This article explains why Spring Boot's @Scheduled annotation executes on every instance in a clustered deployment, causing duplicate tasks like coupon issuance, and demonstrates solutions using ShedLock distributed locks and business-level idempotency keys to ensure exactly-once execution.
A distributed Redis lock collision returns a default success response with null data, causing client-side NullPointerException when unpacking; the article analyzes the root cause, shows the flawed code, and provides fixes for both server and client sides.
This article explains four approaches to implementing distributed locks with Redis: basic SETNX with expiration, atomic Lua scripts for lock acquire/release, the Redisson Java client with watchdog renewal, and the Redlock algorithm for high-availability multi-master deployments.
The article walks through a real production incident where a Redis‑based distributed lock silently failed, causing duplicate point awards, and details the step‑by‑step investigation, root‑cause analysis of transaction‑lock ordering, and three concrete remediation strategies.
The article analyses why XXL‑Job’s separate registration, configuration, and weak sharding cause state inconsistency, observability gaps, and duplicate processing, then proposes JobFlow – a lightweight scheduler that removes redundant components, adds full‑traceId tracing, true sharding with distributed locks, exponential retry, and cloud‑native configuration managed by Nacos, all illustrated with concrete code snippets and deployment diagrams.
Distributed locks are essential for coordinating access to shared resources across multiple machines; this article compares common solutions—Redis locks, database optimistic and logical locks, DB row locks, and Zookeeper locks—detailing their principles, pros, cons, implementation details, and best‑fit scenarios.
This article breaks down the end‑to‑end design of a high‑traffic red‑envelope service, covering its three‑stage lifecycle, a fair double‑mean allocation algorithm, the need to separate grabbing from settlement, and how Redis, Lua scripts, and message queues handle massive concurrent requests.
When a Java application that uses Spring’s @Scheduled moves from a single server to a cluster, the same job may execute on every node, causing duplicate orders, messages, and data; the article explains why this happens, examines common lock‑based fixes and their pitfalls, and proposes a robust, idempotent, sharded task architecture.
The article analyzes Redis distributed locks—its three‑step verification, the limited guarantees it provides, and why small‑to‑medium projects should often prefer database constraints, idempotency keys, or state machines over adding a lock that introduces extra complexity and maintenance overhead.
This article shares real-world experience using Spring StateMachine to replace sprawling if-else logic in order systems, covering state/event/guard modeling, Redis-backed persistence, a three-layer concurrency control pattern (distributed lock + optimistic lock + event idempotency), async decoupling, timeout handling, and compensation strategies with concrete code templates.
This article walks through ten advanced Redis features—including Bloom filters, Redisson distributed locks, delayed queues, token‑bucket rate limiting, bitmaps, HyperLogLog, GEO, Streams, Lua scripts, and RedisJSON—explaining their principles, pros and cons, typical scenarios, and providing complete Spring Boot code examples.
The article explains a three‑layer strategy—pre‑intercept via Redis locks or temporary tokens, mid‑process control using order status and optimistic locking, and post‑fallback with delayed MQ messages and nightly reconciliation—to guarantee that multiple user clicks result in only one successful payment.
This article provides an in‑depth guide to Redis’s advanced capabilities, covering how to use pipeline for batch commands, transactions for ordered execution, Lua scripts for atomic logic, distributed locks with proper token handling, reliable messaging with streams, and specialized data structures such as BitMap, HyperLogLog, Bloom Filter and GEO for efficient large‑scale scenarios.
This article introduces Redisson, a Redis‑based Java client that wraps Redis commands into familiar Java concurrency primitives, compares it with Jedis and Lettuce, explains why custom distributed locks are error‑prone, and provides step‑by‑step code for configuring, using, and integrating its core features such as locks, maps, queues, and rate limiters in Spring Boot.
The article explains that idempotency prevents the same operation from being executed twice, illustrates a real double‑charge bug, evaluates five concrete approaches—including Redis check‑then‑set, DB unique index, optimistic lock, Redis distributed lock, and a message deduplication table—details their failure conditions and suitable scenarios, and recommends combining Redis lock with a database unique index for the most robust protection.
The article explains how non‑existent data amplified by high concurrency can cause cache penetration, distinguishes it from cache breakdown and avalanche, and presents a layered defense—entry validation, Bloom filter existence checks, negative caching, and concurrent‑request convergence—plus practical code, metrics, and operational checklists for robust microservice deployments.
