ByteDance’s recent salary announcements show higher offers up to 40K, while the company’s rigorous backend interview covers Java heap OOM analysis, JVM tuning, thread‑pool configuration, Redis expiration strategies, MQ usage, network request flow, and algorithm challenges, providing a comprehensive technical deep‑dive for candidates.
This article provides a comprehensive guide to Redis Cluster, covering its hash‑slot sharding, master‑slave replication, automatic failover, smart client routing, deployment best practices, operational tips, and the limitations you must know before adopting it.
The article dissects a high‑traffic flash‑sale interview question—how to guarantee zero order loss at 100,000 TPS when the master MySQL instance fails—by explaining the underlying performance‑consistency conflict, the three skills interviewers assess, and presenting five concrete, code‑driven solutions ranging from MySQL parameter tuning to semi‑sync replication, local message tables, group replication, and Redis‑Kafka traffic shaping.
Shopee’s 2023 backend positions offer competitive salaries ranging from 23.5k to 32k in Shenzhen, and the article provides a comprehensive interview guide covering network models, TCP handshake, HTTP/HTTPS differences, MySQL isolation levels, foreign keys, slow query optimization, JWT authentication, RBAC, and Redis sorted sets.
This article walks through the motivation, design, implementation, performance tuning, deployment, and lessons learned when creating a Golang‑based offline Redis RDB parser that efficiently identifies large keys without impacting a live cluster.
JIMDB, a Redis‑based distributed cache, introduces the Big‑Hot Key concept and a multi‑layer proactive governance framework that dynamically identifies resource‑intensive keys, automatically mitigates them, and delivers up to an 80% performance gain while dramatically improving system stability.
JIMDB, a high‑performance Redis‑based distributed cache, introduces the “Big‑Hot Key” concept to dynamically identify keys that strain CPU or bandwidth, and implements a multi‑layer active governance framework—including real‑time detection, server‑side caching, circuit‑breaker, and client‑side consistency—to dramatically reduce resource consumption and boost throughput.
This article combines salary insights for ZTE offers, practical internship interview tips, a deep dive into Spring bean‑injection annotations, a side‑by‑side comparison of MySQL InnoDB and MyISAM, Redis usage patterns, and JVM OutOfMemoryError diagnostics to help you excel in Java backend interviews.
This article explains why a single Redis shard cannot handle extreme traffic, introduces multi‑level caching, and compares four practical methods—MQ synchronization, Redis Pub/Sub, version‑check, and automatic refresh—to keep local caches consistent under heavy load.
This comprehensive guide explains the causes of Redis cache penetration, breakdown, and avalanche, and provides production‑tested solutions such as Bloom filters, distributed locks, logical expiration, random TTL, cache pre‑warming, multi‑level caching, high‑availability deployment, monitoring, and backup strategies.
Redis uses multiple expiration and eviction mechanisms—including lazy deletion, periodic scanning, and memory eviction—to balance performance and memory usage, and this guide explains each strategy, shows how to configure them, and provides Java/Jedis code examples for setting TTLs, handling large objects, preventing cache avalanches, and monitoring stats.
The article compares storing user objects in Redis using plain SET with JSON versus using HASH fields, providing code demos, benchmark results, memory and concurrency analysis, and practical guidelines on when to choose each approach for optimal performance and safety.
To serve billions of users with real‑time timelines, modern social platforms combine push‑based delivery for regular users and pull‑based retrieval for high‑profile accounts, employing hot‑cold separation, Kafka fan‑out, Redis caching, and scalable storage strategies to balance write and read loads.
This comprehensive guide explains the three major Redis cache issues—penetration, breakdown, and avalanche—detailing their causes, impacts, and production‑ready solutions such as Bloom filters, distributed locks, logical expiration, random TTL, multi‑level caching, high‑availability setups, monitoring, backup, and best‑practice recommendations.
This article explains Redis cache penetration, its causes such as malicious attacks and faulty business logic, and presents four mitigation strategies—Bloom filters, caching empty values, rate limiting, and authentication with validation—to protect database performance and system stability.
This article explains four practical idempotency solutions—token tokens, database unique indexes, distributed locks, and request content digests—detailing their concepts, core keywords, and providing ready‑to‑copy Spring Boot code examples, along with implementation tips and a comparison table to help you choose the right approach for high‑concurrency APIs.
