When a business service needs an API that should only be callable within the internal network, this article evaluates three approaches—microservice isolation, gateway‑Redis whitelist, and gateway‑AOP—and demonstrates a concrete AOP implementation to enforce intranet‑only access.
This article explains why traditional session authentication is unsuitable for distributed micro‑service systems, introduces JWT as a stateless alternative, and provides a step‑by‑step SpringBoot implementation—including double‑token handling, Redis blacklist logout, interceptor configuration, and end‑to‑end testing—complete with code snippets and best‑practice recommendations.
Guardian is a lightweight Spring Boot starter that provides both anti‑repeat‑submission protection and API rate limiting; it supports annotation and YAML configuration, flexible key scopes (user, IP, global), Redis or local storage, customizable response modes, white‑list handling, and built‑in observability, making it a drop‑in solution for single‑service projects.
The article explains why many interviewers dismiss Redis‑backed token storage as a bad design, then systematically demonstrates how Redis + token offers controllable logout, multi‑device support, high performance, dynamic permission refresh, and scenario‑driven architecture choices, providing concrete response scripts and advanced enhancements for interview success.
This article documents a complete penetration test on a newly deployed environment, detailing how weak credentials, unauthenticated services, and misconfigurations in MSSQL, Nacos, Oracle, Telnet, OA, NC, Redis, Spring, and frontend assets were systematically discovered and exploited, with step‑by‑step screenshots illustrating each compromise.
The article explains why fixed‑size chunking harms RAG recall, demonstrates three semantic‑chunking strategies—including recursive punctuation splitting, overlapping windows, and parent‑child document mapping—and provides complete Java/LangChain4j code that integrates tokenizers, Redis, and Qdrant to boost retrieval performance.
This article explains the shift from Redis's traditional single‑threaded model to the multithreaded I/O architecture introduced in Redis 6.0, detailing core design principles, performance benchmarks, Go code simulation, interview questions, and practical tuning tips for high‑concurrency workloads.
This guide explains what RedisInsight is, outlines its key features, and provides step‑by‑step instructions for installing the tool on a Linux server, configuring environment variables, deploying it with Kubernetes, and using its web UI to monitor and manage Redis instances.
This tutorial extends the previous FastAPI example by adding Redis caching and Celery asynchronous tasks, showing the project layout, a complete docker‑compose configuration, required Python packages, sample FastAPI and Celery worker code, startup commands, health‑check setup, and key takeaways.
This article walks through a complete performance‑optimization and deployment pipeline for a Spring Boot blog, covering multi‑level caching with Caffeine and Redis, database indexing and cursor pagination, read‑write splitting, asynchronous processing, rate limiting, Docker multi‑stage builds, Nginx reverse‑proxy setup, Actuator monitoring, custom metrics, health checks, alerting, JMeter load testing, and JVM tuning.
This article provides a comprehensive analysis of Redis persistence mechanisms—including RDB snapshots, AOF append‑only logs, and the hybrid RDB+AOF mode—covering their architectures, trigger methods, performance trade‑offs, rewrite processes, interview‑focused questions, and practical recommendations for production environments.
This guide walks Java backend developers through Redis’s eight core scenarios—caching, distributed locks, rate limiting, session sharing, leaderboards, counters, message and delay queues, bitmap statistics, and geolocation—providing complete code, diagrams, and production‑grade best practices.
A night‑time SMS billing attack that drained ¥11,500 in two hours exposed flaws in a naïve Session‑based verification design, prompting a detailed, five‑layer defense architecture that combines gateway rate limiting, Redis token‑bucket controls, advanced captcha tracking, device fingerprinting, blacklist automation, and honey‑pot tactics to raise attack costs.
This article analyzes the constraints of loading a billion 1‑KB log records from HDFS/S3 into MySQL, evaluates single‑table limits, proposes batch inserts, sharding, file‑reading techniques, task coordination with Redis, Redisson, and Zookeeper, and offers practical performance‑tuning recommendations.
This article examines the evolution of Redis distributed lock implementations—from the basic SETNX command to atomic SET with Lua scripts and the feature‑rich Redisson framework—detailing core requirements, common pitfalls, Go code samples, and production‑grade recommendations.
