The Spring Boot annotation that is silently corrupting your database. If you are a Spring Boot developer, you probably use @Transactional every day. If something fails, the database rolls back. Simple, right? But what happens when you write code like this? @Service public class UserService { // The entry point called by the Controller public void registerUser(UserDTO dto) { // ... some validation logic ... saveUserAndSendEmail(dto); } @Transactional public void saveUserAndSendEmail(UserDTO dto) { userRepository.save(new User(dto.email())); emailService.sendWelcomeEmail(dto.email()); // What if this throws an exception? } } What you think happens: If the email service fails and throws an exception, @Transactional catches it, rolls back the database insert, and the user is not saved. What ACTUALLY happens: The database commits the user anyway. Your system now has a user who never got their welcome email, and your database is in an inconsistent state. Why did it fail? The "Proxy Bypass" Trap. Spring’s @Transactional doesn't work by magic; it works by creating an AOP Proxy around your class. When an outside class (like your Controller) calls your Service, it hits the Proxy first, which opens the database transaction. BUT... if you call a method from within the same class (like registerUser calling saveUserAndSendEmail), you are using the internal this reference. You completely bypassed the Spring Proxy. The transaction never started. The rollback will never happen. The Senior Fixes: 1. The Architecture Fix: Move the @Transactional logic to a separate UserRegistrationService. Calling an external class forces you to go through the proxy. 2. The Simple Fix: Put @Transactional on the entry-point method (registerUser) instead of the internal helper method. Stop assuming annotations are magic. If you don't understand how proxies work under the hood, your data is at risk! Have you ever spent hours debugging a @Transactional rollback that just wouldn't trigger? 👇 #Java #SpringBoot #Hibernate #BackendDevelopment #Microservices #SoftwareEngineering #CleanCode #TechTips #LogicLedaMagic

🚀 Launching v1.0.7 — Spring Boot CRUD Generator (IntelliJ Plugin) Hey everyone 👋 I built this plugin to solve a problem every backend developer faces — writing the same CRUD boilerplate again and again. Today, 1000+ developers are actively using it, and I’m excited to release a major update 🎉 🔥 What’s new? 🛢️ Optional database selection (MySQL, PostgreSQL, MongoDB, H2) ⚡ Automatic dependency injection (JPA, Swagger, Security, DB drivers) 🧠 Smarter project setup 🚀 Improved IntelliJ compatibility 💡 What it does From a single JPA entity, it generates: ✔ Controller ✔ Service ✔ Repository ✔ DTO + Mapper ✔ Swagger Docs ✔ Pagination ✔ Exception Handling ✔ Optional JWT Security ⚡ Goal Help developers focus on logic, not boilerplate. Try it — link in comments 👇 Would love your feedback, suggestions, and support 🙌 #ProductHunt #Java #SpringBoot #DeveloperTools #BuildInPublic

I thought our Spring Boot API was fast… until we hit 1,000 users Everything worked perfectly in my local environment. But in production, our dashboard became painfully slow. The logs told the real story: Hundreds of SQL queries… for a single request. I had accidentally introduced the infamous N+1 Query Problem. 🔴 The Mistake I was fetching Users and their Orders using "findAll()". Since the relationship was "FetchType.LAZY" (default), Hibernate did this: • 1 query → fetch all Users • N queries → fetch Orders for each User 👉 With 1,000 users = 1,001 database calls for one page load 😬 🟢 The Fix Used "JOIN FETCH" in a custom query: @Query("SELECT u FROM User u JOIN FETCH u.orders") List<User> findAllWithOrders(); This forces Hibernate to fetch everything in a single SQL join. ⚡ The Result • Database calls: 1,001 → 1 • Response time: 5s → <200ms • Server CPU: Stable again 📌 The Lesson Performance issues often hide in “innocent” code. Don’t blindly trust default JPA behavior. Always monitor SQL logs during development — your future self will thank you. Have you ever been bitten by the N+1 problem? What’s your go-to solution — "JOIN FETCH" or "EntityGraph"? #Java #SpringBoot #Hibernate #BackendDevelopment #Performance #DatabaseOptimization #CleanCode

🚀 Building a Real-Time Spring Boot Project – Fixing Real Issues Today I worked on implementing User Registration & Login APIs using Spring Boot + PostgreSQL. 🔹 What I built: User registration with email, username, and password Secure password storage using BCrypt Login API with password validation 🔹 Issues I faced: ❌ FATAL: database "gmail" does not exist ❌ 401 Unauthorized while accessing APIs ❌ 500 Internal Server Error during login 🔹 How I solved them: Created the PostgreSQL database and fixed datasource config Updated Spring Security config to allow /auth/** endpoints Debugged login issue and found a mismatch between username & email Fixed incorrect usage of Optional<Object> → changed to Optional<User> Removed unnecessary type casting and handled exceptions properly 🔹 Key learnings: Security config plays a major role in API access Small mistakes (like wrong generics) can break the entire flow Debugging step-by-step is more powerful than guessing 💡 Real learning happens when things break and you fix them #Java #SpringBoot #BackendDevelopment #PostgreSQL #LearningInPublic #100DaysOfCode

