The article argues that local caches add significant consistency and management complexity, so they should be avoided unless a genuine performance bottleneck exists, illustrating the point with real‑world promotion spikes, GC concerns, and careful off‑heap testing.
This article walks through 14 real‑world failure scenarios of Spring Boot distributed caching, explains why high cache hit rates are misleading, and provides concrete analysis, code samples, and step‑by‑step recommendations for designing a resilient cache layer that isolates faults, handles hot keys, and ensures data consistency across Redis, local caches, and databases.
The article analyzes Redis distributed locks—its three‑step verification, the limited guarantees it provides, and why small‑to‑medium projects should often prefer database constraints, idempotency keys, or state machines over adding a lock that introduces extra complexity and maintenance overhead.
The article argues that seasoned developers improve system architecture by removing unnecessary components—such as redundant Redis caches or message queues—rather than continuously adding new ones, because each addition raises complexity and maintenance overhead, while simpler designs are easier to manage and evolve.
This guide explains how to build a dynamic IP blacklist for Nginx by comparing OS‑level iptables, Nginx deny rules, and application‑level checks, then detailing the chosen Nginx‑OpenResty, Lua, and Redis architecture, configuration snippets, and Lua script logic for automated blocking and rate limiting.
The article outlines three core architectural choices—channel isolation, a dedicated payment gateway, and robust callback handling with Redis and distributed locks—that together prevent a single payment channel failure from collapsing the entire payment platform.
This article details how a photo‑contest backend migrated from MySQL to Elasticsearch and, through three rounds of optimization—including multi‑level Redis anchor caching, recent‑anchor positioning, and a large‑interval‑plus‑small‑page‑anchor strategy—reduced arbitrary deep‑page response time from ten minutes to about one second, achieving a 600‑fold speedup while exposing remaining data‑drift challenges.
The article explains that although Spring Cloud Gateway offers token‑bucket rate limiting via Redis, adding a coarse‑grained Nginx limit protects the gateway from traffic spikes, reduces JVM pressure, and ensures continuous flow control even during restarts or GC pauses.
The article walks through the evolution of Redis leaderboard architectures, covering why ZSets are used, handling same‑score ordering, seasonal resets, access patterns, synchronous vs asynchronous updates, big‑key and hot‑key issues, sharding with buckets, and data durability strategies.
The article explains that while many teams focus on model accuracy, the real challenges of an AI‑powered tagging system are write spikes, timeouts, duplicate tagging, cost overruns and state inconsistency, and it proposes a five‑layer architecture, async fallback, governance and validation practices to reliably achieve a 100 k QPS target.
The article presents a comprehensive five‑layer risk‑control architecture—covering front‑end behavior verification, device fingerprinting, account profiling, IP network analysis, and backend request sequencing—designed to separate genuine shoppers from scalper scripts during high‑traffic flash‑sale events, using Redis‑based storage and dynamic thresholds to minimize false positives.
This article presents a production‑grade design for a social private‑messaging system that handles billions of messages with millisecond latency, detailing how MySQL serves as the message fact store, RabbitMQ provides low‑latency distribution, Redis manages online state and indexes, and Outbox plus idempotent consumption ensure reliability and ordering.
This article walks through ten advanced Redis features—including Bloom filters, Redisson distributed locks, delayed queues, token‑bucket rate limiting, bitmaps, HyperLogLog, GEO, Streams, Lua scripts, and RedisJSON—explaining their principles, pros and cons, typical scenarios, and providing complete Spring Boot code examples.
An intermittent Redis timeout in a Kubernetes pod was traced not to Redis or the network but to uneven soft‑interrupt handling on a single CPU core, caused by irqbalance moving interrupts across NUMA nodes, and resolved by aligning NIC queues, adjusting irqbalance, and reserving CPUs.
