This article explains the basic principles of how computers work, covering CPU and memory organization, instruction sets, endianness, compiler role, operating system interaction, cache levels, storage technologies, and performance optimization techniques.
This article provides a comprehensive overview of essential backend infrastructure components for Java-based services, covering API gateways, core frameworks, caching, databases, search engines, message queues, file storage, authentication, service governance, scheduling, logging, monitoring, and fault‑tolerance strategies.
This article explains why UIWebView was deprecated, how WKWebView improves performance and memory usage, details its multi‑process architecture, outlines page loading flow, delegate methods, container design, caching strategies, cookie handling across processes, and solutions for white‑screen crashes in iOS apps.
This guide explains how to use OpenResty’s Lua modules to let Nginx read and write Redis directly, compress large payloads, schedule periodic cache updates, and configure URL‑based caching, thereby reducing latency, saving bandwidth, and improving overall backend reliability.
This article explains how to build a highly available, scalable system by decoupling components, adding redundancy, choosing appropriate scaling strategies, employing load balancers, caching, CDN, database replication, sharding, and stateless design to support over 100 million users.
This article explains how to use OpenResty with Lua to integrate Nginx and Redis for high‑performance caching, data compression, timed updates, and configurable URL management, enabling resilient backend services that continue serving content even when application servers fail.
This article presents a comprehensive case study of the technical challenges and optimization strategies—including traffic surges, cache penetration, cache breakdown, limit‑purchase handling, and inventory deduction—encountered during a large‑scale ticket reservation event, and demonstrates how systematic backend improvements achieved over 50% performance gains and 98% cache hit rate.
This guide explores sixteen practical Redis use cases—from caching and distributed locks to rate limiting, bitmaps, shopping carts, timelines, and ranking—detailing data types, commands, and implementation tips that help developers build efficient, scalable backend services.
This article provides a comprehensive overview of large‑scale distributed website architecture, covering its characteristics, design goals, architectural patterns, performance, high‑availability, scalability, extensibility, security, agility, evolution stages, and practical implementation techniques such as caching, load balancing, database sharding, service‑orientation and message queues.
The article outlines the characteristics of high‑traffic websites and traces the architectural evolution from a single‑server model to distributed, cache‑enabled, load‑balanced, and service‑oriented designs, highlighting techniques such as clustering, read/write splitting, CDN, NoSQL, and business micro‑services.
The article explains how a senior architect uses OpenResty and Lua to let Nginx directly access Redis for caching, compresses large responses, schedules periodic updates, and provides a configurable, signature‑verified caching layer to improve web performance and resilience.
This article surveys the most commonly used Java libraries and tools—from Netty and SpringBoot to JUnit, JMH, OkHttp, HikariCP, Caffeine, Hazelcast, logging frameworks, Jackson, Jolokia, Hibernate Validator, and FreeMarker—highlighting their roles, advantages, and integration tips for modern enterprise development.
This article details a step‑by‑step performance overhaul for a weekly report service that will exceed 100 million rows, covering indexing, weekly sharding, Redis caching, and data pre‑warming to dramatically cut query latency and reduce database load.
This article compares the three popular Redis Java clients—Jedis, Redisson, and Lettuce—explaining their core differences, then shows how to configure and use Spring's RedisTemplate, RedissonClient, and annotation‑based caching with practical code examples and configuration files.
This article explains the GraphQL N+1 performance issue, demonstrates how DataLoader batches and caches database calls to eliminate redundant queries, walks through its core TypeScript implementation—including batch scheduling, cache handling, and load methods—while providing practical examples and integration tips for real‑world GraphQL servers.
This article explores sixteen practical Redis patterns—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, tags, product filtering, follow relationships, and rankings—demonstrating how each can be implemented with simple Redis commands to enhance performance and scalability.
This article presents sixteen practical Redis use‑cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, tags, filtering, follow models, and ranking—explaining their data types, commands, and sample code.
This article explains what a Content Delivery Network (CDN) is, how it resolves domain names, distributes content via edge nodes, manages caching and updates, and provides additional features such as secure protocols and protection, illustrating its evolution and impact on web performance.
