This article explains the common reasons why Redis performance degrades—such as network latency, high‑complexity commands, big keys, concentrated expirations, memory limits, fork overhead, huge pages, AOF settings, CPU binding, swap usage, and memory fragmentation—and provides detailed optimization and troubleshooting steps to restore low latency.
The article explains that Redis achieves exceptional performance through four main factors—its in‑memory storage, optimized data structures, a single‑threaded architecture, and non‑blocking I/O—detailing how each contributes to speed and efficiency.
The article explains why using the KEYS command on a large Redis dataset can block the server, introduces the SCAN command as a non‑blocking alternative with cursor‑based iteration, and provides usage examples and best‑practice tips for safely listing prefixed keys.
This article explains the concept of distributed global unique identifiers, outlines four essential characteristics of such IDs, and provides a detailed comparison of five generation schemes—UUID, database‑generated IDs, Redis, Zookeeper, and Snowflake—highlighting their advantages, drawbacks, and suitable use cases.
This article details the end‑to‑end high‑availability architecture—including dual‑center Elasticsearch clusters, Redis caching with distributed locks, and a dual‑center MySQL partitioned setup—that enables a membership platform serving billions of users to sustain massive traffic while ensuring data consistency and rapid recovery.
This article provides a detailed backend interview cheat‑sheet covering Redis data structures and performance issues, MySQL indexing and transaction principles, operating‑system process and thread concepts, TCP/UDP networking differences, and common Java garbage‑collection algorithms, all illustrated with examples and code snippets.
This article explains the importance of rate limiting in microservice systems, compares various governance frameworks such as Dubbo and Spring Cloud, and provides detailed implementations using Guava, Sentinel, Redis+Lua scripts, and a custom Spring Boot starter, complete with code examples and deployment steps.
This article walks through the importance of rate limiting in microservice architectures, compares Dubbo, Spring Cloud, and gateway approaches, explains token‑bucket, leaky‑bucket and sliding‑window algorithms, and provides step‑by‑step AOP implementations using Guava, Sentinel, and Redis with full code samples.
This article explains the Redis Sorted Set data type, its underlying storage mechanisms (listpack and skiplist + dict), the internal structures and algorithms, and demonstrates how to build a real‑time game leaderboard using ZADD, ZRANGE and ZREVRANK commands.
This article explains why API idempotency is crucial in real‑world systems, outlines which operations need it, discusses the trade‑offs of adding idempotency, and presents four practical implementation patterns—including unique primary keys, optimistic locking, anti‑duplicate tokens, and downstream sequence numbers—complete with SpringBoot code examples.
This article explains how Redis stores set and sorted‑set objects using dual encodings—intset vs hashtable for sets and skiplist vs ziplist for sorted sets—detailing their internal structures, upgrade mechanisms, configuration thresholds, and the essential commands for manipulating these data types.
This guide explains why distributed locks are needed, how to correctly acquire and release them, compares Redis and Zookeeper implementations—including single‑master, Redlock, and Zookeeper approaches—and offers practical recommendations for ensuring atomicity, preventing deadlocks, and protecting shared resources in production environments.
This guide walks through the background, implementation, usage examples, and step‑by‑step open‑source contribution process for adding Redis cache expiration and memory‑limit chaos experiments to the ChaosBlade platform, including code snippets, Git workflow, and testing procedures.
This article explains how to design and implement a microservice permission system using Apache Shiro, Spring Boot, Dubbo, and Redis, covering challenges of integrating Shiro with gateways, shared session management, custom Cache and SessionDAO implementations, and provides complete code examples and configuration details.
This article dissects ten common traps when implementing Redis distributed locks—such as non‑atomic setnx/expire, missing expirations, lock release ordering, non‑re‑entrancy, master‑slave replication issues, and the Redlock algorithm—while providing concrete code examples and practical remedies.
A service‑wide avalanche occurred when a Redis 3.x master‑slave failover coincided with Jedis’ default 2‑second connection timeout and six retry attempts, causing up to 60‑second latencies; adjusting connectionTimeout, soTimeout to 100 ms and reducing maxAttempts to two limited latency to about one second and prevented cascade failures.
