This guide explains how to use OpenResty’s Lua capabilities to replace Tomcat‑based Redis access with direct Nginx‑Redis integration, apply compression to reduce bandwidth, set up periodic cache refreshes, configure single‑worker timers, and manage cache settings via a signed backend API.
Redis caching can suffer from avalanche, breakdown, and penetration anomalies, but using staggered expirations, mutex locks, double‑key backups, high‑availability clusters, rate limiting, empty‑value caching, Bloom filters, warm‑up and degradation strategies effectively mitigates these risks and protects database stability.
This article explores Redis cache mechanisms, various caching patterns such as Cache‑Aside, Read‑Through, Write‑Through and Write‑Behind, analyzes consistency challenges in write operations, and presents solutions like delayed double deletion, retry mechanisms, and binlog‑based asynchronous cache invalidation to achieve eventual consistency.
This article explains how to efficiently count daily visits and online users in a Laravel application by leveraging Redis bitmap, detailing its memory‑saving advantages, providing complete PHP code for setting, counting, and checking online status, and comparing alternative methods such as tables, sorted sets, and HyperLogLog.
This guide walks through upgrading a single‑node Redis deployment to a high‑availability setup by building a master‑slave cluster, configuring Sentinel services, testing replication and failover, and enabling client auto‑detection of master changes, all using Docker containers and configuration files.
This article explains how Redis uses Linux epoll and a single‑threaded event‑driven architecture to handle tens of thousands of connections efficiently, detailing the initialization of the server, the event‑loop mechanics, and the processing of client commands from accept to reply.
This article provides a detailed walkthrough of Redis's AOF rewrite mechanism, covering trigger conditions, parent‑child process forking, three communication pipes, key data structures, the child’s rewrite steps, the parent’s handling of concurrent writes, and the final hand‑over that swaps the new AOF file into production.
Learn how to secure a Spring Boot service by limiting request frequency per IP and URL using a custom interceptor combined with Redis distributed locks, automatically blocking abusive IPs after a configurable number of hits within a set time window.
This guide explains how to protect a Spring Cloud Gateway service from packet sniffing by using RSA asymmetric encryption, timestamp validation, unique request IDs stored in Redis, and MD5 signature checks, with complete Java and front‑end code examples.
This article explains how the PIG microservice platform v3.5 adds official Spring Authorization Server support, covering background, Java compatibility, Maven dependency configuration, extended grant types, Redis token storage, token formatting and enhancement, and customized resource server introspection for a seamless OAuth2 migration.
The article presents kk-anti-reptile, a Spring Boot‑based anti‑crawler filter that uses a servlet Filter with a rule‑chain architecture, details its system requirements, configuration, integration steps for backend and frontend, and provides code examples and deployment notes.
This article explains how Redis RDB persistence works, detailing the save configuration parameters, snapshot generation methods, differences between SAVE and BGSAVE, storage locations, and the advantages and disadvantages of using RDB snapshots.
This article explains a complete backend user login process, including phone verification, token generation, token expiration strategies, gateway token validation, logout handling, anonymous request options, blacklist management, and provides a Spring‑Redis implementation for request‑rate limiting.
This article examines common Redis caching challenges in Java high‑performance architectures—such as lock loss, cache hot‑spot rebuild, cache penetration, and avalanche—through eleven real‑world scenarios, and provides practical solutions including RedLock alternatives, Zookeeper, DCL, tryLock, multi‑level caching, and Lua‑based read‑write locks.
This guide explains how to identify when Redis is slow, measure baseline latency, monitor slow commands and latency, troubleshoot network, fork, huge pages, swap, AOF, expiration, and big keys, and provides a practical checklist of solutions.
The article compares five delayed‑task implementations—database polling, JDK DelayQueue, Netty time wheel, Redis sorted sets, and RabbitMQ dead‑letter queues—detailing their concepts, Java code examples, advantages, and drawbacks to help developers choose the most suitable approach for order‑timeout scenarios.
This article explains why Redis is ultra‑fast by exploring its in‑memory design and the nine fundamental data structures—SDS, linked lists, ziplist, hash tables, skiplist, intset, quicklist, and listpack—that power keys, values, and internal objects, complete with code examples and version‑specific changes.
