This guide introduces RedisInsight and RedisMod, explains how to install them via Docker, demonstrates basic data operations, explores the CLI and Profiler features, and shows how to monitor Redis with Grafana and Prometheus, providing a comprehensive workflow for managing modern Redis deployments.
Redis 7.0-rc2 introduces stream consumer group lag tracking, new ACL‑checking APIs, additional command options, extended replies, performance enhancements, and a series of bug fixes, providing a comprehensive update to the open‑source in‑memory database.
The Baidu Aifanfan lead list page evolved through six versions—from rapid MySQL‑based delivery to a metadata‑driven, ES‑backed architecture with Redis synchronization—culminating in a high‑availability, high‑performance system that slashed server latency from 25 seconds to under one second and cut frontend load times and static asset sizes dramatically.
The article explains why caching is essential for high‑concurrency distributed systems, analyzes its benefits and costs, and then details various cache‑update, penetration‑prevention, bottom‑hole, avalanche, and hot‑key‑rebuild optimization techniques, offering practical guidance for reliable and performant backend architectures.
This guide introduces RedisMod’s enhanced modules, shows how to install Redis with Docker, demonstrates native JSON storage using RedisJSON, walks through building and querying a full‑text search index with RediSearch—including Chinese support—and compares its performance against Elasticsearch.
This article explains how Redis uses the simple text‑based RESP protocol for client‑server communication, details each reply type, shows how to capture traffic with tcpdump, and dives into the internal C structures and error codes that power Redis commands.
This article explains why caching is essential for high‑concurrency systems, analyzes the challenges of keeping database and cache data consistent, and compares five practical cache‑invalidation strategies—including write‑after‑DB, delete‑before‑write, delayed double delete, queue‑based deletion, and binlog subscription—highlighting their trade‑offs and suitable scenarios.
This article explores Redis's persistence mechanisms, detailing how RDB snapshots and AOF logs work, how to configure and trigger them, their advantages and drawbacks, and guidance on choosing the appropriate method for reliable data storage.
This article explains why Redis latency spikes can cause system-wide outages, shows how to measure baseline performance, monitor slow commands, and address network, RDB, swap, AOF, expiration, and big‑key problems with practical solutions and a diagnostic checklist.
This guide walks you through installing RedisMod via Docker, using the official RedisInsight visual tool to manage various data types, explore its CLI and Profiler features, and integrate advanced monitoring with Grafana and Prometheus for a comprehensive Redis workflow.
This article walks through Redis’s annotated source code to explain how its single‑threaded architecture initializes the server, loads configuration and data, and continuously processes file and time events within a while‑loop, revealing why Redis achieves high performance without spawning additional worker threads.
This article compares Redis, Memcached, and MongoDB, outlining each system's strengths and weaknesses, performance characteristics, storage capabilities, availability, reliability, consistency, data analysis features, and typical application scenarios to help developers choose the appropriate NoSQL solution.
This article examines the hidden dangers of Redis distributed locks—including non‑atomic operations, forgotten releases, accidental unlocking of others, massive request failures, re‑entrancy, lock competition, timeout handling, and master‑slave replication issues—while providing practical Java code examples and mitigation strategies.
This article examines Redis's single‑threaded architecture by walking through its source code, detailing the main function initialization steps, the event‑loop implementation, and how file and time events are processed, while also noting the promotional messages interspersed throughout.
This article demonstrates how to implement a Redis distributed lock using Jedis in Java, explains lock creation and deletion strategies, and walks through a simulated high‑concurrency order‑grabbing scenario with 100,000 users to illustrate practical usage.
This article provides a step‑by‑step methodology for identifying why a Redis instance becomes slow, covering baseline latency testing, slow‑log analysis, big‑key detection, expiration patterns, memory limits, fork overhead, huge‑page effects, AOF configuration, CPU binding, swap usage, memory fragmentation, network saturation, and practical remediation techniques.
This article explains how to detect Redis latency problems, measure baseline performance, monitor slow commands, and address common causes such as network round‑trip delays, fork‑generated RDB snapshots, transparent huge pages, swap usage, AOF settings, key expiration, and big‑key handling, providing practical troubleshooting steps and solutions.
