This article explains the principles of object pools, details the internal implementation of Go's sync.Pool, and demonstrates through benchmarks how using an object pool can dramatically reduce memory allocation and latency for high‑traffic menu services in a restaurant ordering application.
This article examines the data consistency challenges of writing to both databases and caches, outlines four common double‑write strategies, analyzes their pitfalls—especially under high concurrency—and recommends the optimal approach of writing to the database then deleting the cache, while discussing retry mechanisms, scheduled tasks, message queues, and binlog listeners.
This article explores the challenges of maintaining data consistency under high concurrency in distributed systems, reviewing common consistency issues, distributed lock implementations, optimistic and pessimistic strategies, CAS and ABA problems, and practical solutions such as Redis locks, Zookeeper, and transaction protocols.
This article recounts the end‑to‑end design, bottleneck analysis, and iterative performance tuning—including MySQL, Redis, RocketMQ, compression, sharding, and connection‑pool adjustments—that enabled a volunteer registration platform to meet demanding high‑concurrency and data‑accuracy requirements under limited resources.
The article explains why data inconsistency occurs between MySQL and Redis under high concurrency, analyzes cache‑delete timing issues, and presents two solutions—delayed double‑delete and asynchronous cache updates via MySQL binlog—detailing implementation steps, advantages, drawbacks, and practical considerations.
This article presents a comprehensive guide to high‑concurrency architectural techniques—including microservice decomposition, load‑balancing strategies, distributed caching, asynchronous processing, database sharding, message‑queue integration, rate‑limiting and circuit‑breaking, as well as distributed database options—targeted at building scalable backend systems.
The article analyzes the extreme‑traffic challenges of the 12306 ticket‑seckill service, presents a layered load‑balancing architecture, compares inventory‑deduction strategies, and demonstrates a complete Go‑based prototype with Nginx weighted round‑robin, Redis stock management, and ApacheBench performance testing.
Bilibili’s membership purchase system was re‑engineered by refactoring serial service calls into a responsibility‑chain with parallelism, moving weak dependencies to asynchronous queues, and implementing sharding across 256 tables, boosting peak throughput from ~600 QPS to over 4,000 TPS and eliminating latency incidents during massive promotional traffic spikes.
This article explains the concept of high concurrency, its challenges such as performance degradation and resource contention, and presents comprehensive solutions including various caching mechanisms, multiple rate‑limiting algorithms, and degradation and circuit‑breaker strategies to ensure system stability under heavy load.
This article details Baidu’s internally built unified long‑connection service in Go, covering its motivation, architecture, functional implementation, performance optimizations, multi‑business support, deployment strategy, and lessons learned for delivering secure, high‑concurrency, low‑latency real‑time connectivity across mobile applications.
Baidu’s Go‑based unified long‑connection service delivers secure, high‑concurrency, low‑latency connections for multiple Baidu apps through a four‑layer architecture (SDK, control, access, routing), employing goroutine pooling, two‑layer connection models and binary routing to support tens of millions of concurrent users and million‑level QPS, while simplifying integration and reducing maintenance costs.
This article explores how to achieve fast, scalable data retrieval in million‑level high‑concurrency scenarios by replacing naïve full‑combination rule matching with column‑wise inverted indexes and bitmap operations, dramatically reducing time complexity and improving stability while leveraging RoaringBitmap compression for space efficiency.
This article walks through the full design of a billion‑user group chat system, covering functional and non‑functional requirements, core components such as WebSocket and Redis, face‑to‑face group creation, message flow, database schema, and performance‑oriented scaling techniques.
This article analyzes the massive daily query load on JD Logistics' contract center, identifies cache‑penetration issues, and proposes a three‑layer protection architecture—Bloom filter, Redis cache, and database fallback—detailing implementation steps, performance metrics, and consistency handling.
This article explains why domestic Haiguang/Kunpeng servers lag behind Intel CPUs, presents a quick stored‑procedure method to benchmark CPU performance, details OceanBase tenant CPU specifications and tuning parameters, and offers concrete optimization techniques for high‑concurrency TP and massive‑data AP scenarios, including queuing tables, sequence cache, and index redesign.
This article walks through the design of a billion‑user group chat platform, covering functional and non‑functional requirements, core components, database schema, face‑to‑face group creation, message flow, storage strategies, and performance‑optimizing techniques such as clustering, message queues, multithreading, and Redis caching.
