Redis 7.4 will be released under a dual‑license model (RSALv2 and SSPLv1), ending its long‑standing BSD‑3 clause terms, sparking controversy among open‑source communities, cloud providers, and users while prompting discussions of alternatives like KeyDB, Garnet, and Dragonfly.
This article explains Redis's memory model—including memory statistics, allocation, internal data structures, object types, and encoding—while showing practical examples for estimating usage, optimizing consumption, and troubleshooting fragmentation in high‑concurrency environments.
This article outlines seven typical cache issues—including cache penetration, breakdown, avalanche, data inconsistency, large keys, hot keys, and low hit rates—explains their causes, and provides practical solutions such as parameter validation, Bloom filters, locking, expiration randomization, key sharding, and cache warming to improve system reliability and performance.
This article presents a complete Netty TCP client demo for an IoT project, explaining the project background, architecture, modules, business flow, and detailed Java code—including a local queue, thread pool, client initialization, handler logic, and test controllers—along with deployment notes and source links.
This article explains Redis Cluster's sharding architecture, demonstrates how to configure multiple Redis nodes on different ports, shows commands for creating and testing the cluster, and illustrates failover behavior, highlighting its scalability and high‑availability advantages over Sentinel mode for large‑scale data workloads.
This article explains how Redisson leverages Redis to implement distributed locks, detailing the underlying Lua scripts, lock acquisition and release processes, the watch‑dog mechanism for automatic lease renewal, and provides Java code examples for integrating and testing the lock functionality.
This article provides a comprehensive introduction to Redis, covering its core features, supported data types, common caching use cases in Spring Boot, typical cache‑related problems such as consistency, avalanche, penetration and breakdown, as well as performance reasons, eviction policies, persistence mechanisms, master‑slave replication, and Sentinel high‑availability architecture.
This guide explains why Redis Labs changed its license, introduces the community‑driven Valkey fork, provides step‑by‑step commands to download, compile, install, configure as a systemd service, test with clients, and benchmark performance against Redis.
This article introduces RediSearch, a Redis module for full‑text search, outlines its extensive feature set, compares its indexing and query performance with Elasticsearch, and provides detailed installation steps and command‑line examples for creating, querying, updating, and managing indexes.
An in‑depth investigation of a Redis timeout problem in a Spring Cloud backend reveals that the Lettuce client’s Netty EventLoop threads become blocked by synchronous Pub/Sub callbacks, causing socket buffer buildup, and proposes solutions such as increasing I/O threads or off‑loading processing to avoid the issue.
This article warns developers about several Redis commands—such as KEYS, FLUSHDB/FLUSHALL, SAVE/BGSAVE, and DEBUG SLEEP—that can cause performance degradation, data loss, or security issues in production environments, and encourages careful usage to avoid serious operational risks.
The article explains why Redis stores a single logical collection using two underlying data structures—intset and hashtable for sets, skiplist and ziplist for sorted sets—detailing the encoding rules, upgrade process, example commands, and how this dual‑storage balances O(1) lookups with ordered range queries without duplicating data.
A Docker image upgrade reduced Netty EventLoop threads, causing a Pub/Sub listener’s blocking Future.get() to stall one thread, fill a Redis cluster connection’s receive buffer and trigger widespread Redis timeouts in the custom Lettuce cache framework, which were eliminated by increasing I/O threads or making the callback asynchronous.
This article analyzes how Redis pipelining works, compares Lettuce and Redisson clients under Spring Boot, presents performance benchmarks for batch inserts, discusses practical pitfalls, and explains the internal implementation of pipeline support in both clients to help developers avoid common issues.
This article recounts how a volunteer registration platform was redesigned to meet extreme concurrency demands—handling up to 40k QPS and 20k TPS—by evaluating MySQL limits, adding Redis caching, integrating RocketMQ, applying fault‑recovery mechanisms, and iteratively optimizing through multiple load‑testing rounds.
This article summarizes a Didi second‑round interview covering Redis persistence mechanisms, data structures, memory overflow and leak concepts, thread safety, process vs thread differences, deadlock prevention, TCP/UDP distinctions, and a couple of hand‑written algorithm problems, providing detailed explanations and code examples.