Implementing a distributed delayed message queue in Go with Redis uses a List for ready jobs and a Sorted Set for delayed jobs, periodically migrating expired entries atomically via Lua or transactions, providing O(1) enqueue/dequeue performance, retry support, and scalable concurrency for multi‑instance systems.
This article compares Redis Streams with Kafka, highlighting Redis's simplicity, performance, and built‑in features like consumer groups, while discussing when each solution is appropriate for different project scales and requirements.
Redis 7.0 introduces a suite of groundbreaking features—including the Function command family, Multi‑part AOF, sharded Pub/Sub, and client‑eviction controls—while also expanding use‑case scenarios, improving consistency, and highlighting the growing contributions of the Chinese developer community.
This guide walks through installing a Redis cluster with three masters and three slaves using local ports, explains slot allocation, key hashing, gossip communication, failover, node addition, resharding, and best practices for high availability, while providing practical commands and configuration examples.
This article demonstrates how to protect a Spring Boot service from malicious requests by creating a custom interceptor that tracks URL‑IP request counts, uses Redis for distributed locking, and disables offending IPs after a configurable threshold, with full code examples and configuration steps.
This article analyzes Twitter's early architectural choices for handling massive user growth, detailing how fan‑out, precomputed home timelines stored in Redis, and selective write strategies enabled the platform to serve hundreds of thousands of timeline requests per second while keeping latency low.
This article explains how Redisson builds a robust Redis‑based distributed lock by using Lua scripts for atomic operations, a watchdog to auto‑extend leases, support for re‑entrant, fair, read‑write, multi‑lock and RedLock algorithms, and shows the relevant Java code snippets.
The May 2023 DB-Engines ranking shows Oracle gaining the most points while MySQL and SQL Server plunge, and PostgreSQL and Redis surge dramatically, with detailed score changes, top‑10 lists across database categories, and the methodology behind the popularity metrics.
The article compares delayed and scheduled tasks and presents five backend solutions—database polling with Quartz, JDK DelayQueue, Netty's HashedWheelTimer, Redis ZSET, and RabbitMQ delayed queues—detailing their implementations, code examples, outputs, and pros and cons.
This article explains Redis memory eviction strategies, including volatile and allkeys policies, details the approximate LRU algorithm Redis uses, and provides Java implementations of an LRU cache using LinkedHashMap and a custom doubly‑linked list, complete with code examples and configuration settings.
This article explains how to use OpenResty with Lua to integrate Redis directly into Nginx for cache‑driven request forwarding, data compression, timed updates, single‑process scheduling, and configurable URL caching, thereby improving concurrency, reducing latency, and enhancing backend resilience.
The article explains how Redis Streams, a lightweight in‑memory data structure, can serve as a low‑cost, high‑performance alternative to Kafka for many projects, outlining its features, benefits, and practical usage scenarios while acknowledging when a dedicated MQ is still preferable.
This article explains why concurrency issues like overselling occur in e‑commerce, introduces distributed lock concepts and mainstream implementations (MySQL, Redis, Zookeeper/etcd), and provides a three‑stage quality‑assurance framework with code‑review checklists, testing methods, and data‑reconciliation techniques.
This article explains how to create a Java‑based hot‑search system using Redis ZSets, record and manage personal search histories, retrieve the top ten queries, and integrate a DFA‑driven sensitive‑word filter within a Spring Boot application, complete with full code examples.
This article presents a comprehensive overview of cache optimization techniques, covering benefits and costs, update policies, granularity control, penetration and avalanche mitigation, hot‑key reconstruction, and distributed batch‑operation strategies, with practical examples using Redis and MySQL.
This article provides a comprehensive source-level analysis of Redis 6.0 and later multi-threaded architecture, detailing how the main event loop coordinates with dedicated I/O threads to efficiently distribute, parse, and process concurrent read and write requests while maintaining high throughput and low latency.
The article explains how to prevent duplicate requests on the server side by using unique request IDs, business‑parameter hashing with MD5, exclusion of volatile fields, and atomic Redis SETNX operations, providing a complete Java implementation and testing logs.
