high concurrency

Mike Chen's Internet Architecture

Jun 3, 2025 · Big Data

Kafka High-Concurrency Core Design Explained

This article explains how Kafka achieves high concurrency through its distributed broker cluster, partitioned topics, sequential log writes, message compression, asynchronous producer mechanisms, and OS page‑cache techniques, illustrating the combined architectural and performance optimizations that enable massive throughput.

Kafkaasynchronous producerdistributed architecture

0 likes · 4 min read

Kafka High-Concurrency Core Design Explained

Java Tech Enthusiast

May 31, 2025 · Fundamentals

Why Threads Alone Struggle to Achieve Million‑Scale Concurrency and How Coroutines Provide a Better Solution

The article examines why relying solely on threads cannot achieve single‑machine million‑level concurrency, analyzing thread resource consumption, context‑switch overhead, and contrasting it with user‑space coroutine scheduling that offers predictable, low‑cost switches, making coroutines better suited for IO‑intensive high‑concurrency scenarios.

CoroutinesThreadsconcurrency

0 likes · 6 min read

Why Threads Alone Struggle to Achieve Million‑Scale Concurrency and How Coroutines Provide a Better Solution

IT Services Circle

May 27, 2025 · Fundamentals

Why a Thread‑Only Model Struggles to Reach Million‑Level Concurrency on a Single Machine

The article analyzes why relying solely on operating‑system threads cannot easily achieve single‑machine million‑level concurrency, examining thread stack memory misconceptions, kernel‑level context‑switch costs, and how user‑space coroutine scheduling overcomes these limits.

CoroutinesIO MultiplexingThreads

0 likes · 6 min read

Why a Thread‑Only Model Struggles to Reach Million‑Level Concurrency on a Single Machine

Mike Chen's Internet Architecture

May 15, 2025 · Operations

Nginx High-Concurrency Optimization Techniques

This article explains how to achieve million‑level concurrent connections with Nginx by tuning OS limits, worker processes, epoll event handling, gzip compression, and zero‑copy file transfer, providing concrete configuration snippets and performance rationale for each optimization.

LinuxNginxepoll

0 likes · 4 min read

Nginx High-Concurrency Optimization Techniques

macrozheng

May 12, 2025 · Backend Development

Designing a Billion‑User Real‑Time Leaderboard: Redis vs MySQL

This article explores how to build a scalable, high‑performance leaderboard for hundreds of millions of users by comparing traditional database ORDER BY approaches with Redis sorted sets, addressing challenges such as hot keys, memory pressure, persistence risks, and presenting a divide‑and‑conquer implementation strategy.

RankingRedisbig-data

0 likes · 11 min read

Designing a Billion‑User Real‑Time Leaderboard: Redis vs MySQL

DaTaobao Tech

Apr 28, 2025 · Frontend Development

Front‑End Architecture and Performance Optimization for a Large‑Scale Chinese New Year Interactive Activity

The article details a large‑scale Chinese New Year interactive activity’s front‑end architecture, describing a layered system for business logic, data abstraction, and animation engines, unified data handling, dynamic animation rendering with downgrade paths, high‑concurrency QPS reduction, resilience measures, and extensive performance and workflow optimizations.

animationarchitecturedata management

0 likes · 15 min read

Front‑End Architecture and Performance Optimization for a Large‑Scale Chinese New Year Interactive Activity

Deepin Linux

Apr 25, 2025 · Backend Development

Understanding epoll: Linux I/O Multiplexing, Design, and Practical Usage

This article explains the limitations of traditional I/O models, introduces epoll as a high‑performance Linux I/O multiplexing mechanism, details its design principles, API usage, kernel data structures, and provides practical coding examples and optimization tips for building scalable backend services.