The article details NetEase Cloud Music’s high‑concurrency cache architecture—using lazy‑load, hole‑wrapped objects for penetration protection, placeholder values for missing data, horizontal and vertical scaling with consistent hashing, and asynchronous binlog‑driven invalidation—to achieve sub‑millisecond reads for a read‑heavy, write‑light music library.
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 details Bilibili's end‑to‑end technical planning, traffic‑estimation models, and concrete optimizations—including hotspot caching, traffic dispersion, long‑connection isolation, and automated fault‑injection—that enabled the S13 League of Legends finals to serve over 1.2 billion viewers with stable, low‑latency streaming.
This article introduces Apache Doris, a real‑time analytical database built on an MPP architecture, explains its suitability for massive data workloads and online high‑concurrency reporting scenarios, and details the core technologies—storage models, vectorized query engine, materialized views, partitioning, indexing, row‑store and prepared statements—that enable sub‑second query latency and high QPS, while also showing a real‑world case study and how to join the Doris community.
This article walks through the full design of a large‑scale comment system, covering functional requirements, database schema, distributed architecture, high‑concurrency handling, caching, security measures like Bloom filters, and optional NLP‑based sentiment analysis to moderate live‑stream comments.
This article systematically outlines the e‑commerce product team's responsibilities, the users and consumer pain points it addresses, the core technical challenges such as high‑concurrency reads/writes and AI‑driven automation, and the innovative solutions the team has implemented to keep the product domain healthy, efficient, and intelligent.
This article dissects WeChat's group chat system, covering its functional and non‑functional requirements, high‑level architecture, face‑to‑face group creation, message transmission, storage design, and the real‑time red‑packet allocation algorithm implemented in Go.
This article analyzes the technical challenges of massive flash‑sale events such as Double‑11, including server and network spikes, extreme QPS, and system avalanche, and presents a comprehensive backend and frontend architecture using rate limiting, caching, message queues, and scalable design to ensure reliable high‑concurrency processing.
This article dissects the design of WeChat's group chat system, covering functional and non‑functional requirements, high‑level component architecture, database schemas, face‑to‑face group creation, real‑time messaging, red‑packet algorithms, and Go code implementation, revealing how massive concurrency, performance, and storage challenges are solved.
This article explains the advantages of epoll over select for I/O multiplexing in Linux, covering its event-driven design, edge-triggered vs level-triggered modes, core APIs, practical code examples, and performance considerations for high‑concurrency network servers.
To handle billions of daily users and massive flash‑sale spikes in its live‑ecommerce streams, Kuaishou built a large‑scale elastic container cloud, integrated with Alibaba Cloud in a hybrid‑cloud setup, employing load balancing, caching, message queues, rate‑limiting, and intelligent resource scheduling to achieve million‑request‑per‑second throughput and high availability.
Kuaishou tackles massive live‑e‑commerce flash‑sale traffic by combining an elastic Docker‑based container cloud with a hybrid private‑public cloud architecture, enabling rapid scaling, high concurrency handling, and cost‑effective resource management.
The article describes how Kuaishou handles massive live‑streaming flash‑sale traffic by employing a distributed system, elastic Docker‑based container cloud, and a hybrid IDC‑Alibaba Cloud architecture that automatically scales resources to sustain millions of requests per second with high availability.
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 deeply dissects Kafka Broker's network architecture and request‑processing pipeline, covering sequential, multithreaded, and event‑driven designs, the Reactor pattern, Acceptor and Processor threads, core request flow, and practical tuning parameters for high‑throughput, low‑latency deployments.
This article introduces the 12306 railway ticket system project, detailing its purpose for student learning, two deployment versions, extensive backend architecture, technology stack, code repository structure, design challenges, and resources to help learners master high‑concurrency distributed systems.
This article explores the challenges of designing a flash‑sale (秒杀) system—such as overselling, high concurrency, URL protection, and database bottlenecks—and presents a comprehensive backend architecture using Redis clustering, dynamic URLs, static pages, Nginx, rate‑limiting, asynchronous order processing, and service degradation strategies, complete with code examples.
This article analyzes the challenges of logging in distributed services, compares the traditional ELK stack with Baidu's Tianyan platform, and details Tianyan's architecture, data collection, high‑throughput transmission, storage, retrieval, resource isolation, dynamic cleanup, and best‑practice recommendations, complete with code examples and performance insights.
