A sudden surge in traffic caused a signature interceptor's synchronous database insert to consume all HikariCP connections, crashing core services, and the author resolved it by converting the operation to asynchronous execution while outlining more robust long‑term solutions.
The article explains how using a message queue as a buffer, asynchronous processor, and decoupling layer enables flash‑sale systems to handle tens of thousands of orders per second, reducing database overload, cutting response time from 500 ms to 50 ms, and preventing cascade failures.
The article explains that performance tuning should start with pinpointing bottlenecks using response time, throughput, concurrency and resource utilization metrics, then choose appropriate measures—caching, async processing, database tuning, horizontal scaling, and rate‑limiting—rather than blindly adding a cache.
The article explains how using a message queue such as Kafka lets an order service return quickly by handling non‑critical tasks like SMS, points, and logging asynchronously, thereby improving latency, decoupling services, and smoothing traffic spikes while also covering Kafka's core concepts and trade‑offs.
This article presents nine practical techniques—async processing, caching, database query tuning, response compression, WebFlux reactive endpoints, logging reduction, index optimization, connection‑pool configuration, and CDN static‑asset delivery—to dramatically cut Spring Boot API latency and boost throughput in high‑concurrency environments.
The article explains how message queues can dramatically improve response time, decouple services, and smooth traffic spikes, outlines seven advantages and eight drawbacks, and provides concrete guidelines on when to adopt them, how to prevent loss, duplication, and ordering issues, and how to ensure end‑to‑end reliability.
After mastering core AI Agent capabilities, this article shows how to transform a prototype into a production‑grade service by covering a full architecture overview, stateless design, health‑check and graceful shutdown, asynchronous task queues, multi‑level caching, token‑cost optimization, model fallback, input/output filtering, rate limiting, monitoring, and deployment recommendations for different scales.
The article examines why large database transactions inside API endpoints degrade performance, outlines issues such as data inconsistency, lock contention, undo‑log overhead, and DB pressure, and presents practical optimizations including removing remote RPC calls, using programmatic transactions, batch processing, splitting into smaller transactions, and asynchronous parallel execution with code examples.
This comprehensive guide explains the purpose, core concepts, suitable scenarios, common patterns, benefits, and potential pitfalls of asynchronous processing, offering detailed design, development, review, and operational principles to help teams build reliable, high‑throughput systems.
This article provides a detailed technical walkthrough of a typical payment platform architecture, covering the overall system overview, core transaction and payment modules, service governance mechanisms such as unified context and data consistency, and practical production practices like performance testing and asynchronous processing.
This article explores the fundamental architecture of a payment platform, detailing the separation of transaction and payment cores, the key modules such as transaction abstraction, payment orchestration, service governance, data consistency, asynchronous processing, performance testing, and practical strategies for stability and scalability.
This article explains how a typical payment platform is structured into transaction and payment cores, details the interactions between services, describes governance mechanisms such as unified context and data consistency, and shares practical production techniques like performance testing, async processing, and service degradation.
Increasing a database connection pool size often worsens performance under massive traffic because the bottleneck lies in hardware limits and management overhead, so developers must adopt async processing, read‑write splitting, caching, sharding, and proper pool tuning to sustain high concurrency.
This article examines strategies for low‑latency, consistent hot‑account updates under high concurrency, comparing optimistic DB locking, Redis‑based deduction with asynchronous merging, data sharding, and MQ‑driven batch processing, and also addresses read‑side solutions.
This article breaks down the architecture of a generic payment core system, covering the transaction and payment cores, service governance, data consistency, asynchronous processing, performance testing, and stability measures to help engineers build robust payment platforms.
This article walks through the end‑to‑end design of IMA's AI‑driven knowledge base, covering its definition, core business flow, architecture evolution, data ingestion pipelines, management challenges, asynchronous processing, permission modeling, and the business value demonstrated by the prototype.
This article provides a systematic, step‑by‑step approach for identifying the root causes of slow SQL statements and sluggish API responses, covering monitoring, query analysis, index tuning, N+1 detection, caching, async processing, and architectural best practices.
