Facing 20 billion daily requests, Ctrip’s API gateway evolved from a Zuul‑based prototype to a fully asynchronous, single‑threaded Netty design, employing RxJava, custom flow control, multi‑protocol support, and modular governance to achieve high throughput, low latency, and scalable operations.
This article details how the Sohu video PUGC team refactored a legacy video‑object‑creation API by introducing a reactive, asynchronous, non‑blocking architecture built on RxJava, Vert.x WebClient and a custom Dubbo reactive client, achieving clearer business flow and a 43% latency reduction.
This article explains the fundamentals of reactive programming as a declarative, data‑stream‑based paradigm, describes its core concepts such as streams, events, and operators, and demonstrates how to replace traditional imperative Java code with RxJava‑based reactive implementations for a backend service that processes MQ messages, fetches data, and updates a cache while handling errors, request deduplication, and rate limiting.
This article introduces RxJava as a reactive functional programming framework based on the observer pattern, explains its core concepts, stream types, a wide range of operators, backpressure strategies, and demonstrates its practical use for data migration between MongoDB and MySQL.
This article presents the evolution, architecture, and engineering practices of Ctrip's API gateway, covering its transition to full asynchronous processing with Netty and RxJava, stream‑based forwarding, single‑threaded event‑loop design, performance optimizations, multi‑protocol support, routing, and modular governance for large‑scale microservice environments.
The design separates the original I/O pool into dedicated network, I/O, and polling thread pools, adds comprehensive monitoring of task duration and frequency, enforces unified polling rules, and automatically tunes pool parameters, resulting in a 76 % reduction in UI lag and easier troubleshooting.
This article explains how Hystrix protects distributed Java applications from cascading failures by using circuit breakers, thread‑pool or semaphore isolation, timeout detection, fallback strategies, and health statistics, and includes key source code excerpts illustrating each mechanism.
RxJava brings reactive, event‑driven programming to Java by using Observables and Observers, offering operators, schedulers for thread control, and asynchronous, non‑blocking execution that avoids callback hell, improves performance, and fits UI events, I/O, RPC, and complex service orchestration, as demonstrated by Alibaba’s Xianyu.
This article explains Kotlin coroutine fundamentals, creation, launch and async usage, suspension functions, withContext, and compares coroutine-based implementations with RxJava in Android development, providing code examples and best‑practice guidance, including performance considerations and migration advice.
The article explains reactive programming fundamentals, shows why traditional OO assignments struggle with dynamic data relationships, and introduces the LiveData Extensions library that leverages MediatorLiveData to create lifecycle‑aware, chainable operators (map, filter, merge, etc.), dramatically reducing boilerplate and improving code metrics in Android apps.
This article examines typical Android development challenges such as lack of standard architecture, lifecycle management, and testing difficulties, and presents the Snowball onion architecture—an MVVM‑based, RxJava‑powered framework with reusable components, best‑practice guidelines, and a supporting xueqiu‑onion library.
This guide explains how to identify OutOfMemoryError crashes in Android applications caused by excessive thread‑pool creation, using tools like Android CPU Profiler, adb ps commands, and code hooks, and provides concrete steps to refactor OkHttp, RxJava, and SDK usage to prevent resource leaks.
This article explains how reactive programming, introduced by Microsoft in 2009 and adopted by Java through frameworks like RxJava and Spring Reactor, addresses high‑concurrency server‑side challenges, clarifies concepts such as backpressure, and compares Reactor with RxJava for modern backend engineers.
This article explains how reactive programming, introduced by Microsoft in 2009 and adopted by Java through Spring Reactor and RxJava, addresses high‑concurrency server‑side challenges by using event‑driven, back‑pressure‑aware streams, and compares the key differences between Reactor and RxJava.
This article explains how reactive programming, introduced by Microsoft in 2009 and popularized through Spring 5 and frameworks like Reactor and RxJava, addresses high‑concurrency server challenges by using event‑driven models, back‑pressure mechanisms, and clear distinctions between concurrency and parallelism.
This article explores the rise of reactive programming in Java, explains concurrency versus parallelism, demystifies backpressure, and compares the Reactor and RxJava frameworks, offering practical insights for modern high‑concurrency server development.