This article outlines essential architectural and operational strategies—including page static‑generation, CDN acceleration, caching layers, asynchronous processing, thread‑pool and MQ integration, sharding, connection pooling, read/write splitting, indexing, batch processing, clustering, load balancing, rate limiting, service degradation, failover, multi‑active deployment, stress testing, and monitoring—to build robust, high‑concurrency backend systems.
This article explores comprehensive strategies for handling hot‑key bottlenecks in high‑traffic flash‑sale scenarios, covering DB, Redis, sharding, local cache, failure handling, and scaling techniques across various QPS ranges to ensure reliable inventory control and user experience.
This article breaks down real‑world high‑concurrency strategies—horizontal scaling, caching, Elasticsearch, sharding, message‑queue smoothing, and cellization—explaining when each applies, their trade‑offs, and practical tips for building scalable, reliable backend services.
This article explores comprehensive strategies for designing high‑concurrency systems, covering page staticization, CDN acceleration, caching layers, asynchronous processing, thread‑pool and MQ integration, sharding, connection pooling, read/write splitting, indexing, batch processing, clustering, load balancing, rate limiting, service degradation, failover, multi‑active deployment, stress testing, and monitoring.
This article examines the challenges of scaling inventory pre‑allocation in fast‑growing logistics platforms, outlines architectural principles for a lightweight, Redis‑driven solution that replaces MySQL bottlenecks, details data structures, consistency mechanisms, and performance results, and provides a roadmap for future capacity growth.
This article details the evolution and design of Vivo’s e‑commerce inventory platform, covering its layered architecture, multiple inventory types, deduction workflows, idempotent APIs, anti‑oversell mechanisms, high‑concurrency strategies, hotspot mitigation, and synchronization techniques, while explaining the reasoning behind each technical choice.
StarRocks introduces a Query Cache that stores intermediate aggregation results in memory, enabling reuse across semantically equivalent, partition‑overlapping, or append‑only queries, which can boost query performance by 3‑17× in high‑concurrency scenarios while reducing CPU and disk load.
This article analyzes the high‑concurrency contract‑query workload of JD Logistics, examines call sources, query dimensions and volume patterns, and proposes a three‑layer protection architecture—Bloom filter, Redis cache, and database fallback—along with strategies for cache penetration, breakdown, and avalanche mitigation.
This article defines distributed databases, examines their external traits such as write‑heavy, low‑latency, massive concurrency, massive storage and high reliability, explores internal architectures like client‑side sharding, proxy middleware and unit‑based designs, compares them with Amazon Aurora, and summarizes key takeaways.
This article demonstrates how to simulate a high‑concurrency flash‑sale scenario using SpringBoot, MySQL and JMeter, analyzes why naive lock‑and‑transaction code causes overselling, and presents six refined solutions—including controller‑level locking, AOP locking, pessimistic and optimistic database locks, and queue‑based approaches—along with performance test results.
This article explains the architectural mindset behind high concurrency, defines key performance metrics such as TPS, RPS, RT and VU, analyzes test results, and presents practical techniques like rate limiting, frequency limiting, degradation and caching to balance performance, availability, and resource usage.
This article presents Alibaba Cloud's SLS billion‑scale real‑time log analysis architecture, covering business background, core challenges such as low‑latency queries, massive data scale, high concurrency, and multi‑tenant isolation, and detailing key design solutions like LSM‑based storage, index‑columnar storage, data locality, layered caching, and future directions.
The article examines the March 29 Vipshop data‑center outage that caused over a billion‑yuan loss, explains the cooling‑system failure that triggered a 12‑hour P0 incident, discusses its impact on Tencent services, and analyzes why high‑concurrency crashes remain common, offering availability tier insights and mitigation strategies.
This article outlines the essential design principles, client optimizations, CDN usage, clustering, caching, database tuning, and service governance techniques needed to create robust high‑traffic, high‑concurrency systems that scale reliably and maintain performance.
Apache Doris 2.0 introduces a suite of high‑concurrency data‑serving enhancements—including row‑store format, partition‑bucket pruning, advanced indexing, materialized views, runtime filters, TOPN optimization, short‑circuit point‑query paths, prepared statements, and row cache—enabling single‑node tens of thousands QPS and dramatically reducing query latency.