This talk shares practical high‑availability strategies learned from years of building Taobao’s massive e‑commerce platform and migrating to Alibaba Cloud, covering traditional IDC stability mechanisms, cache and disaster‑recovery designs, cloud‑native fault‑tolerance, capacity planning, rate‑limiting, graceful degradation, and multi‑region resilience.
This article introduces a collection of practical Redis use cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, tags, product filtering, follow relationships, and ranking—explaining their data types, commands, and sample code.
Airbnb replaced duplicated, latency‑prone authorization checks in its new service‑oriented architecture by moving them into data services and building Himeji, a Zanzibar‑inspired centralized permission store that uses triple‑based policies, configurable unions, sharded caching, and Aurora backing to deliver sub‑10 ms latency for millions of checks per second with 99.999 % availability.
This article documents a step‑by‑step performance optimization of a high‑traffic web module, covering requirement analysis, bottleneck detection in database and TCP connections, cache integration, load‑testing results, Linux kernel parameter tuning, and the final achievement of 50k QPS with sub‑60 ms latency.
This article explores sixteen practical Redis use cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, tags, product filtering, follow relationships, and ranking—detailing data types, commands, and code snippets for each scenario.
This article presents nine actionable Java performance‑optimization techniques—from avoiding premature tweaks and profiling bottlenecks to using StringBuilder, primitive types, and caching—complete with code examples and guidance on building a reliable test suite.
This article explores how to leverage Redis for a wide range of backend functionalities—including caching, distributed data sharing, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow relationships, and leaderboards—providing practical code examples and usage patterns.
This article explains the classification of relational and NoSQL databases, analyzes common performance bottlenecks such as query latency, large fields, and write overhead, and presents practical optimization methods including caching, proper indexing, transaction handling, read‑write separation, and sharding for large‑scale systems.
This article explains how to design a high‑concurrency flash‑sale system by addressing instant traffic spikes, page static‑generation, CDN acceleration, button activation, read‑heavy/write‑light patterns, cache breakdown and penetration, inventory pre‑deduction, distributed locking, message‑queue handling, and multi‑level rate limiting.
This article details Dada Group’s transition from a Redis‑based, low‑throughput distance‑calculation system to a high‑concurrency, low‑cost architecture leveraging GEOHASH indexing, cache sharing, and asynchronous processing, achieving billions of daily point‑pair calculations with sub‑100 ms latency and high value‑rate.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering its characteristics, design goals, architectural patterns, performance, high availability, scalability, extensibility, security, agility, and a detailed example evolution from a single‑server setup to a multi‑layer, service‑oriented system.
Shopee Shake’s architecture separates admin and user sides into three layers—access, application, and resource—and uses horizontal scaling, bucketed Redis coin pools, multi‑level caching, asynchronous message queues, precise capacity formulas, and comprehensive monitoring and chaos‑engineered runbooks to reliably handle over 300,000 QPS during major promotional events.
This article documents a step‑by‑step performance optimization of a high‑traffic web module, covering requirement analysis, bottleneck identification, caching, message‑queue offloading, Linux TCP tuning, and the resulting capacity increase to 50,000 QPS with sub‑70 ms latency.
The article categorizes data by change and access frequency, explains cache loading methods, compares local caches (Map, Guava, Spring) with remote solutions like Redis, Memcached, Hazelcast, and addresses common cache issues such as penetration, breakdown, and avalanche, offering practical mitigation techniques including Bloom filters, RoaringBitmap, and load‑balancing strategies.
The article outlines the defining traits of large-scale websites—high concurrency, massive data, 24/7 availability, security, and rapid iteration—and discusses major technical challenges such as scaling, caching, clustering, database read/write separation, CDN acceleration, distributed storage, and service decomposition.
This article provides a comprehensive tutorial on setting up a one‑master‑one‑slave Redis replication environment, configuring read‑write separation, verifying data synchronization, testing read‑only behavior, and performing performance benchmarking with redis‑benchmark, complete with command examples and result analysis.