The article details the architecture, challenges, and solutions of ByteDance's wallet asset platform that unified reward issuance, display, and usage across eight applications during the 2022 Spring Festival, covering high‑traffic order processing, token‑based asynchronous settlement, budget control, stability mechanisms, and future evolution.
This article shares a detailed, step‑by‑step account of three interview rounds at ByteDance, covering the background, technical and algorithm questions on Java, Redis, MySQL, Kafka, and more, along with personal reflections and practical tips for future candidates.
This article explains how caching can accelerate read/write performance and reduce backend load, analyzes its benefits and costs, and details practical design patterns such as update policies, granularity control, penetration, bottom‑hole, avalanche, and hot‑key optimizations for reliable high‑performance systems.
This article analyzes a real‑world migration from MongoDB to the open‑source Pika KV store, demonstrating how adjusting the max‑write‑buffer‑size parameter dramatically improves disk I/O characteristics and raises query‑per‑second throughput from 3 K to 40 K.
This article explains how Redis achieves high‑performance queries by leveraging in‑memory storage, specialized data structures such as SDS, hash tables, skip‑lists and zip‑lists, a single‑threaded event loop with I/O multiplexing, and progressive rehashing techniques.
This article explains Redis Sentinel's architecture, initialization, inter‑process communication, node discovery, down‑detection, leader election, and the step‑by‑step failover process for automatically promoting a slave to master and reconfiguring the old master.
This article explores the architecture and implementation of a high‑concurrency train ticket flash‑sale system, detailing load‑balancing strategies, Nginx weighted round‑robin, Redis‑based inventory management, and Go code examples that demonstrate local and remote stock deduction, performance testing, and fault‑tolerant design.
This article surveys common delayed message implementations—including external storage, RocksDB, Redis, and open‑source MQs like RocketMQ, Pulsar, and QMQ—analyzing their architectures, advantages, drawbacks, and practical considerations for building reliable distributed asynchronous messaging systems.
This article compares Redis dual‑active designs—hot‑standby, various dual‑write models, and bidirectional synchronization—showing hot‑standby’s high cost, dual‑write’s latency or consistency trade‑offs, and arguing that a middleware‑driven bidirectional sync, using replication protocols and fixed‑prefix keys to avoid loops, offers the most practical solution.
This guide demonstrates how to implement a simple hot‑search feature and personal search‑history management using Java and Redis, covering ZSet‑based ranking, timestamp handling, and a DFA‑based sensitive‑word filter with Spring‑Boot configuration and reusable service methods.
Redis 7.0, now officially stable for production, introduces nearly 50 new commands, major features like Redis Functions, ACLv2, command introspection, and Sharded Pub/Sub, while enhancing memory, compute, network, and storage subsystems for better performance and efficiency.
This article explains the design and implementation of a two‑level caching architecture using local Caffeine cache as L1 and remote Redis as L2 in Spring Boot, covering manual cache handling, annotation‑based management with Spring Cache, and a custom annotation with AOP to minimize code intrusion.
This article explains how Redis achieves high‑performance single‑threaded networking by leveraging Linux epoll for I/O multiplexing, detailing the server initialization, event‑loop architecture, connection handling, command processing, and write‑back mechanisms with code examples, providing a deep dive for backend developers.
This article provides a comprehensive technical guide on Redis performance, covering single‑threaded characteristics, master‑slave replication, Sentinel high‑availability mechanisms, Redis Cluster architecture, configuration steps, code examples, and alternative middleware solutions for scaling and fault tolerance.
This article details the end‑to‑end development of a supplier management system using SpringBoot, Thymeleaf, Mybatis‑Plus, MySQL and Redis, covering project planning, code implementation, file upload handling, caching optimization, and deployment considerations.
This article explains Redis 6's client‑side caching feature, describing its purpose, the two tracking modes (default and broadcast), a forward mode for RESP 2 clients, their advantages over traditional remote caching, and provides step‑by‑step command‑line examples with code snippets.
This article shares a curated selection of interview questions and answers covering concurrency programming, MySQL, and Redis, extracted from the author's extensive interview notes, with links to the full content on a personal website and instructions for obtaining the complete material.
