This guide explains Redis replication in depth, detailing the step‑by‑step slave‑of initiation, the PSYNC command’s role in data synchronization, the full and partial sync processes, heartbeat mechanisms, and asynchronous write propagation, while highlighting key configuration parameters and performance considerations.
This article introduces Redis as an open‑source in‑memory store, outlines its core data structures—strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, and geospatial indexes—and provides concrete command examples, application scenarios, and best‑practice guidelines for effective usage.
This article explains the QR‑code login process, detailing how the web front‑end, server, Redis store, and mobile application cooperate to generate a QR code, verify the scan, and complete user authentication using tokens and UUIDs.
This article walks through Redis from a basic overview to advanced features such as persistence, Sentinel, clustering, data types, transactions, Lua scripting, pipelining, and distributed locks, illustrating each concept with practical examples and explaining when and why to use them in real‑world applications.
This article explains the fundamentals of Single Sign‑On (SSO), compares traditional single‑system login with multi‑system authentication, discusses session and cookie challenges, and provides Java Spring code examples for token‑based SSO using Redis and interceptors.
This tutorial shows how to integrate Spring Security and JWT into a Spring Boot backend to implement user login and authorization, configure Swagger‑UI to automatically include the token, and provides all necessary code snippets, configuration files, and utility classes for a complete authentication solution.
This guide walks you through installing Redis, adding Maven dependencies, configuring Spring Boot, creating a Redis service interface and implementation, and building controller endpoints to generate and verify SMS verification codes, complete with code examples and deployment instructions.
This guide explains how to overcome Redis slowlog retention limits by modifying rsbeat to collect and ship slowlog entries—including sentinel and cluster support—to Elasticsearch, where Kibana can be used for detailed analysis and visualization.
This article compiles practical ELK knowledge, covering character‑set conversion, removing unwanted log lines, Grok pattern handling for multi‑line logs, multiline plugin usage in Filebeat and Logstash, date filtering, log type classification, performance optimization, Redis buffering, and Elasticsearch node tuning.
This article traces the historical laws that drove computing growth, examines how Redis, MongoDB and Memcached evolved, compares client‑side, proxy and compute‑storage‑separated architectures, evaluates their trade‑offs, and answers common questions about cloud‑based distributed databases.
This guide walks through setting up a three‑node ELK stack on Alibaba Cloud, configuring Redis as a broker, installing Filebeat for lightweight log collection, wiring Logstash pipelines, deploying Elasticsearch, Kibana and Grafana, and visualizing Nginx logs with geo‑IP maps and dashboards.
The Mall project is a comprehensive e‑commerce solution featuring a front‑end storefront and back‑office management, implemented with Spring Boot, MyBatis, and a suite of modern technologies such as Redis, Elasticsearch, RabbitMQ, Docker, and more, offering modules for products, orders, marketing, and analytics.
This article walks through a NullPointerException triggered by Spring Data Redis cache, analyzes the root cause in the RedisCache implementation, reproduces the issue with a Spring Boot demo, and provides practical solutions including connection‑pool tuning and custom cache handling.
This guide presents comprehensive best‑practice recommendations for designing Redis keys, choosing appropriate data types, managing key lifecycles, safely using commands, optimizing client connections, handling big keys, and selecting eviction policies to ensure high performance and reliability in production environments.
This article examines Redis's I/O multiplexing mechanism, explaining why it uses non‑blocking models, detailing the Reactor pattern, comparing select, epoll, and kqueue, and providing a thorough walkthrough of the underlying C code that implements event creation, addition, deletion, and polling across platforms.
This article examines various distributed ID generation methods—including UUID, MySQL auto‑increment, Snowflake, and Redis—explaining their advantages, drawbacks, and how top companies combine block allocation and double‑buffering to achieve uniqueness, scalability, and high performance.
This article explains why Redis delivers exceptional performance by examining its pure in‑memory operations, single‑threaded design, efficient data structures such as SDS, dictionaries, skip‑lists, zip‑lists, and the encoding and rehash strategies that keep latency low even under heavy load.
This article explains Redis's memory model—including memory statistics, allocation, object structures, internal encodings, and practical optimization techniques—so developers can accurately estimate memory usage, reduce fragmentation, and improve performance of high‑concurrency applications.
