This article walks through the step‑by‑step evolution of a large‑scale e‑commerce backend—from a single‑server setup to microservices, containerization, and cloud platforms—highlighting the technical challenges, key technologies, and design principles that enable millions of concurrent users.
This guide breaks down a real‑world interview scenario where a backend engineer must design a scalable database solution for a billing‑log or feed system handling 100 million daily inserts and 100 k QPS reads, covering partitioning vs sharding, sharding key selection, shard count, read‑write separation, multi‑level caching, consistency patterns, hot‑key mitigation, and online schema changes.
The article traces the evolution from monolithic Java apps to RPC services, explains why sharding and database partitioning cannot alone achieve limitless scaling due to connection limits, and introduces unitization—assigning each application to a specific database shard—to overcome the bottleneck.
This article analyzes the current bottlenecks of Bilibili's private messaging service, explains the technical challenges of massive data volume and traffic spikes, and presents a comprehensive multi‑layer architecture upgrade—including cache strategies, BFF refactoring, database sharding, and consistency mechanisms—to ensure scalability and reliability.
This article explains how to handle hundred‑thousand to million‑level QPS by using vertical and horizontal database sharding, detailing implementation steps, sharding algorithms, and the trade‑offs such as transaction complexity and distributed ID generation.
This article details how Vivo Browser's welfare center was re‑engineered—splitting services, sharding databases, adding arbitration and soft‑transaction mechanisms—to overcome traffic, I/O, and data‑consistency challenges, enabling stable operation at tens of millions of daily active users while reducing storage costs.
This article explains why large MySQL tables degrade performance, introduces vertical and horizontal sharding techniques—including range‑based and modulo‑based partitioning—covers the read‑diffusion problem caused by non‑sharding keys, and presents practical solutions such as auxiliary index tables, Elasticsearch integration, and TiDB as a distributed alternative.
Learn how to seamlessly integrate MyBatis‑Plus with ShardingSphere‑JDBC for high‑performance sharding, covering core principles, step‑by‑step workflow, and detailed solutions to typical pitfalls such as SQL parsing, pagination conflicts, key generation, query routing, transaction handling, broadcast tables, injection risks, and shard algorithm design.
This article explains how MyBatis‑Plus and ShardingSphere‑JDBC work together for sharding and scaling, outlines their core responsibilities, details the step‑by‑step SQL processing flow, and provides practical solutions to typical issues such as SQL parsing errors, pagination conflicts, primary‑key generation clashes, missing sharding keys, distributed transaction complexities, broadcast‑table misconfigurations, injection risks, and uneven sharding algorithms.
This article explains why database sharding becomes essential as user growth and data volume surge, describes horizontal and vertical partitioning methods, compares range and hash routing, and reviews popular sharding middleware with their advantages and drawbacks to help you choose the right solution.
This article explains why and how to shard a MySQL database, covering vertical and horizontal partitioning, distributed transaction handling, unique ID generation, a real‑world e‑commerce case study, and future considerations for distributed architectures.
This comprehensive tutorial explains database sharding concepts, types, strategies, and implementation in Spring Boot using ShardingSphere, covering configuration, entity and repository code, service and controller layers, integration with pagination, Swagger, ActiveMQ, security, batch processing, FreeMarker, WebSockets, AOP, performance testing, FAQs, real‑world cases, and future trends.
This guide explains how to break a monolithic LNMP stack into separate components—dedicated MySQL servers, multiple Nginx/PHP web nodes, shared static storage, and session persistence using Redis—while providing step‑by‑step commands, configuration snippets, and best‑practice recommendations for building a reliable, high‑performance Nginx cluster.
This article introduces the fundamentals of database sharding, outlines architectural evolution, explains vertical and horizontal splitting dimensions and strategies, discusses middleware choices, and presents real‑world case studies while addressing common challenges such as ID generation, join queries, pagination, and distributed transactions.
This guide demonstrates how to set up Docker‑based MySQL instances, configure ShardingSphere‑Proxy for database sharding and read‑write splitting, and integrate ShardingSphere‑JDBC with SpringBoot 2.x using YAML configurations and Maven dependencies, providing complete code snippets and default connection details.