This article explains the three fundamental challenges of distributed locks—availability, deadlock, and split‑brain—compares database and Redis implementations, walks through the five evolutionary steps of Redis locking, and provides a structured interview answer framework to demonstrate deep understanding.
This article outlines nine practical Redis scenarios—including hot‑data caching, distributed locks with Redisson, Bloom filters for cache‑penetration protection, delayed queues using ZSet, token‑bucket rate limiting, bitmap boolean statistics, UV deduplication via Set/HyperLogLog/Bitmap, geospatial indexing, and lightweight Stream queues—explaining their motivations, implementation steps, and trade‑offs.
The article explains why caching is indispensable—from nanosecond‑level CPU caches to millisecond‑level disks—covers the classic pitfalls of penetration, breakdown and avalanche, and walks through consistency strategies such as Cache‑Aside, delayed double‑delete, and Canal‑based binlog syncing for high‑concurrency systems.
This article explores production-grade Spring Boot scheduled tasks, covering dynamic Cron hot-reloading via configuration listeners, cluster duplicate prevention using distributed locks, framework selection between Quartz and XXL-JOB, data sharding strategies, observability, timeout handling, and retry mechanisms with dead-letter queues.
The article analyzes why a basic Redis mutex lock can cause thread blocking, latency spikes, and service collapse under high concurrency, and compares it with logical expiration and never‑expire with proactive updates, explaining their trade‑offs and how to choose the right cache‑breakdown mitigation strategy.
Although idempotent APIs can block duplicate requests, they only guarantee result correctness, while distributed locks control the execution order; the article explains why both mechanisms are complementary and necessary in high‑concurrency scenarios such as stock deduction and money transfer.
The article explains that idempotency guarantees result correctness but does not control concurrent execution order, while distributed locks serialize access; through code examples and scenarios like stock deduction and money transfer it shows why both mechanisms are complementary and often needed together.
This article explains why local locks like synchronized and ReentrantLock fail in microservice clusters, introduces the fundamentals and essential properties of distributed locks, walks through five iterative Redis lock implementations that address dead‑lock, atomicity, and timeout issues, and finally shows how to adopt the production‑grade Redisson library with best‑practice guidelines for key naming, lock scope, and lock type selection.
The article explains ZooKeeper's herd effect, how temporary sequential nodes and chain watching reduce notification storms, how client failures are handled, and why most projects use Curator to simplify fault‑tolerant distributed lock implementations.
The article explains why local locks fail in multi‑machine deployments, defines the three essential properties of a correct distributed lock, walks through seven Redis‑based lock solutions with code samples, highlights common production pitfalls, and provides a decision tree for selecting the right approach.
The article analyzes why simply adding Redis nodes cannot handle a sudden million‑QPS surge, then presents three practical solutions—key sharding, multi‑level caching with hot‑key detection, and distributed‑lock‑based fallback—to build a resilient high‑concurrency backend.
This article explains the three classic Redis cache problems—avalanche, penetration, and breakdown—detailing their definitions, typical symptoms, step‑by‑step troubleshooting procedures, root‑cause analysis, and practical mitigation strategies such as random expiration, empty‑value caching, Bloom filters, distributed locks, and multi‑level cache architectures.
The article explains how to isolate external takeaway platform callbacks—Meituan, Douyin, JD Daojia—into a dedicated service, covering signature verification, idempotent processing with Redisson locks, and the end‑to‑end order handling flow that syncs with the core order domain.
The article explains why a simple JVM lock fails in multi‑instance services, illustrates the overselling problem with an e‑commerce example, and then compares three practical distributed‑lock solutions—database‑based, Redis‑based, and ZooKeeper‑based—detailing their mechanisms, strengths, weaknesses, and suitable scenarios.
The article explains how cache penetration, cache breakdown, and cache avalanche all stem from cache layer failures that let requests flood the database, compares their triggers, impact scopes and risk levels, and presents practical mitigation techniques such as empty‑value caching, Bloom filters, mutex locks, logical expiration, TTL randomization, and multi‑level caching.
The article explains why etcd is the single source of truth for Kubernetes, walks through its internal Raft, WAL, MVCC, and watch mechanisms, analyzes real‑world failure cases, and provides concrete architecture designs, hardware recommendations, configuration parameters, monitoring metrics, backup procedures, and best‑practice checklists to run etcd safely in production.
