Redis 8.0 GA introduces major performance enhancements, adds AI‑focused vector and probabilistic data structures, expands native JSON support, improves the query engine with secondary indexes, and re‑embraces open source by offering an AGPLv3 licensing option.
This article explains why Redis is becoming a DBA's favorite, provides a five‑minute installation guide, essential commands, cache‑design pitfalls and solutions, explores Redis data structures with Java examples, demonstrates distributed locking and flash‑sale implementations, and covers clustering, backup, monitoring, and performance tuning.
Redis 8.0 GA introduces a suite of new data structures, AGPLv3 licensing, multithreaded query execution, horizontal scaling, and significant performance gains, positioning Redis as a more powerful in‑memory database for big‑data, AI, and real‑time applications.
The article explains why traditional auto‑increment primary keys are unsuitable for distributed systems, outlines the business requirements for a distributed ID (global uniqueness, trend and monotonic increase, security), and reviews several generation approaches such as UUID, database auto‑increment, segment mode, Redis, Snowflake, Baidu UidGenerator, Meituan Leaf and Didi TinyID, including their advantages and drawbacks.
Redis, after a year of closed‑source experiments, reopens its source code with version 8 under AGPLv3, offering developers a choice of three licenses, detailing the CEO's regrets, community backlash, emerging forks, and new performance‑focused features that reshape its future.
The production failure where a customer‑service event creation stopped each morning was traced to RedisTemplate’s increment returning null because @Transactional combined with enabled Redis transaction support caused the command to be queued in a MULTI/EXEC block, which is fixed by using separate non‑transactional and transactional StringRedisTemplate beans.
This article examines six different leaderboard implementation strategies—from simple database sorting and cache‑plus‑scheduled tasks to Redis sorted sets, sharded Redis clusters, pre‑computed layered caches, and real‑time stream processing with Flink—detailing their suitable scenarios, advantages, disadvantages, and architectural diagrams to help engineers choose the most appropriate solution.
Operations engineers struggling with countless Redis commands and opaque data structures can simplify their workflow with five recommended visual tools that turn complex Redis operations into intuitive interfaces, complete with monitoring, cluster support, and cross‑platform clients.
Redis, after a year of closed‑source experiments and a controversial dual‑license strategy, announced its return to open source under AGPLv3 for version 8, sparking intense debate over licensing choices, community trust, and the future direction of the in‑memory database ecosystem.
This article explains how to integrate Lua scripts into Spring Boot applications to leverage Redis for atomic operations, performance optimization, and complex data processing, providing detailed Lua fundamentals, practical use‑cases, implementation steps, error handling, security considerations, and best‑practice recommendations.
This guide explains how to use Nginx, Lua (via OpenResty), and Redis to automatically block any IP that exceeds 60 requests per minute, storing the blacklist in Redis with a default one‑day ban and providing performance optimizations and testing steps.
This article examines Japan’s looming 500,000 IT talent shortage, Microsoft’s AI‑driven overhaul of M365, a fraud case involving outsourced development, Redis’s return to open‑source licensing, and Google’s rollout of Gemini AI for children, highlighting the broader implications for the tech industry.
This article presents a continuously updated collection of over 120 Spring Boot 3 practical examples, covering database initialization scripts, switching Redis clients, disabling and customizing security, configuring HTTP clients, and fine‑tuning Spring MVC behavior with code snippets and configuration details.
This guide reviews common backend interview topics, explaining TCP TIME_WAIT behavior, multi‑port listening, full page load steps, Redis data types, Kafka consumer sizing and at‑most‑once semantics, the CAP theorem, Singleton usage, C++ std::map complexity, and an O(n) doubly‑linked list reversal algorithm.
This article traces MySQL’s architectural evolution from single‑node to sharded clusters, explains the fundamentals and classifications of NoSQL databases, and provides a comprehensive guide to Redis—including installation, memory management, and detailed usage of its five core data types with best‑practice considerations.
This tutorial walks developers through setting up a low‑cost, containerized AI service on Windows by installing Docker, deploying Redis and Ollama containers, pulling the DeepSeek‑R1 model, and integrating everything with Spring AI to enable continuous conversation support.