This article introduces ShardingSphere's three components—Sharding-JDBC, Sharding-Proxy, and the upcoming Sharding-Sidecar—explains why sharding is needed for relational databases, demonstrates horizontal data sharding concepts, and provides a complete Spring Boot example with configuration, code, and asynchronous batch insertion.
This comprehensive guide walks through ShardingSphere's core sharding strategies and the twelve built‑in sharding algorithms, showing how to configure each with YAML, illustrating their behavior with code examples, and highlighting practical tips for choosing the right approach in real‑world database partitioning scenarios.
Notion tackled severe PostgreSQL performance limits by sharding its Block table and related tables across 480 logical shards on 32 physical databases, using workspace IDs as shard keys, a dual‑write migration, and rigorous validation to achieve near‑zero downtime and faster response times.
This article explains how to answer interview questions about designing high‑concurrency systems by outlining essential architectural techniques such as system decomposition, caching, message queues, database sharding, read‑write separation, and Elasticsearch, while emphasizing practical considerations and real‑world complexity.
This article explains what constitutes high concurrency, categorizes load levels, and presents practical solutions such as load balancing, database sharding, query optimization, caching, message queues, and rate‑limiting, complete with code examples for implementing these techniques in backend systems.
The article describes a year‑long development experience where a senior architect designs, plans, and implements a sharding solution for loan and repayment tables using ShardingSphere, Spring Boot 3, MySQL, and custom synchronization code to migrate billions of rows while ensuring data consistency and operational continuity.
This article details the end‑to‑end design, configuration, and code implementation of a 50‑table sharding solution for cash repayment and loan application data, covering database schema planning, historical data migration, backend query adaptation, dynamic switches, scheduled consistency checks, and practical pitfalls encountered with Spring Boot 3 and ShardingSphere.
This article explains why database sharding is needed, how to identify performance bottlenecks, and provides step‑by‑step guidance on SQL tuning, table redesign, architectural changes, and practical horizontal and vertical sharding techniques with real‑world e‑commerce examples.
The article details a real‑world case study of building and operating a high‑traffic business middle‑platform for a leading consumer‑goods company, covering business and technical architecture, API gateway selection, SpringBoot microservices, custom MyBatis, logging, SSL, Docker, MyCat sharding, K8s deployment, CI/CD pipeline, and monitoring solutions.
The article explains why and when to apply database sharding and table partitioning, illustrates real‑world growth scenarios, compares horizontal and vertical splitting methods, reviews common sharding middleware such as Cobar, TDDL, Atlas, Sharding‑jdbc (ShardingSphere) and Mycat, and offers practical recommendations for different company sizes.
This article presents a comprehensive, step‑by‑step guide to building large‑scale backend systems—including microservice decomposition, CDN and cache layers, message queues, database sharding, read/write splitting, Elasticsearch search, distributed transaction handling, multithreaded data migration, and massive counting services—illustrated with real‑world examples and practical metrics.
This article explains the concepts of database sharding and partitioning, compares common middleware solutions, and offers practical guidance on choosing and implementing horizontal or vertical splits to handle rapid growth and high concurrency in modern applications.
This article explains the difference between database sharding and table partitioning, illustrates scaling challenges with a fast‑growing startup scenario, and compares popular sharding middleware such as Cobar, TDDL, Atlas, ShardingSphere, and Mycat, offering practical recommendations for different company sizes.
Learn practical ways to enhance a system’s concurrent handling capacity, including traffic distribution via load balancers, database sharding and clustering, message queues, caching layers, CDNs, code and database optimizations, and scaling hardware resources such as servers, CPUs, and memory.
This article explores why distributed primary keys are essential for sharding, compares built‑in ShardingSphere ID generation strategies such as UUID, NanoID, Snowflake, CosId and custom algorithms, details their configuration, performance trade‑offs, and provides code examples for implementing and registering custom key generators.
The tutorial project showcases how to implement database sharding with ShardingSphere-JDBC (versions 4 and 5) in a SpringBoot O2O order service, providing modules for sharding, a gRPC ID generator, detailed configuration of four data sources, sharding algorithms, and Swagger-based testing of order creation and queries.
This article explains the fundamentals of database sharding and partitioning, why they are needed for massive data volumes, various vertical and horizontal splitting strategies, routing algorithms, client vs. proxy deployment models, and the practical challenges and solutions when using ShardingSphere 5.x.