This article analyzes the fundamental conflict between stateful AI agents and the inherently stateless, distributed nature of modern web services, explores time, state, and execution model mismatches, and presents a practical Agent‑as‑API solution using FastAPI, Redis, SSE, and Kubernetes to achieve scalable, fault‑tolerant deployments.
This tutorial walks through why API rate limiting is a critical first line of defense for backend services, compares fixed‑window and sliding‑window strategies, and shows how to create a custom @RateLimit annotation, utility classes, an AOP aspect, Redis or local storage, and unified exception handling, providing complete code and test scenarios for Spring Boot 2.7.x.
This article launches a new series that walks through building a complete technical blog system from the ground up, detailing the chosen Spring Boot and Vue3 stack, phased feature roadmap, database schema, project structure, and initial unified response and global exception handling code.
This article reviews the main Redis deployment options—including single‑node, master‑slave with Sentinel, sharding via consistent hashing, and Redis Cluster—explaining their advantages, high‑availability mechanisms, scalability limits, and recommending suitable scenarios for each architecture.
The article explains JWT fundamentals, why pure stateless tokens cannot be revoked, compares blacklist and whitelist Redis strategies, discusses token renewal with double‑token design, and shows how to articulate the right answer in an interview.
The author recounts five critical production incidents that crippleed an education mini‑program—Redis connection‑pool exhaustion, duplicate bookings, double refunds, mis‑firing no‑show jobs, and inventory oversell—detailing root causes, concrete fixes, and hard‑won lessons for building resilient backend services.
The article explains why merely adding @Cacheable is insufficient and introduces four essential Redis design patterns—penetration protection, avalanche mitigation, serialization control, and consistency handling—plus client‑level considerations, each illustrated with concrete code and diagrams.
In a recent interview question about placing authentication tokens in Redis, experts debate the trade‑offs between pure JWT statelessness and Redis‑backed session control, covering security risks, performance, token revocation, sliding expiration, and practical implementation guidelines.
This article examines how PostgreSQL can take over Redis' roles—caching, pub/sub, and job queues—by leveraging unlogged tables, LISTEN/NOTIFY, and SKIP LOCKED, presenting cost savings, benchmark results, migration steps, and code examples for a practical backend replacement.
This comprehensive guide explains why cache penetration is a high‑risk issue for high‑concurrency systems, distinguishes it from cache breakdown and avalanche, and presents a layered, production‑grade defense that combines parameter validation, gateway rate‑limiting, empty‑object caching, Bloom filters, local caches, distributed locks, and observability to protect both Redis and the underlying database.
The article walks through a step‑by‑step integration of Redis caching into a Spring Boot application, showing how to add dependencies, configure connections, enable caching annotations, customize serialization, simulate slow data sources, and fine‑tune TTLs to turn laggy endpoints into smooth, high‑throughput services.
Redis offers far more than simple key‑value caching; by leveraging its rich data structures—strings, hashes, lists, sets, sorted sets, bitmaps, HyperLogLog, GEO, and streams—developers can implement distributed locks, rate limiting, leaderboards, session storage, counters, geolocation, delayed queues, messaging, bloom filters, and more, all with concise commands.
This article presents sixteen practical Redis use cases—from simple caching and distributed sessions to global IDs, rate limiting, bitmaps, shopping carts, timelines, message queues, likes, tags, filtering, follow relationships, and ranking—each illustrated with commands and code snippets for real‑world backend development.
This guide explains why distributed locks are needed, compares three implementation stages—from basic SETNX+EXPIRE to atomic SET NX EX with Lua scripts, and finally Redisson and RedLock—provides code samples, interview questions, and practical tips for Java developers.
This article walks through the core contradictions of flash‑sale systems, presents a layered architecture using CDN, Nginx, Redis, MQ and a database, explains key implementation details and common pitfalls, and offers incremental optimization steps for building a robust, high‑concurrency service.
This article provides a comprehensive technical analysis of Redis Cluster, covering its data sharding via 16,384 hash slots, master‑slave replication, automated failover mechanisms, and the essential role of smart clients in routing, along with practical engineering guidelines and production‑grade code examples.
The article chronicles the step‑by‑step evolution of a small team’s chaotic food‑delivery backend into a production‑ready system, detailing how they introduced layering, transactions, security, caching, async processing, messaging, observability, scalability, resilience, containerization, and testing using Spring Boot, Kafka, Redis, JWT, and Docker.