The article dissects XXL-JOB's communication layer, showcasing Netty HTTP usage, a full asynchronous processing pipeline, dynamic proxy abstraction, request‑ID based thread wake‑up, and detailed Java code examples that illustrate how the scheduler and executor achieve high‑throughput remote job execution.
This article explains how a large e‑commerce platform implements a high‑performance, asynchronous Excel export mechanism using annotation‑driven declarative programming, AOP interception, Quartz scheduling, RocketMQ messaging, and OSS storage to handle massive data sets without blocking user requests.
This article walks through the complete architecture of a payment platform, detailing the transaction and payment cores, their interactions, service governance, data consistency, DB sharding, asynchronous processing, performance testing, and practical production practices for building robust backend payment systems.
This article walks through a typical payment system architecture—covering the transaction and payment cores, service governance, data consistency, asynchronous processing, performance testing, and production best practices—providing a comprehensive view for engineers building robust financial platforms.
This article explores ten practical use cases for message queues, from system decoupling and asynchronous processing to traffic shaping, data synchronization, log collection, broadcasting, ordered and delayed messages, retry mechanisms, and transactional messaging, providing code examples and best‑practice recommendations for robust backend design.
The article defines high concurrency, explains how to locate bottlenecks and understand user traffic patterns, then outlines practical measures such as distributed architecture, clustering, caching, asynchronous queues, rate limiting, database tuning, preloading, and fault‑tolerant monitoring to build robust high‑throughput systems.
This article explains why Kafka message backlog happens, then details practical solutions such as adding consumer instances, using batch consumption, applying asynchronous processing, and simplifying business logic to improve consumer efficiency.
This article explains how to design and implement a generic asynchronous processing SDK for Java backend services, covering its purpose, advantages, underlying principles, component choices, database schema, configuration, usage patterns, and best‑practice notes, with code examples and diagrams.
This article presents a comprehensive guide to optimizing API performance in Spring Boot projects, covering batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction management, code refactoring, pagination, SQL tuning, and lock granularity, complete with code examples and diagrams.
This article explains how Alibaba Cloud's PAI platform uses an asynchronous inference framework with dedicated queue and inference services to overcome high‑latency challenges, enable load‑balanced request distribution, provide health‑check failover, and support automatic scaling for large‑model AI workloads.
This article introduces the lightweight open‑source XXL‑Job framework, walks through server and executor setup, demonstrates three task creation methods, explains how to trigger and monitor jobs, and highlights the dynamic‑proxy and asynchronous design that enable high‑throughput distributed scheduling in Spring Boot applications.
Airbnb's media team engineered a scalable postcard generation pipeline that matches destinations, handles localized text layout, and delivers high‑performance images across iOS, Android, Web, and external messaging platforms using flexible templates, async processing, and pre‑generation strategies.
This article explains how @Transactional and @Async normally cooperate in Spring Boot, demonstrates cases where the transaction does not roll back when @EnableAsync order is changed, analyzes the underlying AOP proxy creation and bean post‑processor execution order, and introduces new Spring 6.2 transaction‑rollback configuration options.
Learn how to overcome memory and speed bottlenecks when exporting massive datasets to Excel by using streaming APIs like SXSSFWorkbook and EasyExcel, optimizing database pagination, tuning JVM and connection pools, and applying asynchronous shard processing to achieve stable sub‑200 MB memory usage for millions of rows.
This article introduces a non‑intrusive asynchronous processing SDK for Java Spring backends that leverages transaction events, provides fallback mechanisms, and includes configuration, components, usage examples, and code snippets to achieve reliable, eventually consistent async execution while preserving performance and data integrity.
The article outlines a comprehensive approach for backend engineers to manage a sudden 100‑fold increase in traffic, covering emergency response, traffic analysis, robust system design, rate limiting, circuit breaking, scaling, sharding, pooling, caching, asynchronous processing, and stress testing to ensure system stability and performance.
This article introduces a generic asynchronous processing SDK for Java backend systems, explaining its design principles, components, configuration, usage patterns, and advantages such as non‑intrusive design, transaction safety, and eventual consistency, while providing code samples, database scripts, and deployment details.