RedisInsight is a lightweight, cross‑platform visual tool for Redis that offers data browsing, filtering, real‑time command analysis, support for multiple modules like RedisJSON and RediSearch, and includes a Workbench CLI, with installation steps using Node.js and Yarn.
This article provides a comprehensive introduction to Redis, covering its core features, supported data types, common caching problems and solutions, performance characteristics, eviction policies, persistence mechanisms, master‑slave replication, and Sentinel high‑availability architecture.
Redis 7.0 has been officially launched, bringing nearly 50 new commands, major enhancements like Redis Functions, ACLv2, command introspection, and Sharded Pub/Sub, plus extensive performance and stability improvements across memory, compute, networking, and storage subsystems.
The article explains how to prevent duplicate user requests on the server by using unique request IDs, hashing request parameters, excluding volatile fields, and implementing an atomic Redis SETNX‑with‑expiration pattern with Java code examples and a reusable deduplication helper class.
This article explains the concept of Bloom filters, their probabilistic behavior and limitations, demonstrates how to integrate them into Redis via the RedisBloom module, and provides complete Java/Redisson examples for creating, populating, and querying Bloom filters to prevent cache penetration and duplicate processing.
This article explains the structures of B+ trees and skip lists, compares their insertion and search behaviors, and shows why MySQL prefers B+ trees for disk‑based indexing while Redis adopts skip lists for in‑memory operations.
This article explains how to design Redis cache for relational list data such as user timelines or news feeds, covering fixed-length caching, consistency, resource utilization, using ZSET structures, handling additions, deletions, queries, and strategies like preloading, retry mechanisms, and asynchronous rebuilding.
The article explains Redis’s in‑memory architecture, detailing memory components, object encodings, buffer limits, fragmentation, and forked‑process overhead, and offers practical optimization tips—such as using compact encodings, controlling client buffers, disabling THP, and enabling active defragmentation—illustrated by real‑world case studies.
This article examines performance bottlenecks in a high‑traffic e‑commerce product service and proposes data‑centric optimizations—including read‑only focus, field‑level selection via bit‑masking, and Redis hash storage—to reduce payload size, lower GC pressure, and improve latency while maintaining scalability.
The article describes how a large‑scale membership system achieves high performance and fault tolerance by deploying a dual‑center Elasticsearch cluster, isolating traffic with multiple ES clusters, adding a Redis cache with distributed locks, migrating the primary relational store from SQL Server to a partitioned MySQL cluster, and implementing fine‑grained flow‑control and degradation strategies.
This article introduces Redis, explains how to install it on various platforms, demonstrates integrating Redis with Spring Boot using Spring Cache annotations, configures connection pools for Lettuce and Jedis, and provides custom Redis service implementations for flexible cache operations.
This article explains how Redis handles memory pressure using configurable maxmemory limits and a variety of eviction policies—including noeviction, volatile‑lru, volatile‑lfu, allkeys‑lru, and allkeys‑random—while offering guidance on selecting appropriate policies and sizing cache capacity for optimal performance.
This article explains the challenges of mutual exclusion and idempotency in distributed systems, reviews Java concurrency primitives, compares common distributed lock implementations such as Zookeeper, Redis, and Tair, and introduces Cerberus and GTIS as robust solutions for high‑availability and repeatable operations.
The article explains how to prevent duplicate user requests on the server side by using unique request IDs with Redis, constructing business‑parameter keys, computing MD5 digests of sorted JSON payloads, and provides a complete Java utility class with code examples and testing logs.
This article explains the mutual‑exclusion and idempotency challenges in distributed environments, analyzes multi‑thread and multi‑process solutions, introduces the principles and implementations of distributed locks (including ReentrantLock, synchronized, Zookeeper, Redis, Tair, and the Cerberus framework), and presents GTIS as a reliable idempotency guard.
This article explains Redis's original single‑threaded event‑driven design, the problems it causes, how the Lazy Free mechanism and asynchronous deletion were introduced in Redis 4.0, and how Redis 6.0 adds multi‑threaded I/O while discussing its limitations and comparing it with Tair's approach.