The article evaluates common delayed‑task implementations—Redis expiration listeners, RabbitMQ dead‑letter queues, time wheels, and Redisson DelayQueue—explaining their drawbacks, proper use cases, and recommending reliable message‑queue solutions for e‑commerce order‑closing scenarios.
This article explains Redis's two persistence mechanisms—AOF and RDB—detailing how they work, their write‑back strategies, rewrite process, snapshot principles, and the hybrid approach, and provides practical guidance on choosing the right configuration to balance performance and reliability.
This course provides an in‑depth exploration of core backend concepts—including processes, socket programming with TCP/IP, Nginx‑FASTCGI communication, host‑to‑network byte order conversion, and Redis fundamentals—to equip developers, system administrators, and network engineers with comprehensive backend development skills.
This article explains how to design ranking systems for games and social apps, compares database sorting and client‑side approaches, and provides step‑by‑step Java code for implementing Redis‑based counters, leaderboards, performance optimizations, and real‑world use cases.
This article compiles a set of Java interview questions covering the differences between ArrayList and LinkedList, the internal workings and resizing of HashMap, concurrency primitives like synchronized and ReentrantLock, JVM memory layout, garbage‑collection algorithms, reference types, and Redis persistence and distributed‑lock mechanisms.
This article explains how to prevent duplicate submissions in Java backend services by implementing request debouncing using Redis caching and Redisson distributed locks, detailing the design of request lock annotations, unique key generation, and practical code examples for robust idempotent APIs.
Redis achieves its remarkable speed through in‑memory storage, an efficient hash table design, single‑threaded execution that eliminates context switches, epoll‑based I/O multiplexing, progressive rehashing, and cached timestamps, all of which together enable near O(1) operations and minimal latency.
This article explains three Redis‑based rate‑limiting approaches—using SETNX for simple counters, ZSET for sliding‑window limits, and a token‑bucket algorithm with List—providing Java code examples and discussing their advantages and drawbacks.
This article explains why Redis, an in‑memory database, needs persistence, compares the four official persistence options (RDB, AOF, RDB+AOF, and no persistence), details their configurations, performance trade‑offs, and introduces the hybrid RDB+AOF method for balanced durability and speed.
This article explains the concept, necessity, key characteristics, and step‑by‑step Java implementation of Redis‑based distributed locks, providing code examples and guidance for ensuring mutual exclusion, reliability, and performance in backend systems.
When faced with a business requirement to filter up to 100 000 records from a pool of tens of millions and then sort and de‑duplicate them, this article explores four technical solutions—multithreaded ClickHouse pagination, Elasticsearch scroll‑scan, a combined Elasticsearch‑HBase approach, and RediSearch with RedisJSON—detailing their design, implementation, performance testing, and trade‑offs.
This article explains Redis Sentinel’s role in providing monitoring, notifications, automatic failover, and configuration updates to achieve high availability, detailing its heartbeat mechanism, master‑down detection, leader election, failover selection criteria, and the trade‑offs of using this solution.
This article explains why Redis achieves exceptional speed by leveraging in‑memory storage, an efficient hash‑table data structure, a single‑threaded event loop, epoll I/O multiplexing, incremental rehashing, and cached timestamps, providing a comprehensive view of its performance optimizations.
Bloom filters are space‑efficient probabilistic structures that answer “definitely not” or “maybe” membership queries, with a controllable false‑positive rate derived from bit array size, element count, and hash functions, and can be implemented via Guava’s Java library, Redisson’s Redis wrapper, native Redis modules, or custom bitmap code, dramatically reducing memory usage and latency in large‑scale systems such as URL deduplication or user‑product checks.
The article examines how Spring Boot’s Lettuce and Redisson clients implement Redis pipelining, explains the batch‑request principle, benchmarks 100 000 set inserts showing pipeline speeds of 0.5–1.4 seconds versus 162 seconds for single commands, demonstrates Spring Data Redis callback usage, warns of high‑cost command pitfalls, and details Redisson’s RBatch internals, concluding that pipelining dramatically boosts throughput when command costs are modest.
Redis’s versatile list commands—such as LPUSH, RPUSH, LPOP, RPOP, BRPOPLPUSH, LPUSHX, and RPUSHX—enable developers to implement efficient FIFO queues, stacks, blocking queues, and advanced patterns like priority or delayed queues, while offering practical tips for monitoring, persistence, and error handling.
