Vadym Kazulkin 🇺🇦’s Post

New blog post alert 🚨 "Serverless applications on AWS using Lambda with Java 25, API Gateway and DynamoDB – Part 2 Initial performance measurements". In this article, we’ll perform Lambda function initial performance measurements of the sample application using Lambda with Java 25 API Gateway and DynamoDB without any optimizations. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Amazon Web Services (AWS) Oracle #Java #Serverless https://lnkd.in/ejJYXn_4

New blog post alert 🚨 "Serverless applications on AWS using Lambda with Java 25, API Gateway and Aurora DSQL – Part 2 Initial performance measurements". In this article, we’ll perform Lambda function initial performance measurements of the sample application using Lambda with Java 25 API Gateway and Aurora DSQL without any optimizations. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Amazon Web Services (AWS) Oracle #Java #Serverless #PostgreSQL https://lnkd.in/grbkixBZ

New blog post alert 🚨 "Serverless applications on AWS with Lambda using Java 25, API Gateway and DynamoDB – Part 1 Sample application". In this article series, we’ll explain how to implement a serverless application on AWS using Lambda with the support of the released Java 25 version. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Maximilian Schellhorn Andrei Shakirin Lefteris Karageorgiou Matt Meckes Yuriy Bezsonov Raluca Constantin Philipp Page James Ward Amazon Web Services (AWS) Oracle #Java #Serverless https://lnkd.in/eAwyKCQq

💡 How We Reduced Microservice Latency by 70% in a Java + Spring Boot System One of the biggest challenges in large-scale microservices systems is service-to-service latency. In one of our systems, we had this flow: Client → API Gateway → Service A → Service B → Service C → Database The response time was ~1.8 seconds, which was unacceptable for a high-traffic application. After analyzing the architecture, we implemented 4 changes: 1️⃣ Introduced Redis caching Frequently requested data was cached instead of hitting the database repeatedly. 2️⃣ Replaced synchronous calls with Kafka events Instead of blocking REST calls between services, we used event-driven communication. 3️⃣ Added database query optimization Indexes + query refactoring significantly reduced DB execution time. 4️⃣ Enabled asynchronous processing Non-critical operations were handled using message queues and background workers. 📊 Results Response time: 1.8 seconds ➜ ~500 ms System throughput improved significantly during peak traffic. Modern backend systems are not just about writing APIs — they’re about designing scalable distributed architectures. Curious to know how others are optimizing microservices performance in Java systems. What techniques worked best for you? #Java #SpringBoot #Microservices #SystemDesign #SoftwareArchitecture #BackendEngineering #Kafka #AWS #CloudArchitecture #ScalableSystems

Check out the example of AWS Lambda with managed Java 25 runtime using Spring Boot 4 with AWS Serverless Java Container, Hibernate ORM framework, Hikari Connection Pool, and Amazon Aurora DSQL database. 👉 https://lnkd.in/e-2WYWMf 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) #Java #Serverless #Spring #SpringBoot #PostgreSQL #AuroraDSQL

JavaTip #4 – Working with S3 Buckets in Java Applications In many backend systems, storing files directly in the database is not always the best choice. For large-scale applications, object storage solutions like AWS S3 are often used for storing files such as documents, reports, and images. While working with Java backend services, integrating S3 can be a simple yet powerful way to manage file storage efficiently. Here are a few practical points I always keep in mind while working with S3: 🔹 1. Use S3 for Object Storage, not Database Storage Instead of storing large files in DB BLOB columns, storing them in S3 and saving only the file URL or metadata in the database keeps the system lightweight and scalable. 🔹 2. Proper Bucket Structure Organizing files with meaningful folder structures helps in better management. Example structure: user-documents/{userId}/files reports/{year}/{month} This makes retrieval and maintenance easier. 🔹 3. Secure Access with IAM & Pre-signed URLs Instead of making buckets public, it is safer to generate pre-signed URLs when users need temporary access to download files. This ensures: ✔ Controlled access ✔ Better security ✔ No direct exposure of bucket resources 🔹 4. Efficient Upload Handling For larger files, multipart uploads can improve performance and reliability. Cloud storage like S3 allows backend systems to scale without worrying about file storage limits. For backend engineers, understanding how application services interact with cloud storage is becoming an essential skill. #JavaTip #Java #AWS #S3 #BackendDevelopment #CloudComputing #SoftwareEngineering #OpenToWork #Java21 #JavaDevloper