This article analyzes the excessive memory usage and blocking issues in Redis master‑slave replication, explains how the upcoming Redis 7.0 shared replication buffer solves these problems, and details the design, implementation, and key considerations of the shared buffer mechanism.
This article examines various delayed‑message implementations in distributed message‑queue systems, comparing external‑storage approaches using databases, RocksDB and Redis, and reviewing built‑in solutions in open‑source MQs such as RocketMQ, Pulsar and QMQ, while discussing their advantages, drawbacks and design considerations.
This article presents a comprehensive collection of Redis use cases—including caching, distributed locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow relationships, and ranking—along with practical code snippets and command examples for backend developers.
This article explains what constitutes a Redis big key, why it harms performance, how to detect it with tools like bigkeys, redis‑rdb‑tools, and monitoring, and provides practical removal, compression, and sharding strategies to mitigate the issue.
This article presents 16 practical Redis use cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tags, filtering, follow relationships, and ranking—detailing data types, commands, and implementation tips for building scalable backend services.
This extensive guide covers Redis fundamentals, data structures, persistence mechanisms, clustering, sentinel high‑availability, performance tuning, common pitfalls, and practical use‑case implementations, providing detailed answers and code examples for interview preparation and real‑world development.
This article details the design and implementation of an online customer service instant messaging system, covering requirements analysis, client‑server network model, HTTP and WebSocket protocols, distributed architecture choices such as routing rules, Redis shared memory, server‑master sync, and message‑queue broadcasting, and explains why Netty was selected as the development framework.
This article explains three Redis‑based rate‑limiting techniques—setnx with expiration, a sliding‑window implementation using sorted sets, and a token‑bucket algorithm with lists—complete with Java code examples and discussion of their advantages and limitations for high‑concurrency backend services.
This article explains the benefits, costs, and various update, penetration, bottom‑hole, avalanche, and hot‑key rebuilding strategies for caches in high‑concurrency distributed systems, offering practical guidance on choosing eviction algorithms, using Bloom filters, and improving overall system performance and reliability.
The article examines tiered data storage and evaluates four cache‑synchronization strategies—updating the database before the cache, deleting the cache before updating the database, updating the cache before the database, and deleting the cache after a database update—highlighting their trade‑offs and practical solutions such as delayed double deletion, message‑queue retries, and binlog‑driven cache updates.
This article explores Redis persistence mechanisms, detailing the purpose of data durability, the two primary methods—RDB snapshots and AOF logs—their operation, strategies, advantages, drawbacks, and the newer hybrid persistence mode, while also addressing common questions and best‑practice recommendations for reliable deployment.
This article details the research background, environment preparation, and three Redis‑based message‑queue implementations—using List structures, Pub/Sub, and Stream APIs—along with code examples, design considerations, testing strategies, and deployment tips for building a lightweight messaging component in Java Spring Boot.
This comprehensive guide explores Redis fundamentals, internal data structures, persistence options, multi‑threading, replication, sentinel failover, clustering, common performance pitfalls, and practical use‑case patterns, providing detailed explanations and code examples for developers and architects.
This article details how Vivo's push notification platform leverages Redis for massive message throughput, identifies bottlenecks encountered during peak traffic, and outlines a series of capacity, clustering, hot‑key, and client‑side optimizations that reduced load by up to 15% and improved response times dramatically.
This guide outlines a practical seven‑step methodology for mastering Redis source code, from mapping the project structure and preparing prerequisite knowledge to focusing on core execution paths, handling complex functions, exploring side modules, and iteratively filling knowledge gaps.
Redis is an open‑source, in‑memory data‑structure store that serves as a database, cache, and message broker; this guide introduces its fundamentals, explains why NoSQL emerged, details its five primary data types, common commands, and advanced features like transactions and pipelines.
This article compiles a comprehensive set of Tencent interview questions and detailed answers covering HashMap internals, thread safety, red‑black trees, Redis performance, MySQL indexing, TCP reliability, IO multiplexing, RPC, and system bottleneck analysis to help candidates prepare effectively.