Learn how to use the Adapter pattern in Go to unify disparate Redis clients—*redis.Client* and *redis.ClusterClient*—by defining a common interface, implementing concrete adapters, and employing a factory method, resulting in cleaner, maintainable code that adheres to the open/closed principle.
This guide walks through setting up Flink CDC with MySQL on SpringBoot 2.7, covering binlog configuration, Maven dependencies, Java implementation for real‑time change capture, startup options, a custom Redis sink, and a web UI for monitoring the streaming pipeline.
This guide explains how to implement a robust, re‑entrant distributed lock using Redisson in a Spring Boot 2.7 application, covering basic and asynchronous APIs, Maven dependency setup, YAML configuration, custom annotation, AspectJ implementation, and comprehensive test cases.
This article explores sixteen practical Redis patterns—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, tags, filtering, follow relationships, and ranking—demonstrating how each can boost performance and simplify backend architecture.
This article compares JWT and session authentication, explains their differences, advantages, and drawbacks, and provides a complete Java implementation with Redis integration, token generation, validation, renewal, and related interceptor configuration for a robust backend authentication system.
This article explains how to integrate Lua scripts with Redis in a Spring Boot application, covering Lua basics, performance benefits, common use cases, step‑by‑step configuration, Java code examples, error handling, security considerations, and best practices for reliable backend development.
This article explains Redis's three persistence mechanisms—RDB snapshots, AOF append‑only logs, and their hybrid mode—detailing trigger conditions, configuration options, advantages, disadvantages, and practical guidance for selecting the appropriate method in production environments.
This article explains how to design an effective multi‑level caching system in microservice environments, covering client‑side browser caching, application‑layer static resource caching with CDNs and Nginx, and service‑layer caches using in‑process solutions and distributed Redis, while also addressing consistency and deployment considerations.
The article examines Redis's shift to a source‑available license, traces its history, analyzes the impact on cloud providers and open‑source communities, and evaluates emerging forks and alternatives such as Valkey, KeyDB, and Redict as potential replacements.
This article explains how MySQL auto‑increment primary keys work, the practical limits of various internal IDs such as auto‑increment, InnoDB row_id, Xid, trx_id, and thread_id, and compares them with external solutions like Redis‑based auto‑increment keys, providing guidance on choosing the right approach for different scenarios.
The article systematically compares eleven technical approaches for automatically closing expired e‑commerce orders, detailing each method’s implementation steps, trade‑offs, performance characteristics, and ideal deployment scenarios, and finally ranks them to guide practical selection.
This article explains how to extend the Higress cloud‑native gateway with WebAssembly plugins written in Go, C++ or Rust that call Redis for global rate limiting, token‑based AI usage control, and cookie‑based caching, including full code samples, configuration steps, and test results.
The Linux Foundation is backing Valkey, a BSD‑licensed fork of Redis, to preserve an open‑source, community‑driven in‑memory NoSQL database after Redis shifted to stricter RSALv2 and SSPLv1 licenses, drawing support from major cloud providers and sparking debate among industry leaders.
When Redis memory reaches its configured maxmemory limit, it triggers a set of eviction policies—such as noeviction, allkeys‑lru, volatile‑lfu, and others—to decide which keys to discard, and this article explains how to view, configure, and understand each strategy.
This article explains why correctly configuring the Redis client PoolSize in Go is crucial for performance and stability, outlines key factors such as concurrency, latency, and resource limits, and provides practical strategies, benchmarking methods, and sample code to help developers determine the optimal pool size.
This article details how Instagram grew from zero to 14 million users in just over a year using three engineers by applying three core principles and a reliable AWS‑based tech stack covering frontend, load balancing, backend, PostgreSQL sharding, S3 storage, Redis caching, asynchronous task queues, and comprehensive monitoring.
Redis announced a shift from the permissive BSD license to a dual‑license model (RSALv2 and SSPLv1), restricting free use by cloud providers, sparking industry debate and prompting developers to consider alternative implementations and the broader impact on open‑source ecosystems.
