies for robust concurrency control.
This article explains how to prevent duplicate API submissions by applying debounce principles and implementing distributed locks using Redis and Redisson in a Java Spring backend, providing detailed code examples, key generation strategies, and practical testing results.
This article explores how various unique identifier schemes—random UUIDs, sequential numbers, hierarchical paths, and distributed ID generators—are applied across programming languages, file systems, databases, and networks, and explains when and how to enforce uniqueness using pre‑validation, database constraints, or distributed locks such as Redis and Zookeeper, while also linking these mechanisms to CAS‑based concurrency control.
The article outlines practical backend interface performance optimizations—including proper indexing, SQL tuning, parallel remote calls, batch queries, asynchronous processing, scoped transactions, fine-grained locking, pagination batching, multi-level caching, sharding, and monitoring tools—to dramatically reduce latency and improve throughput.
This article walks through eleven practical methods—such as adding proper indexes, optimizing SQL, parallelizing remote calls, eliminating duplicate queries, using async processing, shrinking transaction scope, refining lock granularity, applying pagination, leveraging caching layers, and employing sharding and monitoring tools—to dramatically improve backend interface performance.
This tutorial walks you through using the Lock4j distributed lock component in Spring Boot 3.2.5, covering its features, Maven dependencies, configuration, basic annotation usage, custom key generation, expiration settings, and advanced options like custom executors, key builders, failure strategies, and manual lock handling.
This article explains how to implement distributed locks using Redis, starting with a simple SETNX approach, identifying its shortcomings, and then presenting solutions such as thread‑identifiers, Lua scripts for atomicity, handling TTL with watchdogs, the RedLock algorithm, and practical usage via the Redisson Java client.
This article explains how to implement distributed locks using Redis, starting with a basic SETNX approach, identifying its shortcomings, and then presenting robust solutions such as identifier‑based unlocking, Lua scripts for atomicity, the RedLock algorithm, and Redisson’s advanced features including a watchdog mechanism.
This article explains the concept of request debouncing, outlines ideal debounce component characteristics, identifies which API endpoints need debouncing, and provides detailed Java implementations using shared Redis cache and Redisson distributed locks, including custom annotations, key generation, and practical testing results.
This article introduces etcd as a reliable distributed key‑value store built on Raft, outlines its key features and common use cases such as service discovery and configuration management, and provides a complete Go tutorial covering dependency setup, server launch, client implementation, read/write testing, and distributed lock usage.
This article explains why API debounce is essential, outlines which endpoints need it, describes how to generate unique keys, and provides complete implementations using Redis cache and Redisson distributed locks in a Spring Boot 3 backend, complete with testing results.
This article explains how to prevent duplicate API calls in Java backend systems by using debounce techniques, shared Redis caches or Redisson distributed locks, and provides complete annotation‑based implementations with code examples, key generation strategies, and testing results to achieve idempotent interfaces.
The article details a comprehensive architecture for high‑concurrency services—including a Redis‑backed ranking system, distributed locking, Bloom‑filter nickname deduplication, Netty‑based reliable messaging, IoT MQTT/REST integration, live‑streaming pipelines, layered performance tuning, and automated traffic‑replay testing to ensure scalability and robustness.
This article explains how to prevent duplicate submissions in Java backend services by implementing request debouncing using Redis caching and Redisson distributed locks, detailing the design of request lock annotations, unique key generation, and practical code examples for robust idempotent APIs.
This article explains the concept, necessity, key characteristics, and step‑by‑step Java implementation of Redis‑based distributed locks, providing code examples and guidance for ensuring mutual exclusion, reliability, and performance in backend systems.
This guide explains how to implement a robust, re‑entrant distributed lock using Redisson in a Spring Boot 2.7 application, covering basic and asynchronous APIs, Maven dependency setup, YAML configuration, custom annotation, AspectJ implementation, and comprehensive test cases.
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 walks through the problem of implementing a reliable Redis distributed lock in Go, explains the pitfalls of naive SetNx usage, introduces timeout handling and GetSet replacement, provides step‑by‑step Go code, and demonstrates its correctness with a multithreaded test.
Discover ten practical Redis scenarios—from counting page visits and caching category trees to implementing distributed locks, leaderboards, rate limiting, bitmap analytics, message queues, and global ID generation—each illustrated with concise code snippets and best‑practice tips for robust backend development.
This article provides a detailed introduction to Zookeeper, covering its role as a distributed coordination service, core functionalities such as a file‑system‑like data model, notification and cluster management, the ZAB consensus protocol, node types, leader election, distributed lock implementation, and typical application scenarios.
