Mohitt Chopra’s Post

🚀 Understanding HTTP Status Codes in REST APIs While building APIs, one important thing is not just sending data… 👉 It’s also about sending the right response status. These are called HTTP Status Codes. They help the client understand what happened with the request. 🔹 Common Status Codes ✔ 200 – OK Request was successful Example: Data fetched successfully ✔ 201 – Created New resource created Example: User registered successfully ✔ 400 – Bad Request Invalid input from client Example: Missing or wrong data ✔ 404 – Not Found Requested resource not found Example: User ID does not exist ✔ 500 – Internal Server Error Something went wrong on the server 🔹 Example in Spring Boot @GetMapping("/users/{id}") public ResponseEntity<User> getUser(@PathVariable Long id) { User user = userService.findById(id); return ResponseEntity.ok(user); } If user is not found, we can return: return ResponseEntity.status(HttpStatus.NOT_FOUND).build(); 💡 Why Status Codes are important ✔ Help frontend understand response ✔ Improve API communication ✔ Make APIs more professional ✔ Very common in interviews Understanding status codes helped me connect concepts like exception handling, validation, and API design. #Java #SpringBoot #BackendDevelopment #APIDesign #Learning

APIs started making sense to me when I stopped memorizing… and started understanding the flow 👇 At a basic level, an API is just a way for systems to communicate. But what actually happens when you hit an API? Let’s break it down: 1️⃣ Client sends a request Example: GET /users/1 2️⃣ Request reaches the server → Routed to the correct controller 3️⃣ Business logic runs → Service layer processes the request 4️⃣ Database interaction → Fetch / update data 5️⃣ Response is returned → Usually JSON So the real flow is: 👉 Client → Controller → Service → Database → Response What helped me most was understanding this: • APIs are not just endpoints • They are structured layers working together Also, things started clicking when I focused on: • HTTP methods (GET, POST, PUT, DELETE) • Status codes (200, 404, 500) • Request & response structure Still learning, but understanding this flow made backend development much clearer. If you're learning APIs, focus on the flow — not just the syntax. #BackendDevelopment #APIs #Java #SpringBoot #WebDevelopment #Developers #LearningInPublic

Spring Boot in Real Projects — Day 19 We already know how APIs return data. But what happens when your application grows and starts handling hundreds or even thousands of tasks daily whether from a single user or many users? For example: User1 → 50 tasks   User2 → 120 tasks   User3 → 300 tasks   It's simple to fetch data for user-1 and next user-2 with 120 tasks gets heavy to fetch and for the next user-3 its hard to fetch and the API response may gets slow, to solve this Pagination & Sorting come in What is Pagination? Pagination is the process of dividing data into smaller chunks (pages) and fetching only the required portion instead of loading everything at once. What is Sorting? Sorting allows us to order data based on a specific field like createdAt, title, etc. Flow Client → Controller → Pageable → Repository → Database (applies LIMIT, OFFSET, ORDER BY) → Returns Page → Response to Client #SpringBoot #Java #BackendDevelopment #Pagination #APIDesign #SoftwareEngineering

#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

🚀 Spring Boot – User API Upgrade (Full CRUD) Took my mini project to the next level by implementing complete CRUD operations in Spring Boot. 🧠 What I added: ✔️ GET "/users" → Fetch all users ✔️ PUT "/user/{id}" → Update user ✔️ DELETE "/user/{id}" → Delete user 💡 Now the API supports full lifecycle operations: Create • Read • Update • Delete 🔁 Architecture in action: Controller → Service → In-memory data → JSON response 🧪 Tested all endpoints using Postman and verified the complete flow. ⚠️ Also understood the importance of proper error handling (next step: exception handling instead of returning null). 💻 DSA Practice: • Move zeros to end (array manipulation) • First non-repeating character (HashMap concept) ✨ This step helped me understand how real backend systems manage and manipulate data efficiently. #SpringBoot #Java #BackendDevelopment #RESTAPI #CRUD #DSA #LearningInPublic #SoftwareEngineering

🚨 Fetching all data from DB is a silent performance killer I once saw an API fetching 50,000 records in one go. 💥 Result: High memory usage Slow response Occasional crashes ✅ Fix: Used pagination: Pageable pageable = PageRequest.of(page, size); 💡 Bonus: Added sorting + limit control from API 💡 Takeaway: Never trust frontend to “handle large data” Backend should enforce limits. #Java #SpringBoot #BackendDevelopment #SoftwareEngineering  #Microservices #JPA #RESTAPI  #DeveloperLife #CareerGrowth