This article walks through eleven practical Redis scenarios—from classic caching and distributed locks to rate limiting, leaderboards, timelines, social graph operations, lightweight queues, Bloom filters, hash‑based object storage, unique‑counting, and delayed tasks—providing code samples, advantages, drawbacks, and when to apply each pattern.
Redis 8.0 consolidates many previously external modules—JSON, time‑series, vector search, probabilistic data structures, and more—into a single package, and this article walks through 16 concrete scenarios ranging from field‑level cache expiration to AI‑ready vector similarity search, showing exact commands and when to prefer each feature.
This article walks through sixteen common Redis scenarios—including caching hot data, sharing state across services, implementing distributed locks, generating global IDs, counting events, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, tagging, product filtering, and leaderboards—each illustrated with concrete commands and code snippets.
This article walks through more than ten common Redis scenarios—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow/fan relationships, and ranking—showing concrete command examples and code snippets for each.
This comprehensive guide outlines a step‑by‑step learning path for Redis, covering foundational commands, core data structures, high‑performance internals, persistence options, clustering, common caching pitfalls, performance tuning, monitoring, source‑code exploration, and recommended resources for becoming a Redis expert.
This guide walks Java backend developers through Redis’s eight core scenarios—caching, distributed locks, rate limiting, session sharing, leaderboards, counters, message and delay queues, bitmap statistics, and geolocation—providing complete code, diagrams, and production‑grade best practices.
This article examines the evolution of Redis distributed lock implementations—from the basic SETNX command to atomic SET with Lua scripts and the feature‑rich Redisson framework—detailing core requirements, common pitfalls, Go code samples, and production‑grade recommendations.
The author recounts five critical production incidents that crippleed an education mini‑program—Redis connection‑pool exhaustion, duplicate bookings, double refunds, mis‑firing no‑show jobs, and inventory oversell—detailing root causes, concrete fixes, and hard‑won lessons for building resilient backend services.
This comprehensive guide explains why cache penetration is a high‑risk issue for high‑concurrency systems, distinguishes it from cache breakdown and avalanche, and presents a layered, production‑grade defense that combines parameter validation, gateway rate‑limiting, empty‑object caching, Bloom filters, local caches, distributed locks, and observability to protect both Redis and the underlying database.
Redis offers far more than simple key‑value caching; by leveraging its rich data structures—strings, hashes, lists, sets, sorted sets, bitmaps, HyperLogLog, GEO, and streams—developers can implement distributed locks, rate limiting, leaderboards, session storage, counters, geolocation, delayed queues, messaging, bloom filters, and more, all with concise commands.
This guide explains why distributed locks are needed, compares three implementation stages—from basic SETNX+EXPIRE to atomic SET NX EX with Lua scripts, and finally Redisson and RedLock—provides code samples, interview questions, and practical tips for Java developers.
This article explores sixteen practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tags, filtering, follow relationships, and ranking—demonstrating how Redis can serve as a versatile data store beyond simple caching.
This article presents a comprehensive guide to 16 practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, queues, lotteries, likes, product tagging, filtering, social graphs, and leaderboards—complete with command examples, code snippets, and visual illustrations to help developers implement these patterns efficiently.
This article presents a comprehensive guide to 16 practical Redis use cases—including caching, distributed locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow‑recommendation models, and ranking—complete with code snippets and data‑structure examples.
This article examines how Taobao Flash Sale’s AI Agent uses Alibaba Cloud’s Tair as a high‑performance short‑term memory layer, detailing data model design, latency impact, concurrency control, elastic scaling, bandwidth handling, and TTL‑based cleanup to achieve sub‑second response times during massive traffic spikes.
In high‑concurrency Java services, a Redis lock can appear to expire before its TTL because JVM Stop‑The‑World pauses freeze the application clock while Redis continues counting down, leading to lock loss and data races unless mitigated with watchdogs or optimistic locking at the database level.
In high‑concurrency Java services, a Redis lock that appears to have a valid TTL can still be taken by another thread when a long Stop‑The‑World pause freezes the JVM clock, causing the lock to expire on Redis while the original holder remains paused, leading to data races.
