Optimizing Database Queries for Efficient Backend Code

A single slow Database query almost brought down a production system I worked on. Not because of the query itself, but because it consumed every available thread, leaving nothing for the services that actually mattered. The fix? Bulkhead Semaphore pattern. Isolate your thread pools. Set a queue. Define a fallback. The slides show exactly how. Full Resilience4j docs: https://lnkd.in/dyMxUx_j #java #springboot #backend #resilience4j #softwareengineering

#Post5 In the previous post, we saw how @RequestBody helps us handle request data. Now the next question is 👇 How do we get data from the URL? There are two common ways: • @PathVariable • @RequestParam Let’s understand 👇 👉 @PathVariable Used when the value is part of the URL path Example: GET /users/10 @GetMapping("/users/{id}") public String getUser(@PathVariable int id) { return "User id: " + id; } 👉 @RequestParam Used when the value comes as a query parameter Example: GET /users?id=10 @GetMapping("/users") public String getUser(@RequestParam int id) { return "User id: " + id; } 💡 Key difference: @PathVariable → part of URL @RequestParam → query parameter Key takeaway: Use the right approach based on how data is passed in the request 👍 In the next post, we will explore exception handling in Spring Boot 🔥 #Java #SpringBoot #BackendDevelopment #RESTAPI #LearnInPublic

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

Just published my first technical article on Dev.to! "Building a Database Engine from Scratch" Covered everything: - B+ Tree implementation in C++ - Java + C++ IPC bridge - Docker deployment - Live analytics dashboard Read here: https://lnkd.in/grR247qE Live Demo: fourmulaquery.onrender.com GitHub: https://lnkd.in/gKrjbYGh #buildinpublic #cpp #java #database

Most transaction bugs in Spring Boot are not SQL bugs—they’re transaction boundary bugs. Today’s focus is a deep dive into @Transactional: propagation, isolation, and rollback rules. If you only use the default settings everywhere, you may accidentally create hidden data inconsistencies or unexpected commits. Example: @Service public class PaymentService { @Transactional(propagation = Propagation.REQUIRED, isolation = Isolation.READ_COMMITTED, rollbackFor = Exception.class) public void processPayment(Order order) { paymentRepository.save(new Payment(order.getId(), order.getTotal())); inventoryService.reserve(order.getItems()); } } Key idea: REQUIRED joins an existing transaction or starts a new one, REQUIRES_NEW creates a separate one, and isolation controls visibility of concurrent changes. By default, rollback happens for unchecked exceptions, so checked exceptions often need explicit rollbackFor. Treat @Transactional as an architectural decision, not just an annotation. #Java #SpringBoot #BackendDevelopment

Most transaction bugs in Spring Boot are not SQL bugs—they’re transaction boundary bugs. Today’s focus is a deep dive into @Transactional: propagation, isolation, and rollback rules. If you only use the default settings everywhere, you may accidentally create hidden data inconsistencies or unexpected commits. Example: @Service public class PaymentService { @Transactional(propagation = Propagation.REQUIRED, isolation = Isolation.READ_COMMITTED, rollbackFor = Exception.class) public void processPayment(Order order) { paymentRepository.save(new Payment(order.getId(), order.getTotal())); inventoryService.reserve(order.getItems()); } } Key idea: REQUIRED joins an existing transaction or starts a new one, REQUIRES_NEW creates a separate one, and isolation controls visibility of concurrent changes. By default, rollback happens for unchecked exceptions, so checked exceptions often need explicit rollbackFor. Treat @Transactional as an architectural decision, not just an annotation. #Java #SpringBoot #BackendDevelopment

🔥Today’s DSA Update — #Day62 Today I worked on Implementing Stack using Queue. 💡 The key idea I used a single queue and applied a rotation strategy. Whenever I add a new element: 1. I insert it into the queue 2. Then I move all the previous elements behind it This way, the most recent element always comes to the front, and behaves like the top of a stack. 💡 What I understood Unlike the "Queue using Stacks" (which was efficient over time), this Stack implementation has a O(n) push time. We pay a price in speed to change the fundamental behavior of the data flow Push operation → O(n) Pop operation → O(1) So we are shifting the cost to maintain the stack behavior. 🧠 What I learned today Today’s lesson was that if we understand how data flows in a structure, we can modify its behavior using simple operations. #Java #DSA #Stack #Queue #LeetCode #ConsistencyCurve

How I improved API performance by ~40% 🚀 Problem: Slow response time due to heavy DB queries What I changed: • Optimized SQL queries • Added indexing • Reduced unnecessary joins • Used proper pagination Result: Faster APIs Better user experience Most performance issues are not in code, they’re in the database. #Backend #Java #Performance #Microservices #TechTips

🚀 Day 3/30 – Spring Boot Journey Today I explored @Component, @Service, and @Repository in Spring Boot. 🔹 @Component → Creates Spring Bean automatically 🔹 @Service → Used for business logic layer 🔹 @Repository → Handles database operations + exception handling 💡 Key Learning: All these annotations are special types of @Component, but using them properly improves code structure, readability, and follows clean architecture. Also understood how these layers work together: 👉 Controller → Service → Repository This made Dependency Injection much clearer! #SpringBoot #Java #BackendDeveloper #Learning #Microservices #100DaysOfCode

Is your @Transactional annotation actually doing what you think? 🧐 In Spring Boot, data integrity is everything. But I often see a common trap: Self-invocation. If you call a transactional method from another method within the same class, the Spring Proxy is bypassed. 📉 The result? No transaction starts, and your data might end up inconsistent without any error message. Check: ✅ The Proxy Rule: Spring uses AOP proxies. External calls go through the proxy; internal calls don't. ✅ The Fix: Move the transactional logic to a separate Service or use AspectJ if complexity scales. ✅ Bonus: Always use readOnly = true for fetch operations to improve performance and avoid unnecessary flush calls. It’s not just about using the framework; it’s about understanding the "Magic" behind it. 🚀 Have you ever faced a "phantom" database bug because of this? Let's swap stories! 👇 hashtag #Java #SpringBoot #Backend #Database #CleanCode #SoftwareEngineering #JavaDeveloper