Redis achieves its high speed not only because it is an in‑memory, single‑threaded database with efficient data structures, but primarily thanks to I/O multiplexing, which lets a single thread manage tens of thousands of client connections, dramatically cutting thread‑switch overhead and boosting throughput up to tenfold.
This guide walks through integrating Spring AI 2.0 into a Spring Boot application, covering Maven dependency setup, model configuration, ChatClient usage for synchronous and streaming calls, Redis‑backed chat memory for multi‑turn conversations, and testing with Postman and a UI component.
The article walks through constructing a full‑stack real‑time recommendation system—from user‑behavior collection via Kafka, through Flink streaming jobs for hot‑list, user and item profiling, to storage in Redis, HBase and Elasticsearch, and finally a React/Ant Design console that visualizes the pipeline and enables debugging.
This article walks Java developers through turning Spring AI into an enterprise‑grade AI agent that can query internal databases, access a vector‑based knowledge base, enforce role‑based permissions, persist chat sessions in Redis, add full observability, and be container‑deployed with Docker and Kubernetes.
This article introduces a runnable microservices learning project built with three independent Spring Boot applications (gateway, order, stock) and middleware such as Nacos, MySQL, Redis, RocketMQ, and Seata, guiding readers through cloning the repository, starting the environment with Docker Compose, and verifying the end‑to‑end product‑order‑stock flow.
This article examines the challenges of managing user authentication across many corporate systems, explains traditional session mechanisms, explores session sharing solutions for clustered environments, and provides a detailed walkthrough of implementing single sign‑on using CAS with Java and Redis, including code samples and a comparison with OAuth2.
The article demonstrates how to create a custom @BatchProcess annotation combined with AOP to aggregate high‑frequency requests into batches, persist metadata in Redis, and process them efficiently, thereby reducing connection, I/O, and CPU overhead in distributed, high‑concurrency Spring Boot 3.5.0 applications.
Spring AI 2.0 lets Java developers integrate large‑language‑model capabilities with minimal code by adding a starter, configuring model parameters, injecting a ChatClient bean, and optionally enabling Redis‑backed chat memory for multi‑turn conversations, all demonstrated with runnable examples and screenshots.
This article walks through a production‑grade architecture that uses Spring Boot, Kafka, Redis, and MongoDB to ingest, buffer, order, and store high‑frequency vehicle GPS data from hundreds of thousands of devices while guaranteeing low latency, scalability, fault‑tolerance, and accurate replay capabilities.
After Redis switched from a BSD to a closed‑source license in March 2024, the community quickly forked Valkey, which within a year surpassed Redis in throughput, latency, and memory usage, gained support from major cloud providers and Linux distributions, and continues to accelerate while Redis struggles to regain its position.
This article provides an in‑depth guide to Redis’s advanced capabilities, covering how to use pipeline for batch commands, transactions for ordered execution, Lua scripts for atomic logic, distributed locks with proper token handling, reliable messaging with streams, and specialized data structures such as BitMap, HyperLogLog, Bloom Filter and GEO for efficient large‑scale scenarios.
This article explains WebSocket fundamentals, compares it with HTTP polling, details the handshake and frame format, and provides complete server‑side examples in Python, FastAPI, Go, and Java Spring, followed by a full real‑time chat system with private messaging, database schema, heartbeat handling, and Redis‑based online presence management.
This article explains how to implement an online user counting feature by using Redis sorted sets, covering user identification (token or browser fingerprint), adding users with ZADD, querying current online users with ZRANGEBYSCORE, and cleaning up expired entries via ZREMRANGEBYSCORE and ZREM.
The article explains how combining a local Caffeine cache with a Redis cluster in a three‑tier architecture can protect high‑traffic distributed systems from cache avalanche, detailing expiration strategies, hot‑cold data separation, fault‑tolerant fallback, consistency handling, performance benchmarks, and practical pitfalls.