This comprehensive guide explains Redis's core architecture, the underlying implementation of its various data types, persistence mechanisms (RDB and AOF), high‑availability solutions such as replication, Sentinel and Cluster, as well as performance‑monitoring techniques and common optimization strategies.
This article explains the process of moving hash slots between nodes in a Redis cluster using the redis-cli --cluster reshard command, detailing the required parameters, interactive steps, and verification of successful slot migration.
This article explains the key Redis cluster configuration options found in redis.conf, detailing required settings such as cluster-enabled, cluster-config-file, and cluster-node-timeout, as well as optional parameters like cluster-slave-validity-factor, migration barrier, full coverage requirement, and read‑when‑down behavior.
This article explains the various PHP caching layers—including OPcode, data, page, and HTTP caching—provides configuration examples, code snippets for file, Memcached, and Redis caches, and outlines best practices and invalidation strategies to improve web application performance.
This tutorial introduces NoSQL concepts and provides step‑by‑step Python code examples for installing, connecting, inserting, querying, updating, and deleting data in both MongoDB and Redis, highlighting their suitable use cases and key differences.
This article explains how to design and implement a reliable, resumable multipart file upload system in Java, using Redis for session tracking, FileChannel for zero‑copy merging, progress monitoring, checkpoint files for resume support, and NFS shared storage for Docker‑based distributed deployments.
This article explains the concept of epochs in Redis Cluster, describing the two types—currentEpoch and configEpoch—their roles in versioning and conflict resolution, how they are updated and persisted, and provides example node listings and commands for inspection.
This article explains how Redis cluster nodes use a TCP-based gossip protocol and a dedicated cluster bus to discover each other, detect failures, synchronize configuration, and automatically redirect client commands to the correct node, illustrated with practical command‑line examples.
This article compares several ways to implement order‑closing delayed tasks—message‑queue delayed delivery, Redisson DelayQueue, Redis expiration listening, RabbitMQ dead‑letter queues, and time wheels—explaining their mechanisms, drawbacks, and recommending the most reliable solutions for production systems.
Redis cluster relies on two ports per node, a hash‑slot based sharding scheme, a master‑slave replication model, and provides eventual consistency, making these four fundamentals essential knowledge for developers and operators to.
This guide demonstrates how to horizontally scale a Redis cluster by adding nodes as masters or slaves, configuring them, using redis-cli commands to join or remove nodes, and handling special cases such as removing a failed node, all with practical code examples.
Combining Redis’s distributed, high‑capacity cache with Caffeine’s ultra‑fast local cache creates a two‑level strategy where most reads hit the in‑process cache, fall‑backs use Redis, and only rare misses query the database, dramatically cutting latency, boosting throughput, and easing DB load.
A Redis instance suddenly reported 78.9 GB of used_memory against a 16 GB maxmemory limit, triggering massive key eviction; this article explains how INFO reports used_memory, what the metric actually measures, how Redis 7 changes memory accounting, the exact eviction conditions, and provides a Python script to pinpoint the memory‑hogs.
This article explains the immutable unique node IDs in a Redis cluster, the additional metadata each node maintains, provides a sample `cluster nodes` output, and details the meaning of every field in that output to help with monitoring and interview preparation.
In this interview-style article, the interviewer probes a candidate on why Redis is exceptionally fast, covering its in‑memory storage, single‑threaded design, I/O multiplexing, optimized data structures, memory management tricks, and practical performance‑tuning strategies.
This article introduces RediSearch, a Redis module for full‑text search, outlines its many features, compares its indexing and query performance with Elasticsearch, provides installation methods (source and Docker), and demonstrates command‑line operations for creating indexes, adding documents, searching, and managing indexes.
Learn how to set up Redis with Docker, integrate it into a FastAPI app using a custom caching decorator, and apply Redis for caching API responses, rate limiting, task queues, and distributed locks, complete with practical code examples and deployment tips.
The article explains how to reduce latency in SparkStreaming jobs that heavily interact with Redis by using connection pools, batch sizing, pipeline techniques, and custom JedisCluster pipelines, while also covering Redis deployment modes, Codis proxy, and practical Java/Scala code examples.
This article explains how to use Spring's built‑in cache annotations such as @EnableCaching, @Cacheable, @CachePut, and @CacheEvict to implement Redis‑backed caching in a Spring Boot application, covering configuration, core code, testing, and practical considerations for cache consistency and expiration.
