Common file upload mistakes in production: storing full URLs, skipping compression, and public storage buckets

🚨 Without connection pooling, your DB will struggle under load Spring Boot uses HikariCP by default. But default config isn’t always optimal. 💥 Issue I faced: Under load → DB connections exhausted Root cause: Pool size too small for concurrent traffic ✅ Fix: - Tuned max pool size - Monitored active connections 💡 Takeaway: Database is often the bottleneck. Connection pooling decides how well you scale. #Java #SpringBoot #BackendDevelopment #SoftwareEngineering  #Microservices #JPA #RESTAPI  #DeveloperLife #CareerGrowth

#Post6 In the previous posts, we built basic REST APIs step by step. But what happens when something goes wrong? 🤔 Example: User not found Invalid input Server error 👉 By default, Spring Boot returns a generic error response. But in real applications, we need proper and meaningful error handling. That’s where Exception Handling comes in 🔥 Instead of handling exceptions in every method, Spring provides a better approach using @ControllerAdvice 👉 It allows us to handle exceptions globally Example: @ControllerAdvice public class GlobalExceptionHandler { @ExceptionHandler(Exception.class) public String handleException(Exception ex) { return ex.getMessage(); } } 💡 Why use this? • Centralized error handling • Cleaner controller code • Better API response Key takeaway: Use global exception handling to manage errors in a clean and scalable way 🚀 In the next post, we will create custom exceptions for better control 🔥 #Java #SpringBoot #BackendDevelopment #RESTAPI #LearnInPublic

#Post8 In the previous post, we learned how to create custom exceptions. Now the next question is 👇 How do we validate incoming request data? That’s where validation comes in 🔥 In Spring Boot, we use @Valid along with validation annotations. Example 👇 public class User { @NotNull private String name; @Min(18) private int age; } Controller: @PostMapping("/user") public User addUser(@Valid @RequestBody User user) { return user; } 👉 If invalid data is sent, Spring automatically triggers validation errors 💡 Common validation annotations: • @NotNull • @NotEmpty • @Size • @Min / @Max 💡 Why use validation? • Prevent invalid data • Improve API reliability • Reduce manual checks Key takeaway: Always validate incoming data to build robust APIs 🚀 In the next post, we will handle these validation errors globally 🔥 #Java #SpringBoot #BackendDevelopment #RESTAPI #LearnInPublic

Most developers think Spring handles 500 requests “all at once.” It doesn’t. Here’s what actually happens: Spring Boot (via embedded Tomcat in Spring Boot) uses a thread pool to handle incoming requests. Each request follows this path: → Request arrives at Tomcat → A free thread is assigned → DispatcherServlet routes it to your @RestController → Controller → Service → Database → Response is returned → Thread goes back to the pool That’s it. No magic. ━━━━━━━━━━━━━━━━━━━━ What happens when all threads are busy? → New requests are placed in a queue → If the queue is full, requests may be rejected (e.g., 503 errors depending on configuration) ━━━━━━━━━━━━━━━━━━━━ The real bottleneck isn’t traffic, it’s blocked threads. Consider this: A slow database call takes 3 seconds × 200 threads = Your system can stall under moderate load This is why backend engineers focus on: Thread pool tuning Reducing blocking operations Asynchronous processing (@Async) Efficient database access (connection pooling) Non-blocking architectures (Spring WebFlux) Key takeaway: Performance is not about handling more requests. It’s about how efficiently your threads are utilized. #Java #SpringBoot #Concurrency #BackendDevelopment #SoftwareEngineering #JavaDeveloper

🚀 Spring Boot Tip for Faster Applications One simple improvement that can make a big difference in Spring Boot applications is database connection pooling. Instead of opening a new database connection for every request, Spring Boot uses HikariCP by default to manage connections efficiently. Why it matters: ⚡ Faster response times 📉 Reduced database overhead 🔁 Better handling of high traffic A few useful configurations: • maximumPoolSize – controls the number of connections • connectionTimeout – how long a request waits for a connection • idleTimeout – closes unused connections Optimizing database connections is a small change that can significantly improve application performance. Sometimes performance improvements don’t come from complex architecture, but from tuning the fundamentals. #SpringBoot #Java #BackendDevelopment #JavaDeveloper #Microservices #SoftwareEngineering #TechTips