This article presents a production‑grade, high‑concurrency order system design that separates responsibilities among Redis for traffic control, MySQL as the single source of truth, Kafka for event propagation, and Elasticsearch for search, while detailing state‑machine modeling, outbox patterns, seckill flow, and comprehensive observability and deployment practices.
This article walks through integrating Spring AI 2.0 into a Spring Boot project, configuring model access, implementing synchronous and streaming chat endpoints, and adding Redis‑backed conversation memory to enable true multi‑turn interactions with large language models.
The article explains how caching, especially with Redis, reduces database load in high‑traffic scenarios, but also details three cache failure modes—avalanche, penetration, and breakdown—and provides concrete mitigation techniques to keep systems stable.
This article explains why a single‑node Redis deployment is a single‑point‑of‑failure and walks through building a highly available Redis cluster using master‑slave replication, Sentinel monitoring and automatic failover, split‑brain prevention, production deployment guidelines, common pitfalls, and client‑side connection strategies.
A power outage once erased all cached sessions, counters and leaderboards in Redis, exposing the inherent risk of in‑memory data loss and prompting a deep dive into Redis persistence mechanisms—RDB snapshots, AOF command logging, their trade‑offs, hybrid persistence, and the new Redis 7 Multi‑Part AOF design—so you can choose the right strategy for your workload.
This article explains why Redis can store one million items with ten‑fold memory savings by examining each of the five core data types, their underlying encodings such as SDS, listpack, quicklist, hashtable and skiplist, and shows how automatic encoding switches affect memory usage, performance, and common pitfalls.
This tutorial walks a complete beginner through launching a Redis server with Docker, managing it with Docker Compose, adding master‑slave replication, configuring Sentinel for automatic failover, building a 3‑master‑3‑replica Redis Cluster, and provides production‑grade configuration templates and an online‑deployment checklist.
An in‑depth analysis of a custom SIP‑WebRTC video gateway built on Kubernetes, detailing dual load‑balancing via K8s and Redis RPC, a seven‑layer architecture, media mixing and aggregation pipelines, and graceful shutdown mechanisms that enable reliable SIP call integration with modern WebRTC conferences.
The article explains how a Redis‑backed Bloom filter can deduplicate activity pop‑ups for 30 million users using only 35 MB of memory, compares alternative approaches, and details the underlying bit‑array and hash‑function mechanics, false‑positive rate, parameter sizing, expiration, and practical library choices.
This article walks through Redis fundamentals, explaining its definition, internal modules, the full processing path of a SET command, essential commands, and how Redis differs from Memcached and MySQL, while also covering common connection and performance pitfalls.
The article analyzes the delayed double‑delete cache‑consistency pattern, exposing how its fixed sleep interval, thread blocking, unreliable second delete, and inability to handle concurrent writes make it unsuitable for high‑traffic systems, and it proposes safer cache‑aside alternatives.
A production incident showed Redis keys disappearing at 2 AM without any memory alarms; deep analysis revealed a short‑term memory spike caused by a surge in GET requests, client‑output‑buffer‑limit growth, and LRU eviction, leading to practical mitigation steps.
This article presents a comprehensive guide to building a production-grade SMS verification login system, covering threat modeling, multi-layer rate limiting, state management with Redis, asynchronous message handling, multi‑provider routing, token issuance and operational monitoring to ensure security, cost control, and high availability.
The article introduces Redis's official search module RediSearch, compares its memory usage and query performance against Elasticsearch, presents benchmark results showing faster indexing and four‑times higher throughput, and provides step‑by‑step installation, index commands, and Java integration examples.
This article compares leading open‑source vector databases—including Redis, Milvus, Weaviate, Qdrant, Chroma, pgvector, and Faiss—by examining their architectures, performance benchmarks, deployment models, and suitability for production AI workloads, helping readers choose the right solution for their needs.