I/O multiplexingLinuxbackend development

0 likes · 41 min read

Understanding epoll: Linux I/O Multiplexing, Design, and Practical Usage

IT Services Circle

Apr 18, 2025 · Backend Development

Preventing Duplicate Orders under 100k QPS: Full‑Stack Strategies

This article explains how to prevent duplicate orders in a high‑traffic system handling 100,000 QPS by combining frontend safeguards such as button disabling and token mechanisms with backend techniques including NGINX rate limiting, gateway token validation, idempotent database design, sharding, distributed locks, optimistic locking, and comprehensive logging and monitoring.

Distributed LockIdempotencyRate Limiting

0 likes · 7 min read

Preventing Duplicate Orders under 100k QPS: Full‑Stack Strategies

Deepin Linux

Apr 18, 2025 · Backend Development

Deep Dive into Linux epoll: Design, Implementation, and Performance

epoll is a high‑performance Linux I/O multiplexing mechanism that replaces select/poll by using an event‑driven design with a red‑black tree and ready list, supporting edge‑ and level‑triggered modes, efficient data transfer via mmap, and providing superior scalability for high‑concurrency network applications.

I/O multiplexingLinuxepoll

0 likes · 85 min read

Deep Dive into Linux epoll: Design, Implementation, and Performance

Java Tech Enthusiast

Apr 11, 2025 · Backend Development

Ensuring Message Processing Once in High-Concurrency Scenarios

The article explains how to guarantee that messages are processed only once in high‑concurrency environments by combining production‑side idempotent publishing, broker‑level deduplication with unique IDs, and consumption‑side business idempotency such as database constraints or distributed locks, while also recommending monitoring, metrics, and reconciliation as safety nets.

IdempotencyKafkaMessage Queue

0 likes · 6 min read

Ensuring Message Processing Once in High-Concurrency Scenarios

Java Captain

Apr 4, 2025 · Backend Development

High Concurrency: Principles, Impacts, and Practical Solutions for Backend Systems

This comprehensive guide explains the nature of high concurrency, distinguishes it from parallelism, outlines its potential consequences across application, database, and service layers, and presents a systematic set of mitigation strategies—including rate limiting, asynchronous processing, redundancy, caching, and queue‑based designs—supported by real‑world case studies and code examples.

Database OptimizationRate LimitingRedis

0 likes · 27 min read

High Concurrency: Principles, Impacts, and Practical Solutions for Backend Systems

IT Architects Alliance

Apr 2, 2025 · Backend Development

Designing High‑Concurrency Backend Architecture for E‑commerce Platforms

The article explains how to design a scalable, highly available backend system capable of handling millions of requests per second by defining key performance metrics, estimating traffic with the 2/8 rule, and applying architectural patterns such as load‑balanced clusters, vertical service splitting, distributed caching, and database master‑slave replication, illustrated with a Taobao case study.

Load Balancingbackend architecturedatabase replication

0 likes · 14 min read

Designing High‑Concurrency Backend Architecture for E‑commerce Platforms

Architecture & Thinking

Apr 2, 2025 · Backend Development

Ensuring Fair Flash Sales in Multi-Active Architectures: Strategies & Code

This article examines the challenges of high‑concurrency flash‑sale scenarios in multi‑active architectures, analyzes fairness issues caused by geographic latency, and presents practical solutions such as data‑sharding and global‑clock ordered queues, complemented by a Redis‑based implementation example.

Redisdistributed systemsfairness

0 likes · 12 min read

Ensuring Fair Flash Sales in Multi-Active Architectures: Strategies & Code

Cognitive Technology Team

Mar 24, 2025 · Fundamentals

Understanding Sockets and epoll: Kernel Abstractions and High‑Concurrency Design

Socket provides a file‑descriptor based network communication abstraction in the OS, while epoll uses a red‑black‑tree and ready‑queue mechanism to deliver O(log N) scalable I/O event handling, together forming the core design that enables high‑concurrency servers to efficiently manage thousands of connections.