This article explains how Spring's caching annotations—@EnableCaching, @Cacheable, @CachePut, and @CacheEvict—work together to simplify cache management, includes Maven dependency setup, configuration class, entity and service code, a full Spring Boot example, test cases, and visual illustrations of cache miss, hit, update, and eviction.
This article explains why inventory overselling occurs during massive sales events, analyzes the root causes such as non‑atomic database operations, and presents practical solutions—including optimistic locking, Redis atomic scripts, distributed locks, message‑queue peak shaving, and pre‑deduct strategies—while highlighting common pitfalls and best‑practice combinations for robust e‑commerce back‑ends.
This tutorial shows how to obtain a user's current latitude and longitude via an IP API, send the coordinates to a backend service that stores charging stations in Redis GEO, and query stations within a 20‑kilometer radius, with complete front‑end and back‑end code examples.
This article explains how to reduce Spark Streaming latency caused by heavy Redis interactions by using connection pools, batching commands with pipelines, understanding Redis slot architecture, and implementing custom pipeline logic for both Codis and native Redis clusters, complete with code examples.
This guide walks through initializing a Spring Boot project, handling IDE setup challenges, managing version compatibility between Spring Cloud, Spring Boot, and Kafka, and provides practical code examples for global exception handling, logging, CORS configuration, Swagger integration, and useful development tools.
Learn how to boost Spring Boot 3 application performance by implementing a multi‑level cache that combines local and Redis storage, using custom @MultiLevelCache annotations and AOP aspects, complete with cache clearing mechanisms, code examples, and testing instructions.
This article explains various approaches to delayed task handling—such as database polling, JDK DelayQueue, Redis expiration listeners, Redisson delay queues, RocketMQ delayed messages, and RabbitMQ dead‑letter queues—evaluating their advantages, drawbacks, and best‑practice recommendations for reliable order‑expiration workflows.
This guide explains why Redis clustering is needed for capacity, concurrency and failover, describes Redis 3.0's decentralized cluster architecture, provides step‑by‑step commands to configure, launch and combine six nodes into a cluster, demonstrates slot calculations, client usage with Jedis, and outlines fault recovery, pros and cons, and cleanup procedures.
This article explains the technical selection between JWT and session for authentication, compares their differences, advantages, and disadvantages, and provides a complete Java implementation—including token generation, Redis storage, login/logout, password update, and request interception—demonstrating why JWT was chosen for a distributed backend system.
This article explains why rate limiting is essential for high‑traffic services, compares four classic algorithms (fixed‑window, sliding‑window, leaky‑bucket, token‑bucket), provides Java and Redis implementations, shares production case studies, highlights common pitfalls, and offers performance‑tuning tips for robust backend systems.
This article compares JWT and session-based authentication, detailing their differences, certification processes, advantages, disadvantages, security considerations, performance impacts, token renewal, and revocation strategies, and provides a complete Java implementation using Spring, Redis, and custom utility classes.
This article explains why simple GET‑modify‑SET inventory updates cause overselling in flash‑sale spikes and presents several Redis‑based solutions—including Lua scripts, WATCH‑based optimistic locks, distributed SETNX locks, and asynchronous queue processing—detailing their implementation, advantages, and trade‑offs.
This article introduces the Lock4j distributed lock library, explains its features, shows how to add Maven dependencies, configure Redis, use the @Lock4j annotation for simple and advanced locking scenarios, and provides custom executor, key builder, and failure‑strategy examples for Spring Boot applications.
This article compiles essential Java backend interview topics, covering Redis cache pitfalls and solutions, Spring Boot initialization steps, common Spring annotations, MyBatis advantages, Git workflow commands, Java exception hierarchy, Java 8 enhancements, HashMap internals, Docker isolation mechanisms, and Jenkins CI/CD pipelines, providing a comprehensive technical reference for developers.
Redis achieves remarkable speed through in‑memory storage, I/O multiplexing, optimized data structures such as SDS strings, linked lists, ziplists, skip‑lists, and hash tables, a single‑threaded event loop, and intelligent data encoding, all of which eliminate disk I/O and reduce overhead.