This article introduces Redisson, a Redis‑based Java client that wraps Redis commands into familiar Java concurrency primitives, compares it with Jedis and Lettuce, explains why custom distributed locks are error‑prone, and provides step‑by‑step code for configuring, using, and integrating its core features such as locks, maps, queues, and rate limiters in Spring Boot.
The article outlines five common cache design problems—penetration, concurrency, avalanche, hot‑data management, and business‑cache coupling—illustrates each with a diagram, and provides concrete Redis‑based solutions such as placeholder values, setNX locks, random TTLs, sorted‑set queues, and a Binlog‑Canal‑MQ pipeline.
The article details DeWu's three‑year evolution of its self‑built Redis platform—covering architecture, access method changes, version upgrades, proxy rate limiting, and automated operations—that together reduced request latency by over 90% while supporting more than 1,000 clusters, 160 TB of memory and near‑10‑million QPS.
This article breaks down Redis’s core mechanisms—including its single‑threaded performance tricks, AOF and RDB persistence designs, and the evolution of high‑availability from replication to Sentinel and Cluster—using four clear diagrams to help readers master the system.
The article walks through estimating peak QPS for a 50‑million‑daily‑request system, applies safety margins, and proposes a concrete deployment stack—including LVS, Nginx, Docker‑managed business servers, Redis cluster, MySQL sharding, and Kafka/RocketMQ—to reliably sustain roughly 4,500 QPS.
The article explains that idempotency prevents the same operation from being executed twice, illustrates a real double‑charge bug, evaluates five concrete approaches—including Redis check‑then‑set, DB unique index, optimistic lock, Redis distributed lock, and a message deduplication table—details their failure conditions and suitable scenarios, and recommends combining Redis lock with a database unique index for the most robust protection.
Spring’s multi‑level caching combines Caffeine’s ultra‑fast local store with Redis’s distributed capacity to tackle high‑concurrency challenges such as read amplification, cache storms, consistency, and capacity management, offering a production‑grade design, implementation details, risk boundaries, and evolution paths for robust Spring applications.
The article explains how non‑existent data amplified by high concurrency can cause cache penetration, distinguishes it from cache breakdown and avalanche, and presents a layered defense—entry validation, Bloom filter existence checks, negative caching, and concurrent‑request convergence—plus practical code, metrics, and operational checklists for robust microservice deployments.
The article dissects the delayed double‑delete pattern for Redis‑MySQL consistency, exposing its hidden pitfalls—unreliable delay timing, thread‑blocking sleeps, fragile second‑delete retries, and concurrent‑write anomalies—then recommends cache‑aside and stronger alternatives for production systems.
The article details how the team built a high‑performance, reliable cloud‑native gateway for millions of AI sandbox VNC sessions, addressing challenges of dynamic pod IPs, multi‑stage Web VNC traffic, session consistency, and security by using OpenResty, Lua scripts, Redis‑backed routing, cookie‑based state storage, and extensive Nginx tuning.
The article introduces go-vue-admin, an open-source Go-based backend management system with a Vue3 front‑end, outlines its built‑in modules such as user, role, and monitoring, lists environment requirements and repository links, provides step‑by‑step setup commands, and offers a curated collection of free Go books and video tutorials.
This article provides an in‑depth technical overview of Redis, covering its single‑threaded model, core data structures, persistence options, master‑slave replication, Sentinel and cluster architectures, eviction policies, progressive rehash, skiplist implementation, bitmap usage for massive active‑user counting, and strategies for handling MySQL‑Redis write‑through consistency.
The article details how a sluggish batch‑processing system handling millions of records was redesigned with XXL‑JOB sharding, Redis‑based dynamic task distribution, cursor pagination, and selective transaction scopes, achieving nearly ten‑fold throughput improvement while addressing resource contention, load‑balancing, and reliable result reconciliation.
Redis sustains millions of concurrent operations not merely because it is single‑threaded, but thanks to its non‑blocking event‑loop I/O, compact in‑memory data structures, serialized command execution that eliminates lock contention, and robust production features such as replication, sharding, persistence, observability and governance.