This article examines the challenges of logging in distributed services, compares the traditional ELK stack with Baidu's Tianyan solution, details Tianyan's architecture—including Ingest, Store, Consumer, Elastic Agent, Fleet, APM, Beats, and Disruptor‑based high‑throughput pipelines—covers resource isolation, dynamic cleanup, and best‑practice recommendations for building a scalable, low‑latency log platform.
This article explains what distributed locks are, outlines their essential characteristics and common use cases, compares implementation approaches such as database, Redis, and Zookeeper, and provides step‑by‑step designs—including basic and advanced Redis‑based locks—to handle high‑concurrency inventory reduction safely.
This article outlines the essential design principles, client‑side optimizations, CDN usage, clustering, caching, database tuning, and service governance techniques required to build and maintain high‑traffic, high‑concurrency backend systems that are reliable, scalable, and performant.
The article details the background, design choices, compression code generation methods, database schema, high‑concurrency handling, and implementation of an enterprise‑grade short‑URL service that pre‑generates collision‑free codes to achieve high performance and secure redirection.
This guide walks developers through the essential design principles, client‑side optimizations, CDN usage, clustering, caching, database tuning, and service‑governance techniques needed to build robust high‑traffic, high‑concurrency applications.
Vivo’s e‑commerce inventory system evolved from a monolithic design into a multi‑layered service architecture that separates warehouse, scheduling, and sales layers, introduces distinct stock types, implements deduplication, anti‑oversell, high‑concurrency and hotspot mitigation strategies, and integrates sync mechanisms with warehouses and product systems.
This article explains how JD Supply Chain’s self‑built low‑code rule‑engine platform separates business logic from code, supports ultra‑high concurrency, enables non‑technical users to configure rules, and reduces a typical 10‑day delivery cycle to as fast as 0.5 day across dozens of core projects.
This article describes JD Supply Chain's self‑developed low‑code rule engine platform, outlining its motivation, high‑concurrency performance challenges, value‑centric design, visual rule‑authoring workflow, SDK integration, and the resulting efficiency gains for both development and business teams.
This article explains the true meaning of NoSQL, its theoretical foundation in the CAP theorem, the reasons why it excels in large‑scale and high‑concurrency scenarios, compares it with relational databases, and outlines typical use cases and trade‑offs.
This talk outlines the strategic shift toward domestic, high‑performance databases, comparing shared‑everything, shared‑nothing, and shared‑storage architectures, and presents a seven‑step migration framework—including selection, testing, full and incremental sync, application refactoring, dual‑write or middleware switching, and post‑migration observability—to achieve seamless, low‑impact migration of massive, high‑concurrency workloads.
This article explores the design of a high‑concurrency ticket‑spike system like 12306, covering large‑scale architecture, load‑balancing strategies, inventory deduction techniques, and a complete Go/Redis implementation with performance testing and fault‑tolerance considerations.
This article explains the concept of traffic peak shaving in high‑concurrency internet services, illustrates real‑world examples such as ticket抢购 and flash sales, and presents detailed mitigation techniques including message‑queue buffering and multi‑layer funnel filtering to protect downstream systems.
This article outlines a comprehensive backend architecture for flash‑sale (秒杀) systems, covering traffic‑spike handling, rate‑limiting, Nginx static‑resource separation, Redis locking and caching, message‑queue throttling, database sharding, asynchronous order processing, and captcha‑based anti‑bot measures.
This article compares Nginx and Apache, showing how Nginx’s event‑driven, single‑threaded architecture and efficient memory usage enable it to handle tens of thousands of concurrent connections with far less resources, and explains the underlying Apache work modes and I/O models.
This article presents a comprehensive, seven‑dimensional design for a flash‑sale (秒杀) system, covering business characteristics, overall architecture, peak‑limiting, Nginx configuration, page optimization, Redis clustering, message‑queue throttling, database design, captcha mechanisms, and key precautions for achieving high throughput and reliability.
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.
This article presents a comprehensive backend architecture for handling flash‑sale traffic, covering Nginx front‑end, Redis rate limiting, MQ buffering, async order processing, hotspot isolation, security measures, database sharding, and detailed configuration examples to achieve high availability and low latency.
This article explains the key characteristics of flash‑sale (秒杀) business and presents a comprehensive backend architecture—including Nginx, CDN, gateway, Redis, MQ, asynchronous processing, hotspot isolation, and database strategies—to handle massive simultaneous requests while ensuring performance, reliability, and security.