The article walks through Redis’s two persistence options—RDB snapshots and AOF logs—detailing how each works, their advantages and drawbacks, and why interviewees should understand their differences, including AOF rewrite compression, to answer interview questions accurately.
This article explains the benefits, costs, and various update, penetration, hole, avalanche, and hot‑key optimization techniques for caches in high‑concurrency distributed systems, providing practical guidance on choosing appropriate strategies such as LRU/LFU/FIFO, timeout eviction, proactive updates, Bloom filters, and concurrency‑aware batch operations.
This comprehensive guide walks you through Redis basics, its data structures, commands, installation steps, high‑concurrency principles, persistence mechanisms, replication, caching strategies, Lua scripting, pipelines, distributed locks, and cluster concepts, providing practical examples and best‑practice recommendations for developers and operators.
This article thoroughly examines the safety of Redis-based distributed locks, from basic SETNX implementations to advanced Redlock algorithms, compares them with Zookeeper locks, discusses common pitfalls like deadlocks, clock drift, and network delays, and presents expert debates and practical solutions for robust lock management.
The article explains why data inconsistency occurs when MySQL and Redis are used together under heavy load, analyzes cache‑delete ordering issues, and presents two practical solutions—delayed double‑delete and asynchronous cache updates via MySQL binlog—along with their trade‑offs and implementation steps.
This article explains how to secure API communication with tokens, timestamps, cryptographic signatures, duplicate‑submission prevention, and practical Spring Boot code examples, offering a comprehensive guide for protecting data exchange between services.
This article analyzes why data inconsistencies arise when MySQL and Redis are used together under high load, compares cache‑delete ordering problems, and presents two practical solutions—delayed double‑delete and asynchronous binlog‑driven cache updates—along with implementation tips and trade‑offs.
This article explains how to solve the problem of non‑serializable WebSocket sessions in a load‑balanced Spring application by using Redis publish/subscribe and spring‑session‑redis to share session data across multiple server instances, providing complete configuration and code examples.
To sustain Vivo Push Platform’s massive real‑time traffic, engineers re‑architected two Redis clusters, trimmed capacity by 58 %, split clusters, randomized hotspot‑prone keys, and introduced three‑level caching, cutting peak CPU load by 15 %, halving response time and improving overall Redis efficiency during peak loads.
KeyDB, a multithreaded fork of Redis, offers higher throughput, multi‑master replication, vertical and horizontal scaling, TLS encryption, JavaScript modules, and ARM support, making it a compelling alternative for boosting QPS without additional hardware, while remaining fully compatible with Redis.
This guide shows how to build Undermoon, run a master and replica Memory Broker, verify data replication, and manually switch to the replica via the coordinator API when the master fails, ensuring continued service operation.
This article presents a simulated interview that explains Redis's evolution from single‑threaded to optional multithreading, its in‑memory performance advantages, persistence options (AOF, RDB, hybrid), high‑availability architectures, and the consistent‑hashing based slot allocation used by Redis Cluster.
Undermoon migrates Redis Cluster slots quickly and simply by orchestrating SCAN, DUMP, PTTL, RESTORE and DEL commands through migrating and importing proxies, balancing CPU load, handling delete‑prone keys, and achieving sub‑minute migration of 1 GB with throughput recovery.
This tutorial explains how to quickly set up a Spring Cloud Gateway service, configure static and Nacos‑driven dynamic routes, and implement a Redis‑backed authentication filter that validates tokens, refreshes expiration, and forwards user information to downstream services.
This article details the three‑stage evolution of an e‑commerce recommendation framework—V1.0’s simple strategy‑factory design, V2.0’s vertical business split, and V3.0’s configurable pipeline with dynamic server‑client configuration, addressing scalability, isolation, and AB‑testing challenges.
This article shares a detailed account of a backend engineer's multi‑stage interview process at Huanju (formerly YY), covering personal background, common Java and system design questions, database and caching strategies, distributed lock handling, and reflections on preparation and outcomes.
This article surveys various delayed queue implementations—including Kafka, RocketMQ, Redis (Redisson), and Netty’s HashedWheelTimer—examining their design principles, advantages, drawbacks, and integration strategies, and proposes a unified micro‑service architecture leveraging Kafka topics, Redis ZSETs, and thread‑pool optimizations for reliable message scheduling.