The article analyzes the debate over placing JWT tokens in Redis, compares traditional session and JWT approaches, discusses security and performance trade‑offs, shows practical blacklist code, and explains when a centralized store is justified versus when true stateless JWTs are preferable.
This article walks through the design and production‑grade implementation of a high‑throughput sign‑in service using Spring Boot, Redis BitMap, Lua scripts, and asynchronous reward processing, covering why traditional relational tables fall short, key generation, offset calculation, continuous‑sign‑in logic, architecture layering, scaling, monitoring, and testing strategies.
This article explains how the Guardian Spring Boot starter provides a lightweight solution for preventing duplicate API submissions and applying rate‑limiting, covering Maven dependencies, annotation and YAML configurations, key generation strategies, response handling modes, Redis versus local storage, context‑path compatibility, concurrency safety, and built‑in monitoring features.
This article compares three practical approaches—direct database storage, a Flink‑Kafka‑Redis‑Grafana pipeline, and an ELK stack—to achieve real‑time API request counting for high‑concurrency scenarios, outlining their architectures, advantages, and trade‑offs.
This guide walks operations engineers through building a complete Redis monitoring system—covering why monitoring matters, which metrics to collect, how to gather them with Prometheus and Grafana, and practical Bash scripts for health checks, memory, persistence, replication, client connections, and alert thresholds.
This article explores sixteen practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tags, filtering, follow relationships, and ranking—demonstrating how Redis can serve as a versatile data store beyond simple caching.
This article presents a comprehensive guide to 16 practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, queues, lotteries, likes, product tagging, filtering, social graphs, and leaderboards—complete with command examples, code snippets, and visual illustrations to help developers implement these patterns efficiently.
This article explains why and how to use SpringBoot's built‑in cache annotations—@Cacheable, @CacheEvict, @CachePut and @Caching—covering environment setup, annotation attributes, practical code examples, common pitfalls, and a concise comparison to help developers simplify caching logic and keep data consistent.
This article explains why Redis is essential for Spring Boot projects, walks through adding dependencies, configuring connection and serialization, demonstrates CRUD operations for all Redis data types, shows @Cacheable usage, and lists common pitfalls with practical solutions.
This article presents a comprehensive guide to 16 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‑recommendation models, and ranking—complete with code snippets and data‑structure examples.
This article presents a comprehensive, production‑grade design for a log collection and analysis pipeline that can reliably handle ten‑million‑level concurrent writes by combining Filebeat, Redis, Logstash, and Elasticsearch with careful buffering, scaling, and observability strategies.
The article explains what constitutes a Redis big key, outlines its performance and stability hazards—including bandwidth consumption, request timeouts, memory overflow, and cluster disruption—and presents practical commands and incremental deletion techniques to locate and safely remove such keys.
This article explains why oversized Redis values cause performance issues and presents four practical techniques—splitting the key, compressing the value, applying TTL expiration, and monitoring usage—to mitigate large‑key problems.
This article explains how to implement a SpringBoot HandlerInterceptor to perform login authentication, configure it with a whitelist, return a unified 401 JSON response, and extend it with Redis token validation, advanced usage, and a comparison with servlet filters.
This article presents a production-ready multi-level caching architecture for Spring Boot 3 using Caffeine as L1, Redis as L2, and a database as L3, detailing design goals, hotspot mitigation, consistency handling, failure protection, implementation code, monitoring, and performance evaluation for high-concurrency e-commerce scenarios.
This article explains how to design and implement a three‑level cache pyramid (Caffeine → Redis → MySQL) in Spring Boot 3, covering configuration, a reusable CacheTemplate, hot‑key handling, random TTL, warm‑up, monitoring, and load‑test results that show latency dropping from tens of milliseconds to a few milliseconds while cutting CPU and network usage dramatically.
This article breaks down Redis performance limits, showing that a single node can handle roughly 100‑200k simple GET/SET QPS and up to 500‑700k with multithreaded I/O, while sharded clusters can theoretically reach millions of QPS, though practical factors affect the actual throughput.
This article presents a threat‑model‑driven design for securing private files in Spring Boot, detailing a signed‑URL scheme, RBAC/ABAC access control, Redis‑Lua distributed rate limiting, storage abstraction, HTTPS delivery, observability, key rotation, and production‑grade configuration and deployment practices.