IO MultiplexingLinuxSocket

0 likes · 7 min read

Understanding Sockets and epoll: Kernel Abstractions and High‑Concurrency Design

Bilibili Tech

Mar 18, 2025 · Operations

Technical Practices for Ensuring Stability of Bilibili’s 2025 Spring Festival Gala Live Stream

Bilibili’s engineering team built a scenario‑metadata and one‑click fault‑drill platform, implemented multi‑tier degradation, dynamic capacity planning, and extensive automated fault‑injection testing to guarantee zero‑severity incidents during the high‑traffic 2025 Spring Festival Gala live stream.

Live Streamingcapacity planningfault injection

0 likes · 16 min read

Technical Practices for Ensuring Stability of Bilibili’s 2025 Spring Festival Gala Live Stream

Sanyou's Java Diary

Mar 17, 2025 · Backend Development

Mastering Flash Sale Scalability: Redis, MQ, and Inventory Hint Strategies

This article explores industry‑proven techniques for handling massive flash‑sale traffic, covering pressure‑distribution, Redis + MQ combos, Lua‑based stock deduction, RocketMQ transactional messages, and Alibaba Cloud's Inventory Hint to ensure consistency and performance under extreme concurrency.

Inventory HintLua ScriptMessage Queue

0 likes · 14 min read

Mastering Flash Sale Scalability: Redis, MQ, and Inventory Hint Strategies

Bilibili Tech

Mar 14, 2025 · Backend Development

Evolution and Optimization of Bilibili Membership Ticketing System for High‑Concurrency Scenarios

Bilibili’s ticketing platform evolved from a single‑transaction, synchronous design to an asynchronous batch system and finally to a Redis‑cached inventory layer, adding DB isolation changes, sharding, bloom‑filter protection and adaptive rate limiting, which together enabled handling up to 930 k requests per second and stable high‑concurrency ticket sales.

Database IsolationRedis cachingasynchronous processing

0 likes · 18 min read

Evolution and Optimization of Bilibili Membership Ticketing System for High‑Concurrency Scenarios

Architecture Digest

Mar 12, 2025 · Backend Development

Designing and Optimizing a High‑Concurrency Counting System for Large‑Scale Social Media

This article explains the challenges of storing and serving trillion‑level count data for a massive micro‑blogging platform and presents a step‑by‑step backend design that evolves from simple MySQL tables to sharded databases, Redis caching, message‑queue write‑sharding, and memory‑efficient custom data structures to achieve high performance, low cost, and strong consistency.

MySQLPerformance OptimizationRedis

0 likes · 12 min read

Designing and Optimizing a High‑Concurrency Counting System for Large‑Scale Social Media

Cognitive Technology Team

Mar 9, 2025 · Fundamentals

Understanding Concurrency Levels: Blocking, Starvation‑Free, Obstruction‑Free, Lock‑Free, and Wait‑Free in Java

The article explains five fundamental concurrency levels—Blocking, Starvation‑Free, Obstruction‑Free, Lock‑Free, and Wait‑Free—detailing their definitions, implementation mechanisms in Java, performance trade‑offs, suitable scenarios, and practical decision guidance for building high‑performance, reliable systems.

Javahigh concurrencylocking

0 likes · 6 min read

Understanding Concurrency Levels: Blocking, Starvation‑Free, Obstruction‑Free, Lock‑Free, and Wait‑Free in Java

Cognitive Technology Team

Feb 27, 2025 · Backend Development

High‑Concurrency Seckill Solutions: Redis + MQ, Pressure Distribution, and Inventory Hint Techniques

This article examines common industry practices for handling massive e‑commerce flash‑sale traffic, detailing pressure‑distribution, Redis + MySQL, Redis + MQ, and Alibaba's Inventory Hint approaches, and explains how Lua scripts, transactional MQ messages, and database hints together ensure atomic stock deduction and consistency under extreme load.

Inventory HintMQRedis

0 likes · 13 min read

High‑Concurrency Seckill Solutions: Redis + MQ, Pressure Distribution, and Inventory Hint Techniques