This article explains how to design and implement a high‑concurrency ticket‑seckill system by analyzing the 12306 architecture, introducing multi‑layer load balancing, demonstrating Nginx weighted round‑robin configuration, and providing Go and Redis code to achieve stable, low‑latency ticket purchasing under massive simultaneous requests.
This step‑by‑step tutorial shows how to build Undermoon from source, deploy a memory broker, coordinator, two server proxies and four Redis nodes on a single machine, register them via JSON APIs, create a named cluster, and verify its operation with redis‑cli.
The article explains why service rate limiting is essential for high‑concurrency systems, compares it with service degradation, describes common algorithms such as counter, leaky bucket and token bucket, and shows how to implement distributed rate limiting using Redis.
This guide walks through setting up an Undermoon Redis cluster using Docker Compose, covering prerequisites, building images, registering proxies, creating and scaling the cluster, and demonstrating automatic failover with concrete command‑line examples.
This guide walks through building Helm charts, deploying the Undermoon operator, creating and scaling a Redis Cluster on Kubernetes, and configuring the undermoon‑scheduler to ensure master‑replica placement across nodes, with detailed commands and parameter explanations.
This article shares a step‑by‑step account of a candidate's three‑round ByteDance interview, covering background, technical questions on Java, JVM, Redis, MySQL, Kafka, and algorithm challenges, along with personal reflections on pacing, preparation, and why concise answers matter.
This tutorial explains Redis Bitmaps, covering their underlying concept, core commands such as SETBIT, GETBIT, BITCOUNT, BITOP, BITPOS, BITFIELD, and demonstrates practical uses like online‑status tracking, daily active‑user counting, Bloom filters, marketing segmentation, collaborative drawing, and handling non‑sequential IDs.
This article explains why Redis achieves high query performance by leveraging in‑memory storage, specialized data structures such as SDS, linked lists, dictionaries, ziplists and skiplists, a single‑threaded event loop with I/O multiplexing, and various optimization techniques that avoid common bottlenecks.
Learn how to leverage OpenResty and Lua to replace traditional Tomcat‑Redis pipelines with direct Nginx caching, compress responses, schedule periodic updates, and configure dynamic URL caching, dramatically reducing latency and improving concurrency for high‑traffic web services.
Undermoon is an open‑source system that implements a self‑managing Redis cluster using the Redis Cluster protocol, offering horizontal scalability, high availability, HTTP‑API management, automatic master‑replica failover, and rapid expansion, with its architecture detailed across metadata storage, coordinator, storage cluster, and chunk components.
This article details how the Beike instant‑messaging system was analyzed, bottlenecks identified, and a series of backend optimizations—including business isolation, increased concurrency, computation reduction, and Redis connection‑pool redesign—were applied to boost 300‑person group‑chat throughput from 15 QPS to over 320 QPS, achieving more than a twenty‑fold performance gain.
This article explains how to improve large file upload experience by using instant (MD5‑based) upload, chunked (slice) upload, and breakpoint‑resume upload, detailing the Redis‑based state management and providing Java backend implementations with RandomAccessFile and MappedByteBuffer.
This article explains why Redis needs high availability, defines failure scenarios, compares common HA solutions, and walks through four deployment patterns—from a single instance to a three‑Sentinel architecture—highlighting their trade‑offs and practical implementation details.
This article explains the concept of delayed queues, outlines typical use cases, compares implementation options, and provides a complete Java Spring Boot example that uses Redis Zset to create, manage, and execute delayed tasks with accompanying code snippets and test results.
The article explains how Undermoon’s Chunk, composed of two proxies and four Redis nodes spread across two machines, forms the core building block for a balanced Redis cluster topology and load‑balancing strategy, detailing its node layout, failover behavior, and simple allocation algorithm.
This article explains the design, implementation, and optimization of a Redis‑based delay queue using SpringBoot, Redisson, and distributed locks, covering use cases, architecture, data structures, job lifecycle, core code snippets, and future improvements for reliability and scalability.