This article explains why distributed locks are needed, outlines Redisson's lock properties, walks through its Lua‑based acquisition, renewal, and release processes, examines master‑slave pitfalls, compares RedLock with Zookeeper, and provides practical code examples for Java developers.
This tutorial explains how to add Redis dependencies to a Spring Boot project, configure connection settings, use RedisTemplate for basic operations, enable Spring Cache, employ Redisson for distributed locks, and fine‑tune connection pool and timeout parameters, providing complete code examples.
This guide demonstrates how to use Spring Boot 2.7 with Redis 6.2 to implement Pub/Sub messaging, transactional operations, pipelined commands, and Lua‑based distributed locks, providing code examples, configuration details, and explanations of each feature’s advantages and limitations.
This guide introduces Lock4j, a versatile distributed‑lock component for Java that works with Spring AOP and supports RedisTemplate, Redisson, and Zookeeper, and walks through its features, configuration, annotation attributes, basic usage, and advanced customization options.
The article shows how to build a reliable Redis‑based distributed lock in a Spring Boot 2.7 application by storing a unique UUID token, acquiring the lock with SET NX PX, releasing it atomically via a Lua script, and providing a clean Lock interface, factory component, and JUnit example.
This article explains why request debounce is essential for preventing duplicate submissions, outlines which APIs need protection, and walks through two distributed‑lock solutions—shared Redis cache and Redisson—showing concrete annotations, key‑generation logic, and Spring‑AOP code with full test results.
Redis distributed locks require unique identifiers, atomic Lua releases, and TTL refreshes to avoid deadlocks, while the Redlock algorithm adds majority quorum but remains vulnerable to clock drift and client pauses, so critical systems should combine it with fencing tokens or version checks for true safety.
This article explores ten practical Redis scenarios—from counting page visits and caching category trees to implementing distributed locks, leaderboards, rate limiting, bitmap statistics, cache acceleration, message queues, and global ID generation—providing code snippets and best‑practice tips for each use case.
This article covers Java backend architecture fundamentals, including front‑end/back‑end separation, Maven configuration, distributed lock implementations with Redis and Zookeeper, session handling in distributed systems, database locking strategies, and useful code snippets for common patterns.
The article analyzes the inventory oversell problem caused by concurrent purchase requests and presents four backend solutions—including a Redis distributed lock, MySQL row lock, optimistic locking with version fields, and conditional SQL updates—illustrated with Java code and SQL examples to ensure data consistency.
This guide explains how to use Spring Integration's LockRegistry for distributed locking, covering both database‑based and Redis‑based implementations, complete with Maven dependencies, configuration snippets, bean definitions, and comprehensive test cases to ensure reliable synchronization across multiple nodes.
A Redis lock timeout caused users to be blocked for two hours, revealing problems with lock leakage and mis‑release; the article explains the root causes, proper use of SETNX with EXPIRE, and Lua scripts for atomic operations and safe lock deletion.
This article explores Redis's capabilities beyond simple caching, detailing its use for distributed locks, rate limiting, session storage, complex business scenarios, and various messaging patterns—including List‑based queues, blocking commands, Pub/Sub, and the Stream data structure—while highlighting practical limitations and best‑practice recommendations.
This article explains why scheduled jobs run twice in a multi‑node deployment, shows how to isolate the timing logic into a single service, and demonstrates using ElasticJob with Zookeeper for sharding, distributed locks, and high‑availability to ensure each task executes exactly once.
The article explains how to handle the most complex part of an e‑commerce order flow—stock deduction under high concurrency—by using JVM and distributed locks, Redis atomic operations, two‑phase commit, hot‑cold routing, fault tolerance, and cache rebuilding strategies.
This article outlines a comprehensive strategy for rapidly loading one billion 1 KB log records stored in HDFS or S3 into MySQL, covering data partitioning, B‑tree limits, batch inserts, storage‑engine selection, sharding, concurrent write control, efficient file reading, task coordination, fault‑tolerant progress tracking with Redis, and distributed locking with Redisson.
This comprehensive guide explains Redis fundamentals, its rich data structures, persistence mechanisms (RDB and AOF), enterprise‑level configuration strategies, common usage patterns such as caching, ranking and geolocation, and dives deep into distributed lock implementations with Redisson and Lua scripts, providing practical code examples for real‑world applications.