The upgraded Redis large‑key analysis transforms troubleshooting by delivering a comprehensive memory portrait that includes type, size, TTL, and prefix distributions, plus risk highlights, enabling precise identification of performance bottlenecks without relying on guesswork.
When network timeouts or retries cause the same payment request to be processed multiple times, duplicate requests become a common failure mode in distributed systems; this article explains five practical idempotency strategies—unique DB indexes, token checks, state machines, Redis SETNX, and downstream dedup tables—and offers guidance on choosing the right approach.
This article explains the three fundamental challenges of distributed locks—availability, deadlock, and split‑brain—compares database and Redis implementations, walks through the five evolutionary steps of Redis locking, and provides a structured interview answer framework to demonstrate deep understanding.
The article examines the limitations of using UUIDs for distributed unique identifiers, compares common alternatives such as database auto‑increment and Redis, and then details the Snowflake algorithm’s structure, implementation, advantages, and drawbacks for high‑performance ID generation.
This article outlines nine practical Redis scenarios—including hot‑data caching, distributed locks with Redisson, Bloom filters for cache‑penetration protection, delayed queues using ZSet, token‑bucket rate limiting, bitmap boolean statistics, UV deduplication via Set/HyperLogLog/Bitmap, geospatial indexing, and lightweight Stream queues—explaining their motivations, implementation steps, and trade‑offs.
The article breaks down a complete flash‑sale system into four phases and explains how Redis distributed locks, CDN static pages, Nginx rate limiting, message‑queue peak shaving, and sharding together prevent overselling, crashes, and lost orders even when traffic surges dozens of times.
The article explains the concept of delayed tasks versus scheduled tasks and evaluates several backend solutions—including database polling with Quartz, JDK DelayQueue, Netty's HashedWheelTimer, Redis ZSET, Redis key‑space notifications, and RabbitMQ delayed queues—detailing their implementations, code samples, advantages, and drawbacks for automatically cancelling orders that remain unpaid for a set period.
This article details a real‑time risk control system for e‑commerce and finance built on Apache Flink 2.2.1 and CEP, featuring a dynamic Aviator rule engine, three Kafka event streams, multi‑channel output to Redis, MySQL and Kafka, a Spring Boot‑React management UI, and step‑by‑step deployment instructions.
This article details a production-grade Spring Boot multi-level caching architecture using Caffeine L1 and Redis L2 to solve database IO bottlenecks in read-heavy scenarios, covering consistency solutions via MQ async with idempotency, defenses against cache penetration, breakdown, and avalanche, and load test results showing 78K QPS with 5.2ms P99 latency.
The article explains why caching is indispensable—from nanosecond‑level CPU caches to millisecond‑level disks—covers the classic pitfalls of penetration, breakdown and avalanche, and walks through consistency strategies such as Cache‑Aside, delayed double‑delete, and Canal‑based binlog syncing for high‑concurrency systems.
This article dissects the evolution of a flash‑sale system from a simple monolithic controller to a cloud‑native, micro‑service architecture that can handle over one million requests per second, detailing traffic‑shaping, multi‑level caching, async processing, and inventory‑consistency techniques.
This guide walks through Redis Sentinel’s architecture, explains subjective and objective down states, details the leader election and failover workflow, shows step‑by‑step configuration of a three‑node Sentinel cluster, client integration in Python and Java, and provides best‑practice recommendations, monitoring metrics, and troubleshooting tips.
This article details a production-ready Spring Boot large file upload architecture covering chunked protocol design, async server-side merging with zero-copy FileChannel, OSS/MinIO direct upload via presigned URLs, resumable transfer with Redis chunk tracking, and security hardening including magic-number validation and rate limiting.