This article introduces distributed locks, explains why they are needed in distributed environments, outlines their characteristics and requirements, and compares three implementation approaches—plain Redis, ZooKeeper, and Redisson—detailing their advantages, drawbacks, and code examples.
This article examines how integrating byte‑addressable non‑volatile memory (NVM) with Redis can resolve the performance‑reliability trade‑off of AOF persistence, delivering near‑always‑mode data safety while maintaining everysec‑like throughput and dramatically reducing recovery time.
This article presents comprehensive Alibaba Cloud Redis development standards covering key and value design, lifecycle management, command usage, client integration, tooling, and efficient big‑key deletion techniques to help developers avoid common pitfalls and improve performance.
This article provides a comprehensive overview of Redis, covering its definition, common use cases, core functionalities, differences from Memcached, single‑threaded design, cache‑penetration mitigation, supported data types, Java clients, consistency strategies, persistence methods, distributed lock implementation, memory optimization, eviction policies, and typical performance issues.
This article provides a detailed analysis of Redis's two persistence mechanisms—RDB snapshots and AOF logging—covering their core concepts, implementation details, trigger conditions, data safety, performance impact, and recovery speed to help engineers choose the appropriate method for their workloads.
This article provides an in‑depth overview of why Redis is used, its drawbacks, the reasons behind its single‑threaded speed, data types and use‑cases, expiration policies, memory eviction strategies, consistency challenges with databases, and practical solutions for cache penetration, cache avalanche, and concurrent key competition.
This article explains why traditional in‑process locks fail under massive concurrency, how distributed locks using Redis or Zookeeper ensure atomic updates across many servers, and details practical implementations, pitfalls like deadlocks, and strategies such as key expiration and ephemeral sequential nodes.
Distributed locks provide mutual exclusion across multiple nodes in scalable, reliable systems, and can be implemented via simple Redis SETNX/EX patterns, ZooKeeper’s EPHEMERAL_SEQUENTIAL znodes with watches, or Redisson’s advanced Java‑centric primitives, each offering distinct trade‑offs in simplicity, consistency, and feature richness.
This article explains Spring's annotation‑based caching, analyzes why the default cache key collides in multi‑tenant scenarios, shows how to embed tenant identifiers into keys, and provides a custom CacheManager implementation that parses TTL from the cache name and applies it to Redis entries.
This article analyzes data‑inconsistency problems caused by concurrent multithreaded updates in a CMDB system and presents a step‑by‑step exploration of synchronization solutions, ultimately implementing a robust Redis‑backed distributed lock in Python to guarantee atomic reads and writes while handling edge cases such as crashes and lock expiration.
The article describes the motivation, analysis, architecture, performance optimizations, development challenges, and future plans of JD Finance’s LEGO backend platform, which uses Spring Boot, SpEL, Redis, and reactive programming to enable dynamic, high‑performance page configuration for a high‑traffic mobile app.
This article explains Redis’s core architecture by detailing its event‑driven model, the initialization of the event loop, how file and time events are handled, and walks through a complete client command flow such as a SET operation, illustrated with key source‑code excerpts.
This article explains Redis's memory model—including memory statistics, allocation, internal data structures like redisObject and SDS, object type encodings, and practical techniques for estimating usage, reducing fragmentation, and optimizing memory consumption—providing developers with actionable insights for high‑performance backend systems.
This tutorial demonstrates how to integrate Apache Flink with Redis as a sink, showing the Maven dependency, a custom RedisMapper implementation, and a complete Flink job that reads Kafka messages, performs word count, and stores results in Redis, with plans for HBase and MySQL extensions.
This article explains how idle TCP connections are silently dropped by Alibaba Cloud security groups, causing Redis client timeouts and MySQL JDBC CommunicationsExceptions, and provides step‑by‑step diagnostics and configuration changes—including TCP keepalive and wait_timeout tweaks—to prevent the failures.
This article explains Redis as an in‑memory data store used for caching, database, and messaging, walks through its evolution from simple HTTP caching to dedicated servers, and details server‑side features like persistence, Sentinel, replication, clustering, as well as client‑side capabilities such as rich data types, transactions, Lua scripting, pipelining, and distributed locks.
