Boost Spring Boot Performance with Connection Pooling and Caching

Spring Boot @Conditional — Hidden superpower ⚡ You can load beans conditionally! Examples: ✅ @ConditionalOnProperty ✅ @ConditionalOnMissingBean ✅ @ConditionalOnClass 💡 Use case: Load bean ONLY when needed Example: @Configuration @ConditionalOnProperty(name="feature.enabled", havingValue="true") 👉 Feature toggles made easy 🔥 ✔ Better flexibility ✔ Environment control ✔ Cleaner configs Most developers don’t use this — big mistake ❌ #SpringBoot #Java #BackendDeveloper

Spring Boot HandlerExceptionResolver — Advanced error handling 🔥 Most devs use @ControllerAdvice… but there’s a deeper layer 👇 👉 HandlerExceptionResolver It gives full control over: ✔ Exception handling ✔ HTTP response ✔ Error mapping 💡 Why use it? ✔ Fine-grained control ✔ Custom framework-level handling ⚠️ Use case: When @ExceptionHandler is not enough 👉 Example: Handle all exceptions globally with custom logic This is how frameworks are built internally 💯 #SpringBoot #Java #ExceptionHandling

Spring Boot @Primary — Resolve multiple bean conflicts ⚡ Ever seen this error? “No qualifying bean of type found” ❌ Happens when: 👉 Multiple beans of same type exist Solution 👇 @Primary public class MyServiceImpl implements MyService {} 💡 What it does: Marks default bean when multiple exist Alternative: 👉 Use @Qualifier ⚠️ Mistake: Not handling multiple beans properly 👉 Leads to runtime errors Clean DI = stable application 🔥 #SpringBoot #Java #DependencyInjection

Spring Boot Thread Pool Tuning — Hidden performance killer ⚠️ Most developers ignore this… and it hurts performance badly. By default: 👉 Spring Boot uses a limited thread pool If traffic increases: ❌ Requests get queued ❌ Response time increases 💡 Solution: Configure Thread Pool @Bean public TaskExecutor taskExecutor() { ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor(); executor.setCorePoolSize(10); executor.setMaxPoolSize(50); executor.setQueueCapacity(100); executor.initialize(); return executor; } ⚡ Real impact: ✔ Better concurrency ✔ Faster request handling ✔ Stable system under load 👉 Default config is NOT enough for production Performance tuning starts here 🔥 #SpringBoot #Java #Performance

💡 3 things I wish I knew before my first production deployment: → Caching is not optional at scale — it's survival → A 30% query optimization can feel like buying new hardware → Logs are your best friend at 2AM when things break Backend development taught me that performance is a feature, not an afterthought. #Java #SpringBoot #BackendEngineering #LessonsLearned

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 Streams are killing your performance Java Streams look clean… but they can silently destroy your performance. I saw this in a production audit last week. A simple loop processing 1M records was replaced with streams “for readability”. // ❌ Stream version list.stream() .filter(x -> x.isActive()) .map(x -> transform(x)) .collect(Collectors.toList()); // ✅ Optimized loop List<Result> result = new ArrayList<>(); for (Item x : list) { if (x.isActive()) { result.add(transform(x)); } } 🚨 What happened in production: • CPU usage increased by 35% • GC pressure exploded • Latency x2 under load ❗ Why? Streams: • Create more objects • Add hidden overhead • Are NOT always optimized by JVM ✅ Fix: • Use streams for readability (small datasets) • Use loops for performance-critical paths • Benchmark before choosing https://www.joptimize.io/ Clean code ≠ fast code. Are you using streams in performance-sensitive code? #JavaDev #SpringBoot #JavaPerformance #Backend #SoftwareEngineering

Spring Boot @Async — Not as simple as it looks ⚡ Yes, it runs methods asynchronously… But many developers use it WRONG ❌ Example: @Async public void process() { ... } 💡 Behind the scenes: 👉 Runs in a separate thread pool ⚠️ Common mistakes: ❌ Not enabling @EnableAsync ❌ Calling async method internally (won’t work) ❌ Ignoring exception handling 👉 Important: Self-invocation breaks @Async 😱 ✔ Always call from another bean 🔥 Real-world use: - Email sending - Background processing - Non-blocking tasks Async done right = massive performance gain 🚀 #SpringBoot #Java #Async

🚀 Using Resilience4j with Spring Boot (Java) To use Resilience4j in a Spring Boot application, include the `resilience4j-spring-boot2` dependency. Annotate your methods with `@CircuitBreaker` to enable the circuit breaker functionality. Configure the circuit breaker properties in your `application.properties` or `application.yml` file, such as the failure rate threshold, the slow call rate threshold, and the wait duration in open state. This allows you to control the behavior of the circuit breaker. #Java #JavaDev #OOP #Backend #professional #career #development

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