This comprehensive guide explains the nature of high concurrency, distinguishes it from parallelism, outlines its potential consequences across application, database, and service layers, and presents a systematic set of mitigation strategies—including rate limiting, asynchronous processing, redundancy, caching, and queue‑based designs—supported by real‑world case studies and code examples.
This article explains how AI inference services can tackle five core problems—rate limiting, load balancing, asynchronous processing, user data management, and index enhancement—by leveraging Tair's rich data structures, offering practical code examples, architectural diagrams, and a comparison with alternative solutions.
This article explores eight common scenarios for using message queues in backend development, covering asynchronous processing, service decoupling, traffic shaping, delayed tasks, log aggregation, distributed transactions, remote calls, and broadcast notifications, each illustrated with Java, RocketMQ, and Kafka code examples.
Bilibili’s ticketing platform evolved from a single‑transaction, synchronous design to an asynchronous batch system and finally to a Redis‑cached inventory layer, adding DB isolation changes, sharding, bloom‑filter protection and adaptive rate limiting, which together enabled handling up to 930 k requests per second and stable high‑concurrency ticket sales.
This article explores eight practical scenarios for using message queues—such as asynchronous processing, service decoupling, traffic shaping, delayed tasks, log collection, distributed transactions, remote calls, and broadcast notifications—providing code examples with RabbitMQ, RocketMQ, and Kafka to illustrate each pattern.
This article explores the challenges of handling massive simultaneous requests in distributed architectures and presents practical solutions such as load balancing, distributed caching, asynchronous processing, and sharding, illustrated with case studies from major e‑commerce and social platforms.
This article outlines eight essential high‑performance architecture techniques—including load balancing, asynchronous processing, database optimization, caching, distributed clusters, CDN, microservices, and rate‑limiting/circuit‑breaking—to improve scalability, availability, and responsiveness of large‑scale backend systems.
This article explains Spring Boot’s asynchronous processing mechanism, covering its purpose, the @Async annotation, configuration steps, custom thread pool setup, handling return values, and typical use cases, enabling developers to improve performance and responsiveness in backend applications.
This article explores eight practical use cases for message queues—including asynchronous processing, service decoupling, traffic shaping, delayed tasks, log collection, distributed transactions, remote calls, and broadcast notifications—providing code examples and architectural guidance for building robust backend systems.
This article explains eight typical scenarios for using message queues—including asynchronous processing, service decoupling, traffic shaping, delayed tasks, log collection, distributed transactions, remote invocation, and broadcast notifications—providing clear explanations and Java code snippets for each case.
An extensible asynchronous processing SDK is presented, leveraging Spring transaction events, Kafka queues, XXL‑Job scheduling, and a non‑intrusive architecture to ensure high performance, data safety, and eventual consistency while simplifying integration across Java backend services.
This article examines four architectural approaches for importing Excel data—synchronous, thread‑pooled with MQ, fully asynchronous with Redis locks, and an ideal high‑throughput variant—detailing their trade‑offs, performance impacts, and suitable business scenarios in modern backend systems.
This article presents a detailed case study of four backend design alternatives for importing Excel data—ranging from synchronous processing to asynchronous pipelines with thread pools, message queues, and Redis locks—analyzing their advantages, drawbacks, and suitable scenarios.
This article introduces njs, the JavaScript engine for Nginx, explains its basic "Hello world" example and walks through advanced features such as filesystem APIs, asynchronous I/O, shared memory, response handling, logging, and the global namespace, all with concrete code snippets.
This article dissects the GrowingIO Java SDK, detailing its event‑message construction, asynchronous batch reporting, retry mechanisms, and the underlying design patterns such as Builder, Strategy, and Producer‑Consumer that enable high‑performance user‑tracking.
This article explains how message queues decouple and buffer tasks such as payment processing, inventory updates, and notifications in high‑traffic online stores, improving reliability, scalability, and fault tolerance through asynchronous communication.
This article explains the design, advantages, and implementation details of a generic asynchronous processing SDK that leverages Spring transactional events, Kafka messaging, XXL‑Job scheduling, and a non‑intrusive architecture to ensure data consistency, fault tolerance, and easy integration across services.