This article explains why cache penetration can cripple ultra‑large systems, how storing full data in Redis eliminates the risk, and presents two reliable cache‑update strategies—message‑queue‑driven updates and real‑time MySQL Binlog subscription via Canal—complete with configuration steps and Java code examples.
This article provides a comprehensive overview of Redis, covering its core characteristics, supported data types, common caching challenges such as consistency, avalanche, penetration and breakdown, as well as its high performance mechanisms, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability architecture.
This article provides a comprehensive introduction to Redis, covering its core characteristics, supported data types, caching usage patterns, typical issues such as consistency, avalanche, penetration and breakdown, performance reasons, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability mechanisms.
This article explains the structures of B+ trees and skip lists, compares their read and write performance, and details why MySQL chooses B+ trees for indexing while Redis and RocksDB prefer skip lists, highlighting trade‑offs in disk I/O, fan‑out, and balancing overhead.
This article demonstrates how to build a simple hot‑search functionality with user search history, hot‑keyword ranking, and profanity filtering in a Spring Boot application using Java, Redis ZSet operations, and a DFA‑based sensitive‑word filter, providing complete controller and service code examples.
This article explains how to use Redis key‑expiration events in a Spring Boot application to automatically close unpaid orders by configuring Redis, adding the necessary dependencies, and implementing a listener that reacts to expired keys.
This article provides a comprehensive introduction to Redis, covering its core characteristics, supported data structures, common caching use‑cases and associated challenges such as consistency, cache avalanche, penetration and breakdown, as well as performance reasons, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability mechanisms.
This article surveys various delayed‑queue solutions—including Kafka pause/resume, RocketMQ delay levels, Redis/Redisson zset timers, Youzan's custom design, Netty's HashedWheelTimer, and a Kafka‑based time‑wheel—analyzing their architectures, pros and cons, and offering practical implementation guidance.
This article provides a comprehensive introduction to Redis, covering its core features, supported data structures, common caching patterns, typical cache‑related problems and mitigation techniques, performance characteristics, eviction policies, persistence options, replication mechanisms, and Sentinel high‑availability architecture.
This article explains Redis's two expiration strategies—periodic active deletion and lazy deletion—detailing how the EXPIRE command works, the new NX/XX/GT/LT options introduced in version 7.0, the impact of unsynchronized clocks, and the underlying implementation code.
This article compares native JDK ScheduledExecutorService, Spring Task, Redis‑based approaches, and popular distributed scheduling frameworks such as Quartz, Elastic‑Job, LTS, and XXL‑Job, providing code examples, usage scenarios, advantages, and best‑practice recommendations for implementing reliable timed jobs in microservice environments.
This article walks through adapting a custom Redis client to Spring Cache, explains the cache abstraction, demonstrates annotation‑driven caching, shows how to integrate Caffeine and Redisson, and builds a simple two‑level cache to illustrate advanced scenarios for Spring Cache users.
A recent hardware failure left a physical server's disk read‑only, causing Redis to hang; this article explains the AOF mechanism, its potential pitfalls, log strategies, and practical steps to prevent and mitigate such issues in production environments.
This article explains how to use Redis data structures—Set, Hash, Bitmap, and HyperLogLog—along with Redisson in Java to efficiently count unique page visitors (UV) in high‑traffic mobile internet scenarios, comparing memory usage, precision, and scalability.
The article surveys common delayed‑message solutions in distributed MQ systems—including external storage, RocksDB, Redis, thread‑based scanning, and the designs of RocketMQ, Pulsar, and QMQ—detailing their architectures, advantages, and drawbacks for backend developers.
For deduplicating game recommendations in Redis, RoaringBitMap outperforms intset and Bloom filters by storing 300 auto‑incrementing game IDs in roughly 0.5 KB—over twice the compression of intset and far smaller than the 29 KB Bloom filter—thereby cutting memory use, latency, and hardware costs.
This article demonstrates how to implement Redis distributed locks using Jedis in Java, covering lock creation with SETNX, lock release via Lua scripts, and a high‑concurrency flash‑sale simulation with 100,000 virtual users, illustrating key concepts such as lock expiration, atomic operations, and performance testing.
