Vadym Kazulkin 🇺🇦’s Post

Check out the example of AWS Lambda with managed Java 25 runtime using Spring Boot 4 with AWS Lambda Web Adapter, Spring JDBC, Hikari Connection Pool and Amazon Aurora DSQL database. 👉 https://lnkd.in/ewiHqJWq 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) #PostreSQL #AWS #Java #Serverless #Spring #SpringBoot

🚀 Explored Spring Boot + Redis Caching today — one of the biggest performance boosters in backend development. Here’s the quick takeaway 👇 🧠 Spring Cache → Method-level caching using AOP → Uses Cache-Aside pattern → Enable with "@EnableCaching" 💾 Default Cache (Dev) → JVM memory → No TTL, resets on restart → Not for production ⚡ Redis Cache (Prod) → Fast, in-memory datastore → Supports TTL & persistence → Perfect for scalable systems 🏷️ Key Annotations "@Cacheable" | "@CachePut" | "@CacheEvict" | "@Caching" | "@CacheConfig" 🔧 Pro Tips → Use JSON serialization (avoid JDK default) → Configure TTL smartly → Use "condition" / "unless" for better control Small daily learning = Big long-term growth 💪 #SpringBoot #Redis #Java #BackendDevelopment #Caching #Learning #SoftwareEngineering

🚀 Python on AWS – Scalable Backend Systems Built and deployed backend systems using Python (FastAPI/Django) on AWS, focusing on scalable and high-performance architectures. ☁️ AWS (EC2, Lambda, ECS, EKS, S3, RDS, DynamoDB) ⚙️ REST APIs & Microservices 🔄 Docker, CI/CD (Jenkins, GitHub Actions) 📊 Redis caching & performance optimization 🔐 IAM, security best practices & encryption 🗄️ Database design (PostgreSQL, NoSQL) Always exploring better ways to build cloud-native, distributed systems. #Python #AWS #CloudComputing #Microservices #BackendDevelopment #DevOps #SystemDesign

What’s new in #Java? The fifth preview of Primitive Types in Patterns, instanceof and switch; the proposed release schedule for JDK 27; point releases of Hibernate, LangChain4j, Keycloak and Google ADK for Java; a maintenance release of Helidon; a CVE in Spring Cloud Gateway; and the Junie CLI integrated in JetBrains IDEs. Read more 👉 https://bit.ly/3QGmtFa #InfoQ #JavaUpdates #JavaNews

🚀 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

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

𝐉𝐨𝐛 𝐓𝐢𝐭𝐥𝐞: 𝐒𝐑𝐄 𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧: 𝐑𝐞𝐦𝐨𝐭𝐞. 𝐉𝐃:    Experience in Linux and Python, Shell scripting. Experience of maintaining production systems on AWS and/or GCP. Experience of Kubernetes clusters maintenance, managing and debugging containerized applications (Golang, Java, Python). Understanding of Kafka, Spark, Storm, Cassandra, ElasticSearch, PostgreSQL, Redis (Elasticache), Zookeeper, Nginx, AWS S3/GCP GS. Understanding of infrastructure as code software (e.g. Terraform, AWS and Google Cloud Deployment, CloudFormation). Experience in continuous integration practices & tools (Jenkins, Travis CI, CircleCI, etc. Experience with monitoring solutions such as: CloudWatch, Stackdriver, Prometheus, Thanos, Graphite, Grafana, ELK, Alert Logic, Datadog. Experience with logging service solutions.

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

Optimized a backend system to handle high traffic using Java and Spring Boot. Faced an issue where APIs were slowing down under load. What I did: - Refactored monolithic services into microservices - Introduced asynchronous processing using Kafka - Optimized database queries by reducing redundant joins - Implemented caching using Redis Result: - Improved response time by approximately 40% - Increased system scalability for concurrent users Key takeaway: Performance tuning is not just about code—it’s about architecture. #Java #SpringBoot #Microservices #Kafka #AWS #BackendDevelopment