Facing tight schedules and diverse coding habits, many Chinese projects struggle with API latency; this guide walks through requirement analysis, acceptance standards, common pitfalls, and a comprehensive set of optimization tactics—from configuration and code tweaks to caching, async processing, and observability tools—to dramatically improve API performance.
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 explores seven classic message‑queue use cases—from asynchronous decoupling and traffic‑shaping to delayed tasks, broadcast consumption, distributed transactions, and using Kafka as a data hub—illustrated with real‑world examples and code snippets.
This article shares seven practical scenarios—such as asynchronous processing, traffic smoothing, message bus, delayed tasks, broadcast consumption, distributed transactions, and data hub integration—where message queues like RocketMQ, Kafka, ActiveMQ, and RabbitMQ help solve high‑concurrency challenges in modern backend systems.
This article explains why message queues are needed, outlines RabbitMQ's architecture, describes its basic concepts and working modes such as simple, work, fanout, direct and topic, and discusses reliability features like transactions, confirms, dead‑letter queues, TTL, clustering, ordering, and handling message backlogs.
From asynchronous decoupling to distributed transactions, this article explores seven classic message‑queue use cases—such as peak‑shaving, event buses, delayed tasks, broadcast consumption, and data hub integration—illustrated with real‑world examples from e‑commerce, ride‑hailing, and lottery systems, plus RocketMQ code snippets.
This article walks through a real‑world order‑processing case, demonstrating how successive design‑pattern refinements—starting with the Chain of Responsibility, then adding Factory, Proxy, Template Method, Observer and distributed messaging—enable multi‑tenant, asynchronous, and scalable backend workflows.
This article by a senior architect provides a detailed overview of payment system architecture, covering transaction and payment cores, system interactions, service governance, asynchronous processing, and production practices, while also offering practical performance testing and stability strategies.
This article explains how to boost website response speed by reducing PHP function calls, leveraging built-in functions, caching results, and employing asynchronous processing, providing clear code examples for each optimization technique in PHP.
Facing a traffic surge that pushed a risk‑control engine's response time beyond 250 ms, the team applied a series of systematic optimizations—feature prefetching, batch requests, asynchronous accumulator updates, multi‑level caching with Bloom filters, and log‑compression redesign—resulting in latency dropping to 25 ms, CPU and memory usage falling by up to 90%, and storage costs reduced by over a third.
The article outlines eBay’s key scalability best practices—including functional partitioning, horizontal sharding, eliminating distributed transactions, aggressive asynchronous decoupling, intelligent caching, and pervasive virtualization—to illustrate how large‑scale web systems can achieve linear or sub‑linear growth while maintaining availability and performance.
This article walks through essential techniques for building high‑concurrency systems, covering page static‑generation, CDN acceleration, caching layers, asynchronous processing with thread pools and MQ, sharding, connection pooling, read/write splitting, indexing, batch queries, clustering, load‑balancing, rate limiting, service degradation, failover, multi‑active deployment, stress testing, and monitoring.
This article explains the concept, practical applications, version evolution, and deployment strategy of a virtual phone number service, detailing XB and AXB binding modes, asynchronous retry mechanisms, concurrency handling, and monitoring to ensure reliable integration with third‑party PSTN providers.
This article presents a comprehensive overview of a typical payment system architecture, describing the transaction core, payment core, service governance, asynchronous processing, performance testing, and practical production practices, while illustrating each component with diagrams and implementation details.
The article presents a comprehensive design and implementation of a unified push notification platform that abstracts variable configurations, systematizes immutable logic, and employs asynchronous processing, sharding, rate limiting, and resource allocation strategies to achieve scalable, reliable, and maintainable multi‑business push services.
This article describes the background, design principles, implementation details, performance optimizations, and operational results of a unified push notification platform that abstracts variable configurations, systematizes invariant logic, and supports high‑throughput, reliable messaging across multiple business lines.