This article explains the typical reasons why a Redis instance becomes slow—such as high latency, expensive commands, big keys, concentrated expirations, memory limits, fork overhead, huge pages, AOF settings, CPU binding, swap usage, and memory fragmentation—and provides concrete diagnostics and optimization steps to resolve each issue.
This article provides a detailed interview recap covering Redis in‑memory database concepts, MySQL transaction and indexing fundamentals, OSI network layers, operating‑system memory layout, Java versus Go differences, and key takeaways for future study, all aimed at backend developers.
This article provides Python code examples for using Redis as a database—including connection, basic CRUD, list and sorted set operations, key expiration, publishing/subscribing, distributed locking, and caching—followed by a detailed comparison of Redis and MySQL across data model, persistence, query capabilities, performance, scalability, and transaction support.
Learn how to use Redis with Python for basic CRUD, list and sorted‑set manipulation, key expiration, Pub/Sub messaging, distributed locks, and caching, and understand when Redis is a suitable complement or alternative to MySQL based on data model, performance, and transaction needs.
This article explains how the WeChat multi‑dimensional monitoring platform, which processes billions of data points daily, identified performance bottlenecks in its Druid‑based data layer and applied sub‑query splitting, Redis caching, and sub‑dimension tables to achieve over 85% cache hit rate and bring average query time down to around 100 ms.
The article describes how a membership system serving billions of users across multiple platforms achieves high performance and high availability through dual‑center Elasticsearch clusters, traffic‑isolated three‑cluster ES architecture, Redis caching with distributed locks, dual‑center MySQL partitioning, and fine‑grained flow‑control and degradation strategies.
This article walks through Redis's evolution from a simple single‑instance cache to a highly available, high‑performance cluster, explaining persistence mechanisms (RDB, AOF, hybrid), master‑slave replication, Sentinel automatic failover, and sharding strategies with concrete examples and trade‑offs.
Ximalaya’s KV storage progressed from a simple Redis master‑slave setup to client‑side sharding, then adopted Codis clustering for elastic scaling, integrated Pika’s disk‑based store with cold‑hot separation, introduced KV‑blob separation, fast‑slow command pools, second‑level expansion, ehash fields, large‑key circuit breaking, multi‑active data‑center replication, and now targets cloud‑native deployment, advanced features, and AI‑driven operations.
This article explains why rate limiting is essential for distributed systems, compares it with circuit breaking and smoothing, outlines a generic limiting workflow, and provides complete Java implementations of four algorithms—fixed window, sliding window, leaky bucket, and token bucket—using Redis and Redisson.
This article outlines the design goals, architecture, and implementation details of a high‑performance distributed caching solution for Spring Boot applications, combining Redis as a first‑level cache with Caffeine as a second‑level cache, and provides configuration, usage examples, and future enhancement plans.
The article explains Redis’s set and sorted‑set data structures, detailing how sets use intset or hashtable encodings and how sorted sets employ skiplist or ziplist encodings, including the conditions for each encoding, the underlying C structs, upgrade processes, and common Redis commands.
This article explains how large hotel‑booking platforms such as Airbnb, Booking.com and OYO use a micro‑service architecture—including hotel management, customer search/booking, and view‑booking services—combined with load balancers, Kafka, Elasticsearch, Redis, Cassandra and Hadoop to achieve a seamless, high‑throughput booking flow.
This article explains why rate limiting is needed in high‑traffic backend services, outlines common methods such as token‑bucket with Guava, Redis‑based counters, Nginx configuration, and thread‑pool limits, and provides complete Java code examples for implementing each approach.
This article explains Redis as an in‑memory key‑value NoSQL database, outlines its data types and high‑availability features, and details common cache problems such as avalanche, penetration and breakdown along with practical mitigation strategies, followed by interview commentary on how to discuss Redis effectively.
Explore comprehensive strategies for implementing rate limiting in Java microservice architectures, covering Dubbo and Spring Cloud governance, token bucket, semaphore, Sentinel, Redis+Lua, and custom Spring Boot starter solutions, complete with code samples, configuration steps, and performance testing guidance.
Redis implements approximate LRU and LFU eviction policies by sampling keys and using a compact 24‑bit field to store timestamps and counters, where LRU evicts the least recently accessed items and LFU evicts those with low, decay‑adjusted access frequency, each with trade‑offs for different workloads.