This article explains how to design a high‑performance short‑URL service capable of handling 1 million queries per second, covering system background, URL shortening principles, base‑62 encoding, distributed ID generation options, storage sharding, ambiguity checking with Bloom filters, multi‑level caching, and redirect handling.
This article details the evolution, layered architecture, stock types, deduction strategies, high‑concurrency solutions, and synchronization mechanisms of the Vivo Mall inventory system, illustrating how the backend was refactored to support diverse business scenarios and prevent overselling.
The article details Vivo Mall’s inventory system evolution from a tightly‑coupled service to a layered architecture handling multiple stock types, high‑concurrency flash‑sale deductions, duplicate‑deduction safeguards, hotspot mitigation, and CDC‑based synchronization, outlining current capabilities and future unified management goals.
This article describes JD Supply Chain's self‑developed low‑code rule engine platform, outlining its motivation, high‑concurrency challenges, architectural solutions, visual design capabilities, performance achievements, and how it enables both developers and business users to rapidly deliver complex business rules.
This article outlines fifteen practical techniques—including horizontal scaling, microservice decomposition, database sharding, connection pooling, caching, CDN, message queues, Elasticsearch, circuit breaking, rate limiting, and load testing—to help engineers build robust, high‑concurrency systems that can handle massive traffic spikes.
This article presents a comprehensive backend architecture for flash‑sale systems, covering Nginx static‑dynamic separation, Redis‑based rate limiting and distributed locks, MQ buffering, database sharding, safety protections, page optimization, and detailed Nginx configuration examples to handle massive concurrent traffic.
This article provides a comprehensive overview of Nginx, covering its lightweight high‑performance architecture, core advantages, request handling workflow, event‑driven concurrency model, reverse and forward proxy concepts, directory layout, common configuration directives, load‑balancing strategies, rate‑limiting algorithms, health checks, compression, and best practices for deploying Nginx in production environments.
This article outlines practical strategies for handling online services with extremely high request rates—over 200,000 QPS—by avoiding relational databases, employing multi‑level caching, leveraging multithreading, implementing circuit‑breaker and downgrade mechanisms, optimizing I/O, controlling retries, handling edge cases, and logging efficiently.
The article describes how an e‑commerce product ranking system was migrated to the new “Liao Yue” platform, decoupling it from the search module, introducing fresh metrics and Elasticsearch‑based sorting, then optimizing storage by separating B‑end and C‑end data—cutting costs 60%—with a gray‑scale rollout, dual‑read validation, rollback safeguards, and completing the two‑week, zero‑failure migration that delivered a closed‑loop, faster iteration system.
This article explains why Redis is essential for flash‑sale (秒杀) systems, detailing the load characteristics, the three sale phases, stock‑checking and decrement strategies, atomic operations, distributed locks, data modeling, and practical deployment recommendations for high‑concurrency back‑end services.
This article explains why Redis is essential for flash‑sale systems, detailing load characteristics, the three sale phases, Redis features such as high concurrency and atomic operations, and practical implementations using Lua scripts and distributed locks to ensure safe stock checking and deduction.
This article presents a comprehensive analysis of flash‑sale (seckill) business characteristics, technical challenges such as traffic spikes, database overload, and order handling, and offers detailed architectural solutions spanning frontend static pages, CDN caching, request throttling, queue‑based processing, optimistic locking, database sharding, high‑availability designs, and anti‑cheat mechanisms to ensure correctness and scalability under extreme concurrency.
This article provides a comprehensive overview of Nginx, covering its definition, advantages, application scenarios, request processing flow, high‑concurrency mechanisms, proxy types, directory layout, configuration directives, load‑balancing algorithms, rate‑limiting strategies, health checks, gzip compression, and best practices for deployment and scaling.
By manually modifying a few hundred lines and using an automated script, we transformed the multithreaded RocksDB storage engine into a coroutine‑based version with PhotonLibOS, achieving near‑identical functionality and, in heavy I/O and high‑concurrency scenarios, up to double the throughput compared to the original.
This article outlines the technical strategies and best practices for guaranteeing high‑quality user experiences during large‑scale mobile event rollouts, covering background, core guarantees, business characteristics, performance metrics, high‑concurrency handling, dynamic configuration, loading optimization, runtime safeguards, and monitoring.
This article outlines practical strategies for guaranteeing high‑concurrency, high‑flexibility, and stable performance of large‑scale mobile event pages, covering user experience goals, technical indicators, load and runtime optimizations, dynamic configuration, and monitoring to keep millions of users satisfied.