Scrapy is a powerful Python-based web scraping framework designed for large-scale and complex website data extraction. It offers high-level abstractions, built-in data extraction tools using XPath and CSS selectors, asynchronous processing for parallel requests, and flexible pipelines for data storage, making it ideal for efficient and scalable web scraping projects.
Taobao’s open platform gateway has evolved into a modular, decentralized system that uses full asynchronous processing, multi‑cluster deployment, token‑based authentication, and advanced metadata caching to boost scalability, security, and performance while protecting privacy and enabling efficient large‑scale reporting.
This article explores the comprehensive architecture of a payment platform, detailing the transaction and payment cores, service governance, data consistency, asynchronous processing, and production practices, illustrated with diagrams to help engineers design robust, scalable payment systems.
Discover a comprehensive set of ten practical techniques—including batch processing, asynchronous execution, caching, pooling, parallelization, indexing, transaction management, and SQL tuning—to dramatically reduce API latency and improve efficiency in legacy backend systems, illustrated with real-world examples and code snippets.
This article explains the core components and interactions of a typical payment system, describing the transaction and payment cores, their abstractions of payment types, service governance mechanisms, data consistency strategies, asynchronous processing, and practical production practices for high‑performance, reliable payment platforms.
Learn practical ways to ensure that failures when invoking third‑party services don’t roll back database inserts, by using transactions, asynchronous processing with threads, thread pools, job schedulers, message queues, Redis queues, or binlog listeners, and understand their trade‑offs and ordering guarantees.
This article presents a comprehensive set of backend API optimization strategies—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction management, code restructuring, deep pagination, SQL tuning, and lock granularity—to dramatically reduce response times and improve system efficiency.
This article presents a detailed overview of payment system architecture, describing the transaction and payment cores, their interactions, service governance, asynchronous processing, data consistency, and practical production practices, while illustrating each concept with diagrams and code snippets.
This article presents a comprehensive overview of a typical payment system architecture, detailing the transaction core, payment core, service governance, data consistency, asynchronous processing, and production practices such as performance testing and stability management.
This article presents a detailed analysis of modern payment system architecture, covering the transaction and payment cores, service governance, data consistency, asynchronous processing, and practical production practices such as performance testing and stability management, illustrated with numerous diagrams.
This article presents a comprehensive guide to optimizing API latency by covering batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelism, indexing, transaction management, code refactoring, pagination, SQL tuning, and lock granularity, each illustrated with practical code examples and diagrams.
This article explains why message queues are used to decouple services, enable asynchronous processing, smooth traffic spikes, and improve performance, while also detailing the new challenges they introduce such as reduced availability, increased complexity, duplicate consumption, data consistency, message loss, ordering, and backlog, along with practical solutions for each issue.
This article explains why message queues are essential for decoupling services, enabling asynchronous processing, and smoothing traffic spikes, then details the new challenges they introduce—such as availability, complexity, duplicate consumption, ordering, and data consistency—and offers concrete mitigation strategies for each issue.
The article presents a branch‑path graph scheduling framework that extends traditional finite‑directed‑graph scheduling with branch nodes and AND/OR activation semantics, converting it into a deterministic directed acyclic graph, thereby eliminating exponential state growth and improving scalability, readability, and maintainability of Vivo’s real‑time advertising estimation service.
This article explains the Observer pattern, its core roles and typical use cases, then dives into Spring's event mechanism—including ApplicationEvent, ApplicationListener, and ApplicationEventPublisher—showing how to configure synchronous and asynchronous event processing with code examples.
This article presents a comprehensive overview of payment system architecture, detailing the core transaction and payment modules, system interactions, service governance, data consistency strategies, asynchronous processing, and practical production practices such as performance testing, stability management, and service isolation.
This article describes the motivation, requirements, design, and implementation of a lightweight asynchronous message processing framework built by ByteDance to handle near‑real‑time metadata changes for DataLeap's Data Catalog, detailing its architecture, thread model, state management, delay handling, monitoring, and operational experiences.
This article explains why RabbitMQ is used for decoupling, asynchronous processing, and traffic shaping, describes its architecture and core components such as broker, exchange, queue, binding, routing key, vhost, producer, consumer, and channel, and outlines its six operating modes with illustrative diagrams.