This article presents a comprehensive guide for backend developers, covering ten practical techniques—including local and distributed caching, parallel execution, asynchronous processing, connection pooling, sharding, SQL tuning, pre‑computation, batch operations, lock granularity, and context propagation—to dramatically improve API performance.
This article explores the end‑to‑end design of a modern dating platform, covering requirement analysis, micro‑service architecture, gateway routing, sharded MySQL, CDN caching, matchmaking, recommendation scoring, high‑concurrency strategies, load balancing, database optimization, message queues, and spatial proximity algorithms such as grid, quadtree, and GeoHash.
This article explains why database sharding and partitioning become necessary, outlines performance bottlenecks, presents software and hardware optimization techniques, details horizontal and vertical splitting strategies, and discusses the added complexity such as cross‑database joins, distributed transactions, and ID generation.
This article recounts the step‑by‑step evolution of a ride‑hailing order service’s data layer—from a single SQLServer database through SQL optimization, read‑write splitting, business‑domain sharding, caching and MQ integration, to a custom sharding component and smooth migration to MySQL—highlighting challenges, solutions, and lessons learned.
This guide explains how to design and expand server infrastructure to handle millions of concurrent users by using load balancers, database sharding, caching, CDNs, hardware selection criteria, and redundancy techniques, ensuring high availability and performance 24/7.
This article outlines the progressive architectural evolution of an e‑commerce platform, describing how increasing traffic drives the transition from a single‑module monolith through horizontal scaling, read/write separation, sharding, CDN caching, dual‑datacenter deployment, and finally to a unit‑based micro‑service architecture.
This article explains how to assess MySQL table size, understand performance degradation caused by large B+‑tree indexes, and apply three practical solutions—table partitioning, database sharding, and cold‑data archiving—to improve query speed and maintainability.
This article outlines the progressive evolution of e‑commerce system architecture from a monolithic 1.0 design through layered scaling, read/write separation, CDN caching, sharding, and finally unit‑based modularization, highlighting the performance bottlenecks and solutions at each stage.
The article explains how a meeting control system tackled severe storage pressure from high concurrent traffic by introducing a proxy layer, multi‑active disaster‑recovery, identity‑based data isolation, dynamic‑static key separation, multi‑level caching, overload protection, sharding with dual‑write migration, and extensive monitoring to meet 100k QPS and ensure reliability.
This article explains why single‑database resources hit limits, introduces horizontal and vertical sharding techniques, discusses how to choose effective split keys, and reviews common key‑generation methods such as auto‑increment, UUID, and Snowflake IDs for scalable database architectures.
This article examines the high connection‑count problem of Sharding-JDBC in JD Logistics, explains sharding concepts, compares four mitigation strategies, and presents a custom table‑partitioning algorithm with configuration changes that dramatically lowers MySQL instance connections while maintaining performance.
This article examines how JD Logistics tackled MySQL connection limits by redesigning its sharding strategy using ShardingSphere's Sharding-JDBC, detailing the background, concepts of database and table sharding, analysis of bottlenecks, and step‑by‑step refactoring to reduce connections and improve scalability.
This tutorial walks through the fundamentals and architecture of ShardingSphere, compares it with other sharding tools, and demonstrates how to implement database sharding in a Spring Boot project using both YML configuration and pure Java code, including detailed code snippets and deployment tips.
Database sharding and table partitioning are essential scaling techniques that separate data across multiple databases and tables to handle growing user volume, data size, and concurrency, with explanations of when to split, methods (horizontal vs vertical), and a comparison of popular middleware solutions.
Facing a MySQL transaction table exceeding 50 million rows and growing rapidly, a finance team redesigned the schema using sharding‑jdbc, chose transaction time as the shard key, tackled multi‑data‑source transactions and cross‑shard pagination, and executed a phased migration to ensure stability and performance.
This article explains why database sharding is needed when performance bottlenecks appear, outlines the criteria and timing for adopting sharding, and provides detailed step‑by‑step guidance on both database‑level and table‑level partitioning, including common pitfalls and solutions.
This article explains the concept, motivations, design methods, common patterns, and trade‑offs of database sharding (both table and database level), offering practical code examples, scalability criteria, and guidance on avoiding over‑engineering while considering alternatives like TiDB.