The article explains how the Rust‑based Undermoon project implements the Redis Cluster Protocol using a server‑side proxy, detailing the protocol’s responsibilities, the advantages of this approach over traditional proxies, and providing step‑by‑step commands and code examples for deployment.
This article explains why Redis needs high availability, defines failure scenarios, compares several HA architectures—including single‑instance, master‑slave with one or multiple Sentinel processes, and a three‑node solution with a virtual IP—and provides practical guidance for building a reliable Redis service.
This article explains the concept of distributed locks, compares three implementation methods—database unique indexes, Redis (using SETNX), and Zookeeper—provides detailed SQL table design and Java code examples for acquiring and releasing locks, and discusses reentrancy, lock release timing, and single‑point failure considerations.
This guide walks through deploying a Redis cluster on Kubernetes, covering the challenges of stateful workloads, the use of StatefulSets and Headless Services, configuration of persistence, and step‑by‑step commands to create, initialize, and expose the cluster.
This tutorial walks through creating a Spring Boot lottery application that caches activity, prize, and record data in Redis, covering project setup, Maven dependencies, YML configuration, MyBatis‑Plus code generation, Redis key management, lottery API, event‑driven data loading, stock handling, and asynchronous record saving.
This guide shows how to use the Bitnami Helm chart to quickly deploy a Redis Cluster in a Kubernetes "redis" namespace, customize its values.yaml, retrieve the auto‑generated password, and add RedisInsight with a persistent volume for web‑based management.
This article presents a step‑by‑step guide to building a Spring Boot lottery application that leverages MyBatis‑Plus and Redis for caching activity, prize, and record data, covering project setup, dependencies, configuration, code generation, Redis integration, lottery logic, and stock management.
This article compares the Java Redis clients Jedis, Redisson, and Lettuce, explains their distinct features and thread‑safety models, and provides detailed Spring Boot configuration and code examples for using RedisTemplate, RedissonClient, and annotation‑based caching to implement distributed locks, hash operations, and cache management.
This guide explains how to implement fast, reliable large file uploads in Java using chunked (slice) uploads, instant (秒传) transfer via MD5 deduplication, and breakpoint resume, detailing Redis state tracking, RandomAccessFile and MappedByteBuffer server‑side code, and practical deployment considerations.
This article explains the challenges of at‑least‑once delivery in distributed message middleware like RocketMQ, examines simple and concurrent deduplication techniques, and presents both transaction‑based and non‑transactional exactly‑once solutions using database tables or Redis, along with practical Java code examples.
This article explains Redis’s original single‑threaded event‑driven architecture, its performance bottlenecks, and how versions 4.0 and 6.0 introduced Lazy Free and multithreaded I/O mechanisms—including asynchronous key deletion, background freeing, and I/O thread pools—to improve scalability while outlining their implementation details, limitations, and comparisons with Tair.
Vivo’s self‑built database and storage platform evolved from a fragmented 2018 system into a two‑layer DaaS solution offering unified management, automated security, high availability, and cost‑effective scaling across MySQL, Redis, TiDB, object storage, and future autonomous services within a hybrid‑cloud architecture.
This article explains how to implement fast (MD5‑based) upload, chunked upload, and resumable (break‑point) upload for large files in Java, detailing the Redis‑based status tracking, server‑side file handling with RandomAccessFile or MappedByteBuffer, and the required front‑end and back‑end coordination.
This article outlines the first, second, and HR interview rounds for a CCBank online position, detailing over thirty technical and behavioral questions covering Elasticsearch, Redis, Spring Cloud, JVM, data structures, performance optimization, and candidate queries, followed by a concise summary.
This article explains how to select Redis data structures such as Bitmap, HyperLogLog, Set, List, Sorted Set, and Hash to efficiently handle massive statistical scenarios like user login status, UV counting, ranking, and set aggregation, while providing concrete command examples and best‑practice recommendations.
This article examines common delayed (scheduled) message implementations in distributed systems, comparing external storage approaches using databases, RocksDB, and Redis, as well as built-in solutions in open-source MQs like RocketMQ, Pulsar, and QMQ, and discusses their advantages, drawbacks, and design considerations.