The article describes the architecture and optimization techniques of Qunar's inventory search service, covering background business flow, challenges of high‑traffic multi‑channel integration, and detailed solutions such as product cataloging, channel cache utilization, request replay, cache isolation, unified cache management, cold‑hot segregation, and full‑process asynchronous handling to improve cache hit rate, update efficiency, and overall throughput.
This article explains the fundamentals of Redis master‑slave architecture, covering why high‑availability replication is needed, the three topology patterns, the three replication modes (continuous, full, partial), the replication backlog buffer, PSYNC command handling, heartbeat mechanisms, and the practical considerations for operators and architects.
This article details the step‑by‑step architectural evolution of JD Daojia's product system—from the initial 1.0 monolithic design, through 2.0 high‑availability and performance enhancements, to the 3.0 domain‑driven microservice architecture—highlighting the motivations, technical solutions, and future outlook.
This article provides a comprehensive overview of Redis interview questions, covering persistence mechanisms, cache avalanche and penetration, hot vs. cold data, differences from Memcached, single‑threaded performance, data structures, internal architecture, expiration policies, clustering options, distributed locks, and transaction handling.
This article explains how to design a flash‑sale (秒杀) system that can handle sudden spikes of traffic by using static pages, CDN acceleration, caching strategies, distributed locks, message‑queue asynchronous processing, rate limiting and other backend techniques to ensure reliability and prevent overselling.
This article provides a comprehensive overview of caching in large distributed systems, covering theory, common components, classification, CDN and reverse‑proxy caches, local application caches, popular implementations like Ehcache, Guava, Memcached and Redis, and a detailed comparison of their features and trade‑offs.
Redis, traditionally seen as a high‑performance cache, also offers rich data structures, persistence options, and clustering modes that enable it to function as a primary database for many internet services, supporting use cases such as user profiles, counters, leaderboards, friend relationships, distributed locks, rate limiting, and more.
This article outlines the new features of Redis 6.0 and 5.0, compares various deployment architectures such as master‑replica, single‑replica, cluster and read‑write split, presents typical usage scenarios, and provides a Java Jedis code sample for connecting to a Redis cluster.
The article explains four classic cache‑update design patterns—Cache Aside, Read Through, Write Through, and Write Behind—detailing their workflows, consistency trade‑offs, and practical optimizations for high‑concurrency backend services that combine Redis with MySQL.
This article reviews various cache system types, compares popular distributed caches such as Memcache, Tair, and Redis, explains Redis cluster high‑availability mechanisms, discusses sharding strategies, and outlines common cache problems and solutions, providing practical configuration examples for Java backend developers.
This guide explains how to build a highly available, scalable architecture for supporting hundreds of millions of users, covering decoupling, redundancy, vertical and horizontal scaling, load balancing, database replication, sharding, caching, CDN, GeoDNS, and best practices for progressive system expansion.
This article explains how to use OpenResty with Lua to build a high‑performance caching layer that directly accesses Redis, compresses large responses, schedules periodic updates, forwards requests intelligently, and provides configurable URL caching, thereby improving concurrency, reducing latency, and optimizing bandwidth usage.
The article explains what high concurrency means, outlines key performance metrics such as response time, throughput, QPS, TPS and concurrent users, and presents vertical and horizontal scaling techniques—including hardware upgrades, caching, load balancing, sharding, micro‑services, multithreading and CDN—to improve a system's ability to handle massive parallel requests.
This article presents a comprehensive guide to using Redis for various backend scenarios, including caching hot data, sharing sessions, implementing distributed locks, generating global IDs, counting, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, tags, product filtering, follow relationships, and ranking, all illustrated with concrete code examples.
By illustrating how to replace simple HashMap caching with Redis‑based distributed caches and locks—using SETNX, Lua scripts, and Redisson—the article shows Spring Boot developers how to prevent cache breakdown, ensure data consistency, and dramatically improve throughput in high‑concurrency web applications.
The article discusses how to approach performance for entire front‑end applications—especially ReactJS—by considering real‑world data flow, state management, caching strategies, and the limitations of benchmark‑driven choices, while offering practical references and future topics.
