Spring Boot Circular Proxy Issue and AOP Solution

While building a recent Spring Boot application, I realized... The Hook: Stop defaulting to .parallelStream() to make your Java code "faster." 🛑 The Insight: It’s a common misconception that parallel streams always improve performance. Under the hood, parallelStream() uses the common ForkJoinPool. If you are executing CPU-intensive tasks on a massive dataset, it’s great. But if you are doing I/O operations (like database calls or network requests) inside that stream, you will exhaust the thread pool and bottleneck your entire application. The Pro Tip: Always benchmark. For I/O bound tasks, look into asynchronous programming (like CompletableFuture) or Java 21's Virtual Threads instead of parallel streams. #Java #PerformanceOptimization #SoftwareEngineering #CleanCode

💡 Those cascading instanceof chains? Java 21 made them obsolete. Pattern matching in switch lets you match types, deconstruct records, and add guard clauses, all in one expression. The compiler enforces exhaustiveness, so missing cases become compile errors, not runtime surprises. Cleaner code. Safer code. Same performance. #Java #Java21 #PatternMatching #JavaDeveloper #CleanCode #SoftwareDevelopment

Spring Boot Filters vs Interceptors — Most developers confuse this 🤯 Let’s simplify 👇 ✅ Filter (Servlet level) - Works BEFORE DispatcherServlet - Used for logging, authentication, request modification ✅ Interceptor (Spring level) - Works AFTER DispatcherServlet - Used for business-level checks 💡 Flow: Request → Filter → DispatcherServlet → Interceptor → Controller ⚡ Real use case: - Filter → JWT validation - Interceptor → role-based access 👉 Choosing wrong = messy architecture Know the difference = cleaner backend 🔥 #SpringBoot #Java #BackendDeveloper

Java 26 introduces warnings for illegal final field mutation. Future releases will deny it by default. Start migrating now: inventory with debug, enforce in CI. https://lnkd.in/d8Kcz7m5 ← Learn how to avoid breakage & unlock safer JVM optimizations with Babneet Singh! #Java26 #DevOps #Java

Cache bugs are often not bugs at all. They are contracts we forgot to read. This tutorial walks through Quarkus cache failure semantics with a small pricing service: - `@CacheResult` on `Uni` - resolved-value caching - success-only `@CacheInvalidateAll` - stale data after failed methods with surviving side effects - forced invalidation with `CacheManager` The practical question is simple: when is stale data better than a cache miss, and when is it dangerous? https://lnkd.in/dShFxDDQ #Quarkus #Java #Caching #ReactiveJava

⚠️ Why @Transactional Sometimes Doesn’t Work in Spring Boot A common mistake: Calling a @Transactional method   from another method inside the SAME class. Result? Transaction doesn’t trigger. Why? Because Spring uses proxies. Internal method calls bypass the proxy,   so transaction management never happens. Fix: → Move logic to another service   OR   → Call through Spring-managed bean Small detail. Big production issue. #springboot #java #backendengineering

Spring Boot Bean Scope — Not just Singleton 🤯 Most developers only know this: 👉 Default scope = Singleton But there’s more 👇 ✅ Prototype → New instance every time ✅ Request → Per HTTP request ✅ Session → Per user session 💡 Why it matters: ✔ Memory optimization ✔ Better state handling ⚠️ Mistake: Using singleton for stateful data ❌ 👉 Leads to concurrency issues 🔥 Real lesson: Choose scope based on use case, not default Backend bugs often start here 🚨 #SpringBoot #Java #Architecture

Spent 15 minutes wondering why my REST API was returning empty response The endpoint was working fine yesterday Checked the controller and service layer Everything looked correct Turns out I forgot @ResponseBody on the controller method @GetMapping("/users") public List<User> getUsers() {   return userService.getAllUsers(); } Without @ResponseBody Spring tries to resolve a view instead of returning JSON Quick fix was adding @RestController instead of @Controller #Java #SpringBoot #REST #BackendDevelopment

🔍 Filters vs Interceptors in Spring Boot — and why most devs mix them up Both intercept HTTP requests. Both let you run logic before and after your controller. But they live at completely different layers — and choosing the wrong one leads to subtle bugs. Here's the mental model that clears it up: Filter = Servlet Container layer. Runs before DispatcherServlet even knows a request came in. It sees everything — including static resources. Interceptor = Spring MVC layer. Runs after DispatcherServlet, before your controller method. It knows which handler is being called. The one-line rule I use: If it needs to apply to every request, use a Filter. If it needs to know which endpoint is being called, use an Interceptor. This distinction matters for: → Security — JWT pre-checks live in Filters, role validation in Interceptors → Logging — request metadata at filter level, execution time at interceptor level → Clean architecture — putting logic at the right layer keeps things testable Swipe through the full breakdown with code examples and comparison table 👇 💬 Drop a comment: Which one do you use more in production, and for what? #SpringBoot #Java #BackendDevelopment #Microservices #SpringMVC #JavaDeveloper #SoftwareArchitecture #TechInterview #SpringFramework #Programming

Double-checked locking in Java looks correct… but it’s broken without volatile. Why? Because of instruction reordering by the JVM. Object creation is NOT atomic: 1. Allocate memory 2. Assign reference 3. Initialize object Steps 2 and 3 can be reordered. So another thread might get a reference to an object that is not fully initialized ❌ This leads to subtle and hard-to-debug bugs. Fix: Use volatile with double-checked locking. private static volatile Singleton instance; Lesson: Concurrency bugs don’t fail fast — they fail silently. #Java #Multithreading #Concurrency #Backend #SoftwareEngineering #JVM