The article details vivo’s transition from a monolithic mall to a micro‑service transaction platform, outlining a multi‑tenant architecture with ShardingSphere‑sharded MySQL, Snowflake IDs, Elasticsearch search, configurable state machines, generic delayed tasks, Seata and local‑message distributed transactions, plus high‑availability safeguards, emphasizing pragmatic solution selection.
This article outlines a comprehensive flash‑sale system architecture covering front‑end separation, Nginx static handling, traffic shaping, Redis caching and locking, message‑queue throttling, database sharding, and captcha mechanisms to achieve high concurrency while ensuring security and reliability.
Learn why large-scale internet applications need database sharding, understand the evolution from single databases to split databases and tables, and follow practical steps for assessing, planning, and implementing horizontal and vertical sharding, including sizing calculations, key selection, and query challenges.
This article explains how to expand a sharded database from three to four nodes, compares five migration schemes—including stop‑service, stop‑write, log‑based, dual‑write, and smooth 2N approaches—and provides step‑by‑step instructions for MariaDB installation, master‑slave configuration, Keepalived high‑availability setup, and dynamic data‑source integration using ShardingJDBC.
The article details how a rapidly growing airline ticket order system was re‑architected by identifying performance bottlenecks, applying vertical and horizontal sharding, optimizing cache layers, implementing dual‑write mechanisms, and planning a phased migration to achieve ten‑fold QPS growth while reducing resource usage and operational risk.
This article explains the need for database sharding as applications scale, describes horizontal and vertical partitioning techniques, compares popular sharding middleware, and offers practical guidance on choosing and deploying sharding solutions for different workloads.
Database sharding—splitting data across multiple databases and tables—is essential as applications scale, and this article explains the motivations, differences between sharding and partitioning, horizontal and vertical splits, common middleware options like Cobar, TDDL, Atlas, ShardingSphere, and Mycat, and how to choose the right solution.
This article details the architectural evolution of JD's VOP message warehouse, describing the challenges of massive data volumes, the transition from V1.0 to V3.0+ through database sharding, MongoDB adoption, traffic governance, stability improvements, and cost reduction strategies, while presenting performance metrics and future outlook.
The article explains how to handle billions of daily orders by classifying data into hot and cold segments, storing them in MySQL, Elasticsearch, and Hive, and applying sharding and partitioning techniques at both table and database levels to achieve scalable performance.
This article chronicles the step‑by‑step evolution of a ride‑hailing order service's data layer, covering the shift from a single SQLServer database to read‑write separation, business‑domain sharding, caching, message queues, a self‑built sharding component, and a data exchange platform for smooth migration and ongoing scalability.
This article walks through turning a basic personal‑blog architecture—single server, MySQL, and Node.js—into a high‑concurrency system by adding Redis caching, Nginx load balancing, horizontal scaling, read‑write splitting, sharding, NewSQL databases, and message‑queue techniques.
This article introduces Sharding-JDBC and ShardingSphere, explains their history, core architecture, vertical and horizontal sharding concepts, sharding rule configuration, SQL parsing and rewriting, routing and result merging, and provides a quick‑start guide with Maven dependencies and Spring Boot configuration for Java applications.
This article explains how to select and implement routing keys for database sharding, covering key requirements, common scenarios such as time‑based archiving, hash‑based distribution, and detailed strategies for both user‑side and merchant‑side routing in multi‑database order platforms.
The article introduces eight widely used software architecture design patterns—including single‑database single‑application, content distribution, query separation, microservices, multi‑level caching, sharding, elastic scaling, and multi‑data‑center modes—explaining their structures, typical use cases, and the pros and cons of each.
This article introduces eight core software architecture design patterns—including single‑database single‑app, content distribution, query separation, microservices, multi‑level caching, sharding, elastic scaling, and multi‑datacenter deployment—explaining their structures, typical use cases, advantages, and drawbacks to help developers choose the right architecture for their systems.
This article explains why database sharding (both vertical and horizontal) is needed to overcome performance bottlenecks, outlines practical thresholds for splitting tables, describes vertical and horizontal partitioning techniques, and discusses key issues such as distributed transactions, cross‑node queries, global primary keys, and scaling strategies.
This article details the design and implementation of Baidu's transaction middle‑platform asset system, covering its C2C payment model, core asset management capabilities, consistency guarantees, double‑entry accounting, hot‑account handling, sharding and archiving strategies, and compliance with national financial regulations.
