Spring Boot Redis Caching Performance Booster

Not Redis, but something similar to Redis 👇 Designed a Redis-like in-memory cache using LRU eviction and TTL support. Key things I focused on: • O(1) get/set using HashMap + Doubly Linked List • LRU eviction when cache is full • TTL handling with lazy expiration • Clean and modular design You can refer this code to understand low level system design of Cache System GitHub Link:- https://lnkd.in/d5fAVsrP #LowLevelDesign #SystemDesign #Caching #Java #Backend

Spring Boot Caching — Easy Performance Boost 🚀 Instead of hitting DB every time ❌ Cache the result ✅ Example: @Cacheable("users") public User getUser(Long id) { return userRepository.findById(id); } 💡 What happens: First call → DB Next calls → Cache ⚡ ✔ Faster response ✔ Reduced DB load ⚠️ Important: Choose proper cache strategy (TTL, eviction) 👉 Tools: - Redis - EhCache 🔥 Real impact: Huge performance improvement with minimal effort Are you using caching in your project? #SpringBoot #Caching #Java #BackendDeveloper

🧠 Most backend performance issues are not about code. They’re about NOT using caching correctly. After working with Java, Spring Boot, and AWS, I’ve seen this pattern a lot 👇 ⚖️ Quick breakdown: 🔹 Without caching ❌ Repeated database queries   ❌ Higher latency   ❌ Unnecessary load on your system  🔹 With caching (e.g. Redis) ✔ Faster responses   ✔ Reduced database load   ✔ Better scalability  But here’s the catch 👇 🚨 The mistake: Adding caching… without a strategy. No TTL.   No invalidation logic.   No understanding of stale data. 💡 Rule of thumb: Cache what is: • Frequently read   • Expensive to compute   • Not changing constantly  Example: User profile → ✅   Real-time stock price → ❌  Good caching is not about speed. It’s about knowing WHAT (and WHEN) to cache. Do you use caching in your backend systems? #Backend #Java #SpringBoot #Redis #AWS #Performance #SoftwareEngineering

I've just spent the holiday building 4 backend systems, each targeting a different domain: 1. ecom-poc (Go): Kafka outbox, Redis, gRPC, Elasticsearch https://lnkd.in/gJUPY2hG 2. uber-poc (Java): reactive WebFlux, Redis GEO, real-time location matching https://lnkd.in/gVGxztEe 3. chat-poc (Node.js): SocketIO, Redis Pub/Sub, presence, typing indicator https://lnkd.in/g95GrXqW 4. dropbox-poc (Go): file chunking, SHA-256 dedup, delta sync, WebSocket conflict detection https://lnkd.in/gq32ZH-G #backend #golang #java #typescript P.S. If you find it useful, you can star the repos.

💡𝗕𝗔𝗖𝗞𝗘𝗡𝗗 𝗦𝗖𝗘𝗡𝗔𝗥𝗜𝗢: 𝗣𝗥𝗘𝗩𝗘𝗡𝗧𝗜𝗡𝗚 𝗢𝗩𝗘𝗥𝗦𝗘𝗟𝗟𝗜𝗡𝗚 𝗜𝗡 𝗘-𝗖𝗢𝗠𝗠𝗘𝗥𝗖𝗘 🛒 𝗣𝗥𝗢𝗕𝗟𝗘𝗠: 1 item left, multiple users try to buy at the same time ⚠️ 𝗥𝗜𝗦𝗞: Overselling → Negative Inventory → Poor User Experience 🧠 𝗣𝗢𝗦𝗦𝗜𝗕𝗟𝗘 𝗦𝗢𝗟𝗨𝗧𝗜𝗢𝗡𝗦: 1️⃣ Optimistic Locking (DB) Use versioning → Retry on conflict 2️⃣ Pessimistic Locking Lock row → Only one user proceeds 3️⃣ Redis Distributed Lock Ensure single access across services 4️⃣ Queue-Based Approach (Kafka/SQS) 🚀 Process orders sequentially → Best for scale 🔥 𝗜𝗡 𝗥𝗘𝗔𝗟 𝗦𝗬𝗦𝗧𝗘𝗠𝗦: Combination of Queue + Cache + DB is used Always discuss → Trade-offs + Scalability + Failure Handling #SystemDesign #Backend #Java #SpringBoot #AWS #Microservices #Scalability #DistributedSystems #Kafka #Redis #HighConcurrency #Ecommerce #SoftwareEngineering #CleanCode #BackendDeveloper #TechInterview #CodingInterview #Architecture #SystemDesignInterview #Engineering #TechCareers #DeveloperLife #CloudComputing #PerformanceOptimization #LowLevelDesign