This article explains practical techniques to stop Redis cache penetration—including using Bloom filters to pre‑check keys, caching empty responses, applying request rate limiting, and adding authentication checks—while outlining each method's advantages and drawbacks.
Learn how to restrict API calls per user within a specific time window by creating a custom @RequestLimit annotation, an interceptor that checks Redis counters, and configuring Spring Boot's WebMvc to enforce rate limiting, with complete code examples and usage instructions.
The article explains why using the KEYS command in Redis is a blocking operation that can cripple production systems, demonstrates the safe, incremental SCAN approach, and proposes an index‑based architecture or replica scans for high‑frequency or offline key‑lookup scenarios.
Learn how to set up Redis Sentinel for high‑availability caching, covering prerequisites, anti‑patterns, detailed configuration of master, replicas and Sentinel nodes, firewall rules, monitoring, failover testing, troubleshooting, performance tuning, backup, rollback and best practices—all achievable within a 30‑minute deployment.
This guide details seven critical Redis hardening steps—including timeout, syslog, strong password, non‑root execution, strict file permissions, trusted bind addresses, and command renaming—to transform insecure default settings into a verifiable, production‑ready security baseline.
This article explains Redis cache eviction policies—including noeviction, allkeys‑lru, volatile‑lru, random, ttl, and lfu—detailing when to use each, how the eviction process works, and practical solutions for cache penetration, breakdown, and avalanche to keep your backend stable under high load.
This article explores why precise per‑minute API call statistics are essential for performance bottleneck detection, capacity planning, security alerts, billing, and troubleshooting, and presents five practical implementations—including fixed‑window counters, sliding windows, AOP‑based interception, Redis time‑series storage, and Micrometer‑Prometheus integration—along with their trade‑offs and capacity‑planning guidelines.
This guide walks you through installing RedisInsight, configuring its environment variables, deploying it on Kubernetes, and using its GUI to monitor Redis metrics, edit data, and analyze memory, complete with command‑line examples and visual screenshots.
The article presents a step‑by‑step, code‑driven design of a Spring Cloud SaaS multi‑tenant solution that balances resource sharing and strict data isolation by using a shared‑database, shared‑schema approach with tenant_id filtering, ThreadLocal context, MyBatis‑Plus interceptors, Redis key prefixing, Sa‑Token session segregation, and Spring Boot auto‑configuration.
The article shares recent JD backend salary data ranging from 24k to 32k RMB per month, explains the compensation structure, and provides a step‑by‑step interview preparation guide covering project presentation, JWT, Redis, thread pools, MySQL‑Elasticsearch sync, isolation levels and performance analysis.
Explore Redis cache eviction policies, understand how strategies like allkeys‑lru, volatile‑ttl, and noeviction work, and learn practical solutions for cache penetration, breakdown, and avalanche—including Bloom filters, mutex locks, and staggered expirations—to keep your backend resilient under high load.
This article explains how to build a JWT blacklist in Spring Boot 3, covering token fingerprinting, storage options using Caffeine or Redis, request interception, configuration, and sample login/logout endpoints to revoke tokens before they expire.
This article walks through a production Redis connection timeout incident, detailing how concurrent AOF persistence and RDB snapshots caused disk I/O blockage, the diagnostic steps taken, and the optimization measures implemented to eliminate the timeouts and improve performance.
During a Double‑11 promotion, an oversized Redis cache key caused a cascade of failures—cache miss, network bandwidth saturation, and a full‑blown cache avalanche—prompting the team to implement big‑key mitigation, compression, lock‑based cache back‑source, and monitoring measures to safeguard future deployments.
This article analyzes a production incident where Redis failed to retrieve keys at 2 AM, tracing the root cause to a short‑term memory write‑full condition caused by massive GET request bursts that overflowed client output buffers, and outlines both emergency fixes and long‑term mitigations.
This article presents a complete, step‑by‑step analysis of a high‑concurrency Redis counter component that supports up to 100 000 QPS, covering business pain points, architectural design, two core counting strategies, sharding, local batch optimization, code walkthroughs, and performance benchmark results.
The article introduces Microstrategy, a US‑based BI firm with a large Hangzhou R&D center, outlines its generous 9‑5‑style work environment, details the interview stages—including written test, technical and non‑technical rounds—and provides concrete advice on self‑introduction, project presentation, system design, Canal‑MySQL sync, XXL‑Job video transcoding, Redis, MongoDB, SQL injection, Java exceptions, OOP concepts, and interview puzzles.