The article examines the Chengdu COVID‑19 testing system failure, analyzes its root causes such as oversized MySQL tables and insufficient load handling, and then presents a step‑by‑step high‑concurrency roadmap—including single‑machine, service‑database separation, caching, load balancing, read/write splitting, sharding, hardware and DNS load balancing—to help backend developers design scalable systems.
To improve capacity and robustness as business complexity grows, this article explains system splitting—including horizontal, vertical, business, and database sharding—and architectural evolution techniques such as caching, indexing, microservices, and service governance, illustrating practical strategies for scalable, resilient backend architectures.
This article explains why high‑traffic systems need database sharding, compares table‑level and database‑level partitioning methods, presents concrete MySQL table‑creation scripts and Java service code, and discusses the advantages, drawbacks, and common middleware solutions such as Sharding‑JDBC and Mycat.
This article explains the reasons for database sharding, compares vertical (scale‑up) and horizontal (scale‑out) partitioning methods, illustrates vertical database/table splits and horizontal partition strategies such as HASH, RANGE, LIST, KEY and composite, and discusses the challenges like distributed transactions, cross‑database joins and increased management overhead.
This article traces the historical development of computer architecture from the ENIAC era through mainframes, the rise of distributed systems, and modern scaling techniques such as clustering, replication, middleware, read‑write separation, caching, and service decomposition, illustrating each stage with clear examples and diagrams.
This article details Ctrip's comprehensive redesign of its airline ticket order database, covering the background performance bottlenecks, vertical and hot‑cold data splitting, sharding key selection, multi‑level caching, cross‑shard query optimization, dual‑write mechanisms, fault‑tolerance strategies, project planning, and the resulting improvements in scalability and cost.
This article explains the concepts, motivations, strategies, and challenges of database and table sharding, covering vertical and horizontal partitioning, range and hash algorithms, when to adopt them, and popular middleware solutions for scalable MySQL architectures.
This article explains the concepts of database and table sharding, why they are needed, various vertical and horizontal splitting strategies, range and hash partitioning methods, their advantages and drawbacks, timing considerations, common challenges such as transactions and distributed IDs, and introduces popular sharding middleware.
This article provides a comprehensive guide to Sharding-JDBC, covering the challenges of database sharding such as distributed transactions, cross‑node joins, pagination, global key collisions, and demonstrates practical configuration and code examples for vertical and horizontal partitioning using Spring Boot.
This article examines JD Baitiao’s journey from monolithic MySQL to NoSQL and DBRep, detailing how the team evaluated and ultimately adopted Apache ShardingSphere for scalable, low‑coupling database sharding, highlighting performance, maintenance, and business-driven architectural decisions.
This article uses Taobao as a case study to trace the step‑by‑step evolution of a high‑traffic backend system—from a single‑machine setup through caching, load balancing, database sharding, microservices, ESB, containerization, and finally cloud‑native deployment—while highlighting the key technologies and design principles at each stage.
This article outlines the background, analysis, design, testing strategy, migration plan, and production quality results for implementing horizontal sharding, table splitting, and Elasticsearch integration in a growing enterprise database system.
This article walks through the step‑by‑step evolution of a basic single‑server e‑commerce system into a large‑scale, distributed architecture, explaining core concepts, each architectural upgrade, the technologies involved, and key design principles for building scalable, high‑availability services.
This article explains the business model, capabilities, service types, order lifecycle, and detailed architectural evolution of Baidu's transaction middle‑platform order system, covering instruction orchestration, performance optimizations such as caching, async execution, ThreadLocal handling, and database sharding for high‑throughput scenarios.
This article walks through Taobao’s journey from a single‑server setup to a cloud‑native, micro‑service architecture capable of handling tens of millions of concurrent requests, explaining each scaling step, the technologies involved, and key design principles for high‑availability systems.
This article details how ZhaiZhai tackled severe order database performance and capacity issues by adopting ShardingSphere for sharding, describing the challenges, mitigation steps, shard key design, migration process, and the significant performance improvements achieved.
The article outlines a comprehensive, multi‑layer architecture—including DNS routing, L4/L7 load balancing, micro‑service or monolithic deployment, caching, database sharding, hybrid‑cloud deployment, elastic scaling, and tiered degradation—to reliably support systems handling from millions to billions of daily active users under sudden traffic spikes.