🚀 Just built a High-Performance Data Retrieval System using Redis Caching! I was exploring backend development and decided to build a Student Management API to understand how caching works in real-world applications. 🛠️ Tech Stack: → Java 17 + Spring Boot 3.2 → PostgreSQL + Hibernate JPA → Redis (Distributed Caching) ⚡ Most interesting part — the performance difference: → Without Redis (DB query): ~50-200ms → With Redis (Cache HIT): ~2-10ms → Same data, 100x faster! ⚡ 💡 Key things I learned: → How @Cacheable works behind the scenes → Cache HIT vs Cache MISS in real-time → Why big companies use Redis in production 🔗 GitHub: https://lnkd.in/gCP5J-Kk #Java #SpringBoot #Redis #PostgreSQL #BackendDevelopment #Programming #SelfLearning

🚀 Just leveled up my Spring Boot skills with Redis caching! After diving deep into Redis integration with Spring Boot, I've successfully implemented a robust caching layer for my job portal application. Here's what I learned: 🔧 Tech Stack: - Spring Boot 3.x - Redis (with Docker) - Lettuce connection pool - Jackson for JSON serialization 💡 Key Implementations: 1️⃣ Smart Caching Strategy @Cacheable(value = "companies", key = "'all_companies_admin'") @CacheEvict(value = "companies", allEntries = true) - Reduced database load by 70%+ for read-heavy operations - Automatic cache invalidation on data mutations 2️⃣ Dockerized Redis Setup: redis:  image: redis:latest  command: ["redis-server", "--appendonly", "yes"] - Persistent storage with AOF (Append Only File) - Connection pooling for optimal performance 3️⃣ Custom Serialization - GenericJackson2JsonRedisSerializer with JavaTimeModule - Polymorphic type handling for complex DTOs - TTL configurations: 30 min default, 1 hour for critical data 📊 Results: ✅ Faster API responses (5-10x improvement) ✅ Reduced database queries ✅ Scalable architecture ready for production 🐳 Pro tip: Always configure connection pooling! The difference in performance is noticeable under load. 🔗 https://lnkd.in/dXCg553r Next up: Implementing Redis Pub/Sub for real-time notifications! 🔄 #SpringBoot #Redis #Java #Caching #Microservices #BackendDevelopment #SoftwareEngineering

12/30 Performance Post 5: Most developers think Redis is just for caching. But in reality, Redis is powering some of the most critical parts of modern systems, from rate limiting and background jobs to real-time messaging and leaderboards. In this carousel, I’ve broken down practical Redis use cases in a simple and easy-to-understand way, the kind you actually use in real projects. If you're working with backend systems, this is something you should not ignore. 💡 Save this for later , you’ll definitely need it. #redis #systemdesign #backenddevelopment #softwareengineering #nodejs #webdevelopment #scalability #developers #programming #tech #learning #30daysofcode

Check out the example of AWS Lambda with Custom Runtime based on GraalVM Native Image (with version 25) using Spring Boot 4 with AWS Serverless Java Container, Spring JDBC, Hikari Connection Pool and Amazon Aurora DSQL database 👉 https://lnkd.in/ePQRJaxu If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories a star! Please also check out my website 👉 vkazulkin.com for more technical content and upcoming public speaking activities. Amazon Web Services (AWS) GraalVM #PostreSQL #Java #Serverless #Spring #SpringBoot