The article explains why traditional Cron‑based scheduling fails under high load, walks through three architectural stages—from database polling to Redis delay queues and finally a full distributed scheduler—showing code examples, design trade‑offs, and performance numbers that enable thousands of tasks per second.
The article explains why AI‑generated code often fails when prompts lack clear context, demonstrates real comparisons between vague and detailed requests, and provides a practical three‑step framework—background, purpose, and requirements—to craft precise prompts that yield reliable, production‑ready code.
Facing slow page loads in a legacy WAF reporting system, we dissected a 1000‑line Java method, introduced hourly aggregation, Redis auto‑increment counters, and scheduled synchronization, eliminating costly SQL scans and achieving sub‑second queries on 1.5 million logs, while outlining remaining optimization opportunities.
This article explains how Redis achieves lightning‑fast performance with a single‑threaded architecture by leveraging in‑memory operations, IO multiplexing, and event‑driven design, and it details the four key advantages of this approach as well as the evolution introduced in Redis 6.0.
This guide walks through ten classic Redis scenarios—caching, session storage, rate limiting, leaderboards, message queues, Pub/Sub, real‑time analytics, distributed locks, geospatial queries, and shopping carts—providing Go code examples, best‑practice tips, and performance considerations for each pattern.
This article explains why adding Redis alone may still be slow, introduces a three‑level cache pyramid (Caffeine L1, Redis L2, DB L3) built with Spring Boot 3, and provides complete configuration, code, warm‑up, monitoring, and benchmark results that reduce response time from 28 ms to 2 ms while cutting CPU usage by 35%.
The article dissects an Alibaba interview question on counting 100 million daily active users, showing why HyperLogLog’s error and lack of per‑user state make it unsuitable, and presents a detailed Bitmap‑based architecture—including sharding, pre‑computation, and ClickHouse integration—to achieve precise, high‑performance analytics.
Guardian is a lightweight Spring Boot starter that provides independent anti‑duplicate submission and rate‑limiting modules, offering annotation and YAML configuration, multi‑dimensional key scopes, customizable response modes, Redis or local storage, and built‑in monitoring, all illustrated with step‑by‑step code examples.
The article presents a complete, event‑driven architecture for a game achievement system that uses the Observer pattern and Redis to achieve high cohesion, low coupling, millisecond‑level performance, and easy extensibility for millions of users and thousands of achievement types.
This article provides a comprehensive, production‑ready walkthrough of implementing Redis‑based rate limiting using token bucket and sliding window algorithms, covering algorithm fundamentals, code examples, performance testing, multi‑layer architecture, dynamic configuration, and best‑practice recommendations for high‑traffic backend services.
The article explains how Redis guarantees atomic operations through single‑command atomicity, MULTI/EXEC transactions, and the WATCH optimistic‑lock mechanism, providing Java/Jedis code examples and discussing their role in ensuring data consistency in distributed systems.
Discover how to transform a basic Beego V2 project into a high‑performance, concurrent service by replacing reflection‑based controllers with functional routing, implementing a distributed Redis‑Lua token‑bucket rate limiter via AOP filters, and securing the built‑in Toolbox with essential production hardening steps.
In high‑concurrency Java services, a Redis lock can appear to expire before its TTL because JVM Stop‑The‑World pauses freeze the application clock while Redis continues counting down, leading to lock loss and data races unless mitigated with watchdogs or optimistic locking at the database level.
This guide explains three ways to automatically cancel orders that remain unpaid for 30 minutes in a Spring Boot application, covering scheduled tasks, RabbitMQ delayed queues, and Redis key‑expiration events, with complete code examples and configuration details.
This article explains three practical ways to automatically cancel orders that remain unpaid for 30 minutes in a Spring Boot application, covering a scheduled task, a RabbitMQ delayed queue, and Redis key‑expiration events, with complete code examples and configuration steps.
Facing high memory overhead, operational complexity, and scaling limits of a large Redis cluster, we migrated to DragonflyDB using a three‑stage shadow, dual‑write, and cut‑over process, achieving up to 25‑fold throughput increase, 80% cost reduction, and simpler maintenance while preserving compatibility with existing Redis clients.
This article compiles a comprehensive set of 240 Go interview questions—including fundamentals, concurrency, runtime, and related topics such as containers, Redis, and MySQL—each paired with answers, offering candidates a structured study guide to repeatedly practice and improve their chances of securing a Go development position.