🚀 Built a Complete Spring Boot REST API with CRUD Operations I’m excited to share my latest project where I developed a RESTful API using Spring Boot and MySQL. This project demonstrates full CRUD functionality and follows a clean layered architecture. 🔧 Tech Stack: • Spring Boot • Spring Data JPA • MySQL • REST API • RAPID API (Testing) 📁 Architecture: Client → RestController → Service → Repository →Entity-> Database 📌 Features Implemented: ✅ Create Student (POST) ✅ Get All Students (GET) ✅ Get Student By ID (GET) ✅ Update Student (PUT) ✅ Delete Student (DELETE) 🔗 API Endpoints: POST /students GET /students GET /students/{id} PUT /students/{id} DELETE /students/{id} This project helped me understand: • REST API design • Layered architecture • Database integration using JPA • Testing APIs using RAPID API CLIENT Looking forward to feedback and suggestions! #SpringBoot #RESTAPI #Java #MySQL #BackendDevelopment #SpringDataJPA #Learning #CRUD #Developer

🧠 My Spring Boot API just got a real upgrade today 👀 I implemented full CRUD operations using Spring Boot + JPA 🚀 Here’s what my API can do now 👇 ✅ CREATE → Add new data ✅ READ → Fetch data ✅ UPDATE → Modify existing data ✅ DELETE → Remove data Flow remains clean 👇 Client → Controller → Service → Repository → Database What I used 👇 ✅ Spring Boot ✅ Spring Data JPA ✅ MySQL ✅ REST APIs 💡 My takeaway: This is where backend development starts feeling real — you’re not just reading data, you’re managing it ⚡ #Java #SpringBoot #CRUD #BackendDevelopment #LearningInPublic

If your tests are slow, your learning curve is slow too—H2 can change that today. 🚀 In Spring Boot, H2 is a lightweight in-memory database that starts instantly, making it perfect for repository and integration tests. You get realistic SQL behavior without managing an external DB during development. <dependency> <groupId>com.h2database</groupId> <artifactId>h2</artifactId> <scope>runtime</scope> </dependency> spring: datasource: url: jdbc:h2:mem:testdb username: sa password: h2: console: enabled: true Use @DataJpaTest to validate repository logic in isolation and catch mapping/query issues early. A great workflow is: H2 for fast local feedback, then PostgreSQL integration tests for production parity. Common pitfall: assuming H2 behaves exactly like PostgreSQL/MySQL in every SQL edge case—it does not. Treat H2 as a speed layer, not your only verification layer. #Java #SpringBoot #BackendDevelopment

🚀 Improving SQL Performance in Spring Boot Applications While working on a backend system, I noticed some APIs were taking longer than expected to respond. After analysis, the issue was inefficient SQL queries. Here’s what I did to optimize performance: 🔍 Identified Slow Queries Used logs and query analysis to find bottlenecks ⚡ Applied Indexing Added indexes on frequently queried columns, which significantly reduced query execution time 🔄 Optimized Hibernate Usage Reduced unnecessary joins Used lazy loading where required Avoided N+1 query problem 📉 Result Improved API response time and reduced database load 💡 Key Learning: Even a well-written application can perform poorly if database queries are not optimized. ⚙️ Tech Used: Java, Spring Boot, Hibernate, SQL If you're facing performance issues, start by analyzing your queries—you might find quick wins there. #Java #SpringBoot #SQL #PerformanceTuning #Backend #Hibernate

Recently focused on improving both system reliability and user experience in a Spring Boot backend. 🎁🎁 Key improvements: • Migrated Java 11 → Java 17 to modernize the runtime and leverage newer JVM performance optimizations • Designed Excel-style filtering supporting both selection and unselection logic (complex dynamic query building) • Implemented pagination on filtered results to handle large datasets efficiently • Stored per-user filter state in Redis, allowing users to continue exactly where they left off without extra DB load • Added Optimistic Locking to prevent lost updates during concurrent modifications by users and admins Engineering focus:✔ concurrency safety✔ scalable querying✔ state management✔ performance optimization✔ Stack: 💣Java 17 💣Spring Boot 💣Redis 💣MySQL 💣REST APIs

Day 3 – Tech Stack Tech Stack used in my project: Java Spring Boot Spring Data JPA MySQL REST APIs Focused on building a scalable backend system. #TechStack #Backend