Slow APIs vs Broken APIs: Performance is Part of Design

#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

Day 7 — The API Latency Trap Your API feels fast locally… but suddenly takes 1.2 seconds in production. Here’s what’s really happening 👇 You’re calling multiple external services: • User API • Order API • Payment API Each takes ~400 ms. User user = userClient.getUser(id); Order order = orderClient.getOrder(id); Payment payment = paymentClient.getPayment(id); Looks clean, right? But these calls are sequential. 👉 Total latency = 400 + 400 + 400 = 1200 ms This works fine in testing… but in production, it kills user experience. ⸻ ✅ The Fix: Parallel Calls CompletableFuture<User> userFuture = CompletableFuture.supplyAsync(() -> userClient.getUser(id)); CompletableFuture<Order> orderFuture = CompletableFuture.supplyAsync(() -> orderClient.getOrder(id)); CompletableFuture<Payment> paymentFuture = CompletableFuture.supplyAsync(() -> paymentClient.getPayment(id)); CompletableFuture.allOf(userFuture, orderFuture, paymentFuture).join(); User user = userFuture.join(); Order order = orderFuture.join(); Payment payment = paymentFuture.join(); 👉 Latency drops from 1200 ms → ~400 ms ⸻ 💡 Senior-Level Insight • Don’t rely on default thread pools → use custom executors • Add timeouts + fallbacks (Resilience4j) • Prefer WebClient (non-blocking) for scalable systems ⸻ 🎯 The Lesson Sequential API calls are silent performance killers. Parallelism is not an optimization — it’s a requirement. ⸻ If your service depends on multiple APIs… fix this before production exposes it. ⸻ #BackendDevelopment #Java #SpringBoot #Microservices #SystemDesign #Performance #APIs #DistributedSystems

🌐 REST APIs: Simple in Theory, Tricky in Reality Creating APIs is easy. Designing GOOD APIs is hard. Here’s what I learned: ❌ Bad: /getUserData ✅ Good: /users/{id} ❌ Returning everything ✅ Returning only required data (DTO) ❌ Ignoring status codes ✅ Using proper HTTP responses (200, 404, 500) Small improvements = Professional APIs 🚀 What’s one API mistake you’ve seen often? #RESTAPI #Java #SpringBoot #Backend

Most APIs function correctly, but very few are designed well Swipe to understand what good REST API design actually involves Early on, I approached APIs as simple CRUD implementations define endpoints, connect services, and move on Over time, it became clear that building scalable systems requires more than that This breakdown highlights key aspects that often get overlooked • Applying REST principles beyond basic implementation • Choosing the right HTTP methods based on intent • Structuring resources in a clear and consistent way • Using status codes and headers effectively • Considering authentication, caching, and rate limiting from the start The shift from writing endpoints to designing systems changes how backend development is approached What aspects of API design have been the most challenging in your experience #BackendDevelopment #Java #SpringBoot #RESTAPI #SoftwareEngineering #SystemDesign #JavaDeveloper

Recently worked on improving API performance in a backend system ⚡ 📉 Problem: High response time under load 🔧 What I did: Optimized DB queries Introduced caching Refactored inefficient logic 📈 Result: ~40% performance improvement 🚀 💡 Lesson: Performance issues are rarely about one thing — it’s always a combination. Small improvements → Big impact. #BackendDevelopment #PerformanceOptimization #Java #Engineering

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

@Service in Spring Boot @Service is used to define the business logic layer in a Spring Boot application. It tells Spring: “This class contains the core logic of the application.” Key idea: • Processes data • Applies business rules • Connects Controller and Repository Works closely with: • @Repository → Fetches data • @RestController → Handles requests In simple terms: @Service → Handles Logic Understanding @Service helps you keep your application clean, organized, and maintainable. #SpringBoot #Java #BackendDevelopment #LearningInPublic

If you haven’t heard about this, it aligns strongly with the long‑standing approach Azul has taken with Zulu Builds of OpenJDK. Worth a read for anyone building or maintaining Java‑based enterprise applications. Check it out here. #JavaDevelopers #Java #UIDevelopment #OpenJFX

If you haven’t heard about this, it aligns strongly with the long‑standing approach Azul has taken with Zulu Builds of OpenJDK. Worth a read for anyone building or maintaining Java‑based enterprise applications. Check it out here. #JavaDevelopers #Java #UIDevelopment #OpenJFX

I worked on improving the performance of one of our Java microservices, and I’m excited to share a quick win We were seeing API response times averaging around 800ms, which wasn’t ideal for user experience or system efficiency. So I took a step back and focused on a few key areas: Optimized database queries by improving indexing and reducing unnecessary calls Introduced a more effective caching strategy to avoid repeated data fetches Implemented asynchronous processing to better utilize system resources The result ? We brought response time down to 600ms — a 25% improvement. This experience reinforced something simple but powerful: small, targeted optimizations can make a big impact when you approach problems systematically. Always learning, always iterating #Java #Microservices #BackendDevelopment #SoftwareEngineering #PerformanceOptimization #SystemDesign #Scalability #APIs #TechCareer #BuildInPublic