This article explains why distributed locks are needed beyond Java's JVM‑level locks, compares three common implementation approaches—database unique indexes, Redis SETNX, and Zookeeper ephemeral sequential nodes—and provides concrete SQL and Java code examples with analysis of re‑entrancy, release timing, and single‑point risks.
This article explains the business scenarios that require delayed processing, compares several existing delayed‑queue solutions, and details a Redis‑backed delayed‑queue design—including its data structures, message format, multi‑node deployment, and runtime workflow—providing practical guidance for building scalable backend systems.
This article demonstrates how to use Redis key‑expiration notifications together with Spring Boot to automatically close orders that remain unpaid after a configurable timeout, eliminating inefficient polling and providing a clean backend solution for order lifecycle management.
This article introduces essential Redis operational tools, covering the INFO command for monitoring, Prometheus‑based redis‑exporter visualization, the Redis‑shake data migration utility, Redis‑full‑check consistency verification, and the CacheCloud platform for comprehensive cluster management.
This article examines whether Redis can serve as a reliable message queue by exploring its List, Pub/Sub, and Stream data structures, detailing their usage, advantages, drawbacks, and how they compare to dedicated queue middleware.
This article explains how to implement a distributed lock using Redis by setting a unique key-value pair, handling expiration, addressing lock timeout issues, and leveraging Redisson's watchdog mechanism with automatic renewal, including detailed code examples for tryLock, subscription, and renewal processes.
The article details a week‑long investigation of unresponsive APIs in a sandbox environment, tracing the root cause to Redis connection pool blocking in a Spring Boot service, and presents step‑by‑step debugging, code analysis, configuration fixes, and best‑practice recommendations for proper Redis usage.
The article explains a typical payment‑timeout scenario and compares four backend solutions—database polling, Java DelayQueue, Redis sorted set, and RabbitMQ TTL with dead‑letter exchange—detailing their mechanisms, advantages, and drawbacks for implementing delayed task cancellation.
This article explains how to implement a Role‑Based Access Control (RBAC) permission model in Spring Cloud Gateway, covering the design, loading URL‑role mappings into Redis, customizing UserDetailsService, and integrating OAuth2.0 with code examples and database schema.
This article explains how to protect a Spring Boot API from traffic spikes by applying common rate‑limiting algorithms, configuring Redis for distributed throttling, writing Lua scripts for atomic counters, and integrating a custom @Limit annotation with AOP to enforce limits across a cluster.
Redis distributed locks rely on a key-value marker with unique client IDs, expiration times, and careful release checks; the article explores lock timeout challenges, automatic renewal via watchdog mechanisms, Redisson's tryLock implementation, Lua‑based renewal scripts, and best practices to avoid deadlocks and resource waste.
This article explains how to implement a distributed lock using Redis, discusses challenges with lock expiration, describes automatic renewal techniques such as watchdog mechanisms, and provides detailed Java code examples of Redisson's tryLock and renewal processes.
The article explains skip list fundamentals, shows how probabilistic multi‑level indexing yields O(log n) search and O(n) space, provides a complete Go implementation with insertion, deletion and search functions, compares Redis’s enhanced skip list used in sorted sets, and answers common design questions.
This article explains the concept of delayed tasks, compares them with scheduled tasks, and presents five implementation approaches—database polling, JDK DelayQueue, time‑wheel algorithm, Redis sorted sets, and message‑queue based delay queues—detailing their code, advantages, and drawbacks.
In distributed e‑commerce systems, achieving Redis cache consistency requires invalidating stale entries via reliable DB change detection (e.g., binlog listeners) and optionally using versioned writes that only update when newer, combined with safeguards like TTLs, double deletes, and business‑specific designs to mitigate race conditions and ensure eventual consistency.
This tutorial explains how to achieve automatic API idempotency in Spring Boot by leveraging Redis for token storage, creating a custom @AutoIdempotent annotation, configuring an interceptor to validate tokens, and providing complete code examples and test cases to prevent duplicate operations.
The article details a step‑by‑step investigation of a Spring‑based backend where API requests hung due to Redis connection pool misconfiguration, showing how thread dumps, jstack, and Arthas were used to pinpoint blocking in Jedis pool acquisition, and provides corrected configuration and proper connection handling recommendations.