This article explains the design and architecture of a cloud-based shopping cart system, covering its functional modules, layered and cluster designs, distributed goals such as stability and elasticity, three-level caching, asynchronous checks, heterogeneous storage, payment solutions, and anti‑scalping measures.
This article details how to optimize a high‑traffic weekly report service by adding composite indexes, partitioning data into weekly tables, leveraging Redis caching, and employing data pre‑warming to dramatically reduce query latency and database load.
Designing a flash‑sale (秒杀) system for massive concurrent users requires careful handling of instant traffic spikes, page staticization, CDN acceleration, caching strategies, distributed locks, rate limiting, asynchronous processing, and reliable stock management, with nine detailed techniques to ensure stability and prevent overselling.
This article recounts how a Vue‑SSR e‑commerce site was repeatedly overloaded during three promotional phases and how the team applied static‑dynamic data separation, CDN integration, application‑level caching, auto‑degrade rendering, rate‑limited APIs, and hydration fixes to achieve stable high‑traffic performance.
This article explains how cache penetration occurs in high‑traffic systems and demonstrates how Bloom filters—using minimal memory and fast hash checks—can efficiently prevent such attacks, including construction details, Redis BitMap experiments, and Java Redisson implementation examples.
This article outlines a universal caching strategy for low‑frequency static data in microservice systems, explaining why in‑memory caches like Redis are needed, detailing a six‑component architecture with services, queues, and consistency checks, and weighing trade‑offs such as cache eviction, persistence, and scalability.
This article explores how backend developers can handle intricate e‑commerce search filters by first attempting a monolithic SQL solution, then improving performance with index analysis and query splitting, and finally achieving fast, scalable results using Redis sets, sorted sets, and transaction commands.
This article outlines ten actionable techniques—including reverse proxy deployment, load balancing, caching, compression, SSL/TLS optimization, HTTP/2 adoption, software upgrades, Linux tuning, web‑server tweaks, and real‑time monitoring—that together can boost a web application's speed and reliability by up to ten times.
This article explains the architecture of a cloud‑based shopping cart, covering its functional roles, layered and cluster designs, distributed technical architecture goals, three‑tier caching, storage heterogeneity, payment solutions, Nginx+LUA aggregation, anti‑bot measures, and multi‑dimensional user feature identification.
This article presents a comprehensive overview of 58.com’s database architecture, covering availability through replication and dual‑master setups, read‑performance enhancements via indexing, replicas and caching, consistency strategies, rapid horizontal scaling, sharding patterns, and practical SQL considerations alongside Codd’s 12 relational rules.
This article introduces Spring Cache as a unified caching solution that abstracts away the specifics of Redis, Ehcache, and other providers, explains its core concepts, annotations, configuration, and demonstrates practical usage with code examples to eliminate manual cache handling.
This article describes the architecture and optimization of JD.com’s coupon platform, covering the JimDB distributed in‑memory database for core storage, a massive distributed task system for product coupons, high‑availability strategies for store coupons, and the overall middle‑platform design that ensures scalability, low latency, and data consistency across millions of daily transactions.
This article explores the fundamental concepts of software architecture, illustrates scaling challenges with examples like Uber and Weibo, and details multi‑tier designs, caching strategies, service decomposition, monitoring, and operational practices for building and maintaining high‑performance, billion‑user backend systems.
This article explains how Flutter loads and caches images, analyzes the underlying source code of key classes such as PaintingBinding, ImageCache, ImageProvider, and RenderImage, and provides practical techniques for reducing memory usage and preventing OOM in large Flutter applications.
This article explores the role of multi‑level caching—from distributed and local caches to direct memory and CPU cache—detailing performance gains, cache‑miss handling, consistency challenges, false sharing issues, and practical mitigation techniques such as approximate LRU, random TTL, delayed double‑delete, padding, and lock‑free designs.