The article explains what occurs when a Redis distributed lock expires before a business operation completes, and details how Redisson’s watch‑dog mechanism automatically renews the lock, covering the underlying principles, configuration, code examples, and comparisons with alternative renewal approaches.
When half a million users simultaneously try to buy 5,000 concert tickets, naive ordering logic leads to race conditions and double bookings, so the article walks through the root causes, compares pessimistic, optimistic, and Redis distributed locks, and presents an industrial‑grade microservice design with a three‑stage state machine to ensure strong consistency and high availability.
When a Spring Boot application using @Scheduled is deployed on multiple servers, each instance triggers the same job, leading to duplicate processing; this article explains why the problem occurs, reviews simple single‑node and Redis lock approaches, and provides a step‑by‑step guide to integrate the ShedLock framework for reliable, annotation‑driven distributed locking.
This guide explains why @Scheduled jobs run on every Spring Boot instance in a cluster, and presents three practical solutions—Spring Integration's Redis lock, Redisson, and ShedLock—complete with Maven dependencies, configuration snippets, code examples, and runtime screenshots to guarantee that only one node executes the scheduled task at a time.
This article walks through the evolution of Redis distributed locks—from basic SETNX mutual exclusion to atomic SET with expiration, Lua‑based safe unlocking, Redisson's WatchDog auto‑renewal, and the RedLock algorithm—highlighting pitfalls, best‑practice implementations, and interview‑style Q&A for robust production use.
This guide explains how Redis implements distributed locks, outlines common pitfalls such as lock contention, zombie locks, and mismatched expiration times, and provides step‑by‑step solutions—including single‑node SET commands, Redlock high‑availability algorithm, Lua‑based safe release, and best‑practice recommendations for real‑world deployments.
The article explains the delayed double‑delete cache‑invalidation pattern, its four major drawbacks in high‑concurrency environments, and presents four production‑grade alternatives—event‑driven binlog updates, distributed‑lock with versioning, write‑through proxy layers, and logical‑delete with async cleanup—used by leading Chinese internet firms to achieve reliable data consistency.
The article explains how Redisson implements a re‑entrant distributed lock using Redis hash structures and the atomic HINCRBY command to manage client identity, re‑entry counting, concurrency safety, and graceful release, providing a complete technical analysis with code, Lua scripts, and best‑practice guidelines.
This article dissects the core architecture of a miniature ride‑hailing platform, covering domain‑driven design, layered microservice structure, Redis GEO for fast location queries, distributed locking, Netty‑based real‑time messaging, and hot‑cold data separation to handle massive traffic and ensure reliability.
When a scheduled‑task server crashes, simply restarting it is insufficient; a robust solution must combine clustering, distributed locks, idempotent designs, checkpointing, and monitoring to ensure tasks resume correctly across non‑runtime and runtime failures, as detailed with SpringTask‑Redis and XXL‑JOB implementations.
A detailed investigation reveals that MySQL's lock matrix and MVCC‑based transaction isolation caused a concurrent synchronization job to lose metric and dimension data, and proposes a distributed‑lock solution together with a long‑term read‑calc‑write refactor to eliminate the issue.
This article explains four common idempotency strategies—token, database unique index, distributed lock, and request‑body digest—detailing their core ideas, key concepts, and providing ready‑to‑copy Spring/Redis code examples to prevent duplicate requests in high‑traffic backend systems.
This article explains four practical idempotency solutions—token tokens, database unique indexes, distributed locks, and request content digests—detailing their concepts, core keywords, and providing ready‑to‑copy Spring Boot code examples, along with implementation tips and a comparison table to help you choose the right approach for high‑concurrency APIs.
This article explains the concept of debounce for backend APIs, identifies which endpoints need it, and provides two distributed solutions—shared Redis cache and Redisson lock—complete with annotations, key generation logic, code examples, testing results, and tips for achieving true idempotency.
This article explains how tldb implements language‑agnostic distributed locks, describes the Lock, TryLock, and Unlock APIs, and provides practical Go and Java code samples along with test results to help developers integrate reliable distributed locking into their systems.
Valkey 9.0, the latest community‑driven fork of Redis, introduces multi‑path TCP support, per‑field hash expiration, atomic slot migration, and new safety commands, offering developers enhanced performance, finer‑grained data control, and more reliable cluster operations.
This article explains how to handle massive concurrent requests in a group‑buy scenario by using Redis distributed locks to avoid database overselling, MQ delayed messages for timeout processing, and a local outbox table to guarantee reliable cross‑service notifications.