This article explains how to set up a dynamic IP blacklist using Nginx, Lua scripts, and Redis, covering environment preparation, design options, configuration of nginx.conf, Lua script implementation, and advanced features such as rate limiting, white‑listing, and automated detection to protect servers from malicious traffic.
This article examines the challenges of high‑concurrency flash‑sale scenarios in multi‑active architectures, analyzes fairness issues caused by geographic latency, and presents practical solutions such as data‑sharding and global‑clock ordered queues, complemented by a Redis‑based implementation example.
The article explains why using Redis expiration or RabbitMQ dead‑letter queues for delayed order‑cancellation tasks is unreliable, compares several approaches such as message‑queue delayed delivery, Redisson delay queues, and time wheels, and recommends robust solutions like RocketMQ or Pulsar for accurate timing.
This article compiles a comprehensive collection of over two hundred interview questions covering Linux system administration, Nginx web server, MySQL databases, Redis caching, Kafka messaging, and core network security topics, providing a one‑stop resource for candidates preparing for major tech company interviews.
This article walks through a complete sharding implementation that splits a user table across four MySQL databases and sixteen tables, discusses challenges such as distributed IDs, transactions, data migration and pagination, and provides full Spring Boot, Sharding‑JDBC, Elasticsearch and Redis configurations with code examples.
This article explains Redis's three clustering solutions—master‑slave replication, Sentinel mode, and sharding—detailing their architectures, setup steps, synchronization mechanisms, advantages, drawbacks, split‑brain handling, and common interview questions for developers.
This article details Kuaishou's migration of massive Redis clusters to Kubernetes using the KubeBlocks Operator, covering architecture, multi‑layer management requirements, federated cluster deployment, custom controllers, performance and stability considerations, and the resulting operational benefits.
This article analyzes the essential requirements of massive‑scale game leaderboards, explains how Redis sorted sets and hash tables provide fast ranking and lookup, and presents a multi‑layered architecture—including hot‑key sharding, dynamic partitioning, tiered storage, read/write separation, pipeline batching, and hybrid persistence—to achieve real‑time, billion‑user performance.
This article explores why batch execution in Redis improves command efficiency, simplifies client logic, and enhances transaction performance, and then details four core batch query methods—MGET, HMGET, Pipeline, and Lua scripting—along with practical SpringBoot examples and best‑practice considerations.
This guide introduces Tiny RDM, a modern, lightweight, cross‑platform Redis client with over 10k GitHub stars, details its key features, installation via Docker, usage tips—including theme switching, connection creation, and data operations—and showcases a real‑world e‑commerce project that leverages Redis.
This article examines the most frequent Redis performance bottlenecks—including high memory usage, network latency, misconfiguration, poor data‑structure choices, and suboptimal persistence—explains why they occur, and provides concrete optimization techniques, monitoring commands, real‑world case studies, and emerging trends to keep your cache fast and stable.
This article compiles the most frequently asked Redis interview questions, covering fundamentals, data structures, persistence mechanisms, high‑availability features, clustering, performance tuning, and troubleshooting, providing clear explanations and practical guidance to help candidates confidently tackle any Redis interview.
This guide walks through creating a Docker Compose file that defines a custom network, assigns static IPs to Redis, MySQL, and a Java container, and shows how to configure SpringBoot to connect to these services using the assigned addresses.
This article describes how Tubi improved the latency of its Redis‑backed online feature store for machine‑learning inference by analyzing query patterns, measuring client‑side bottlenecks, and applying optimizations such as binary Avro encoding, MGET usage, virtual partitioning, and parallel deserialization to meet a sub‑10 ms SLA.
After being laid off, the author interviewed only four companies, detailing each interview’s technical questions—from Spring Cloud and MySQL to concurrency and design patterns—and reflecting on the interviewers’ professionalism, the relevance of his skill set, and the importance of matching resumes to job requirements.
This article explains three Redis-based methods—Hash, Bitmap, and HyperLogLog—for tracking website user visits, detailing how each structure works, their implementation steps, memory and accuracy trade‑offs, and guidance on choosing the best approach for different traffic scenarios.
This article outlines a fraud‑detection use case that leverages Redis GEO commands to compare user order addresses with known malicious locations, discusses technology choices among MySQL, Redis, and Elasticsearch, explains Redis’s Sorted‑Set and GeoHash implementation, and provides Java code examples for GEOADD, GEOPOS, GEODIST, and GEORADIUS.