This article introduces the Lock4j distributed lock library for Java, outlines its key features and supported backends, provides step‑by‑step Maven dependency setup, Redis configuration, annotation details, and demonstrates both basic and advanced usage patterns including custom executors and lock‑failure strategies.
This article explains what distributed locks are, outlines their key properties, classifies implementation approaches, and provides detailed walkthroughs of MySQL, Redis, Etcd, and ZooKeeper based locks—including code examples, trade‑offs, and best‑practice recommendations.
This article explains how to apply Redisson's distributed lock in Java, demonstrates the basic lock and tryLock APIs, shows how to create a custom @DistributedLock annotation and an AOP aspect to handle locking automatically, and provides a practical usage example with two services.
This article explains how Redis can be used for distributed locks, rate limiting, various message‑queue patterns, delayed queues, distributed sessions and complex business scenarios, while also discussing the limitations of using Redis as a message queue and recommending dedicated solutions like RocketMQ or Kafka.
This article explains why Quartz jobs can overlap when their execution time exceeds the trigger interval, demonstrates how to prevent concurrent execution with the @DisallowConcurrentExecution annotation, discusses distributed lock and idempotency solutions for clusters, and covers thread‑pool limits, exception handling, and a complete Java demo.
This article analyzes a coupon‑claiming race condition that caused stock to become negative under load and presents four solutions—including Java synchronized blocks, SQL conditional updates, Redis SETNX locks, and Redisson client locks—to reliably prevent over‑issuance in backend systems.
This article explains why traditional local locks fail in distributed systems, surveys common distributed‑lock approaches, and provides a step‑by‑step guide to building a robust Redis‑based lock in Java—including expiration handling, UUID safety, Lua‑script atomicity, re‑entrancy, automatic renewal, and the RedLock algorithm—while comparing its performance against plain local locks.
This article examines why local locks fail in distributed micro‑service environments, introduces Redis‑based distributed locking, walks through five incremental lock designs—from a simple SETNX implementation to a Lua‑script atomic solution—highlighting each scheme's trade‑offs, code examples, and practical pitfalls.
The article explains how Redis’s rich data structures and persistence options enable it to serve not only as a distributed cache but also as a primary store for user profiles, counters, leaderboards, relationships, active‑user tracking, distributed locks, rate limiting, messaging, geospatial queries, and more, while also discussing high‑availability and hot‑cold data challenges.
This article introduces Redisson, a Java framework built on Redis for distributed systems, detailing its extensive features such as distributed objects, locks, rate limiters, and task scheduling, and provides step‑by‑step guidance on integrating Redisson’s distributed lock into Spring Boot applications with code examples.
This article explains how Redis can be used to implement distributed locks, outlines common pitfalls such as non‑atomic operations, lock expiration, incorrect unlocking, lack of re‑entrancy and waiting mechanisms, and presents Lua scripts, Java examples, and cluster‑level considerations to mitigate these issues.
This article introduces Redisson, a high‑performance Java Redis client built on Netty, explains its key advantages, and provides a detailed walkthrough of the RedissonLock implementation—including tryLock, blocking lock, unlock logic, associated Lua scripts, and the automatic watchdog renewal mechanism.
This article explains Zookeeper's role as an open‑source distributed coordination service, covering its observer‑pattern design, data model, installation (single‑node and cluster), leader election, watcher mechanism, and demonstrates how to integrate Zookeeper as a service registry and distributed lock in Spring Boot using Curator, complete with code examples.
This article explores five common Redis applications—caching, session storage, distributed locking, rate limiting, and leaderboards—explaining how each leverages Redis’s in‑memory speed, atomic commands, and data structures to improve performance, scalability, and reliability of modern web services.
This article explains the concept of distributed locks for ensuring data consistency in clustered environments, outlines common implementation approaches, and provides a comprehensive guide to using Redisson in Java—including Maven setup, YAML configuration, core source‑code analysis, Lua scripts for lock and unlock operations, and practical test cases.
This article explains how distributed locks solve data‑consistency problems in clustered environments, introduces Redisson as a Redis‑based locking library, provides Maven and YAML configuration, shows Java code for Redisson setup and test cases, and analyses the underlying Lua scripts and source‑code mechanisms.
This article details the evolution and design of Vivo’s e‑commerce inventory platform, covering its layered architecture, multiple inventory types, deduction workflows, idempotent APIs, anti‑oversell mechanisms, high‑concurrency strategies, hotspot mitigation, and synchronization techniques, while explaining the reasoning behind each technical choice.