This tutorial walks through why Redis is essential for Go services, how to choose and install the go‑redis client, and provides step‑by‑step code for connections, common commands, cache‑aside, distributed locks, leaderboards, and lightweight queues, followed by performance tuning and best‑practice recommendations.
Learn how to prevent duplicate execution of Spring Boot @Scheduled tasks in multi-instance deployments by integrating ShedLock, including Maven dependencies, H2 database setup, LockProvider configuration, and annotation usage to ensure each job runs only once across all nodes.
This tutorial explains how tldb implements distributed locks, detailing the lock, trylock, and unlock methods, their parameters, and usage examples in Go and Java, including Maven configuration, spin‑wait techniques, and performance test results with illustrative images.
This article walks through the evolution of a payment API’s idempotency design—from an initially flawed implementation, through naive Redis deduplication and token‑based approaches, to a robust solution that combines distributed locks, double‑checked caching, and state management for reliable, concurrent processing.
This article deeply explores the ordered consumption mechanisms of RocketMQ and its derivative DDMQ, explaining the implementation differences, source‑code details of sequential sending, broker locking, client‑side processing, and compares the two systems' approaches to guarantee message order.
This article introduces Easy-Cache, a Spring-AOP based caching framework that provides annotation-driven, multi-level Redis and local cache with dynamic upgrade/downgrade, elastic expiration, and Lua-script-ensured data consistency, eliminating repetitive cache code and handling failures, cache penetration, breakdown, and eventual consistency challenges.
This article provides a comprehensive guide to Redisson's distributed lock implementation, covering its architecture, comparison with Jedis and Lettuce, basic lock creation, Lua scripts for atomic operations, reentrant lock handling, watchdog-based lock renewal, RLock usage, and the design of fair locks using Redis data structures.
This article explains how Redis and Zookeeper implement distributed locks, compares their performance, consistency, and reliability, and provides guidance on choosing the appropriate lock mechanism for different backend scenarios.
This article explains how to protect time‑consuming business operations using a Redis‑based distributed lock implemented with a custom annotation, Spring AOP interception, automatic lock renewal via a scheduled executor, and proper lock release, ensuring data consistency across concurrent requests.
This article explains how to use segmented (sharded) locks and Redis‑based routing strategies to parallelize inventory deduction and order creation, dramatically increasing orders processed per second while maintaining atomicity and fault tolerance.
Learn how to design and implement a Redis-based distributed lock in Java using custom annotations, AOP interception, and a scheduled executor to automatically extend lock expiration, ensuring safe concurrent access to critical resources while handling timeouts, retries, and thread interruptions.
This article explains what debounce means for web APIs, identifies which endpoints need it, describes how to detect duplicate requests, and provides complete Java implementations using shared Redis caches and Redisson distributed locks with code examples and key generation strategies.
This article explains how to implement distributed locks using Redis, covering basic SETNX/EXPIRE techniques, common pitfalls, atomic Lua scripts, the RedLock algorithm, and practical Redisson usage, while providing Java code examples and best‑practice recommendations.
ZooKeeper is an open‑source distributed coordination service that provides a simple, efficient mechanism for managing shared state, and this guide explores seven typical scenarios—including distributed locks, service registration, configuration management, queues, leader election, notifications, and barriers—complete with Java code examples and usage details.
This article explains how ZooKeeper provides reliable distributed locks using ordered temporary nodes and watchers, details the underlying mechanisms, step‑by‑step implementation in Java, compares Curator framework options, and discusses the advantages, drawbacks, and real‑world use cases.
This article explains how to implement request debouncing in Java backend services using Redis and Redisson, covering the concept, interface types needing debouncing, duplicate request detection, shared cache and distributed lock solutions, with complete code examples and testing results.
This article explains how to build a robust order service using Seata distributed transactions, Redisson distributed locks, CompletableFuture for asynchronous operations, token-based duplicate‑submission prevention, and RabbitMQ delayed queues for automatic order cancellation, with full Java code examples.
This article explores five practical lock-based approaches—including database primary keys, pessimistic and optimistic locks, state machines, and distributed locks—to achieve reliable API idempotency in distributed systems, highlighting implementation details, trade‑offs, and code examples.
