What’s New in RocketMQ 5.x? A Deep Dive into Cloud‑Native Features, Proxy, Pop, and Tiered Storage

This article explores Apache RocketMQ 5.x’s new cloud‑native capabilities—including the Proxy component, gRPC client, Pop consumption model, timer‑based delayed messages, tiered storage, distributed rate‑limiting, and containerized deployment on Tencent Cloud—while outlining architecture changes, practical usage patterns, and future directions.

RocketMQcontainerizationdistributed rate limiting

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What’s New in RocketMQ 5.x? A Deep Dive into Cloud‑Native Features, Proxy, Pop, and Tiered Storage

Architecture Digest

Jul 22, 2022 · Backend Development

Understanding Interface Idempotency and Distributed Rate Limiting: Concepts, Algorithms, and Java Implementations

This article explains the principle of interface idempotency, presents practical techniques such as version‑based updates and token mechanisms, and then delves into distributed rate‑limiting dimensions, common algorithms like token‑bucket and leaky‑bucket, and concrete implementations using Guava, Nginx, Redis and Lua with full code examples.

Backend DevelopmentIdempotencyNginx

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Understanding Interface Idempotency and Distributed Rate Limiting: Concepts, Algorithms, and Java Implementations

IT Architects Alliance

May 24, 2022 · Backend Development

How to Ensure API Idempotency and Implement Distributed Rate Limiting in Java

This guide explains the principles of API idempotency using unique business IDs or token mechanisms, explores distributed rate‑limiting dimensions, compares token‑bucket and leaky‑bucket algorithms, and provides concrete implementations with Guava RateLimiter, Nginx configuration, and a Redis‑Lua script integrated into Spring Boot, including annotation‑based AOP for easy usage.

API idempotencyGuava RateLimiterNginx

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How to Ensure API Idempotency and Implement Distributed Rate Limiting in Java

Architecture Digest

May 14, 2018 · Backend Development

Implementing and Optimizing a High‑Concurrency Flash Sale System with Optimistic Lock, Distributed Rate Limiting, Redis Cache, and Kafka

This article walks through building a Java‑based flash‑sale (秒杀) service, diagnosing overselling issues, and progressively enhancing it with optimistic locking, distributed rate limiting, Redis caching, and asynchronous Kafka processing to achieve higher throughput and data consistency under heavy concurrency.

JavaKafkaPerformance Testing

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Implementing and Optimizing a High‑Concurrency Flash Sale System with Optimistic Lock, Distributed Rate Limiting, Redis Cache, and Kafka