This guide presents comprehensive Redis best practices covering key naming, value design, command selection, client configuration, memory‑efficient data structures, and practical Java code for safely deleting large keys, helping developers build high‑performance, reliable Redis deployments.
Facing metadata inconsistency, ten‑thousand‑device scale, and the need for intelligent, event‑driven automation, Tencent Cloud Redis’s sole operations lead built a unified DB‑CMDB, a job‑platform for reusable workflows, and AI‑assisted scheduling, transforming DBAs into developer‑operators and driving 300% efficiency gains.
This article explains how to implement ordinary Redis distributed locks and the advanced Redlock algorithm using Redisson across single‑node, sentinel, and cluster architectures, provides complete Java code examples, and addresses common questions about expiration, high availability, and comparisons with Zookeeper.
This article reviews the evolution of Redis‑based distributed lock implementations—from the simple V1.0 pattern to the more robust V3.1 and Redlock algorithm—highlighting their mechanisms, common pitfalls, and guidance on selecting the most suitable lock for a given scenario.
This article compiles a comprehensive set of interview questions encountered across four technical rounds and an HR session for a major Chinese tech company, covering Java backend architecture, microservices design, RocketMQ, Redis internals, database consistency, networking, algorithms, and practical troubleshooting tips.
This article provides a comprehensive overview of Redis, covering its core concepts, common use cases, and detailed explanations of internal objects such as redisObject, dictEntry, SDS, and the underlying data structures for String, List, Hash, Set, and ZSet, including code snippets and performance characteristics.
This article walks through the evolution of Redis clustering—from early Replication+Sentinel setups, through Proxy‑based solutions, to modern Redis Cluster—detailing their architectures, advantages, drawbacks, and provides practical interview tips and high‑impact answers for candidates.
This article presents thirty practical recommendations for deploying, configuring, and maintaining Pika—a high‑capacity, RocksDB‑backed Redis‑compatible storage system—covering version selection, thread settings, hardware choices, key design, memory management, replication, backup, compaction, security, and monitoring to achieve reliable and high‑performance operation.
Pika 3.0, the open‑source C++ Redis‑compatible storage built on RocksDB, introduces a new Blackwidow engine, enhanced binlog, and optimized server layer, delivering higher interface performance, reduced disk usage, extended key length, full ZSET compatibility, and improved overall efficiency.
This article explains the classic Redis distributed lock using SETNX and Lua, highlights its pitfalls, introduces the advanced Redlock algorithm with step‑by‑step acquisition and release procedures, and provides concrete Java Redisson code examples, illustrating how to implement reliable distributed locks across multiple Redis nodes.
Zhihu built a self‑service analytics platform on Druid, introduced a multi‑level Redis caching strategy, split long‑duration queries across multiple brokers, and added automatic cache invalidation to dramatically improve query latency and resource usage for massive daily request volumes.
This guide reviews Redis’s evolution, highlights key data structures such as bitmap and streams, and provides actionable recommendations on expiration policies, pipelines, namespaces, Lua scripting, configuration tweaks, and monitoring to help developers use Redis efficiently and avoid common pitfalls.
Redis, a high‑performance NoSQL database, offers multiple clustering approaches—including client‑side sharding, proxy‑based solutions like Twemproxy and Codis, and the native Redis Cluster—each with distinct trade‑offs in scalability, availability, operational complexity, and performance, guiding engineers to select the optimal architecture for their workloads.
This article reviews the progression of Redis high‑availability solutions—from simple replication with Sentinel, through proxy‑based setups, to native Redis Cluster—explains their advantages and drawbacks, and provides practical interview Q&A tips for candidates.
This article explains the business background of a flash‑sale (seckill) scenario, analyzes naive locking approaches, introduces Redis‑based distributed locks, and provides a complete Java demo with custom annotations, dynamic proxies, and a multithreaded test to ensure correct inventory decrement under high concurrency.
This article explains Redis’s key features and common use cases, highlights the risks of single‑node deployments, compares two master‑slave replication architectures, and outlines their pros and cons, and details how Redis Sentinel provides automated monitoring, failover, and configuration to achieve high availability.
This article walks through the design of a highly available Redis deployment, explains common failure scenarios, compares single‑node, master‑slave, and multi‑Sentinel architectures, and shows how adding a virtual IP and three Sentinel instances can provide robust HA while keeping client usage simple.