This article explains how to achieve efficient pagination and multi‑condition fuzzy searches in Redis by leveraging Sorted Sets for ordered paging, Hashes with HSCAN for pattern matching, and combining both techniques with caching and expiration strategies to optimize performance.
This article explains how to install and configure Redis, connect to it using Python's redis library, and demonstrates storing and retrieving various data structures—including strings, hashes, lists, sets, and sorted sets—followed by proper disconnection, providing a practical guide for Python developers.
This guide walks through building a SpringBoot architecture that updates MySQL first, deletes Redis cache asynchronously via Canal and RabbitMQ, resolves common Canal meta‑data mismatches, and configures manual RabbitMQ acknowledgments to guarantee reliable cache invalidation.
This guide walks through essential Redis operations, covering real‑time monitoring with the INFO command and Prometheus‑compatible exporters, data migration using Redis‑shake, consistency verification via Redis‑full‑check, and comprehensive cluster management with CacheCloud, providing practical tools for reliable Redis administration.
This article provides an in‑depth tutorial on designing and implementing various rate‑limiting strategies—such as token bucket, leaky bucket, and sliding window—in Java microservice architectures, with practical code examples using Guava, Sentinel, Redis+Lua, and a reusable Spring Boot starter.
This article explains why rate limiting is critical for microservice architectures, reviews common limiting algorithms, and provides step‑by‑step implementations using Dubbo, Spring Cloud, Guava token‑bucket, Sentinel, Redis+Lua, and a reusable Spring Boot starter with custom annotations and AOP.
This article explains how to protect high‑concurrency Java applications using rate‑limiting techniques, covering basic algorithms such as counter, leaky‑bucket and token‑bucket, demonstrating a single‑node implementation with Guava’s RateLimiter and custom annotations, and showing a distributed solution based on Redis and Lua scripts.
This article explains how to implement API rate limiting in PHP by connecting to Redis, defining a RateLimiter class with limitRequests method, and demonstrates usage to restrict requests per time window, while detailing the underlying logic and code examples.
This guide explains why Logstash may import fewer rows from a large PostgreSQL table into Elasticsearch, and provides two practical solutions—ID comparison via shell scripts and accelerated comparison using Redis—to quickly identify and resolve data inconsistencies.
This article explains the Bloom filter data structure, its space‑efficient probabilistic nature, common scenarios such as cache‑bypass and URL deduplication, and provides practical Java Guava and Redis module implementations with code examples.
This article examines the common consistency challenges between Redis cache and MySQL, explains why strict consistency is hard to achieve, and presents four practical approaches—cache‑first delete, delayed double delete, DB‑first update with lock, and MQ‑based retry—to minimize inconsistency windows and ensure reliable data synchronization.
This article shares a detailed account of a Tencent testing engineer interview, covering self‑introduction, Go knowledge, Redis performance, distributed lock implementation, semaphore mechanics, RPC vs HTTP, concurrency usage, Git security practices, Linux commands, and a few brain‑teaser and algorithm questions.
This article demonstrates how to use Redis as a database with Python code examples for basic operations, advanced features like pub/sub and distributed locks, and compares Redis to MySQL across data model, persistence, query capabilities, performance, scalability, and transaction support.
This article explains how to use Redis sets, sorted sets, lists, bitmaps, and HyperLogLog to perform aggregation, ordered queries, binary state tracking, and cardinality estimation for large‑scale applications such as sign‑in systems, e‑commerce comments, and social‑network friend lists.
This article examines various order timeout handling techniques in e‑commerce, detailing JDK’s DelayQueue, RabbitMQ delayed messages, RocketMQ timer‑wheel, Redis expiration listeners, and distributed batch processing with SchedulerX, comparing their advantages, drawbacks, and suitability for different latency and scale requirements.
This article reviews the latest Mica releases, highlighting version compatibility with Spring Boot, new Redis and XSS features, core utility enhancements, bug fixes, and upcoming support for Spring Boot 3.1.x, providing developers with a concise overview of the library’s recent advancements.
Aeraki Mesh 1.3.0 (code‑named Dragonboat) adds Istio 1.16.x compatibility, introduces connection multiplexing, enables MetaProtocol routing on gateways, expands Dubbo service governance options, provides Redis traffic management, and migrates official images to GitHub Packages, with a detailed changelog and configuration examples.