This article presents fourteen practical scenarios covering the design, pitfalls, strategies, and implementation details of distributed caching in Spring Boot, including cache breakdown prevention, write‑through vs. write‑behind, eviction policies, negative caching, secondary caches, cache‑aside pattern, warming, monitoring, consistency, and custom key generation, all illustrated with concrete code examples.
The article analyzes why a basic Redis mutex lock can cause thread blocking, latency spikes, and service collapse under high concurrency, and compares it with logical expiration and never‑expire with proactive updates, explaining their trade‑offs and how to choose the right cache‑breakdown mitigation strategy.
Although idempotent APIs can block duplicate requests, they only guarantee result correctness, while distributed locks control the execution order; the article explains why both mechanisms are complementary and necessary in high‑concurrency scenarios such as stock deduction and money transfer.
The article explains how a production‑grade coupon‑grab service can survive millions of requests per second by treating rate limiting as a business admission layer, separating stock capping from throttling, making explicit CAP trade‑offs, and implementing a hybrid Redis‑based token‑bucket limiter with local fallback, monitoring, and deployment best practices.
The article explains that idempotency guarantees result correctness but does not control concurrent execution order, while distributed locks serialize access; through code examples and scenarios like stock deduction and money transfer it shows why both mechanisms are complementary and often needed together.
This article provides a step‑by‑step engineering‑level walkthrough of a real Redis mining breach, covering everything from the initial alert, evidence collection, and process termination to crontab cleanup, SSH key removal, system hardening, monitoring setup, and post‑mortem analysis.
The article explains why RocketMQ inevitably delivers duplicate messages under at‑least‑once semantics, analyzes root causes in producer, broker and consumer stages, and presents a production‑grade idempotent solution that combines business keys, Redis caching, Redisson locks, a MySQL idempotent table, AOP interception, and comprehensive monitoring to guarantee exactly‑once business outcomes even under high concurrency and Kubernetes graceful shutdown.
This article analyzes how to import one billion 1 KB log records stored in HDFS or S3 into MySQL, detailing why a single table cannot hold that volume, the B‑tree index limits, batch‑write strategies, storage‑engine choices, sharding, file‑reading methods, task coordination, and reliability mechanisms.
This guide details a step‑by‑step, zero‑downtime expansion of a 3‑master‑3‑slave Redis Cluster to a 4‑master‑4‑slave setup, covering node standardization, network checks, big‑key handling, full backups, monitoring, slot migration planning, progressive migration methods, replica addition, post‑expansion validation, rollback procedures, and practical lessons learned.
The article explains why the simple fixed‑window counter is a hidden trap for high‑traffic systems, outlines five essential questions for production‑grade rate limiting, and compares three practical deployment patterns—single‑node Guava token bucket, Redis‑based distributed sliding window, and Sentinel‑driven microservice governance—complete with code and operational tips.
The article explains how massive simultaneous expiration of Redis keys can overload the single‑threaded server, causing brief request blocking, response‑time spikes, and even memory‑related issues in master‑slave setups.
The article explains the funnel model for flash‑sale systems, detailing how layered filtering—via CDN static delivery, gateway rate limiting, Redis pre‑checks, inventory validation, and final database writes—shifts cost to cheap resources, improves scalability, and incorporates time‑based traffic shaping.
This article offers a comprehensive 2026 overview of Redis commands, organized by function, with clear usage examples, return values, performance notes, and best‑practice recommendations such as avoiding KEYS in production and using SCAN, making it a practical reference for developers and architects.
An autonomous AI security tool discovered a two‑year‑old Use‑After‑Free remote code execution flaw (CVE‑2026‑23479) in Redis, detailing its discovery timeline, three‑stage exploit chain, affected versions, patches, and why AI succeeded where traditional scanners failed.
To handle the massive, short‑lived traffic of flash‑sale events, architects must combine static content delivery, Redis‑based inventory pre‑loading, asynchronous order processing, distributed rate‑limiting, stateless services, Kubernetes auto‑scaling, graceful degradation, circuit breaking, and robust monitoring to ensure reliability and prevent overload.