Discover how Nacos 3.0 introduces a high‑availability distributed lock feature that eliminates the need for external services like Redis or ZooKeeper, learn step‑by‑step integration with Spring Boot, explore its core components, and unlock advanced extensibility via Java SPI.
This article explains how using a custom @DistributeLock annotation together with @Transactional can cause idempotency issues due to aspect execution order, and demonstrates how to prioritize the lock aspect using @Order to ensure the lock is applied before the transaction, with code examples and best‑practice recommendations.
This article explains why API debounce is needed in web back‑ends, identifies the types of endpoints that require it, outlines how to detect duplicate requests, and provides two concrete distributed solutions—shared Redis cache and Redisson lock—complete with annotation design, key generation logic, and full Java code examples.
This article explains why Redis is becoming a DBA's favorite, provides a five‑minute installation guide, essential commands, cache‑design pitfalls and solutions, explores Redis data structures with Java examples, demonstrates distributed locking and flash‑sale implementations, and covers clustering, backup, monitoring, and performance tuning.
The article describes how the FoxFriend team built a scalable, configurable lottery‑activity platform that replaces manual feed‑based draws with a modular micro‑service architecture, featuring a flexible prize‑tier data model, pre‑occupied inventory buckets, multi‑tenant isolation, high‑concurrency stock deduction, user risk controls, accurate probability handling, and a roadmap toward AI‑driven optimization.
This article summarizes the key changes from the Nacos 3.0 beta to the official release, covering JDK and Spring Boot upgrades, new Admin APIs, default authentication, AI Model Content Protocol, unified namespaces, beta distributed lock, fuzzy listening, and native xDS support for cloud‑native microservice architectures.
Version 3.0 of Nacos upgrades to JDK 17 and Spring Boot 3.4.1, introduces AI‑focused MCP, enhanced Admin API with default authentication, unified namespaces, beta distributed lock and fuzzy listening features, and native xDS protocol support, while highlighting related open‑source SpringBoot + Vue e‑commerce projects.
Nacos 3.0 introduces major upgrades—including JDK 17 and Spring Boot 3.4.1 support, a new Admin API, default authentication, AI‑focused MCP, unified namespaces, beta distributed lock and fuzzy listening features, plus native xDS protocol support—positioning it as a powerful cloud‑native service discovery and configuration platform.
To prevent overselling during flash sales, the article explains how non‑atomic database updates cause negative stock and presents solutions such as optimistic DB locking, Redis Lua atomic deductions, Redisson distributed locks, transactional message queues, and pre‑deduction with rate limiting, recommending a combined approach that achieved 120 000 QPS with zero oversell.
The article explains how Spring AOP can cause object escape leading to concurrency safety problems, describes the impact of asynchronous threads and caching, and provides practical guidance on ordering AOP advices such as distributed lock, @Retryable, and dynamic datasource to avoid business anomalies.
Learn how to set up Redis with Docker, integrate it into a FastAPI app using a custom caching decorator, and apply Redis for caching API responses, rate limiting, task queues, and distributed locks, complete with practical code examples and deployment tips.
This article explains how to prevent duplicate orders in a high‑traffic system handling 100,000 QPS by combining frontend safeguards such as button disabling and token mechanisms with backend techniques including NGINX rate limiting, gateway token validation, idempotent database design, sharding, distributed locks, optimistic locking, and comprehensive logging and monitoring.
This article explains why simple GET‑modify‑SET inventory updates cause overselling in flash‑sale spikes and presents several Redis‑based solutions—including Lua scripts, WATCH‑based optimistic locks, distributed SETNX locks, and asynchronous queue processing—detailing their implementation, advantages, and trade‑offs.
This article explains Redis persistence mechanisms (RDB and AOF), common cache problems and their solutions, the various Redis data structures and their use cases, cluster deployment options, master‑slave replication, transaction commands, and how to implement distributed locks with SETNX and expiration.
This article introduces the Lock4j distributed lock library, explains its features, shows how to add Maven dependencies, configure Redis, use the @Lock4j annotation for simple and advanced locking scenarios, and provides custom executor, key builder, and failure‑strategy examples for Spring Boot applications.
This article explores seven practical retry solutions—from simple loops and Spring Retry to Resilience4j, message queues, scheduled tasks, two‑phase commits, and distributed locks—explaining their scenarios, core code, and how they prevent costly system failures.