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

⚡ A simple thing that improved my API performance While working on a backend API, I noticed the response time was higher than expected. The issue wasn’t complex logic — it was this: 👉 Fetching unnecessary data from the database Here’s what I changed: ✔ Avoided using SELECT * ✔ Fetched only required fields ✔ Reduced multiple DB calls ✔ Added proper indexing Result? 🚀 Faster response time 🚀 Better performance 💡 Lesson: Small optimizations in database queries can make a big difference in real-world applications. What’s one small change that improved your system performance? 👇 #Java #SpringBoot #BackendDevelopment #SQL #Performance #Microservices

What I learned today while exploring Spring Boot. Instead of just going through theory, I focused on understanding how Controllers actually work in a real backend application. Here’s what I covered: 🔹 @RestController — used to expose REST APIs and return data directly (JSON/XML) 🔹 @Controller — mainly used for returning views (like in MVC-based applications) 🔹 Mapping requests properly using: → @RequestMapping (base mapping) → @GetMapping (fetch data) → @PostMapping (create data) → @PutMapping (update data) → @DeleteMapping (delete data) → @PatchMapping (partial update) 🔹 Handling client input: → @PathVariable for dynamic values in URL → @RequestParam for query parameters → @RequestBody for sending JSON data What I’m realizing is — Controllers are not just annotations, they define how the client and backend communicate. Focusing more on clarity and real usage rather than just memorizing. Learning step by step ⚡ #SpringBoot #Java #RestAPI #LearningJourney #Consistency #LearningJourney #Consistency

🚀 Learning Update: Why Single-Threaded Server is Not Enough? After building a Single-Threaded Client-Server application, I explored its real limitations — and this changed how I think about backend systems. --- ⚠️ Drawbacks of Single-Threaded Server: ❌ Handles only one client at a time → If one client is connected, others must wait ❌ Blocking nature → "accept()" and I/O operations block the entire server ❌ Poor scalability → Cannot handle real-world traffic (multiple users) ❌ Slow response under load → Requests get queued → delay increases --- 💡 Solution: Multi-Threaded Server Instead of one thread handling everything, we use multiple threads. 🔁 How it works: - Each client request is handled by a separate thread - Server can process multiple clients simultaneously --- 🔥 Benefits of Multi-Threading: ✅ Handles multiple clients at the same time ✅ Better performance and responsiveness ✅ Efficient CPU utilization ✅ Foundation of real-world backend systems --- 🧠 Real Understanding: Single-threaded server = 🚶♂️ One person handling all work Multi-threaded server = 👥 Team handling multiple tasks together --- 🚀 Next Step in My Journey: I’ll implement a Multi-Threaded Server using Thread & ThreadPool to handle concurrent client requests efficiently. --- #Java #Multithreading #BackendDevelopment #SystemDesign #LearningInPublic #JavaDeveloper #100DaysOfCode

🚀 Spring Boot — Why I stopped using Entity in request body Earlier, I used to accept Entity directly in my APIs: @PostMapping("/users") public User createUser(@RequestBody User user) { return userService.save(user); } It worked… but later I realized the problem. ⸻ Issues I faced: ✔ Unnecessary fields coming from request ✔ Hard to control what client sends ✔ Tight coupling with DB structure What I do now: Use DTO instead: @PostMapping("/users") public User createUser(@RequestBody UserDto dto) { return userService.create(dto); } ✔ Only required fields ✔ Better validation ✔ Cleaner API contract ⸻ ⭐ Simple rule: 👉 Entity → database 👉 DTO → request/response Small change, but made APIs much safer. Do you accept Entity or DTO in your request body? #SpringBoot #JavaDeveloper #BackendDevelopment #LearningInPublic #Java