Vadym Kazulkin 🇺🇦’s Post

I'm currently working on updating my examples to measure the performance of the #AWS Lambda function using #Java runtime (the managed version, like 25 or deployed as a GraalVM Native Image as AWS Custom Runtime) and Spring Boot 4. I started with providing the examples using AWS #Serverless Java Container for Spring Boot 4. See my GitHub repo 👉 https://lnkd.in/eKafFg7p I previously always used NoSQL Amazon #DynamoDB in my examples, and now I would like to add some examples of using the relational Amazon Aurora DSQL database to compare the results. For this, I use a simple application to store and retrieve products. You can already see some working examples below (others will be added later). Lambda cold and warm start measurements and optimization techniques (Lambda SnapStart with primings) will be published in my upcoming articles. AWS Lambda with Managed Java 25, Spring Boot 4, AWS Serverless Java Container for Spring Boot 4 : 1) With DynamoDB 👉 https://lnkd.in/eg-FMF4R 2) With Spring JPA (Hibernate as provider), Hikari CP and Aurora DSQL 👉 https://lnkd.in/e7zbh3QQ AWS Lambda as a Custom Runtime containing GraalVM Native Image built with 25.0.2 version, Spring Boot 4, AWS Serverless Java Container for Spring Boot 4 : 1) With DynamoDB 👉 https://lnkd.in/ex5wJJMQ 2) With Spring JPA (Hibernate as provider), Hikari CP and Aurora DSQL 👉 https://lnkd.in/eTW6JUZH 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. Maximilian Schellhorn Andrei Shakirin Lefteris Karageorgiou Matt Meckes Yuriy Bezsonov Marc Bowes Raluca Constantin Philipp Page James Ward Amazon Web Services (AWS) Oracle #Hibernate #PostgreSQL #AuroraDSQL #Spring #SpringBoot

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

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

Day 47 & 48 – Spring Boot + PostgreSQL Integration Complete 💾🚀 Successfully connected my Spring Boot URL Shortener project to PostgreSQL and verified the complete backend flow end-to-end. What I did: ✅ Removed H2 and configured PostgreSQL ✅ Fixed authentication & SessionFactory errors ✅ Auto-created table using Hibernate (ddl-auto=update) ✅ Sent POST request and stored real data ✅ Verified insertion using SELECT * FROM url_mapping; Flow now working: Request → Controller → Service → JPA → PostgreSQL → Data Stored Big takeaway: Most backend issues are configuration problems, not logic problems. Debugging calmly is a superpower. Next: Implement redirect logic + improve validation. #SpringBoot #Java #PostgreSQL #BackendDevelopment #LearningInPublic #100DaysOfCode

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

𝗢𝘃𝗲𝗿 𝘁𝗵𝗲 𝗽𝗮𝘀𝘁 𝗳𝗲𝘄 𝗱𝗮𝘆𝘀, 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗦𝗽𝗿𝗶𝗻𝗴𝗕��𝗼𝘁-𝗔𝗣𝗜, 𝗮 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀 𝘂𝘀𝗶𝗻𝗴 𝗝𝗮𝘃𝗮 𝗮𝗻𝗱 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁. After working with the PERN stack, I wanted to go deeper into backend engineering with Java + Spring Boot and learn how production-style APIs are typically designed (security, caching, scalability, and clean architecture). 𝗪𝗵𝗮𝘁 𝗜 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱: • JWT Authentication + Refresh Token flow (refresh token via HTTP-only cookie + stored with TTL in Redis). • Role-based access control (USER / ADMIN protected routes). • Validation for request/data integrity (required fields + controller-level validation). • Rate limiting using Bucket4j to protect endpoints from abuse. • Redis caching using Spring Cache annotations to improve performance. • Pagination + sorting + filtering for scalable endpoints. • Dockerized setup with Docker Compose so the full stack (Spring Boot + PostgreSQL + Redis) runs locally with one command. This project helped me understand how Spring applications are structured, and how backend concerns like security + performance fit together. Github Link: https://lnkd.in/gsPY9vpk #SpringBoot #Java #BackendDevelopment #MERN #RESTAPI #PostgreSQL #Redis #Docker #DockerCompose #JWT #SoftwareEngineering #CollegeProjects #LearningByBuilding

🚀 Today’s update on my Library Management App (Spring Boot + PostgreSQL) Worked on the Category feature and connected it properly to Book creation. Ran into an issue where books weren’t saving through the API. Traced it through Hibernate logs and found the root cause the category ID being passed didn’t exist in the database. Once that was fixed, the flow worked exactly as designed: validate → persist → done. Also refined DTO ↔ Entity mapping to keep the data flow clean and maintainable. Lesson learned: A lot of backend “bugs” aren’t logic problems they’re data consistency issues. Always verify your data before doubting your code. Building step by step. Solid over flashy 👍 . #Java #SpringBoot #BackendEngineering #PostgreSQL

Built a modular microservice platform combining Job, Company, and Review services using Spring Boot, PostgreSQL, and Docker. Each service manages its own data but communicates via APIs.

🚀 Day 21 – Java Backend Journey | Redis Basics Today I focused on learning the basics of Redis, a powerful in-memory data store widely used for caching and high-performance applications. 🔹 What I practiced today ✔ Understanding Redis Redis is an in-memory key-value database that stores data in RAM, making it extremely fast compared to traditional databases. ✔ Basic Redis Commands I explored some commonly used Redis commands: Set value SET user:1 "Akash" Get value GET user:1 Delete key DEL user:1 Check all keys KEYS * ✔ Working with Data Structures Redis supports multiple data types: • Strings → Simple key-value pairs • Lists → Ordered collection • Sets → Unique values • Hashes → Key-value pairs inside a key Example: HSET user:1 name "Akash" email "akash@gmail.com" HGETALL user:1 🔹 What I learned • Redis works as a NoSQL database • Data is stored in memory for ultra-fast access • Supports multiple data structures • Used in caching, session management, and real-time systems 🔹 Why Redis is important ✔ High performance (low latency) ✔ Reduces database load ✔ Supports scalable backend systems ✔ Widely used in real-world applications 🔹 Key takeaway Redis is not just a cache—it’s a powerful in-memory data store that plays a critical role in building fast and scalable backend systems. 📌 Next step: Implement Redis caching in real APIs and explore advanced features. #Java #SpringBoot #Redis #BackendDevelopment #Caching #SoftwareEngineering #LearningInPublic #JavaDeveloper #100DaysOfCode