This guide walks through setting up a Redis Cluster with three master and three replica nodes on Rocky 9.7 VMs, covering environment preparation, source compilation, configuration files, service creation, cluster creation, verification, and production‑grade tuning.
This guide curates essential AI Agent Skills for the Java ecosystem, covering backend tools like Spring Boot, MyBatis‑Plus, and Redis, frontend frameworks such as Vue and React, installation commands, custom Skill examples, and recommended full‑stack workflows to boost project productivity.
The article explains how layered caching in Java back‑ends—combining MyBatis first‑ and second‑level caches with a service‑layer Caffeine + Redis cache—affects cache granularity, consistency, distribution, and performance, and provides concrete configuration examples, code snippets, and best‑practice guidelines.
The article examines the challenges of multiple corporate systems requiring separate logins, explains traditional session mechanisms and their limitations in clustered environments, compares session replication versus centralized storage, and presents a complete Java Spring implementation of CAS‑based single sign‑on with code samples and a discussion of differences from OAuth2.
SpringBoot developers can dramatically boost throughput and cut latency by pairing a microsecond‑level local cache (Caffeine or Guava) with Redis, using a two‑level architecture that isolates hot data in JVM memory, reduces network and serialization overhead, and provides configurable eviction policies for various use cases.
This guide shows how to install, configure, and manage MySQL/MariaDB, Redis, and PostgreSQL inside Windows Subsystem for Linux, including remote access setup, common command‑line operations, GUI client recommendations, and scripts for one‑click start/stop and backup.
An INT auto‑increment primary key can exhaust its 2‑31‑1 limit in just 80 days under 10 M QPS, so the article explains how ID consumption is amplified, the high cost of migrating to BIGINT, the single‑point bottleneck of auto‑increment, and compares Snowflake, segment, UUIDv7, and Redis distributed ID solutions.
This article provides a detailed walkthrough of the 15 most commonly used Redis commands—including key, hash, list, set, sorted‑set, and monitoring operations—showing syntax, return values, typical use cases, performance characteristics, and cautions for production environments.
The article explains JWT fundamentals, the drawbacks of pure stateless tokens, why adding a Redis blacklist or whitelist can solve revocation issues, compares the approaches, and provides a structured way to discuss these choices in a technical interview.
The article explains why the default SpringBoot cache manager is insufficient for production, then walks through creating custom Caffeine and Redis CacheManager beans, configuring expiration, key prefixes, serialization, and multi‑level caching to solve issues like cache penetration, key collisions, and performance bottlenecks.
This article explains how to implement duplicate submission prevention in Spring Boot using a token mechanism with Redis, AOP, and custom annotations, covering idempotency concepts, solution comparisons, code implementation with Lua scripts for atomic operations, and common pitfalls.
This article provides a comprehensive interview self‑test on Spring Cache, covering annotation differences, AOP internals, SpEL expressions, key generation, Redis serialization, multi‑level caching, consistency strategies, and Caffeine eviction policies, complete with code examples, diagrams, and practical recommendations.
The article explains why placing rate limiting at the Spring Cloud Gateway layer, using Redis and Lua scripts, provides a high‑performance, distributed defense against traffic spikes, and walks through three algorithms, configuration parameters, code examples, and custom error handling for robust backend services.
The article explains how CDN works as a distributed reverse‑proxy cache, details its DNS‑based load balancing, cache‑hit/miss flow, expiration policies and refresh strategies, compares CDN caching with browser and Nginx caches, outlines scenarios where CDN or Redis is appropriate, and provides typical interview follow‑up questions and practical tips.
This tutorial explains the JWT structure, shows how to add custom claims such as user ID, department and roles, implements token blacklisting for logout, handles refresh token logic, and provides step‑by‑step code and testing instructions for a Spring Security OAuth2 authentication system.