This article introduces Caffeine Cache, explains its W‑TinyLFU eviction algorithm, compares it with Guava, and provides detailed usage examples—including manual, synchronous, and asynchronous loading, eviction policies, Spring Boot integration, annotations, and configuration snippets—for building high‑performance backend caches.
The article outlines practical principles for building scalable, stateless backend architectures—choosing the right tools, using multiple servers, applying caching and rate limiting, delegating responsibilities to databases, handling large data volumes, and presenting example configurations from small to massive deployments.
By consolidating all Yanhuobao category settings into a single Mach‑managed configuration defined by a JSON schema and introducing a generic Guava LoadingCache with a ConfigValueParser, the service now adds new categories with just three fields, reduces parsing overhead, lowers maintenance costs, and improves consistency and QPS performance.
This tutorial explains how to integrate Ehcache into a Spring Boot application, covering Maven dependencies, YAML and XML configuration, enabling caching with @EnableCaching, using cache annotations, and manually operating caches via CacheManager, complete with code examples.
This article explains how Streamlit, a free open‑source Python framework, simplifies the creation of interactive machine‑learning applications by letting developers write pure Python scripts that automatically rerun on each interaction, cache results, and integrate seamlessly with existing tools and workflows.
This article explains the evolution and core concepts of caching in backend services, covering service‑cache interaction, hit rate, consistency challenges, granularity choices, cache penetration risks, and strategies to prevent cache avalanche through high availability and traffic control.
This article explains the differences between in‑process caches and external cache services like Redis, outlines the drawbacks of process‑level caching in distributed systems, and provides concrete criteria and strategies for deciding when to migrate to a dedicated cache service.
This article provides a comprehensive overview of caching in large‑scale distributed systems, covering cache fundamentals, classification, major implementations such as CDN, reverse‑proxy, local, and distributed caches, detailed analyses of Memcached and Redis, common design challenges, and real‑world industry solutions.
This article explains a reusable caching architecture for low‑frequency static data in microservice systems, covering why caching is needed, the role of Redis, persistent queues, consistency checks, and trade‑offs such as cache expiration and operational complexity.
This article uses a fictional bank scenario to illustrate how database architectures evolve from a single MySQL instance through caching, master‑slave replication, vertical splitting, horizontal sharding, clustering, and finally the adoption of NoSQL and search engines to handle growing traffic and data complexity.
This article explores how introducing various cache layers—from database and distributed caches to local and CPU caches—bridges the gap between fast CPUs and slow I/O, detailing performance benefits, cache miss handling, consistency strategies, memory management, and techniques to avoid false sharing.
This article explains the design, architecture, and implementation of Transparent Multilevel Cache (TMC), a solution that adds application‑level hotspot detection and local caching to reduce cache hot‑spot pressure, improve consistency, and boost performance for Java services in high‑traffic scenarios.
This article examines the rapid growth of Qunar's domestic flight pricing system, identifies response time bottlenecks caused by increased request volume and complex calculations, and details a series of architectural and algorithmic optimizations—including caching, search structure changes, election logic refinement, and fault‑tolerant mechanisms—that reduced average latency by up to 40%.
This article walks through the step‑by‑step architectural evolution of a Java Web e‑commerce platform, covering initial single‑machine deployment, separation of application and database servers, clustering, load‑balancing algorithms, session handling, read/write splitting, search integration, caching, database sharding, microservice decomposition, SOA governance, and the introduction of message middleware.
This comprehensive guide walks you through Redis fundamentals, data structures, persistence, high‑availability, performance tuning, and real‑world caching scenarios, providing the essential knowledge and interview tips needed to ace backend developer interviews focused on Redis.
This article explores the fundamentals and advanced techniques of cache optimization, covering multi‑level caching, read/write performance gains, cache miss handling, consistency strategies, heap versus direct memory, CPU cache effects, false sharing, and practical mitigation patterns.
This article examines the challenges of caching low‑frequency static data in microservice systems and proposes a comprehensive solution that combines business services, Redis, persistent queues, and consistency‑checking programs to achieve high‑performance, near‑real‑time data access while mitigating cache‑related risks.