This curated collection presents technical insights on plugin-based frontend design, high‑concurrency architecture, modern 3D web development, Next.js innovations, React Three Fiber particle effects, functional programming perspectives, a face‑api.js virtual avatar demo, and Base64 encoding fundamentals.
This guide explains how to design, implement, and configure rate‑limiting solutions for high‑traffic flash‑sale scenarios, covering architecture goals, algorithm choices, single‑node vs distributed approaches, cloud‑native gateway settings, scaling with Kubernetes, and practical Go code examples.
This article details the end‑to‑end design of a coupon system for a rental listing platform, covering business flow, MySQL table schemas, activity lifecycle management, state‑machine and observer patterns, high‑concurrency claim handling with Redis and MQ, and future scalability optimizations.
This article examines the challenges of deploying dozens of deep‑learning models at Zuoyebang and compares three serving architectures—Gunicorn + Flask + Transformers, Tornado + PyTorch, and Tornado + Triton—highlighting performance trade‑offs and presenting a final high‑concurrency, low‑latency solution in production.
This article explains the core challenges of handling massive simultaneous requests and presents a comprehensive high‑concurrency architecture, covering infrastructure deployment, service‑side design, application‑level optimizations, clustering, database sharding, caching layers, message‑queue smoothing, service governance, resource isolation, and practical techniques such as multithreading and coroutine usage.
This comprehensive guide explains what Nginx is, its advantages, deployment scenarios, request processing, high‑concurrency mechanisms, proxy types, directory layout, configuration directives, cookie vs session, threading model, load‑balancing algorithms, rate limiting, health checks, compression, and practical examples for building robust, scalable web services.
Managing 20,000+ concurrent SQL queries requires moving beyond a single‑instance design; this guide explains why simple architectures fail, how to shard databases into multiple instances, route traffic using geographic hashing, and implement read‑write separation to sustain high read loads while keeping latency low.
JD-hotkey is a high‑performance backend framework that detects billions of hot keys daily, captures crawlers and hot items, and pushes them to service memory within milliseconds, dramatically reducing data‑layer load and boosting application performance.
This article presents a comprehensive analysis of flash‑sale (秒杀) system requirements, performance estimations, non‑functional constraints, and detailed architectural designs—including dynamic‑static separation, traffic layering, high‑availability, and stock‑deduction strategies—along with the key technologies needed to support million‑level concurrent traffic.
This article details the end‑to‑end design of a high‑traffic flash‑sale (秒杀) system, covering functional and non‑functional requirements, performance estimations, dynamic/static separation, traffic‑layer control, high‑availability strategies, inventory‑deduction methods, and the key technologies needed for a resilient, scalable backend.
This article details the architectural evolution of China Telecom's Tianyi Account gateway from its initial Zuul‑based 1.0 version through a Kong‑based 2.0 redesign to the high‑performance, multi‑language, hybrid‑deployment 3.0 architecture that now supports over 100 k QPS and 99.96% SLA.
This article explains how to build a high‑concurrency ticket‑seckill system that can handle one million users buying ten thousand tickets simultaneously by using distributed load‑balancing, Nginx weighted round‑robin, Go‑based HTTP services, Redis atomic stock deduction, and practical performance testing.
This article examines a high‑traffic video‑watching scenario, identifies database write bottlenecks, and presents four asynchronous solutions—thread‑pool, in‑memory queue with scheduled tasks, message‑queue, and Agent‑plus‑MQ—detailing their implementation steps, trade‑offs, and best‑practice guidelines.
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.
This article analyzes the extreme traffic spikes of China's 12306 train ticket platform, detailing its distributed high‑concurrency architecture, load‑balancing strategies, inventory‑deduction techniques, and Go/Redis code examples that enable stable, oversell‑free ticket purchasing during peak holidays.
This article details the architecture, compression‑code generation strategies, database schema, high‑concurrency handling, and Java implementation of a short‑URL service capable of supporting hundreds of millions of short links, including code snippets and deployment considerations.
This article explores the architecture and implementation of a high‑concurrency ticket‑snatching system, covering load‑balancing strategies, Nginx weighted round‑robin configuration, Redis‑based inventory management, and Go code examples that together enable handling millions of simultaneous requests while preventing overselling and ensuring high availability.