This guide explores ten practical Redis non‑cache patterns—including distributed locks, message queues, leaderboards, real‑time counters, Bloom filters, session stores, distributed counters, rate limiting, unique ID generation, and delay queues—detailing their data structures, key commands, and typical application scenarios.
This guide walks you through integrating SMS4J into a Spring Boot application, covering dependency setup, multi‑vendor configuration, code examples for sending verification codes, Redis‑based rate limiting, fallback handling, common pitfalls, and a ready‑to‑use project structure.
This guide explains what n8n is, compares it with Coze and Dify, and provides step‑by‑step instructions for quick installation via npx or Docker, detailed multi‑stage Dockerfile, production environment settings, database, SSL, Redis, monitoring, and backup strategies for reliable workflow automation.
The article reveals Xiaohongshu’s unusually high 2023 campus recruitment packages—over 51 w annual total—while also providing an extensive backend interview preparation guide covering TCP vs UDP differences, design patterns, workflow versus rule engines, message‑queue selection, and Redis data structures and eviction policies.
This article explains the fundamental ideas behind pessimistic, optimistic, and distributed locking, compares their strengths and weaknesses, outlines typical application scenarios such as inventory deduction, banking transfers, and cache protection, and provides concrete implementation examples using SQL row locks, version fields, CAS, Redis WATCH, and Lua scripts.
This article examines the limitations of traditional threshold‑based database inspections, introduces AI‑driven anomaly detection techniques such as DoubleRollingAggregate, SeasonalAD, and LevelShiftAD, and details practical implementations, tuning strategies, and real‑world use cases for MySQL and Redis monitoring.
This tutorial explains how to integrate Lua scripts with Spring Boot and Redis, covering Lua fundamentals, advantages, real‑world use cases, step‑by‑step implementation in Spring Boot, performance gains, error handling, security measures, and best practices for reliable backend development.
A Java Spring application repeatedly stalled due to Redis connection pool deadlocks, and the investigation walks through using system tools, JVM thread dumps, and Arthas to pinpoint the issue, then shows how proper pool configuration and safe Redis access prevent the API from freezing.
After disabling Redis in production and carefully testing on PostgreSQL 18, the author reduced p95 latency, simplified the system, and lowered operational overhead by replacing the cache with a covering index, a generated column, and a materialized view, while providing detailed query examples, configuration tweaks, and performance measurements.
This guide walks through RedisInsight’s key features, step‑by‑step Linux installation, environment‑variable configuration, service startup, Kubernetes deployment via a yaml manifest, and basic UI operations for monitoring Redis memory, connections, and data, illustrated with concrete commands and screenshots.
The article shares ZTE salary offers and interview tips, then dives into technical deep‑dives on Spring bean injection annotations, compares MySQL InnoDB and MyISAM, explains Redis usage patterns, and walks through JVM OutOfMemoryError diagnosis with concrete code and tooling examples.
This article explains how to build a high‑throughput, low‑latency, and scalable queue system in Go by leveraging Kafka, RabbitMQ, and Redis, covering core concepts, practical code examples, performance optimizations, and guidance on choosing the right solution for different workloads.
This guide explains the three major Redis cache failure patterns—avalanche, penetration, and breakdown—detailing their causes and offering concrete mitigation techniques such as staggered TTLs, empty‑object caching, Bloom filters, logical expiration, distributed locks, high‑availability clusters, and comprehensive monitoring to ensure robust high‑availability systems.
This article walks through a systematic emergency response for Redis outages, explains how Redis Cluster promotes a replica, quantifies the typical data‑loss window from hundreds of milliseconds to several seconds, and provides detailed persistence configurations (RDB, AOF, and hybrid) to minimise downtime and data loss.
Effective Redis key naming is essential for building robust, scalable applications; this guide outlines clear conventions—meaningful names, colon-separated namespaces, concise keys, proper ordering, TTL usage—and provides concrete examples across data types, common pitfalls, and a universal key template to improve readability, maintenance, and performance.
This guide explains three practical ways to achieve dynamic IP blocking in Nginx—using Fail2ban, Lua with Redis, and Nginx's built‑in limit modules—detailing configuration steps, code examples, advantages, drawbacks, and best‑practice recommendations for various deployment scenarios.