Redis announced it will replace its BSD‑3‑Clause license with a source‑available dual licensing model—SSPLv1 and RSALv2—starting with Redis 7.4, keeping the community edition free for developers while requiring cloud providers to secure commercial agreements, yet leaving existing enterprise users and client‑library developers under unchanged, permissive terms.
Redis is widely known as a high‑performance cache, but its rich data structures, persistence options, and distributed features enable it to serve as a primary storage engine for many internet services, from user profiles and leaderboards to rate limiting, messaging, and geo‑location, while presenting scalability and reliability challenges.
This article compiles a comprehensive Java backend interview guide covering Redis fundamentals, thread creation methods, thread‑pool pitfalls, Spring ecosystem relationships, IoC/AOP principles, shallow vs deep copying, collection cloning, differences between interfaces and abstract classes, and string handling classes, providing concise explanations and code examples for each topic.
This article introduces Bloom filters, explains their probabilistic principles, advantages and drawbacks, details how to add and query elements, derives false‑positive formulas, provides Guava, Redisson, Redis‑module, and custom bitmap implementations with code samples, and showcases real‑world business scenarios and performance benefits.
Redis announced that starting with version 7.4 it will abandon the BSD 3‑Clause license in favor of a dual SSPLv1/RSALv2 model, making the source code source‑available while keeping the community edition free, and clarifying the impact on cloud providers, partners, and end‑users.
This tutorial explains how to install Redis Exporter, configure Prometheus to scrape Redis metrics, visualize them in Grafana, and set up alert rules for Redis, providing step‑by‑step commands, configuration snippets, and screenshots for a complete monitoring solution.
This article explains how to improve large‑file upload experiences by using instant transfer (MD5‑based deduplication), chunked upload, and resumable upload techniques, and provides complete Java backend implementations with Redis coordination and code examples using RandomAccessFile and MappedByteBuffer.
Redis officially announced that starting with version 7.4 it will abandon the BSD 3‑Clause license and adopt a dual SSPLv1/RSALv2 source‑available model, while keeping the community edition free for developers and assuring no changes for existing users or client libraries.
This article details DeWu's self‑built high‑performance distributed Redis cache system, covering its Proxy‑based architecture, core components like ConfigServer, Redis‑Proxy and Redis‑Server, the automated operations platform for deployment and scaling, as well as monitoring, alerting, stability measures and future roadmap.
This article provides a comprehensive overview of Redis' eight common data structures—including String, List, Hash, Set, Sorted Set, Bitmap, HyperLogLog, and Geospatial—detailing their internal implementations, core commands, practical code examples, and typical application scenarios for developers.
This article explains how to integrate Lua scripts into Spring Boot applications using Redis, covering Lua basics, advantages of Lua in Redis, practical use cases, step‑by‑step implementation with code examples, performance optimization, error handling, security considerations, and best practices for reliable backend development.
This article analyzes the shortcomings of fragmented monitoring systems, defines key metrics such as MTTA and MTTR, proposes a unified alert convergence architecture using Redis delayed queues, and details design, implementation, and future AI‑enhanced improvements to reduce alert fatigue and accelerate incident response.
This article dissects Redis's memory architecture, explains each memory component, walks through object encoding rules, buffer handling, fragmentation, and child‑process memory, then presents two production incidents with step‑by‑step analysis and concrete optimization actions to prevent future memory alarms.
The article explains why data inconsistency occurs between MySQL and Redis under high concurrency, analyzes cache‑delete timing issues, and presents two solutions—delayed double‑delete and asynchronous cache updates via MySQL binlog—detailing implementation steps, advantages, drawbacks, and practical considerations.
This article analyzes multiple engineering solutions for automatically cancelling orders after the payment timeout, covering single‑machine and cluster scheduled‑task designs (Quartz, ElasticJob, XXL‑JOB), delayed‑message techniques (RocketMQ, custom delay service, Redis), and essential concurrency and monitoring best practices.
This article provides a comprehensive overview of Redis, covering its core concepts, deployment architectures—including single instance, high‑availability, Sentinel, and cluster setups—its replication mechanisms, gossip protocol, and the various persistence options such as RDB, AOF, and fork‑based snapshots.