Learn why inventory over‑selling occurs under high concurrency, and explore three practical solutions—pessimistic locking, optimistic locking with version control, and Redis‑Lua scripting—detailing their implementation steps, advantages, and drawbacks to help you choose the best approach for reliable stock management.
This article explains the principles behind TCP's three‑way handshake and four‑way teardown, covering connection establishment, data transfer, termination, header fields, state diagrams, sequence‑number wraparound, SYN‑flood attacks, mitigation techniques, and a practical Redis packet‑capture analysis, all illustrated with diagrams and code snippets.
This article explains the RESP protocol used by Redis, details its five data types, demonstrates pipeline handling with raw sockets, examines the parsing and command‑packing logic of the redis‑py client, analyzes the pipeline implementation and limitations of redis‑py‑cluster, and speculates on upcoming RESP3 features.
The article describes the challenges of handling book outbound operations—matching SN codes, ensuring data consistency, and avoiding lock contention—and outlines a comprehensive refactor that separates business logic, standardizes APIs, adopts asynchronous processing, and leverages Redis sets to achieve high‑performance, reliable SN allocation.
This article details SuNing Financial’s red‑envelope platform architecture and evolution, covering high‑concurrency challenges, traffic shaping, asynchronous processing, multi‑level caching, Redis‑based distributed locks, task scheduling, payment‑chain isolation, high‑availability deployment, multi‑layer flow control, monitoring, and future scalability directions.
This article introduces CIM, a cross‑platform instant messaging framework, detailing its architecture, core components, registration and routing mechanisms, client‑server interaction, custom protocol, built‑in commands, and step‑by‑step deployment using SpringBoot, Netty, Redis and Zookeeper.
This article explains how Redis stores and encodes its five data types by using various in‑memory data structures such as SDS strings, linked lists, quicklists, hash tables, skip‑lists, intsets and zip‑lists, and it also covers object mapping, expiration policies, LRU eviction, and the RDB/AOF persistence mechanisms.
The 2018 DB-Engines ranking crowned PostgreSQL as the top DBMS, highlighting its 30‑year evolution, strong community, and expanding features, while MongoDB and Redis secured the runner‑up and third places respectively, and the January 2019 popularity scores showed continued shifts among major database systems.
This article provides a detailed overview of Redis as an open‑source, in‑memory data store, covering its core features, common use cases such as caching and leaderboards, high‑performance characteristics, essential data structures and commands, configuration options, and practical recommendations for production deployment.
This article compares Redis and Memcached across network I/O models, supported data structures, memory management mechanisms, persistence options, consistency guarantees, and clustering approaches, highlighting the strengths and trade‑offs of each in‑memory data store.
Learn how to implement Redis distributed locks in both single‑node and cluster environments, covering lock acquisition and release commands, key pitfalls like master‑slave failover, and the advanced Redlock algorithm with step‑by‑step guidance, Lua scripts, and a PHP lock class example.
This article explains Redis's memory model—including memory statistics, allocation strategies, object types, internal encodings, and practical optimization techniques—so developers can accurately estimate memory usage, reduce fragmentation, and improve overall system efficiency.
This article explains the five Redis data types—String, List, Hash, Set, and Zset—their characteristics, typical use cases, and how to combine them effectively, illustrated with a real‑world hot‑top cache design for a high‑traffic group‑buying system.
The 2018 3306π Shenzhen annual conference, co‑hosted by the 3306π community, Zhishu Hall and vivo, attracted over 150 attendees and featured expert talks on MySQL 8.0, open‑source data‑transfer middleware, TDSQL for finance, RadonDB, InnoDB enhancements, large‑scale Redis clusters, and Youzan database operations, with presentation materials available online.
In high‑concurrency scenarios, using Redis as a buffer can improve performance, but without proper strategies such as delayed double‑delete, cache expiration, or asynchronous binlog‑driven updates, inconsistencies between Redis and MySQL may arise, so this article explains the causes and presents four practical solutions.
This article explains Redis's core data types—String, Hash, List, Set, and Sorted Set—its internal object model, persistence options (RDB and AOF) with their pros and cons, transaction characteristics, master‑slave replication mechanisms, and common caching scenarios and design considerations.