This article outlines the key building blocks of a microservice architecture—including Nginx as the traffic entry, Spring Cloud Gateway, service registration, Redis caching, MySQL persistence, Elasticsearch, message queues, ELK logging, distributed scheduling, and object storage—providing practical guidance on design choices and high‑availability setups.
This article explores four powerful Redis batch query methods—MGET, HMGET, pipeline, and Lua scripting—detailing their advantages, usage examples in Spring Boot, and practical considerations such as network latency reduction, client simplification, transaction performance, and atomic execution to enhance system efficiency under high-concurrency workloads.
The article explains why interviewers often reject flashy project tech stacks, shows how to evaluate the practicality of your own solutions, and lists common over‑engineered patterns such as unnecessary micro‑service suites, misuse of Redis, and needless sharding.
This article details how to design and implement a scalable, low‑cost content moderation pipeline that combines a local Trie + Aho‑Corasick engine, Redis‑based hot‑updates, MySQL persistence, and third‑party machine‑review fallback to achieve millisecond‑level response, high accuracy, and controllable costs.
This article walks through the design and implementation of a simplified TikTok‑style recommendation system using Spring Cloud microservices, covering business requirements, service decomposition, Eureka registration, Feign clients, Redis and Kafka integration, circuit‑breaker fallback, and end‑to‑end testing.
The article combines a surprising look at XiaoHongShu's record‑breaking graduate salaries with a comprehensive backend interview guide covering Redis advantages and drawbacks, common data structures, persistence and high‑availability, MySQL lock types, optimistic vs. pessimistic locking, message‑queue fundamentals, RabbitMQ vs. Kafka differences, deadlock avoidance, and a sample algorithm question.
This article explains how to achieve conditional queries and pagination in Redis using Sorted Sets and Hashes, outlines the implementation steps for pagination, fuzzy multi‑condition search, their combination, and discusses performance optimizations such as expiration and real‑time data handling.
This article explains why front‑end debouncing is insufficient for preventing duplicate orders, then walks through four backend strategies—local cache with AOP, Redis atomic operations, database unique indexes, and token verification—providing core principles, code examples, and pros/cons for each.
This article explains how to restrict the number of requests to a Spring Boot API within a given time window by using a custom @RequestLimit annotation, a Redis‑backed interceptor, and the necessary configuration to register the interceptor in the application.
This article explains what Redis is, why it delivers microsecond‑level performance through in‑memory storage, single‑threaded event handling, and highly optimized C code, and it covers its persistence options, memory management techniques, and practical use cases such as caching and real‑time leaderboards.
This article walks through a complete Netty TCP client demo for IoT messaging, covering project architecture, module layout, business flow, detailed code explanations—including a local BlockingQueue replacement for RocketMQ, thread‑safe client creation with Redis locks, and testing endpoints—providing a practical reference for building robust socket‑based services.
This article combines a post‑holiday job‑hunting update for Kingsoft WPS with a comprehensive interview guide covering OSI and TCP/IP models, HTTP connection handling, TCP three‑way handshake, SYN‑flood mitigation, Redis Bloom filters, CAP theorem, and I/O multiplexing techniques such as epoll.
This guide outlines practical Redis best‑practice rules covering key naming conventions, data‑size limits, expiration strategies, script and transaction usage, client connection handling, and security measures to help you build fast, stable, and secure production systems.
This article examines the diminishing premium of overseas degrees, suggests one‑year master programs for career advancement, and then provides a comprehensive Baidu interview guide covering C++/PHP fundamentals, memory allocation, networking protocols, process vs thread, Redis performance, and a complete quicksort implementation in C++.
JetCache, an open‑source Java caching library, combines local (Caffeine) and distributed (Redis) caches into a multi‑level system, offering annotation‑driven lazy loading, automatic refresh, consistency strategies, cache pre‑warming, monitoring, and safeguards against penetration, avalanche, and memory leaks.
A sudden Redis memory surge from 2 GB to 20 GB was traced to a misconfigured list-compress-depth parameter, revealing how uncompressed lists and queue backlogs can cause ten‑fold memory growth, and outlining step‑by‑step diagnostics, compression fixes, and long‑term optimization strategies.
This guide introduces a lightweight, high‑performance PHP queue library built on Redis 5.0+ Streams, detailing its features such as high concurrency, delayed tasks, multi‑producer/consumer support, ACK handling, message replay, audit mode, and provides step‑by‑step installation, configuration, and usage examples with code snippets.