This article introduces HertzBeat, an open‑source real‑time monitoring and alerting system that offers powerful template‑based monitoring without agents, explains its Docker‑quick start, demonstrates how to monitor Redis and SpringBoot services, and walks through email alarm configuration.
The article explains the concepts of cache penetration, cache breakdown, and cache avalanche, illustrates why they cause massive database load, and provides practical mitigation techniques such as parameter validation, storing null placeholders, Bloom filters, rate limiting, hot‑key pre‑warming, distributed locking, and cache pre‑warming strategies, with Java code examples.
This article walks through the problem of implementing a reliable Redis distributed lock in Go, explains the pitfalls of naive SetNx usage, introduces timeout handling and GetSet replacement, provides step‑by‑step Go code, and demonstrates its correctness with a multithreaded test.
The ioredis author @Luin announced the library's acquisition by Redis Inc., recounting its nine‑year evolution from a personal side project created to address missing Promise support, aesthetic command syntax, and lacking Cluster/Sentinel features into the most popular Node.js Redis client today.
This article explains the concept of delayed tasks, distinguishes them from scheduled tasks, and presents six practical Java implementations—including Quartz database polling, JDK DelayQueue, Netty's HashedWheelTimer, Redis sorted sets, Redis key‑space notifications, and RabbitMQ delayed queues—along with their advantages and drawbacks.
This article explains what constitutes a large Redis key, the performance problems it can cause such as CPU spikes, client timeouts and memory imbalance, and presents practical solutions including key cleanup, splitting, compression, pipeline usage, and alternative storage options.
The ioredis author announced its acquisition by Redis, detailing the library's evolution from a personal side project to the leading Node.js Redis client, its technical advantages over earlier libraries, and providing links to the new repository and author’s profile.
The article explains what cache penetration is, why it can crash databases under malicious traffic, and presents practical mitigation techniques such as Bloom filters, null-value caching, data pre‑warming, and request validation to protect Redis‑backed systems.
This article explains how a high‑throughput, low‑latency flight search system handles billions of daily requests by employing multi‑level caching, distributed Redis and MongoDB stores, AI‑driven TTL optimization, and advanced load‑balancing techniques such as pooling and overload protection.
To ensure the massive, multi‑platform membership service remains fast and reliable, this article details a multi‑center architecture using Elasticsearch for unified member data, Redis caching, and MySQL partitioning, along with traffic isolation, fault‑tolerant syncing, and fine‑grained flow‑control and degradation strategies.
ioredis, a high‑performance Node.js Redis client created by luin, uses a single‑socket pipeline for efficient command batching, has attracted enterprise users like Alibaba, and after years of growth was officially acquired by Redis, with its source now hosted on GitHub.
This article introduces Redis's five extended data types—Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog—explaining their concepts, common commands, and practical code examples, and highlights how they address specific internet‑scale scenarios more efficiently than traditional relational solutions.
The ioredis project, a robust Node.js Redis client created by Luin to address shortcomings in existing libraries, has been acquired by Redis Ltd, with its repository moved under the official Redis organization, highlighting its nine‑year evolution from a personal side project to a leading open‑source database client.
This article explains three practical Spring Boot approaches—scheduled tasks, delayed RabbitMQ queues, and Redis key‑expiration events—to automatically cancel orders that remain unpaid for 30 minutes, complete with ready‑to‑use Java code examples.
This article explains what a distributed ID is, outlines its essential requirements such as global uniqueness, high performance, high availability, and ease of use, and then reviews common generation strategies—including database auto‑increment, segment mode, NoSQL approaches, UUID, Snowflake, and several open‑source frameworks—providing code examples and practical trade‑offs.
This article explains Redis's five basic data types—String, List, Hash, Set, and Sorted Set—detailing their typical use cases, common commands, and providing concrete CLI examples that demonstrate how to manipulate each type.
This article introduces J2Cache, a two‑level caching framework for Spring Boot, explains its L1 (Caffeine/Ehcache) and L2 (Redis/Memcached) architecture, and provides a complete hands‑on example covering Maven dependencies, configuration, core service classes, REST controller, and testing procedures.
This article explains why a custom business risk‑control (rate‑limiting) component is needed for AI‑intensive services, compares open‑source solutions, and provides a complete backend implementation using Redis+Lua scripts, Kotlin code, Spring AOP annotations, and real‑time adjustable rules.
