Improved API Performance by 40% with DB Optimization and Caching

Recently, I shared how reducing multiple API calls improved performance. This time, I ran into a different issue. My API response looked fine… until I saw the payload size. While working on a feature, everything was working as expected. But the response felt heavier than it should be. After checking, I realized I was returning full objects, even when only a few fields were actually needed. So for a single request, I was sending a large JSON response unnecessarily. I made a small change: Instead of returning everything, I started sending only what the client actually needs. Used DTOs to control the response and removed unused fields. That made a clear difference: - Smaller payload - Faster response - Cleaner API Last time, the issue was too many API calls. This time, it was too much data in one call. (Check the previous post: https://lnkd.in/gxSKFVbk) Sometimes performance issues are not about complex fixes… they’re about reducing unnecessary work. #Java #BackendDevelopment #SpringBoot #API #Performance #LearningInPublic

One thing I’ve learned working on backend systems is that not all problems need a new solution. Sometimes the first instinct is to add something new. A new service, a queue, or another layer. But in many cases, the issue is in how the current system is designed or used. I’ve seen systems improve a lot just by simplifying flows or removing unnecessary steps. Not every problem needs more architecture. Sometimes it just needs less. #SoftwareArchitecture #Microservices #BackendEngineering #Java

What actually happens when you hit a REST API? 🤔 Let’s break it down step by step 👇 1️⃣ Client sends HTTP request 2️⃣ Request hits DispatcherServlet 3️⃣ HandlerMapping finds the correct controller 4️⃣ Controller processes request 5️⃣ Service layer applies business logic 6️⃣ Repository interacts with DB 7️⃣ Response is returned as JSON 💡 Behind the scenes: - Jackson converts Java → JSON - Spring handles dependency injection - Exception handling via @ControllerAdvice ⚡ Real benefit: Understanding this flow helps you: ✔ Debug faster ✔ Write better APIs ✔ Optimize performance Next time you call an API, remember — a lot is happening inside 🔥 Follow for more backend deep dives 🚀 #SpringBoot #Java #RestAPI #BackendDeveloper

I had an API returning correct data. But response time was too high. Issue turned out to be unnecessary data being fetched from DB. Reduced fields, optimized query. Small change, big impact. #Backend #Java #SpringBoot #Performance

Logs are not observability Many teams think they have observability because they have logs. That’s not enough. When a production issue happens, I want to know: - Which endpoint degraded? - Which dependency is slow? - Which service is failing? - Which customer flow is impacted? - Where exactly the request broke? That means I need more than logs. I need: - metrics - tracing - health signals - correlation IDs - alerting In distributed systems, this becomes non-negotiable. Because once requests travel through: - API - service layer - DB - Kafka - external integrations - ... Debugging without observability becomes pure guesswork. A backend that cannot be observed Cannot be operated professionally. #Observability #OpenTelemetry #Micrometer #Java #SpringBoot #SRE #Backend

🚀 Developed a basic REST API using Spring Boot to handle HTTP requests and responses. 🔹 What I implemented: Created a REST Controller using @RestController Used @RequestMapping to define base URL (/api) Built a GET API using @GetMapping("/student") 🔹 API Endpoint: http://localhost:8080/api/student 🔹 Output: "Student data" 🔹 Key Learnings: How Spring Boot handles HTTP requests Understanding request → controller → response flow Basics of REST API development Excited to move next into POST APIs and sending real data using @RequestBody 🔥 #SpringBoot #Java #BackendDevelopment #LearningJourney #CSE 

🚀 Java Streams: Sequential vs Parallel — When to use what? A simple concept, but often misunderstood 👇 🔹 Sequential Stream → Runs on a single thread (one CPU core) → Processes data step-by-step → Lower overhead → Best for: small datasets, simple operations 🔹 Parallel Stream → Uses multiple threads (ForkJoinPool) → Splits data across multiple CPU cores → Processes tasks concurrently → Best for: large datasets, CPU-intensive operations 💡 Key Insight: Parallel streams are NOT always faster. ⚠️ They introduce: - Thread management overhead - Context switching cost - Possible issues with shared mutable state ✔️ Use Parallel Stream when: - Data size is large - Task is CPU-bound - Operations are stateless & independent ❌ Avoid when: - Small datasets - I/O operations (DB calls, API calls) - Order matters strictly 💼 Real-world example: In one of my use cases, processing large collections (like aggregations/search results) using parallel streams improved performance — but only after ensuring operations were stateless and thread-safe. ⚡ Pro Tip: Always benchmark before switching to parallel — assumptions can be misleading. #Java #StreamAPI #Java8 #Performance #Backend #SoftwareEngineerin

🚨Application is running in production and appears to be working properly. However, even when there are no API calls or incoming requests, it still results in an OutOfMemory(OOM) error. 👉Memory Leak Objects are created but never deleted - memory fills up slowly over time 👉 Background Jobs  Scheduled tasks run silently on timer - not on user requests. They consume memory 24/7. 👉Cron Jobs  Cron jobs are fire at fixed intervals - every minute, hour, or day. Each run can load large data into memory and never release it if not handled properly. 👉JVM Schedulers  Internal JVM schedulers like ScheduledExecutorService, Timer, and TimeTask run completely inside the JVM. If tasks are registered without cancellation and shutdown, they hold memory reference silently 👉Too Many Threads Each thread eats 1MB of RAM. 1000 idle threads = nearly 1 GB gone - no traffic needed. 👉Cache Has No Limit  Your cache keeps storing data but never removes old entries - it grows forever. 👉 Logging Buffers Async loggers hold messages in a queue. If it fills up, it eats your memory. 👉Wrong JVM / Container Configuration  JVM asks for 3GB , Container only has 2.5GB - when your app tried to go beyond 2.5, the OS kills your process or application silently ➡️How to Overcome Memory Leaks -> heap dump analysis  Background Jobs -> use try-finally cleanup Cron Jobs -> batch processing  JVM Schedulers -> shutdown on app exit  Threads -> use fixed thread pool size Cache -> configure time to live on every cache  Logging -> bounded async queue  JVM Config -> match heap to container size 🎯"Memory doesn't need traffic to leak - it just needs time." #OutOfMemory #MemoryLeak #Java #JVM #JavaDeveloper #ProductionIssues #Microservices #Debugging