This comprehensive guide explains Redis fundamentals, its rich data structures and common commands, then dives into performance optimization, replication, and clustering, offering practical examples and best‑practice recommendations for building reliable, high‑performance Redis deployments.
During a Redis container migration, slowlog entries suddenly reported 1800 ms latency despite low QPS, leading to a deep investigation that traced the anomaly to a kernel bug affecting the Time Stamp Counter on Skylake‑X CPUs and improper use of gettimeofday for timing.
This guide walks through downloading and installing Redis, adding Java dependencies, creating a Redis DAO with Protostuff serialization, configuring Spring beans, caching seckill data, implementing a MySQL stored procedure for atomic execution, mapping it with MyBatis, updating service and controller layers, and testing the entire workflow.
This guide explains how to monitor critical Redis health indicators—including ping response, client connections, blocked clients, memory usage, fragmentation, cache hit rate, OPS, persistence status, expired keys, and slow logs—to ensure optimal performance and prevent failures.
This article details the design and performance optimization of a live quiz platform, covering overall architecture, user info retrieval, answer submission handling, anti-cheating measures, and Tomcat configuration, highlighting techniques such as request distribution, in‑process caching, asynchronous processing, MQ peak‑shaving, and Redis aggregation.
This guide outlines comprehensive best‑practice recommendations for Alibaba Cloud Redis, covering key naming conventions, value size limits, safe command usage, client connection strategies, memory eviction policies, and useful auxiliary tools to improve reliability and performance.
This article provides a comprehensive guide to Redis high‑availability solutions, detailing Sentinel principles, multiple HA architectures such as DNS‑based, VIP‑based, Keepalived, Redis Cluster, Twemproxy and Codis, and shares practical best‑practice recommendations for deployment and failover.
Redis and Memcached differ significantly in data structure support, memory efficiency, performance, memory management, persistence options, and clustering capabilities, with Redis offering richer data types, server‑side operations, configurable persistence, and native clustering, while Memcached provides simpler key‑value storage, slab allocation, and client‑side distribution.
This article walks through the NewLife.Redis library’s two‑layer architecture, demonstrates basic and advanced usage with C# examples, shows pressure‑testing results, and shares practical tips such as GetAll/SetAll, pipelines, and serialization tricks for high‑throughput big‑data scenarios.
This article introduces Zhuanzhuan's multi‑service storage layer—including MySQL, TiDB, ZZRedis, ZZKV/ZZList, and ZZOS—detailing their applicable scenarios, architectural designs, and the improvements made to address scalability, security, and resource‑usage challenges.
Redis is an open-source, in‑memory key‑value store written in C that supports multiple data structures, offers high‑performance caching, persistence via RDB and AOF, various clustering solutions such as Codis and native Redis Cluster, and is widely used for caching, ranking, session management, queues, and pub/sub.
The article explains how engineers can design robust game special‑event systems by abstracting features into reusable components, applying hook‑based extensibility, and using asynchronous message middleware to handle diverse, time‑critical, high‑risk operations, illustrated with the 37 Platform’s event architecture and configuration‑driven flexibility.
Hotspot keys, caused by massive read‑heavy traffic or server limits, can overload network cards, crash cache shards, and trigger DB bottlenecks, but by employing server‑side caching, Memcache/Redis, local caches, read‑write separation, and proactive hotspot detection, systems can mitigate these issues and scale efficiently.
This article explains the main factors that cause Redis latency—including network bottlenecks, Redis’s single‑threaded design, deployment choices, OS settings, and slow commands—and provides concrete commands and best‑practice solutions to diagnose and reduce the delay.
This guide explains how to create a dynamic IP blacklist using Nginx, Lua, and Redis, covering the background, architecture options, step‑by‑step implementation, and the advantages of a lightweight, shared, and easily updatable solution.
This article recounts a Redis server hijack incident, detailing the detection, forensic investigation, removal of malicious files and cron jobs, and provides practical hardening recommendations to prevent similar attacks on Linux environments.
This article explains how to build a high‑performance red‑packet (hong‑bao) service by configuring a RedisTemplate in Spring, writing an atomic Lua script for stock management, persisting results to a MySQL database via JDBC batch processing, and using @Async to off‑load long‑running tasks, complete with code examples and deployment steps.