This article explains how to integrate Lua scripts into Spring Boot applications using Redis, covering Lua fundamentals, advantages of Lua in Redis, practical use cases, step‑by‑step implementation, performance optimization techniques, error handling, security considerations, and best practices for reliable backend development.
This article presents a step‑by‑step technical walkthrough of a SpringBoot‑based Seckill project, covering password MD5 hashing, distributed session management, unified exception handling, page and object caching, static page generation, memory flag with Redis pre‑decrement and RabbitMQ async processing, oversell prevention, and API rate limiting, followed by a visual demonstration of the system’s UI and source code download.
This article provides a comprehensive overview of distributed locks, detailing their four essential conditions, three implementation methods—including a Redis-based solution with Java code examples—and explains how to acquire and release locks safely in high‑concurrency distributed systems.
This article reviews eight popular distributed ID generation methods—including UUID, database auto‑increment, Redis INCR, Snowflake, database segment, Meituan Leaf, Didi Tinyid, and Baidu UidGenerator—detailing their principles, code examples, advantages, disadvantages, and practical considerations for choosing the right solution.
This article explains the classic cache consistency problem between MySQL and Redis, analyzes why inconsistencies occur, and details four common design patterns—delete‑then‑update, update‑then‑invalidate (Cache‑Aside), Read/Write‑Through, and Write‑Behind—along with their advantages, drawbacks, and typical execution flows.
A week‑long API freeze in a sandbox environment was traced to Redis connection pool misconfiguration, where threads waited indefinitely for a Jedis resource, leading to Tomcat request threads blocking and 500 errors, and the issue was resolved by adjusting pool settings and using proper connection release patterns.
This article compares Redis Streams and Kafka, examining their architectures, ordering guarantees, consumer group models, scalability, and trade‑offs, and shows how Redis can emulate Kafka‑like semantics using the Runnel library, while highlighting memory‑speed benefits versus Kafka’s durable, unlimited log storage.
Discover ten practical Redis scenarios—from counting page visits and caching category trees to implementing distributed locks, leaderboards, rate limiting, bitmap analytics, message queues, and global ID generation—each illustrated with concise code snippets and best‑practice tips for robust backend development.
This article details Qunar.com's Redis automation operations system, covering background challenges, the high‑availability cluster architecture, resource management, automated deployment, various migration strategies, scaling mechanisms with RedisGate, inspection processes, and future AI‑driven enhancements.
This article walks through the fundamentals of HTTP session handling, the challenges of session sharing in clustered environments, and presents a step‑by‑step design of a Single Sign‑On solution using CAS, including concrete code examples, Redis‑based session storage, and a comparison with OAuth2.
This article presents sixteen practical 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 leaderboards—each explained with data types, commands, and code examples.
This article explains why distributed locks are needed, outlines Redisson's lock properties, walks through its Lua‑based acquisition, renewal, and release processes, examines master‑slave pitfalls, compares RedLock with Zookeeper, and provides practical code examples for Java developers.
This tutorial explains how to add Redis dependencies to a Spring Boot project, configure connection settings, use RedisTemplate for basic operations, enable Spring Cache, employ Redisson for distributed locks, and fine‑tune connection pool and timeout parameters, providing complete code examples.
This article explains how to integrate Lua scripts into Spring Boot applications using Redis, covering Lua fundamentals, performance benefits, practical use cases, step‑by‑step implementation, error handling, security considerations, and best practices for optimizing backend services.
This article introduces Redis as an in‑memory key‑value data‑structure server, explains its primary use cases, walks through deployment options such as single instances, high‑availability, Sentinel and Cluster, and details its persistence mechanisms including RDB, AOF and forking.
Learn how to seamlessly integrate Spring Data Redis Repositories in a Spring Boot 2.7 environment, covering object mapping, CRUD operations, indexing with @Indexed, query‑by‑example, batch saves, and setting expiration using @TimeToLive, complete with code examples and visual data representations.
The article explains why the KEYS command can freeze a Redis instance with millions of keys, analyzes its O(n) blocking behavior, and demonstrates how the incremental SCAN command with cursor, MATCH, and COUNT parameters provides a non‑blocking alternative for retrieving prefixed keys.