The talk by Tang Gengyang from Citic Baixin Bank details the challenges faced in risk feature engineering, presents two solution frameworks (1.0 and 2.0) for accelerating deployment, improving reuse, handling offline/online consistency, and outlines future enhancements for a more efficient, automated feature pipeline.
Time management in programming spans human concepts of time, language-specific handling of zones and timestamps, 32‑bit overflow risks, sync versus async processing, log timestamping, business‑level period calculations, and common pitfalls, emphasizing that mastering these nuances prevents bugs, improves performance, and enables reliable analytics.
This article explains how to handle large Excel uploads in Java by offloading parsing and validation to a thread pool, using the Template Method pattern to reduce boilerplate, and finally applying a custom AOP annotation to manage logging, error handling, and asynchronous execution in a clean, reusable way.
This article presents a comprehensive guide to designing high‑concurrency backend solutions, covering server architecture, load balancing, database clustering, caching layers, message queues, asynchronous processing, service‑oriented design, redundancy, and automation to ensure reliable performance under massive user traffic.
This article explains what a Message Queue (MQ) is, why it is used for traffic shaping, system decoupling, and asynchronous processing, reviews popular MQ products such as Kafka, ActiveMQ, RocketMQ and RabbitMQ, and details RabbitMQ's core concepts and advanced features like dead‑letter, delayed, idempotent, priority, and lazy queues.
The article explains how to redesign a high‑traffic user‑registration flow by introducing an asynchronous queue layer, detailing the broker architecture, commitlog, consumeQueue, page‑cache, mmap, topic/tag routing, high‑availability strategies, and the role of a nameserver in a RocketMQ‑style system.
This article explains how RocketMQ tackles registration latency, synchronous coupling, and traffic spikes by introducing an intermediate queue, designing a persistent high‑availability broker, leveraging Linux page cache and memory‑mapped files, and employing topics, tags, and sharding to enable efficient asynchronous processing and scalable consumption.
This article explains what message middleware (or message queues) is, the problems it solves such as decoupling, ordering, routing, asynchronous processing and peak shaving, and details its components, patterns, advantages, common protocols, and popular implementations.
This article explains the motivations for introducing message queues—such as decoupling, asynchronous processing, and traffic shaping—then details the typical challenges like availability, complexity, duplicate consumption, data consistency, message loss, ordering, and backlog, and provides practical solutions for each issue.
This article explains the purpose of message queues, outlines RabbitMQ's architecture, describes common MQ patterns such as simple, work, fan‑out, direct, and topic, and discusses reliability features like transactions, confirms, dead‑letter queues, TTL, clustering, and ordering guarantees.
This article analyzes the challenges of building a flash‑sale system—such as overselling, massive concurrent requests, URL exposure, and database strain—and presents a comprehensive backend design that includes separate databases, dynamic URLs, Redis clustering, Nginx load balancing, rate‑limiting, asynchronous order processing, and service degradation strategies.
The article explains the characteristics of flash‑sale scenarios, presents core design principles such as rate limiting, peak shaving, asynchronous processing and scalability, and details a complete backend and frontend architecture—including Redis‑based queueing and caching—to build a robust, high‑throughput seckill system.
This article presents a comprehensive guide to designing high‑concurrency backend systems, covering server architecture, load balancing, database and NoSQL clustering, caching strategies, concurrency testing tools, message‑queue solutions, first‑level cache, static data handling, layering, distribution, asynchronous processing, redundancy and automation.
This article demystifies message queues by comparing them to parcel lockers, explains their role in asynchronous processing and system decoupling, details point‑to‑point and publish‑subscribe models, introduces JMS, AMQP, RabbitMQ, ActiveMQ, RocketMQ, and Kafka, and outlines their architectures and high‑availability mechanisms.
This article explains the fundamentals of message queues, their role in asynchronous processing and system decoupling, compares point‑to‑point and publish‑subscribe patterns, introduces Java Message Service (JMS) and its API, and outlines popular implementations such as ActiveMQ, RabbitMQ, RocketMQ, and Kafka with their architectures and high‑availability mechanisms.