An incident where a PHP engineer ran a Redis KEYS * command caused a massive lock, CPU spike, and a cascading database crash, prompting a review of Redis safety rules, command restrictions, and best‑practice alternatives like SCAN to prevent future outages.
The article compares traditional and cloud databases, outlines the performance and management benefits of Tencent Cloud's database SaaS, explains migration tools like DTS, details audit and data‑subscription features, and highlights future trends for cloud‑native data services.
A comprehensive collection of Alipay senior Java interview questions covering JVM garbage collection, concurrency, J.U.C utilities, performance tuning, data structures, MySQL indexing, distributed systems, Redis clustering, middleware, and high‑concurrency solutions for preparation.
This article explains the causes of hot key problems in high‑traffic scenarios, outlines their potential impact on system performance, and presents multiple mitigation strategies—including server‑side caching, Memcache/Redis, local caches, read‑write separation, and proactive hot‑data detection—while comparing their advantages and trade‑offs.
This article explains how to build a high‑concurrency flash‑sale (秒杀) system by separating static page traffic with CDN, using read‑write‑split Redis for early request filtering, applying Lua scripts for atomic inventory deduction, and employing Redis as a simple message queue for asynchronous order persistence.
In this talk, Tencent Cloud senior engineer Zou Peng outlines cloud‑native best practices for Redis 4.0 clusters, detailing a smart‑proxy architecture with gossip‑based management, lazy‑scan big‑key handling, automatic fail‑over, the high‑performance CKV engine, and practical guidance on scaling, sharding, monitoring, and cost‑effective high‑availability deployment.
This article provides a comprehensive introduction to Redis, covering its core concepts, advantages, data structures, persistence mechanisms, eviction policies, clustering, common usage scenarios, and typical interview questions for developers working with this high‑performance in‑memory key‑value store.
This comprehensive guide introduces Redis fundamentals, explores its diverse data structures and essential commands, and delves into advanced topics such as persistence, memory management, pipelining, transactions, clustering, and Java client choices, offering practical tips for deployment, optimization, and architectural decisions.
The article describes how to build a Go‑based backend for a WeChat lottery mini‑program using the Echo framework, Tencent Cloud load balancer, Nginx, Redis and MySQL, detailing the system architecture, API design, database and Redis integration, WeChat login flow, activity state machine, and lessons learned.
Pika 3.0, a C++ open‑source Redis‑compatible storage built on RocksDB, introduces a new blackwidow engine, an improved binlog system, and a more efficient server layer, delivering higher performance, reduced disk usage, extended key length limits, and full Redis ZSET compatibility.
This article explains the performance bottlenecks of MongoDB's geoNear queries in high‑traffic LBS services, analyzes why dense point clusters cause massive scans, and details the introduction of hintScan and hintCorrectNum parameters that reduced scan size and increased QPS by more than tenfold, even outperforming Redis 3.2.
This article explores the fundamentals and practical details of server‑side caching in distributed web systems, covering local vs. distributed caches, Redis instance planning, key‑value modeling, persistence, eviction policies, and CRUD operations with performance‑focused recommendations.
This article details Meitu's engineering of a Redis/Memcached proxy platform, describing why twemproxy was chosen, the limitations of its upstream version, the multi‑process redesign with live configuration reload, added latency metrics, reuse‑port handling, Redis master‑slave support, performance testing, and remaining challenges.
This article explains why Redis is a popular in‑memory key‑value store, defines high availability, enumerates failure scenarios, and walks through four incremental architectures—single instance, master‑slave with one Sentinel, dual Sentinel, and three‑Sentinel with VIP—to achieve a robust, production‑grade Redis deployment.
The article details Meitu's adaptation of Twitter's twemproxy into a multi‑process, hot‑reloading, high‑availability Redis/Memcached proxy, describing its architecture, custom features, performance testing, remaining challenges, and open‑source resources for other engineering teams.
This guide walks through Logstash fundamentals, from creating basic pipelines with input, filter, and output sections to using common plugins such as grok, mutate, date, geoip, multiline, and integrations with NGINX, rsyslog, Redis, and Docker‑based Logspout, providing practical configuration examples and command‑line tips.
