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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 explains how to extract AOP annotation code, implements a Redis-based rate limiter using Redisson in Java, analyzes the underlying Lua script, and extends the utility to support automatic expiration of the limiter, providing complete code examples and detailed explanations.
The article examines various approaches for implementing accurate order‑cancellation timers in e‑commerce systems, compares message‑queue delayed delivery, Redisson delay queues, Redis expiration listeners, RabbitMQ dead‑letter queues, and time wheels, and recommends reliable solutions while warning against common pitfalls.
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.
The article explains why using Redis expiration or RabbitMQ dead‑letter queues for delayed order‑cancellation tasks is unreliable, compares several approaches such as message‑queue delayed delivery, Redisson delay queues, and time wheels, and recommends robust solutions like RocketMQ or Pulsar for accurate timing.
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 the concept of request debouncing, identifies which API endpoints need it, and provides a detailed tutorial on implementing distributed debounce using shared Redis cache or Redisson locks in a Spring Boot backend, complete with annotation design, unique key generation, and code examples.
This article explains the probabilistic Bloom filter data structure, its characteristics and typical use cases such as cache‑penetration prevention, details its simple implementation steps, demonstrates how Redis (via Redisson) provides built‑in Bloom filter support with Java code examples, and summarizes its practical benefits.
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 presents a comprehensive engineering guide for importing one billion 1 KB unstructured log records stored in HDFS or S3 into MySQL, covering data sizing, B‑tree limits, batch insertion strategies, storage‑engine choices, sharding, file‑reading techniques, concurrency control, and reliable task coordination using Redis, Redisson, and Zookeeper.
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 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 article explains how Redisson's DelayedQueue works internally, covering its data structures, the basic workflow for sending and receiving delayed messages, the initialization process, and the scheduling logic that moves expired items from the delay queue to the target queue.
To import one billion 1 KB log records stored in HDFS or S3 into MySQL efficiently, the article examines data partitioning, B‑tree index limits, batch insertion, storage engine choices, concurrency control, file‑reading methods, task scheduling with Redis, Redisson, and Zookeeper for reliable, ordered, high‑throughput loading.
This article explains how to prevent duplicate submissions in Java backend APIs by applying debounce techniques using shared Redis caches or Redisson distributed locks, detailing the design of a @RequestLock annotation, key generation, aspect implementation, and practical testing results.
This article explains how to implement interface debounce in Java backend services, covering the concept, identifying suitable APIs, generating unique keys, and applying distributed locking mechanisms with Redis and Redisson, including code examples and practical testing results.
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 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 explains why request debouncing is essential for backend APIs, identifies the types of endpoints that need it, and provides two distributed solutions—shared Redis cache and Redisson locks—along with complete Java annotation and aspect implementations, testing results, and best‑practice recommendations.
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.