This guide introduces RedisInsight, outlines its key features, provides step‑by‑step Linux installation, environment‑variable configuration, service startup, Kubernetes deployment instructions, and demonstrates basic UI usage for monitoring, CLI interaction, and memory analysis of Redis instances.
DragonflyDB, a Redis‑compatible high‑performance KV store, delivers up to 25× the throughput of Redis while using 15‑30% less memory, and can be integrated into Spring Boot projects with zero code changes, offering faster latency, better resource utilization, and seamless migration.
This article explains how to design a high‑concurrency ticket‑snatching service that can handle millions of requests by combining multi‑layer load balancing, weighted Nginx round‑robin, in‑memory stock with Redis‑backed global inventory, and Go’s native concurrency, complete with code samples and performance results.
In high‑concurrency Java services, a Redis lock that appears to have a valid TTL can still be taken by another thread when a long Stop‑The‑World pause freezes the JVM clock, causing the lock to expire on Redis while the original holder remains paused, leading to data races.
This article introduces Guardian, a lightweight Spring Boot starter that provides anti‑duplicate‑submission protection and request rate limiting, explains how to integrate it via Maven, configure it with annotations or YAML, and details its internal workflow, storage options, concurrency handling, and monitoring capabilities.
Redis Pipeline batches commands to reduce network round‑trip overhead, offering up to ~50× speedup for bulk writes, improved stability under high concurrency, and lower latency for critical services; the guide explains its mechanics, Go implementation with go‑redis/v9, performance benchmarks, suitable scenarios, and pitfalls to avoid.
Although JWT is marketed as a decentralized, stateless solution, the majority of real‑world applications still rely on a Token + Redis approach because it simplifies logout handling, avoids Redis outages, and aligns better with practical security requirements.
Redis Cluster can lose writes due to asynchronous replication, network partitions, inadequate persistence settings, or unconfirmed client operations, but enabling AOF, configuring replication acknowledgments, using Sentinel or built‑in HA, and adding client retry logic can mitigate these risks, as demonstrated with a Java Jedis example.
Engula is a Redis‑compatible, high‑performance in‑memory database that cuts memory usage by up to 50% through compression and metadata optimization, while incurring only about 10% performance overhead, and its architecture, testing methodology, and benchmark results are detailed in this article.
The article outlines the motivation, phased planning, architecture, task breakdown, implementation, and testing of a compact reminder platform built with Java 17/SpringBoot 3, Vue 3, Uniapp, MyBatisPlus, Redis, and RocketMQ, detailing each functional module and UI screen.
This guide introduces RedisInsight—a visual Redis GUI—covers its key features, provides step‑by‑step instructions for Linux and Kubernetes installation, explains environment variable configuration, shows how to start the service, and demonstrates basic usage for monitoring and managing Redis instances.
This guide walks through three defensive layers for MySQL performance—optimizing the connection pool, configuring the query cache and memory tables, and designing multi‑level application caches (including Cache‑Aside, Write‑Through, and Redis integration) with concrete code examples and best‑practice recommendations.
The article introduces Guardian, a lightweight Spring Boot starter that provides independent anti‑duplicate‑submission and rate‑limiting modules, explains why a custom solution is preferable to raw Redis locks, and walks through annotation and YAML configurations, key generation, response handling, storage options, concurrency safety, and observability.
This article explains how to build a real‑time online user counter by identifying users via tokens or browser fingerprints, storing them in a Redis sorted set, and using ZADD, ZRANGEBYSCORE, ZREMRANGEBYSCORE and ZREM commands to add, query, and clean the data.
This article explores how to design a high‑concurrency ticket‑spike service inspired by China’s 12306 platform, covering multi‑layer load balancing, local stock pre‑allocation, Redis‑based global inventory control, Go implementation details, and performance testing that demonstrates handling millions of simultaneous requests.
This tutorial explains how to add the EasyCaptcha library to a Spring Boot 3.5.0 project, covering dependency setup, generating various captcha types (arithmetic, Chinese, font‑based), converting them to images or Base64, and integrating the captcha flow with Redis in a Spring MVC controller.
This article demonstrates how to encapsulate common RedisTemplate operations—such as deleting single or multiple keys, setting expiration, checking existence, and performing String and Hash CRUD actions—by providing concrete Java code examples and step‑by‑step method implementations.