Spring Framework Learning Roadmap I’ve been strengthening my knowledge in the Spring Ecosystem, which is one of the most powerful frameworks for building Java-based enterprise applications. 📌 Key areas I am focusing on: 🔹 Spring Core & Spring MVC – Dependency Injection, Annotations, Scheduling 🔹 Spring Boot – Auto-configuration & production-ready applications 🔹 Spring Data & Hibernate – JPA, JDBC, Transactions, Entity Lifecycle 🔹 Databases – MySQL, PostgreSQL, MongoDB (Queries, Joins, Indexing, Locking) 🔹 Spring Security – OAuth2, Form Authentication, Basic Auth, JWT 🔹 Testing – JPA Test, MockMVC, Service Testing 🔹 Microservices Architecture – Docker, Spring Cloud, API Gateway, Eureka 🔹 Message Queues – Kafka, RabbitMQ, AWS SQS This roadmap helps in understanding how different components of the Spring ecosystem work together to build scalable, secure, and production-ready applications. Always learning and improving! 💻 #Java #SpringBoot #SpringFramework #Microservices #BackendDevelopment #Hibernate #SpringSecurity #Kafka #SoftwareDevelopment

🚀 Day 23 – Java Backend Journey | Caching getUserById API Today I practiced applying Redis caching to a real API, specifically the getUserById endpoint, to improve performance and reduce database load. 🔹 What I practiced today I implemented caching for the GET API: GET /users/{id} This API fetches a user by ID from the database. 🔹 Caching Implementation Used Spring Boot caching with @Cacheable: 🔹 How it works • First request → Cache miss → Data fetched from DB → Stored in Redis • Next requests → Cache hit → Data returned from cache (no DB call) 🔹 What I observed ✔ Reduced database queries ✔ Faster response time ✔ Improved performance for repeated requests 🔹 Why this is important Caching frequently accessed APIs like getUserById is a common optimization technique used in real-world backend systems. It helps in: • Scaling applications • Handling high traffic • Improving user experience 🔹 Key takeaway Adding caching to APIs is a simple but powerful way to make backend systems efficient and high-performing, especially for read-heavy operations. 📌 Next step: Implement cache eviction using @CacheEvict to handle data updates. #Java #SpringBoot #Redis #Caching #BackendDevelopment #SoftwareEngineering #LearningInPublic #JavaDeveloper #100DaysOfCode

New blog post alert 🚨 "Serverless applications on AWS with Lambda using Java 25, API Gateway and Aurora DSQL – Part 1 Sample applications". In this article series, we’ll explain how to implement a serverless application on AWS using Lambda with the support of the released Java 25 version. One application uses JDBC and another Hibernate, both with Aurora DSQL. If you like my content, please follow me on GitHub (github.com/Vadym79) and give my repositories like this https://lnkd.in/epud2eRf a star! Maximilian Schellhorn Andrei Shakirin Lefteris Karageorgiou Matt Meckes Yuriy Bezsonov Raluca Constantin Philipp Page James Ward Amazon Web Services (AWS) Oracle #Java #Serverless #AuroraDSQL #PostgreSQL https://lnkd.in/eMSzrJGy

Spring Framework Ecosystem – My Learning Map While learning Spring Boot & Backend Development, I created this mind map to understand the complete Spring Framework Ecosystem. It covers important concepts like: ✔️ Spring Boot (Auto-Configuration & Production Ready Features) ✔️ Spring MVC, Dependency Injection & Annotations ✔️ Spring Security (OAuth2, Basic Auth, JWT) ✔️ Spring Data JPA & Hibernate ✔️ Databases (MySQL, PostgreSQL, MongoDB) ✔️ Microservices with Spring Cloud & Docker ✔️ Messaging Queues (Kafka, RabbitMQ, SQS) ✔️ Testing (JPA Test, Mock MVC) Creating this helped me understand how different components of the Spring ecosystem work together in real-world backend applications. Always learning and improving! 💻 #SpringBoot #Java #BackendDevelopment #Microservices #SpringFramework #LearningJourney #SoftwareDevelopment