This tutorial walks through why Redis is essential for Go services, how to choose and install the go‑redis client, and provides step‑by‑step code for connections, common commands, cache‑aside, distributed locks, leaderboards, and lightweight queues, followed by performance tuning and best‑practice recommendations.
This guide demonstrates how to integrate WebSocket and STOMP into a Spring Boot application, covering setup, core modules, code examples for broadcasting and targeted messages, and a cluster mode using Redis for distributed session forwarding.
This guide explains why recent Redis versions require Python, shows how to install necessary dependencies, demonstrates using redis-cli --bigkeys and custom shell scripts to locate large keys, and outlines the risks and practical solutions for handling big keys in production.
This article examines common high‑availability challenges across applications, databases, caches, message queues, containers, and GC, presenting real JD engineering cases, root‑cause analyses, and practical mitigation strategies to help engineers design more resilient systems.
This article explains why Redisson's RMapCache.size() includes expired entries, demonstrates how to obtain the exact number of currently valid key‑value pairs using keySet().size() or readAllEntrySet().size(), and provides performance tips and a real‑world e‑commerce use case.
This guide walks you through setting up a Python 3.8/Django 2.2 vulnerability scanner that uses Celery for asynchronous port and background scanning, integrates the AWVS API for vulnerability detection, and requires MySQL and Redis configuration, with detailed deployment steps and troubleshooting tips.
This article provides a detailed technical walkthrough of RAGFlow's architecture, covering its microservice design, directory layout, layered structure, cloud‑native deployment, core modules such as DeepDoc, RAG engine, Agent system, and web UI, as well as multi‑tenant isolation, streaming responses, asynchronous task handling, concurrency controls, scalability strategies, and a complete request‑lifecycle example for document upload.
This guide compiles high‑frequency Java interview questions—from core language concepts and collections to JVM tuning, MySQL indexing, Redis caching, Spring frameworks, micro‑services, and MQ reliability—providing a comprehensive reference for candidates targeting small‑to‑mid‑size companies.
This article explains Redis’s three clustering options—master‑slave replication, Sentinel high‑availability, and sharding—detailing their architectures, setup steps, synchronization mechanisms, advantages, drawbacks, and common interview questions, helping readers choose and implement the right solution for high‑performance, scalable data storage.
This comprehensive guide walks you through real‑world Redis failure stories, explains why Redis is a critical backbone for modern applications, and provides step‑by‑step high‑availability designs, troubleshooting mind maps, monitoring setups, security hardening, automation scripts, cloud‑native deployments, and future‑proofing tips for engineers.
This article provides a comprehensive comparison of JWT and Token‑Redis authentication schemes, covering their underlying principles, Java implementations, advantages and disadvantages, performance and security trade‑offs, suitable use‑cases, and practical guidance for choosing the optimal solution in modern web and mobile applications.
This comprehensive guide walks you through diagnosing and resolving Redis memory issues, covering the underlying architecture, common pitfalls such as memory leaks and fragmentation, practical diagnostic commands, automated monitoring scripts, and optimization techniques to prevent costly outages and improve performance.
This article explores the challenges of duplicate message consumption in distributed systems, explains why naive de‑duplication fails under high concurrency, and presents four progressively robust idempotency strategies—from database pessimistic locks and local message tables to a state‑machine approach with Redis or MySQL, highlighting their trade‑offs.
This article explains how using a bitmap can reduce the memory needed to store billions of integers from about 15 GB to under 500 MB, describes the bitmap concept, introduces Bloom filters, outlines their principles, advantages, common use cases, and provides Java and Redis code examples for implementation.
This article walks through essential ByteDance Java interview topics, covering thread safety fundamentals, thread‑pool configuration, Redis performance tricks, Zset and skip‑list internals, memory‑policy handling, cache‑consistency patterns, and MySQL transaction ACID properties, providing code examples and best‑practice recommendations.
This article explains Java thread‑safety concepts, how to use and configure ThreadPoolExecutor, Redis performance tricks, Zset internal structures, memory‑eviction policies, node‑failure handling, cache‑DB consistency patterns, and MySQL transaction ACID properties with MVCC differences, providing practical code examples throughout.
The article explains how to perform fuzzy queries in Redis using KEYS, SCAN, and collection‑type scans, enumerates commands that can block the single‑threaded server such as KEYS *, HGETALL, LRANGE, and provides practical mitigation strategies like incremental scans, key sharding, and asynchronous deletion.