This guide explains how to use Redis cluster key tags to align multiple keys to the same slot, enabling multi-key commands like MGET, and includes step‑by‑step examples of connecting, setting keys, checking slots, encountering CROSSSLOT errors, and resolving them with tagged keys.
The article explains how Redis vector search, powered by RedisSearch’s FLAT and HNSW algorithms and supporting various data types and precisions, enables fast AI-driven similarity queries for text, image, and audio, and provides implementation guidance, optimization tips, and a real‑world customer‑service use case.
The article details practical monitoring strategies for both remote Redis and local Caffeine caches in high‑traffic game services, including prefix‑based Redis key tracking, Aspect‑oriented instrumentation, Caffeine statistics collection, and real‑world case studies that illustrate how these metrics identify hot‑keys, cache‑miss spikes, and reduce system load.
This article explores industry‑proven techniques for handling massive flash‑sale traffic, covering pressure‑distribution, Redis + MQ combos, Lua‑based stock deduction, RocketMQ transactional messages, and Alibaba Cloud's Inventory Hint to ensure consistency and performance under extreme concurrency.
This article explains Redis cluster hash slots, how they partition data across 16,384 slots, the CRC16-based slot calculation, slot allocation to nodes, and demonstrates dynamic scaling operations such as adding, reshaping, and removing nodes with practical command‑line examples.
This article explains how to use Redis as a high‑performance caching layer—covering local and remote caches, support for multiple data structures, expiration and eviction policies, persistence mechanisms like RDB and AOF, a simple TCP protocol, and advanced modules—enabling services to handle tens of thousands of queries per second without overloading MySQL.
This article provides a comprehensive guide to Redis Cluster, covering its background, overall architecture, deployment steps, configuration templates, cluster creation commands, basic key‑value operations, high‑availability testing, and how to manually assign master‑slave relationships for robust distributed caching.
The article introduces Redis Streams as a Kafka‑like messaging structure, explains its fundamental concepts and seven core commands (XADD, XRANGE, XREAD, XGROUP CREATE, XREADGROUP, XACK, XTRIM), demonstrates integration with Spring Boot, and evaluates its fit for lightweight, low‑backlog queue scenarios.
This article explains how Redis achieves million‑level concurrent connections by using an event‑driven Reactor model, non‑blocking I/O, efficient I/O multiplexing such as epoll, and in‑memory data storage to deliver ultra‑low latency and high throughput.
Redis offers three persistence options—AOF, which logs every write command; RDB, which creates periodic snapshots; and hybrid persistence, which combines both—to balance data safety, recovery speed, file size, and performance, with configurable settings for sync policies, rewrite processes, and compression.
This article introduces a comprehensive Spring Boot 3 practical case collection and walks through building a reentrant, auto‑renewing distributed lock using Redis and Lua scripts, providing full code snippets and configuration guidance for robust backend concurrency control.
Redis Cluster distributes data using 16,384 hash slots calculated via CRC16, a design that offers flexible slot allocation, simpler data migration, and better performance compared to traditional consistent hashing, and this article explains the slot mechanism, node scaling, client routing, and the reasons behind the 16K slot choice.
This article explains how Redis uses memory as a cache, details the maxmemory setting and its configuration methods, describes various eviction policies—including LRU, LFU, and random strategies—and outlines expiration handling, replication considerations, and best‑practice recommendations for stable high‑load deployments.
Redis evolved rapidly, with versions up to 5.x handling all core CRUD commands on a single worker thread, while Redis 6 introduced multi‑threaded network I/O to leverage multi‑core CPUs, yet still processes core business commands single‑threadedly for performance and simplicity, as explained with diagrams.
This guide explains how to integrate Redis with Spring Security and OAuth2 to create a distributed, stateless authentication system that supports multiple client types, details request encryption, token generation, persistence, and the complete server‑side login flow.
Redis 8.0-M03 adds an RDB channel replication feature that parallelizes RDB file transfer with write command streaming, reducing master node memory pressure and CPU load while improving full‑sync performance through a dedicated connection and configurable buffer limits.