One of the most abused annotations in Spring is @Transactional. People treat it like a “wrap everything and forget” switch. I’ve seen methods like this: 👉 save to DB 👉 call external payment API 👉 send email 👉 call another microservice 👉 update DB again All inside ONE transaction. Looks clean. Feels safe. It’s not. Because the moment you mix DB + external calls inside a transaction, things start getting messy: ⚠️ external APIs are slow ⚠️ transactions stay open longer (locks, performance issues) ⚠️ if API fails → DB rolls back, but external system doesn’t ⚠️ debugging becomes painful Now you have inconsistent systems and no clear recovery path. The better way? Keep @Transactional boring. ✔️ only DB operations inside ✔️ keep it short ✔️ do external calls before or after ✔️ think about failure scenarios explicitly @Transaction is not a safety net. It’s a boundary. Use it wrong → hidden bugs Use it right → stable systems Learned this the hard way 🙂 #Java #SpringBoot #BackendEngineering #SystemDesign #Microservices #CodeQuality

𝗔𝗳𝘁𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝗣𝗥𝘀, 𝗜 𝗸𝗲𝗲𝗽 𝘀𝗲𝗲𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀𝘀𝘂𝗲. The developer did everything right: clean code, proper repository, correct relationships. And yet, the application was making 𝟱𝟬 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 to display a single page. Nobody noticed. Until production. This is the N+1 problem. In simple terms: instead of 1 query, your app makes 1 + N, one for each item. You don't see it in your code. You see it in your logs: 1 query for orders, then 1 query per customer. 500 orders = 501 queries. The reflex fix? Switch to EAGER loading. That's often the wrong answer. The real fix is understanding 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂'𝗿𝗲 𝗹𝗼𝗮𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗲𝗻. Need the data always? use JOIN FETCH Need it sometimes? use @EntityGraph Need full control? use DTO query N+1 is not a JPA bug. It's a judgment problem: the difference between code that works locally and systems that perform in production. I've attached a visual breakdown of these 3 approaches with real examples. Have you ever shipped something that worked perfectly in dev… but broke in production? #Java #SpringBoot #JPA #Hibernate #Backend #Performance #SoftwareEngineering

Not every API should return Entity objects. Spring Boot experts use DTOs and Projections for read performance. Most developers do this: @GetMapping("/users") public List<User> getUsers() { return userRepository.findAll(); } It works… but it’s not optimal. ⸻ ❌ Problem Returning Entity: • Loads unnecessary fields • Fetches relationships lazily • Slower response • Tight coupling with DB ⸻ 🧠 Better Option 1 — DTO public class UserDto { private String name; private String email; } Mapping: return users.stream() .map(user -> new UserDto( user.getName(), user.getEmail())) .toList(); ⸻ ⚡ Best Option — Projection (Expert Way) Spring Boot supports interface-based projections: public interface UserProjection { String getName(); String getEmail(); } Repository: List<UserProjection> findAllProjectedBy(); Only required fields are fetched. ⸻ 💡 When to Use Use Entity → write operations Use DTO → API responses Use Projection → read-only optimized queries ⸻ 🧠 Expert Rule Write APIs for write flexibility Read APIs for performance ⸻ Day 18 of becoming production-ready with Spring Boot. Question: Do you use DTO or Projection for read APIs? ⸻ #Java #SpringBoot #BackendEngineering #Performance #SoftwareArchitecture

I stopped treating backend development as “just CRUD APIs” and started building systems the way they actually run in production. Recently, I designed and implemented a user management service using Spring Boot with a focus on clean architecture and real-world constraints. Instead of just making endpoints work, I focused on: • Strict layer separation (Controller → Service → Repository) • DTO-based contracts to avoid leaking internal models • Validation at the boundary using @Valid and constraint annotations • Centralized exception handling with @RestControllerAdvice • Pagination & filtering using Pageable for scalable data access • Query design using Spring Data JPA method derivation • Handling edge cases like null/empty filters and invalid pagination inputs I also implemented authentication with password hashing (BCrypt) and started integrating JWT-based stateless security. One thing that stood out during this process: Building features is easy. Designing them to be predictable, scalable, and secure is where real backend engineering begins. This project forced me to think beyond “does it work?” and start asking: How does this behave under load? What happens when input is invalid? How does the system fail? That shift in thinking changed everything. Always open to feedback and discussions around backend architecture, API design, and Spring ecosystem. #SpringBoot #BackendEngineering #Java #SystemDesign #RESTAPI #SoftwareEngineering