This comprehensive guide explains Redis fundamentals, its rich data types, differences from Memcached, thread model, persistence mechanisms (AOF, RDB, hybrid), memory eviction policies, clustering for high availability, cache design patterns, distributed locking, Redlock algorithm, and practical techniques for identifying and safely deleting big keys.
This article explains how to create Redis‑based distributed locks, discusses common pitfalls such as deadlocks and accidental deletions, and provides practical solutions including atomic expiration commands, Lua scripts, and a complete Redisson Java example for acquiring and releasing locks safely.
This guide explains why distributed locks are needed, how to correctly acquire and release them, compares Redis and Zookeeper implementations—including single‑master, Redlock, and Zookeeper approaches—and offers practical recommendations for ensuring atomicity, preventing deadlocks, and protecting shared resources in production environments.
This article dissects ten common traps when implementing Redis distributed locks—such as non‑atomic setnx/expire, missing expirations, lock release ordering, non‑re‑entrancy, master‑slave replication issues, and the Redlock algorithm—while providing concrete code examples and practical remedies.
This article provides Python code examples for using Redis as a database—including connection, basic CRUD, list and sorted set operations, key expiration, publishing/subscribing, distributed locking, and caching—followed by a detailed comparison of Redis and MySQL across data model, persistence, query capabilities, performance, scalability, and transaction support.
Learn how to use Redis with Python for basic CRUD, list and sorted‑set manipulation, key expiration, Pub/Sub messaging, distributed locks, and caching, and understand when Redis is a suitable complement or alternative to MySQL based on data model, performance, and transaction needs.
This article explains how to connect to Redis from PHP, demonstrates core Redis commands such as set, get, list, hash, and sorted‑set operations, and describes typical use cases like caching, counters, distributed locks, and message queues.
The article examines multiple techniques—including direct SQL updates, compare‑and‑swap, optimistic locking, Redis‑based distributed locks, and MySQL shared locks—to ensure correct and consistent account balance modifications and order status changes in high‑concurrency scenarios.
This article demonstrates how to use Redis as a database with Python code examples for basic operations, advanced features like pub/sub and distributed locks, and compares Redis to MySQL across data model, persistence, query capabilities, performance, scalability, and transaction support.
This article explains how Redisson extends Redis to provide a rich set of distributed Java objects and services, walks through integrating Redisson into a Spring Boot project, demonstrates various lock types (reentrant, read‑write, semaphore, latch) with code examples, and details the internal watchdog mechanism that guarantees atomic lock acquisition and automatic renewal.
This article explains Redisson's distributed lock features, comparing it with Jedis and Lettuce, detailing lock acquisition, reentrancy, Lua scripts, watchdog renewal, and the implementation of fair locks using Redis lists and sorted sets, with code examples and architectural insights.
This article explains the concept of distributed locks, outlines various implementation strategies, and dives deep into Redis-based solutions—including SETNX, GET, GETSET, EXPIRE, and Lua scripting—to achieve mutual exclusion, safety, reentrancy, and automatic lock release in high‑performance systems.
This article explains the concepts, designs, and practical implementations of distributed locks using databases, Redis, and Zookeeper, compares their advantages and drawbacks, and provides code examples and best‑practice recommendations for reliable concurrency control in distributed systems.
The article explains the concept of idempotency for APIs, distinguishes request and business idempotency, highlights the risk of idempotent breakdown under high concurrency, and presents a practical solution using a three‑step process—lock, check, and update—often implemented with Redis distributed locks and database uniqueness constraints.
This article explains the e‑commerce payment flow, identifies why duplicate payments occur—such as missing idempotency, lost orders, and multi‑channel issues—and presents practical backend solutions like distributed locking, result caching, in‑progress flow cancellation, refund handling, active polling, and push/pull synchronization.
This article outlines a comprehensive Redis training program covering fundamentals, distributed lock implementation, high‑availability mechanisms, clustering, persistence strategies, and practical projects such as Bloom filter integration and flash‑sale systems, providing learners with the knowledge to master advanced Redis usage.
This article explains why distributed locks are needed, describes the five essential characteristics of Redis locks, compares three common implementation methods, and provides detailed Java code examples with watchdog, re‑entrancy, and expiration handling to guide developers in building reliable distributed locking solutions.
This article explains how to prevent overselling in high‑concurrency scenarios by using Redis‑based distributed locks in PHP, detailing initialization, lock acquisition with setnx, unlocking with del, and providing complete sample code along with best‑practice considerations.