This article explores the fundamental principles of system architecture, using Sina Weibo as a case study to illustrate multi‑layer design, scaling challenges, caching strategies, service decomposition, distributed tracing, and operational practices required for handling millions of users and billions of requests.
This article compiles 40 frequently asked Redis interview questions covering fundamentals, data types, persistence, clustering, performance tuning, memory optimization, security, and advanced usage such as pipelines and distributed locks, providing concise answers to help candidates prepare confidently for technical interviews.
This article compiles 40 common Redis interview questions and detailed answers, covering Redis fundamentals, data types, persistence mechanisms, performance characteristics, clustering, replication, memory optimization, eviction policies, and practical usage scenarios to help candidates confidently succeed in technical interviews.
This article examines common performance pitfalls in Elasticsearch—including slow queries, cluster architecture bottlenecks, and business‑scenario challenges—and provides practical guidance such as caching key fields, data pre‑heating, hot‑cold separation, avoiding joins, and using tribe nodes to improve accuracy and response time.
This article explores the definition of software architecture, reviews essential technologies such as distributed systems, clustering, caching, queues, multithreading, and rate limiting, discusses security threats like SQL injection and XSS, and highlights common design mistakes to avoid.
This article provides a detailed introduction to Nginx, covering its architecture, core configuration directives, installation steps, reverse proxy and load‑balancing setups, caching, HTTPS, CORS handling, gzip compression, and practical examples to help developers and operations engineers configure and use Nginx effectively.
In high‑traffic e‑commerce systems, caching—ranging from simple in‑JVM HashMap caches to Guava, Caffeine, and Redis distributed stores—reduces latency by applying eviction policies such as FIFO, LRU, LFU, or W‑TinyLFU, while employing consistency strategies like expiration, write‑through, and cache‑aside to mitigate breakdown, avalanche, and penetration issues.
This article explains how Zhihu built a highly available, high‑performance, and easily extensible read‑service for its homepage, detailing the system architecture, caching strategies, query performance requirements, and the migration from MySQL to TiDB with TiDB 3.0 enhancements.
This article documents a Python web service performance optimization case, detailing the initial bottlenecks, architectural redesign with caching and message queues, load‑testing methodology, Linux TCP time‑wait tuning, and the final results achieving 50k QPS with sub‑70 ms latency.
This article presents a comprehensive guide to improving Laravel application performance by covering configuration caching, route caching, class map optimization, autoload optimization, session storage with Memcached, professional cache drivers, database query tuning, dataset caching, JIT compilers, and front‑end asset bundling.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering characteristics, goals, common patterns, high‑performance and high‑availability designs, scalability, extensibility, security, agility, a seven‑layer reference model, and the evolutionary steps of modern e‑commerce systems.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering characteristics, goals, common patterns, high‑performance and high‑availability designs, scalability, extensibility, security, agility, a seven‑layer reference model, and a detailed e‑commerce case study with practical optimization steps.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering characteristics, design goals, architectural patterns, performance, high availability, scalability, extensibility, security, agility, evolution stages, and a detailed e‑commerce case study.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering characteristics, design goals, high‑performance, high‑availability, scalability, security, and detailed patterns such as layered design, caching, clustering, load balancing, database sharding, CDN, and service‑oriented decomposition.
Progressive Web Apps (PWA) are web applications that leverage Service Workers and modern web technologies to deliver native-like functionality, including offline capabilities, push notifications, and home screen installation, enhancing user experience across devices.
This article explains how to improve Spring Cloud microservice performance by adding local and Redis distributed caching, demonstrates the necessary code changes, and discusses cache pitfalls such as penetration, avalanche, and breakdown along with lock‑based solutions.
This article provides an extensive overview of the HTTP protocol, covering its layered network architecture, request/response processes, detailed header fields, caching directives, status codes, message bodies, as well as related components such as cookies, proxies, and servers.
This article compiles 40 common Redis interview questions covering fundamentals, data types, persistence, clustering, performance tuning, memory optimization, and practical usage patterns, providing concise answers that help candidates confidently tackle Redis topics and secure job offers during the competitive hiring season.