This article explains Redis’s persistence mechanisms—including RDB snapshots, AOF append‑only logs, and the hybrid approach—detailing how to enable each method, their commands, configuration options, performance trade‑offs, rewrite processes, and recovery procedures to help you choose the right strategy for reliable data durability.
A detailed investigation reveals that excessive Redis latency was caused by misconfigured JedisPool parameters, leading to long connection wait times and frequent connection churn, and shows how adjusting pool settings dramatically improved response times and overall system performance.
This article analyzes why Redis's increment operation returns null when used inside a Spring @Transactional method with transaction support enabled, details the debugging steps, experiments, and presents two practical solutions to prevent the issue.
This article explains Redis Pub/Sub fundamentals, demonstrates channel‑based and pattern‑based subscription with command‑line examples, dives into the internal data structures that power them, and shows how to integrate Redis Pub/Sub in Spring Boot using Redisson for lightweight messaging.
This article explains how to implement efficient pagination and multi‑condition fuzzy queries in Redis using Sorted Sets and Hashes, outlines the underlying commands, discusses combining both techniques, and offers optimization strategies such as expiration handling to maintain cache performance.
This article explains three progressively refined approaches to caching paginated list data—directly caching whole pages, caching individual object entries after querying ID lists, and caching both ID lists and object entries—highlighting trade‑offs, implementation details, and performance considerations using Java and Redis.
This article explains how Redis Sentinel provides automated failover, details the replication process that keeps master and slave data synchronized, and compares the roles of master‑slave replication, Sentinel, and Redis Cluster in achieving high availability and horizontal scalability.
This article explains how to limit the number of requests an API endpoint can receive within a specified time window by creating a @RequestLimit annotation, a Redis‑backed interceptor, and the necessary Spring Boot configuration and controller examples.
Redis is an open‑source, in‑memory key‑value store written in C that uses hash tables, various data structures, and a single‑threaded event loop to achieve high performance, and this article explains its basic types, persistence mechanisms (AOF and RDB), replication, sentinel failover, and cluster sharding.
This article explains how to set up and use ThinkPHP 6’s email queue functionality, covering Redis installation, queue configuration, creating a SendMailJob class, pushing jobs to the queue, and running the queue listener to improve email sending performance and reliability.
This guide demonstrates how to create a powerful web-based Redis CRUD interface by integrating Django for the backend API, MySQL for metadata storage, and Vue for a responsive frontend, covering environment setup, project scaffolding, model and view implementation, routing, and deployment steps.
This article provides a comprehensive overview of Redis's ten data types—including strings, hashes, lists, sets, sorted sets, streams, hyperloglog, geospatial, bitmap, and bitfield—detailing their structures, encoding mechanisms, application scenarios, and underlying implementation details with code examples.
This article explains why publicly exposed APIs are vulnerable, describes the concepts of anti‑tampering and anti‑replay protection, and provides a complete Spring Boot implementation—including request signing, timestamp and nonce validation, and Redis‑based replay detection—to safeguard API endpoints.
This article details the design and implementation of a highly available, high‑performance membership platform serving over a billion users, covering Elasticsearch dual‑center clusters, traffic‑isolated clusters, deep ES optimizations, Redis caching strategies, MySQL dual‑center partitioning, seamless migration, and fine‑grained flow‑control and degradation mechanisms.
The article details how WeChat's multi‑dimensional metric monitoring platform, which handles billions of data points per minute, reduced average query latency from over 1000 ms to around 140 ms and achieved over 85% cache hit rate by analyzing query behavior, redesigning the data layer architecture, splitting queries into sub‑queries, adding Redis caching, and introducing sub‑dimension tables.
The article examines the newly released open‑source memory cache Dragonfly, compares its performance and architecture against Redis through detailed benchmark tests, discusses Redis's response and design principles, and concludes with insights on future directions for in‑memory data stores.
The article explains the structural differences between B+ trees and skip lists, compares their read‑write performance in MySQL and Redis, and shows why MySQL prefers B+ trees while Redis adopts skip lists for its in‑memory ZSET implementation.