This article outlines a comprehensive architecture for handling millions of daily active users, covering DNS routing, four‑layer and seven‑layer load balancing, monolithic versus microservice deployment, caching, database sharding, hybrid‑cloud strategies, full‑link elastic scaling, and a three‑tier degradation mechanism to ensure resilience under sudden traffic spikes.
Database sharding improves performance by partitioning tables vertically or horizontally and routing rows with consistent hashing, while ShardingJDBC rewrites SQL for physical tables, adds missing columns, corrects pagination, splits batch inserts, and merges results using sorting, grouping, aggregation, and pagination engines.
The article outlines a comprehensive backend architecture for handling million‑to‑tens‑of‑million daily active users, covering DNS routing, L4/L7 load balancing, monolithic versus microservice deployment, caching, database sharding, hybrid‑cloud strategies, elastic scaling, and multi‑level degradation mechanisms.
This article walks through the step‑by‑step evolution of a basic single‑server MVP architecture into a large‑scale distributed system, using a simulated Taobao example to illustrate ten‑plus architectural stages, key technologies, design principles, and the eventual shift to cloud‑native microservices.
This article presents a comprehensive set of design practices for building a highly available, high‑performance, and secure user‑center system that can handle hundreds of millions of users, covering service architecture, API design, token degradation, data sharding, security, asynchronous processing, and monitoring.
This article shares practical experience on designing and operating a high‑concurrency order‑delivery system, covering infrastructure choices, database scaling techniques such as read‑write separation and sharding, architectural patterns like caching, message queues and service governance, as well as application‑level optimizations including compensation, idempotency, async processing and warm‑up strategies.
This article provides a comprehensive technical summary of large‑scale distributed website architecture, covering characteristics, goals, architectural patterns, performance, high‑availability, scalability, extensibility, security, agility, and a detailed evolution roadmap with practical examples and recommendations.
This article provides a step‑by‑step tutorial on configuring MySQL master‑slave replication using Docker, explains redo log and binlog mechanisms, and demonstrates how to implement Sharding‑JDBC read/write splitting and database sharding with Spring Boot, including code examples and configuration details.
This article provides a comprehensive overview of the design principles, architectural patterns, performance, availability, scalability, security, and operational considerations for building large distributed web sites, illustrated with a step‑by‑step evolution from a single‑server setup to a multi‑layer, cloud‑native architecture.
The article explains how Ant Financial scales Double‑11 payment traffic to hundreds of thousands of TPS by employing logical data centers (LDC), unit‑based system design (RZone, GZone, CZone), database sharding, CAP theorem analysis, Paxos‑based consensus, and the OceanBase distributed database, while also detailing disaster‑recovery and traffic‑shifting mechanisms.
Effective MySQL sharding requires sustainable, low‑skew designs, favoring hash‑based methods with proper coprime counts, two‑stage partitioning, routing tables, or consistent hashing, while supporting expansion via doubling or flexible consistent‑hash growth to avoid hot spots and uneven data distribution.
This article chronicles JD Baitiao's data‑architecture evolution from Solr + HBase to MongoDB and finally to Apache ShardingSphere, highlighting the challenges of massive data growth, the need for decoupling, and the performance, scalability, and operational benefits achieved by adopting ShardingSphere.
This article provides a comprehensive guide on using ShardingSphere-JDBC for read‑write splitting, configuring MySQL master‑slave replication with Docker, explaining redo log and binlog mechanisms, demonstrating two‑phase commit, and showing how to implement database sharding and distributed primary keys in a Spring Boot application.
This article explains how to decide between database and table sharding, describes vertical, horizontal, and range partitioning techniques, discusses their trade‑offs and implementation details, and offers practical guidance for solving query challenges in high‑traffic, write‑intensive systems.
The article explains MySQL vertical partitioning techniques—including instance‑level database splitting, schema‑level splitting, and column‑level table splitting—detailing their advantages, drawbacks, and step‑by‑step command‑line procedures with code examples for implementing each method in production environments.
This article introduces Sharding-JDBC (now ShardingSphere), explains core sharding concepts such as shards, data nodes, logical and physical tables, sharding keys, algorithms and strategies, shows how it extends JDBC, and provides a step‑by‑step Spring Boot + MyBatis‑Plus example for building a sharded database application.
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.