This article explains the common Redis caching tools, introduces Spring Data Redis's bound operation APIs, and provides step‑by‑step configuration examples for RedisTemplate, helping developers choose and set up a Redis client in Java applications.
This article explains how to access the Redis CLI on Linux, connect to remote servers, and provides detailed examples of common commands for each of Redis's five data types—String, List, Set, Sorted Set, and Hash—highlighting key syntax and usage patterns.
This comprehensive guide walks you through the real costs of Redis memory problems, explains the three‑layer memory architecture and five major consumers, provides a toolbox of INFO and MEMORY commands plus monitoring scripts, and offers step‑by‑step solutions for seven common issues, best‑practice optimizations, real‑world case studies, a daily checklist, and advanced techniques such as Lua scripts and smart cache warm‑up.
This article walks through the complete setup of a hybrid retrieval pipeline on two CPU‑only Linux servers using Redis 8, Qwen‑3‑Embedding vectors, and RediSearch to combine BM25 keyword scores with cosine‑based vector similarity, showing environment details, index creation, data ingestion, the hybrid_search function implementation, result normalization, and a common pitfall of forgetting to set the query language to Chinese.
This guide walks through the limitations of Redis Sentinel, introduces Redis Cluster’s sharding architecture, shows how to configure multiple nodes on a single server, demonstrates cluster creation, key slot calculation, basic CRUD tests, and explains automatic failover behavior when a master node goes down.
Using a 600k‑record IP range dataset, we built identical tables in ClickHouse and Doris, and a Redis skip‑list store, then ran three Flink‑Kafka streaming jobs to compare query latency across the three databases under varying traffic rates, revealing Redis as fastest, ClickHouse second, Doris slowest.
The article explains what occurs when a Redis distributed lock expires before a business operation completes, and details how Redisson’s watch‑dog mechanism automatically renews the lock, covering the underlying principles, configuration, code examples, and comparisons with alternative renewal approaches.
The article explains why naïve reliance on databases causes performance bottlenecks, introduces cache fundamentals, details Redis’s strengths, and walks through a full Spring Boot‑Redis integration deployed with Docker, including configuration, annotations, and practical API testing.
Redis introduces a multithreaded query engine that keeps average latency under 10 ms while delivering up to 16× higher throughput for vector‑search workloads, enabling faster retrieval‑augmented generation (RAG) applications and outperforming pure vector databases and managed Redis services in benchmark tests.
The article traces the evolution of an e‑commerce recommendation platform through three architectural versions, detailing the scalability, modularity, and configurability challenges of V1.0, the pipeline‑based improvements of V2.0, and the dynamic configuration service, AB testing, and distributed recall and prediction services introduced in V3.0.
This comprehensive guide examines why Redis persistence is critical, compares RDB snapshots and AOF logging in depth, provides configuration examples, monitoring scripts, performance benchmarks, real‑world incident analyses, and practical recommendations for selecting and tuning the optimal persistence strategy.
This article presents four practical Go rate‑limiting implementations—a sliding‑window algorithm, a token‑bucket approach, the built‑in golang.org/x/time/rate package, and a Redis‑backed distributed limiter—complete with code samples, usage guidance, and recommendations for different deployment scenarios.
The article demonstrates how to achieve a ten‑fold reduction in API response time by building a three‑level cache pyramid (Caffeine L1, Redis L2, DB L3) in Spring Boot 3, covering dependencies, configuration, core template code, warm‑up, monitoring, load‑test results and common high‑concurrency pitfalls.
This article explains what cache breakdown is in high‑traffic scenarios, why it can overload databases, and presents practical solutions such as pre‑warming hot data, scheduled cache refreshes, and multi‑level caching with code examples in Java and Redis.
The article explains how Redis eviction policies work, why configuring maxmemory and a proper policy is essential to avoid OOM crashes, details each of the eight policies, shows practical configuration and monitoring commands, and dives into the source‑code implementation of LRU/LFU eviction.
When half a million users simultaneously try to buy 5,000 concert tickets, naive ordering logic leads to race conditions and double bookings, so the article walks through the root causes, compares pessimistic, optimistic, and Redis distributed locks, and presents an industrial‑grade microservice design with a three‑stage state machine to ensure strong consistency and high availability.