Bilibili’s relationship‑chain service, which handles follows, blacklists, whispers and mutual follows, migrated from a single sharded MySQL instance to an internal distributed KV store and introduced a three‑tier cache (memcached, Redis and a Bloom filter) plus automated hotspot routing, achieving near‑million QPS, lower latency, and preparing for multi‑tenant reuse.
This guide introduces a lightweight Redis graphical management tool that offers full CRUD support for all Redis data types, outlines its Spring Boot backend and Vue frontend stack, provides step‑by‑step installation and Docker deployment instructions, and details current features, UI screenshots, and future development plans.
This article explains Redisson's distributed lock features, comparing it with Jedis and Lettuce, detailing lock acquisition, reentrancy, Lua scripts, watchdog renewal, and the implementation of fair locks using Redis lists and sorted sets, with code examples and architectural insights.
This article explains how to set up Docker on Windows, integrate it with IntelliJ IDEA, build and run a Spring Boot application inside a container, and connect the containerized service to a Redis instance, including configuration, Maven plugin setup, and troubleshooting steps.
This article examines synchronous versus asynchronous microservice communication, outlines the benefits of async messaging, and compares three popular message brokers—RabbitMQ, Kafka, and Redis—by evaluating their scale, persistence, consumer models, and ideal use cases to help developers choose the right solution.
This article describes the background, design goals, core technical solution, versioned implementation process, and performance improvements of a real‑time final‑price calculation module that integrates product price, coupons, and promotions to deliver up‑to‑date pricing for e‑commerce users.
This article explains the inner workings of Redis Hashes, covering the dict and listpack storage mechanisms, memory‑optimizing conditions, rehashing strategies, and the underlying C structures that enable fast O(1) field lookups and efficient memory usage.
This article explains Redis 6 client‑side caching, compares its default and broadcast tracking modes, outlines performance advantages, clarifies common misconceptions, and provides step‑by‑step telnet demos—including code snippets and image illustrations—to help developers implement the feature in real projects.
This article details the design and implementation of a highly available, high‑performance membership system that serves over a billion users, covering Elasticsearch dual‑center HA, traffic‑isolated clusters, Redis caching, MySQL dual‑center partitioning, seamless migration, and refined flow‑control and degradation strategies.
This article compiles a series of practical SpringBoot implementations—including AES encryption with Vue, a Netty‑based TCP client that parses hex data into MySQL, multiple Redis integration patterns, strategy‑factory designs, custom annotations for rate limiting, global exception handling, and scheduled tasks—each linked to detailed examples.
This guide walks through setting up MySQL master‑slave replication in Docker, configuring the instances, testing synchronization, then deploying Canal and integrating it with a Spring Boot 2.7.8 application to capture database changes and push them into Redis.
This article introduces a lightweight Redis graphical management tool, outlines its backend (Spring Boot) and frontend (Vue) architecture, provides step‑by‑step installation instructions including Docker usage, describes current features like multi‑user management and permission control, and shares future development plans with screenshots and the project repository link.
This document introduces a lightweight Redis graphical management tool, outlines its Spring Boot backend and Vue frontend architecture, provides step‑by‑step installation instructions including Docker deployment, details recent feature enhancements such as multi‑user support and permission control, and mentions future plans for clustering and sentinel modes.
This article demonstrates how to ensure data consistency between Redis and MySQL using Python by implementing a synchronization mechanism that monitors MySQL binlog changes and updates Redis cache in real-time.
This article explains the causes of Redis cache penetration, avalanche, and thundering herd, and provides practical mitigation strategies such as caching null values, using white‑lists, Bloom filters, pre‑warming hot keys, staggered expirations, multi‑level caching, and lock mechanisms.
This tutorial explains how to build a PHP-based single sign‑on system using a main login application, a shared authentication service backed by MySQL and Redis, and multiple external applications, providing complete code examples for database setup, login handling, session sharing, and logout.
This article explains why Redis offers transactions, introduces the five core transaction commands, demonstrates practical examples including WATCH and CAS optimistic locking, and discusses error handling and the reasons Redis does not support rollback.
This article compares B+ trees and skip lists, explaining their structures, insertion and search complexities, and why MySQL chooses B+ trees for disk‑based indexing while Redis prefers skip lists for in‑memory sorted sets, highlighting trade‑offs in read/write performance and I/O costs.