RxJava implements ReactiveX’s observer‑based, pull‑model paradigm by using Observable as a factory and template class whose static creators and chainable operators (e.g., flatMap) apply factory, template, observer and decorator patterns, enabling Java 8‑style asynchronous pipelines that developers can reason about, debug, and optimize.
Recent tech headlines reveal a programmer’s server lockup leading to a startup’s collapse, Microsoft’s entry into the OpenJDK project, Spring Framework 5.1.11 and RxJava 2.2.14 releases, PyCharm 2019.3 EAP adding R support, and Samsung’s decision to dissolve its in‑house CPU team in favor of ARM.
Learn how to create a reactive REST API using Spring Boot and RxJava, covering prerequisite knowledge, required dependencies, CRUD endpoint definitions, service layer implementation with RxJava Single, and web controller integration, plus essential Maven setup and tips to avoid common HttpMediaNotAcceptableException errors.
This article explains RxJava2's backpressure mechanisms, compares the five Flowable strategies (MISSING, ERROR, BUFFER, DROP, LATEST), demonstrates their behavior with practical experiments, and shows how to use Subscription and FlowableEmitter to build a demand‑driven, memory‑safe data pipeline.
This article provides a comprehensive source‑code walkthrough of RxJava’s Observable subscription flow, detailing object creation, reverse subscription, thread scheduling, and operator handling with step‑by‑step code analysis.
This article explains RxJava's Scheduler abstraction, lists the built‑in schedulers, demonstrates thread control with subscribeOn() and observeOn() through a detailed code example, and walks through the underlying source code to show how tasks are scheduled and executed on different threads.
By adopting an event‑driven reactive architecture and leveraging RxJava’s asynchronous, message‑oriented programming, Youzan Retail decouples services, parallelizes complex initialization and data‑transformation tasks, reduces response time and coupling, and enhances robustness, scalability, and throughput for its rapidly growing retail platform.
This article explains RxJava’s origins, core concepts, advantages, common operators, threading strategies, and practical experiences, illustrating how adopting RxJava with Java 8, Lambda, and Stream API can simplify complex, rapidly changing backend systems and improve code readability, maintainability, and performance.
By refactoring Xianyu’s key features with RxJava 2.0, the team replaced blocking logging, HTTP, RPC, and cache operations with asynchronous streams, cutting response time by half, boosting CPU utilization to 97%, raising throughput about 30%, and demonstrating a unified, core‑size thread‑pool model.
This article reviews the evolution of Android app architecture over several years, describing the shortcomings of a simple two‑layer design, the adoption of RxJava and MVP patterns, and the resulting benefits and remaining challenges for building maintainable mobile applications.
This article provides Android developers with practical guidance on using Kotlin instead of Java, covering basic syntax, data classes, singletons, activity navigation, Retrofit with OkHttp, and RxJava to streamline development and improve productivity.
The article clarifies that Android MVVM is an architectural pattern distinct from Google’s DataBinding tool, contrasts MVC and MVP shortcomings, outlines MVVM’s View, Model, and ViewModel layers with detailed responsibilities and sample RxJava/Retrofit code, and recommends using a global Messenger for decoupled ViewModel communication to achieve low‑coupling, testable, data‑driven UI development.
This article explains how to combine MVP architecture with RxJava in Android development, covering framework selection, step‑by‑step MVP construction, thread management using RxJava, and practical Q&A on testing, modularization, and memory‑leak prevention, providing a comprehensive guide for building decoupled, responsive mobile apps.
This article explains how to choose an Android framework, build a layered MVP architecture, and use RxJava to move network requests off the main thread, providing clear code examples and practical tips for scalable, testable mobile apps.
This comprehensive guide walks Android developers through Retrofit’s fundamentals, usage patterns, URL configuration, parameter handling, custom converters, call adapters, and advanced techniques like file uploads, mock servers, and RxJava integration, providing code examples and deep insights to master network requests efficiently.
An ex‑WeChat engineer shares practical strategies to accelerate Android development, covering RxJava for reactive streams, MVP/MVVM architecture, REST client generation, image‑loader choices, quality monitoring tools, and workflow best practices that can help startups build high‑performance apps efficiently.