This article explains how to prevent duplicate user requests on the server side by leveraging unique request IDs, hashing request parameters (excluding time fields) to generate MD5 signatures, and implementing an atomic Redis SETNX‑with‑expiration solution in Java, complete with utility code and test logs.
This article explains how to prevent harmful duplicate write requests by using unique request IDs or parameter‑based signatures, leveraging Redis for atomic SETNX with expiration, computing MD5 hashes of sorted JSON payloads, and providing a reusable Java helper class for robust backend deduplication.
This article presents a detailed evaluation of Intel® Optane™ Persistent Memory, including benchmark comparisons with DRAM and NVMe using Redis and RocksDB workloads, analysis of latency and throughput, and step‑by‑step configuration instructions for Memory Mode, App Direct, and KMEM DAX.
This article explains how to handle flash‑sale (seckill) scenarios in PHP by reducing database pressure with caching, preventing stock oversell using row locks, transactions, file locks, and Redis queues, and provides complete code examples for each technique.
This article introduces CIM, a developer‑oriented distributed instant messaging platform, outlines its current features and roadmap, explains its SpringBoot‑Netty‑Redis‑Zookeeper architecture, and provides detailed installation, configuration, and usage instructions with code examples.
This article explains why data loss can occur in Redis master‑slave clusters due to split‑brain scenarios, outlines step‑by‑step troubleshooting methods—including checking replication lag, analyzing client logs, and monitoring resource usage—and recommends configuration settings such as min‑slaves‑to‑write and min‑slaves‑max‑lag to prevent the issue.
Redis 7.0-rc3 introduces new cluster commands, configuration ordering changes, ACL restrictions, function load adjustments, performance optimizations, enhanced CLI behavior, expanded INFO fields, module API extensions, and a comprehensive set of bug fixes, providing developers with important updates and potential migration considerations.
This article examines why Redis HSCAN can cause high CPU usage on ziplist‑encoded hash objects, compares its performance with HGETALL through benchmark tests, and provides practical recommendations for avoiding costly full‑scan operations in production environments.
This guide explains caching fundamentals, purposes, and optimal use cases, details hit rates and eviction policies such as FIFO, LRU, LFU, TTL/TTI, compares client‑side (HTTP, browser) and server‑side caches (CDN, Redis, Memcached), explores multi‑level architectures, common pitfalls like avalanche, penetration and breakdown, and best‑practice strategies including pre‑warming, update patterns, and consistency management.
Server‑side caching improves performance by trading space for time, using local caches like HashMap, Guava, Ehcache, and Caffeine, distributed caches such as Redis, and multi‑level architectures that combine in‑process, distributed, and database layers, while employing consistency patterns, monitoring, and hot‑key detection.
This article provides a step‑by‑step guide to installing RedisInsight, configuring its environment variables, launching the service on Linux, deploying it with Kubernetes, and using its graphical interface to monitor and manage Redis clusters, keys, and memory.
This article compares three popular open‑source message brokers—RabbitMQ, Kafka, and Redis—examining their scalability, persistence, consumer models, and ideal use cases to help developers select the most suitable asynchronous communication solution for their microservice architecture.
This article explains the challenges of generating globally unique identifiers in distributed systems and presents various solutions—including UUID, database auto‑increment, Redis atomic counters, and the Snowflake algorithm—detailing their requirements, implementations in Java and SpringBoot, and their pros and cons.
This article details the design and implementation of a highly available, high‑performance membership system serving over a billion users, covering Elasticsearch dual‑center clusters, traffic isolation, Redis caching, MySQL migration, and fine‑grained flow‑control and degradation strategies.
The article explains how to prevent duplicate requests on the server side by using unique request identifiers stored in Redis, hashing request parameters (excluding volatile fields) with MD5, and provides a reusable Java helper class with example code and test logs.
This article explains why storing JSON strings in Redis is inefficient, compares JSON with binary‑safe formats like MessagePack, evaluates compression algorithms such as ZSTD and LZ4, and provides Python examples showing how to reduce latency and memory usage when using Redis strings.