This article explains why a custom business rate‑limiting component is needed, outlines the required rules, chooses Redis + Lua for counting, and provides complete Kotlin/Spring code—including a Detect annotation, Lua scripts, and usage examples—to enforce daily, hourly, and combined limits with real‑time adjustments.
This article provides a comprehensive English guide to Redis’s ten core data types—including strings, hashes, lists, sets, sorted sets, streams, hyperloglog, bitmap, and bitfield—detailing their definitions, typical application scenarios, underlying storage mechanisms, conversion rules, and sample Redis commands with code examples.
This article explains the three major Redis cache issues—penetration, avalanche, and breakdown—detailing their causes, real‑world examples, and practical mitigation strategies such as caching null values, using Bloom filters, staggering TTLs, multi‑level caching, and lock mechanisms to protect database stability.
To protect the outbox Redis cluster from extreme read amplification during hot events, the service adds a resident local cache for hot creators’ latest posts, using a threshold‑based list, change‑broadcast updates, and checksum verification, which achieved over 55% cache hits and cut peak Redis load by roughly 44% and CPU usage by 37%.
Dragonfly, an open‑source C/C++ memory cache system released by a former Google and Amazon engineer, claims superior speed and lower memory usage than Redis, but Redis’s team counters with benchmark results showing higher throughput and discusses architectural differences, scaling strategies, and future feature considerations.
This article explains how to design and use various client‑side, CDN, and server‑side caching techniques—including HTTP Cache‑Control, Redis data structures, cache consistency patterns, and mitigation of cache penetration, breakdown, and avalanche—to improve performance and reliability of high‑traffic distributed applications.
This article introduces Redis HSET, covering its definition, suitable use cases, common commands for creating, reading, updating, and deleting hash fields, as well as the underlying encoding mechanisms (ziplist vs hashtable) and best practices, including the deprecation of HMSET.
This article presents a comprehensive engineering case study of a high‑traffic membership system, detailing the dual‑center Elasticsearch high‑availability design, traffic‑isolated three‑cluster ES architecture, Redis caching strategy, dual‑center MySQL partitioning and migration plan, abnormal member relationship governance, and future fine‑grained flow‑control and downgrade policies.
This article outlines a comprehensive Redis training program covering fundamentals, distributed lock implementation, high‑availability mechanisms, clustering, persistence strategies, and practical projects such as Bloom filter integration and flash‑sale systems, providing learners with the knowledge to master advanced Redis usage.
This guide demonstrates how to integrate Spring Boot with Redis to implement personal search history, hot search ranking, and sensitive word filtering using a DFA algorithm, including Maven dependencies, YAML configuration, utility classes, service implementations, and controller endpoints with full Java code examples.
This article examines various distributed ID generation techniques—including UUID, database auto‑increment, segment mode, Redis INCR, Snowflake, Meituan Leaf, Baidu UidGenerator, and Didi TinyID—detailing their principles, advantages, drawbacks, and code examples to help developers select the most suitable solution for their systems.
This comprehensive guide explains Redis fundamentals, covering NoSQL basics, the five core data types and their internal storage mechanisms, persistence options (RDB and AOF), replication, Sentinel failover, publish/subscribe patterns, and common cache pitfalls such as penetration, breakdown, and avalanche, providing practical insights for developers and architects.
This article explains how to set up Redis monitoring using Prometheus, covering installation of Redis Exporter, Prometheus configuration, Grafana visualization, and alert rule creation, providing step‑by‑step commands and guidance to ensure high availability and performance of Redis instances.
In this article we walk through five successive optimizations—adding Redis caching, scheduling asynchronous jobs, introducing local Caffeine cache, enabling Nginx GZip compression, and slimming Redis data—to dramatically improve the performance of a SpringBoot‑Thymeleaf classification‑tree query, reducing response size from 1 MB to 100 KB and raising QPS from 100 to over 500.
This article explains how to reduce database pressure by counting likes and views in Redis, designing a hash‑based storage structure, synchronizing cached data to MySQL with Quartz, and marking articles as liked for each user, while also outlining current limitations.
Redis provides two primary persistence mechanisms—RDB snapshots that capture the dataset at specific moments and AOF write‑ahead logs that record every write command—along with a hybrid model combining both, each with distinct strategies, advantages, drawbacks, and configuration options to ensure data durability and fast recovery.