The article explains why Redis needs asynchronous IO threading, describes the shortcomings of previous IO‑thread models, details the design of the new async IO thread architecture with event‑notified client queues and thread‑safety mechanisms, and presents performance test results showing up to double the QPS and significantly lower latency.
The article explains what constitutes a Redis large key, outlines its performance and stability risks, describes common scenarios and root causes, and provides practical detection commands, mitigation techniques such as splitting, compression, proper data modeling, and monitoring strategies to prevent future issues.
This article explains how to build a two‑level cache architecture using Caffeine as a local cache and Redis as a remote cache in a Spring Boot project, covering manual implementation, Spring cache annotations, and a custom AOP‑based solution while discussing advantages, consistency challenges, and best‑practice code examples.
This article explains Redis Pipeline—a technique that batches multiple commands in a single request to cut round‑trip latency, details how to implement it in Java using Jedis, and outlines its advantages, drawbacks, and ideal scenarios for high‑throughput backend applications.
Redis achieves exceptional speed by storing all data in memory, using a single‑threaded event‑driven architecture with epoll/kqueue, employing efficient I/O multiplexing, optimizing data structures such as strings, hashes and sorted sets, and providing flexible persistence and high‑availability options, all of which are detailed in this article.
This article examines common industry practices for handling massive e‑commerce flash‑sale traffic, detailing pressure‑distribution, Redis + MySQL, Redis + MQ, and Alibaba's Inventory Hint approaches, and explains how Lua scripts, transactional MQ messages, and database hints together ensure atomic stock deduction and consistency under extreme load.
This article walks through the problem of locking out users after multiple failed login attempts, explains why IP‑based locking with Redis and Lua scripts is effective, and provides a complete Spring Boot implementation—including front‑end HTML, custom annotations, AOP aspect, Redis configuration, and sample controller code—to enforce a configurable login‑attempt limit.
This article details Redis's expiration mechanisms, comparing lazy (passive) deletion with periodic (active) deletion, and outlines the auxiliary memory eviction policies, highlighting their principles, pros, cons, and ideal use cases.
The article details how to engineer a Web‑based group chat that supports one million members by selecting WebSocket for real‑time communication, using read‑diffusion storage, a three‑layer architecture with Redis routing and Kafka queues, ensuring ordered, reliable delivery via TCP, ACKs and UUID deduplication, calculating unread counts with Redis ZSETs, and handling massive traffic through rate‑limiting, protobuf compression and message chunking.
Redis 6.0 adds multithreaded network I/O to improve throughput, latency, and bandwidth utilization while keeping command execution single‑threaded for simplicity and data safety.
The paper details a live‑streaming highlight system that integrates heterogeneous data sources, uses a three‑stage pipeline with MySQL/Redis storage, applies sliding‑window interval optimization and AI‑driven title generation, scoring, and segment selection, managed by a shared state‑machine, and outlines future stability and observability improvements.
This guide explores 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 models, and ranking—providing code snippets and implementation tips for each pattern.
Redis offers three persistence mechanisms—RDB snapshots, AOF command logging, and a hybrid approach—each with distinct trigger methods, performance impacts, and trade‑offs in recovery speed, data safety, and file size, helping you choose the optimal strategy for your workload.
Redis, a non‑relational database, implements transaction-like behavior using commands such as MULTI, EXEC, DISCARD, and WATCH, allowing queued execution, optimistic locking, and various rollback granularities, though its semantics differ from traditional relational database transactions.
This guide walks through why Redis clustering is needed for high availability, explains the decentralized cluster architecture introduced in Redis 3.0, provides step‑by‑step configuration of multiple redis.conf files, demonstrates creating a six‑node cluster, shows key slot calculations, fault‑tolerance handling, and includes Java Jedis code for connecting to the cluster.
This article compares JWT and session authentication, discusses their differences, advantages, security and performance considerations, and provides a complete Java implementation with Redis integration, including token generation, validation, renewal, and interceptor configuration for secure backend services.
Redis stores data directly in memory rather than on disk, leveraging microsecond‑level access, a single‑threaded Reactor event loop with I/O multiplexing, optimized data structures like strings, hashes, lists, sets, and a simple text protocol, all of which combine to deliver exceptionally high performance for high‑frequency read/write workloads.