This guide presents a comprehensive overview of Redis core data structures—String, List, Set, Sorted Set, and Hash—detailing their most common commands, practical code examples, and typical application scenarios such as counters, distributed sessions, locks, ranking lists, and shopping‑cart storage.
This article explains how Redis handles key expiration, the TTL and TTI concepts, periodic and lazy deletion strategies, their impact on persistence and replication, and the various memory eviction policies—including LRU and LFU—so developers can efficiently manage data and improve system performance.
This article details Kaltura's migration of Argo CD's built‑in Redis to AWS ElastiCache Serverless for Valkey, explaining the cost, performance, and operational challenges of the default setup, the step‑by‑step migration process, and the substantial savings and reliability gains achieved.
After a midnight outage that erased 6 GB of cache, this comprehensive guide explores Redis persistence mechanisms—RDB snapshots and AOF logs—detailing their trade‑offs, configuration nuances, performance impacts, and practical optimization techniques to ensure data safety, fast recovery, and compliance in production environments.
This article examines five frequent Redis cache anti‑patterns—cache avalanche, unbounded local cache, stale data, missing invalidation, and oversized objects—explaining their pitfalls with code examples and showing concrete fixes that dramatically improve latency, throughput, and memory usage.
This article provides a comprehensive overview of Redis, covering its core concepts, features, data types, caching patterns in Spring Boot, common cache pitfalls and solutions, performance reasons, eviction policies, persistence options, replication, and Sentinel high‑availability mechanisms.
This guide explains how Redis, a high‑performance in‑memory database, works with the RedisInsight visual tool, covering its key features, step‑by‑step installation on Linux, environment‑variable configuration, service startup, Kubernetes deployment via YAML, and basic usage for monitoring, data manipulation, and memory analysis.
This article recounts how the Ruby and Rails community propelled Git from a Linux‑kernel‑only tool to the dominant version‑control platform and helped Redis gain widespread adoption, illustrating the powerful influence of a vibrant developer ecosystem on technology evolution.
This guide explains how to implement an online user counting feature using Redis sorted sets (ZSET) in a Spring Boot application, covering user identification via tokens or browser fingerprinting, adding, querying, and cleaning up entries with zadd, zrangeByScore, zremrangeByScore, and zrem commands.
The article explains what a Redis big key is, why it harms performance, typical scenarios that generate big keys, and provides a step‑by‑step online optimization plan—including key sharding, dual‑write synchronization, progressive migration with HSCAN, gray‑scale traffic switch, non‑blocking deletion using UNLINK, and monitoring with rollback procedures—to answer the interview question confidently.
This article explains how Redis achieves ultra‑high concurrency by combining a single‑threaded event‑driven core, highly optimized in‑memory data structures, efficient I/O multiplexing, smart memory management, asynchronous persistence, and scalable clustering, allowing it to comfortably serve hundreds of thousands of simultaneous requests.
Redis, the high‑performance in‑memory key‑value store, uses a single‑threaded event‑driven architecture that handles client connections, command parsing, execution, and reply transmission; this article explains its deployment modes, core modules, and step‑by‑step command processing flow, including code snippets and key data structures.
This article explains how to evaluate system performance bottlenecks, choose between hard and soft rate‑limiting, and implement a non‑intrusive, high‑concurrency traffic‑control solution using OpenResty, Lua scripts, and Redis, covering architecture design, queue management, rate limiting, resource release, and performance testing.
Redis 8.0, released on May 1, 2025, introduces a major license shift to AGPLv3, eight new native data structures—including vector sets, JSON, and time‑series—alongside a rebuilt query engine that delivers up to 16‑fold performance gains, enhanced security, and cloud‑native capabilities.
Redis achieves million‑level concurrency by storing all data in memory, leveraging ultra‑fast read/write speeds, employing optimized data structures such as strings, hashes, lists, sets and sorted sets, using I/O multiplexing to handle many connections in a single thread, and scaling through master‑slave replication for high availability.
This article explains how to implement an online‑user counting feature using Redis sorted sets (zset) by leveraging the four core commands zadd, zrangeByScore, zremrangeByScore, and zrem, with Java/Spring code examples and optional browser‑fingerprinting for unauthenticated visitors.
An in‑depth guide compares Redis Sentinel and Redis Cluster, covering design philosophies, performance benchmarks, operational complexity, scalability, high‑availability, and migration strategies, helping architects and engineers choose the optimal solution for demanding production environments.
