Spring Strategy Pattern with Dependency Injection

 𝐋𝐨𝐨𝐬𝐞 𝐂𝐨𝐮𝐩𝐥𝐢𝐧𝐠 𝐢𝐧 𝐒𝐩𝐫𝐢𝐧𝐠:- Loose Coupling is one of the most important design principles in Spring that helps build flexible and maintainable applications. Instead of depending on specific implementations, classes rely on abstractions, which reduces direct dependency between components. In Spring, this is achieved using Dependency Injection (DI), where objects are not created manually but are provided by the Spring container. This approach separates object creation from business logic, making the system more organized and easier to manage. Loose coupling allows developers to change implementations without affecting existing code. It also makes applications scalable, as new features or components can be added without breaking the system. This is especially useful in real-world projects where requirements frequently change. Another major advantage is improved testability. Since dependencies are not tightly bound, they can be easily replaced with mock objects during testing. This leads to better code quality and faster development cycles. Overall, loose coupling promotes clean architecture, reusability, and long-term maintainability. It is a core concept in Spring that enables developers to build robust and future-ready applications. #SpringFramework #SpringBoot #LooseCoupling #DependencyInjection #Java #BackendDevelopment #CleanCode #SoftwareDesign #JavaDeveloper #Codegnan Anand Kumar Buddarapu Uppugundla Sairam Saketh Kallepu

🚫 Stop Writing new User() Everywhere While reviewing a simple login system, I noticed something common in junior code: User user = new User();user.setId(existingUser.getId()); At first glance, it looks harmless. But this is where design starts breaking. ⚠️ The Problem This isn’t just about object creation. It’s about not understanding object lifecycle. Creating objects without purpose Breaking shared state Increasing memory overhead Reducing code clarity ✅ The Fix (Simple Login System Thinking) In a login flow: Request → create new (external input) DB → fetch existing User Response → create new (output model) 👉 That’s it. No unnecessary new User() in between. 💡 Better Approach User user = userRepository.findByEmail(email); // reuse If needed: return new LoginResponse(user.getId(), "SUCCESS"); 🧠 Architect Mindset Every new should answer one question: “Why does this object need a new life?” If you don’t have an answer, you’re not designing—you’re just coding. 🔥 Final Thought In frameworks like Spring Boot, you don’t manage objects—you define their lifecycle. #SystemDesign #CleanCode #Java #SpringBoot #SoftwareArchitecture #CodeReview

Architecture vs. Speed: When is 8 classes for one task actually worth it? 🚀 As a Senior Developer, I often see a common struggle: when to keep it simple and when to build a robust architecture. In my latest video for Let’s Code Java, I took a simple console app and transformed it into a professional, service-oriented system. The result? I added 8 new classes (builders, services, exception hierarchy), but technically... I didn’t add a single new feature. Was it worth it? It depends. If you're building a prototype, this level of engineering will only slow you down. But if you’re working on a long-term enterprise project, skipping this foundation will cost you twice as much time later when you have to refactor "dirty" code. In this episode, I dive deep into: ✅ Building a Custom Exception Hierarchy that doesn't mess up your logic. ✅ Implementing a Service Layer to isolate business rules from I/O. ✅ Using the Builder Pattern to ensure object validity from the start. ✅ Preparing the ground for Spring Boot & REST API. If you want to see how to design Java applications that are ready for the real world (and why "perfect" code isn't always the goal), check out the video in the first comment. Question for my fellow devs: Where do you draw the line between "clean architecture" and "over-engineering"? Let’s discuss in the comments! 👇 #Java #SoftwareArchitecture #CleanCode #BackendDevelopment #JavaDeveloper #ProgrammingTips #SpringBoot #LetsCodeJava

"Depend on things that change less often than you do." ― Practical Object-Oriented Design by Sandi Metz I need to admit it. I never spent more than a few minutes whether service X should depend on service Y or the other way round ? Two classes. Either can depend on the other. The result is the same. So the direction doesn't matter — until your codebase starts to grow. Then it matters a lot. Choosing what depends on what is a design decision with long-term consequences. Get it right and changes stay local. Get it wrong and a small tweak cascades through the entire system. This applies at every level — from two classes collaborating, to two services in a Spring application, all the way up to microservices communicating across a system. #SoftwareDesign #CleanCode #POODR #ObjectOrientedDesign #SoftwareEngineering #Java

Most developers don’t realize this… Your API is not judged by how it works when everything is correct. It is judged by how it behaves when things go WRONG. Today I built a complete Exception Handling Architecture from scratch. Not just try-catch… A proper system: → Custom exception hierarchy → Centralized handling (@RestControllerAdvice) → Standardized ErrorCode enum → Clean JSON responses → Proper HTTP status mapping Now instead of random errors, my backend responds like this: { "status": 400, "errorCode": "OTP_INVALID", "message": "Invalid OTP", "path": "/api/auth/verify-otp" } This is the difference between: ❌ Writing code ✅ Designing systems Most beginner projects ignore this layer… But this is exactly what makes a backend production-ready. And honestly… once you see this architecture, you can’t go back to messy error handling. I’ve attached the full architecture breakdown 👇 Let me know what you think. #SpringBoot #Java #BackendDevelopment #SystemDesign #FullStackDeveloper

𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 — 𝗗𝗮𝘆 𝟯 Day 3 was where things started getting more real. This section focused on the core concepts of the Spring Framework — the things that actually make Spring powerful behind the scenes. The total duration was around 2 hours 18 minutes, but again, it took me 1 week to properly understand and connect everything. Here’s what I learned: 🔹 What a Web Framework is 🔹 Introduction to Spring Framework 🔹 Tight Coupling vs Loose Coupling (with hands-on) 🔹 Core concepts of Spring (Java Web Development basics) 🔹 Spring Container & Configuration 🔹 Setting up a Spring project 🔹 Beans in Spring • Creating your first Bean • Bean Lifecycle 🔹 Dependency Injection (DI) • Constructor Injection • Setter Injection 🔹 Inversion of Control (IoC) 🔹 Autowiring • By Name • By Type • By Constructor Understanding concepts like Dependency Injection and IoC really changed how I think about writing code. It’s less about creating everything manually and more about letting Spring manage things efficiently. Also, concepts like loose coupling finally started making practical sense when I saw them in code. 📌 Next part coming soon. #SpringBoot #JavaDeveloper #BackendDevelopment #LearningJourney #Day3 #TechGrowth #Consistency

As developers, we all say *“async = separate thread”*… but when I went deeper during development, a few real questions hit me 👇 Can I actually **see which thread is running my async task?** Yes — using `Thread.currentThread().getName()` you can log and observe it. But then the bigger question… 👉 **How many threads are actually created?** 👉 Is it always a new thread per task? 👉 Or are threads reused? I came across a post saying **“by default 8 threads are used”** — but is that really true in all cases? From what I understand so far: * It depends on the **thread pool configuration** * Frameworks like Spring Boot often use executors (not raw thread creation) * Threads are usually **reused**, not created every time But I want to hear from real-world experience 👇 💬 How does async actually behave in production systems? 💬 What decides the number of threads? 💬 Any pitfalls you’ve seen while working with async? Let’s learn from each other 🚀

Most Go codebases don’t suffer from a lack of interfaces—they suffer from too many of them. I wrote about how overusing interfaces in Go quietly makes your code harder to read, harder to test, and slower to evolve. The core idea is simple: 👉 Interfaces in Go should be discovered, not designed upfront 👉 Premature abstraction adds indirection without real value A lot of this comes from bringing patterns from Java/TypeScript into Go—where they don’t translate well. If you’ve ever: created an interface with only one implementation added interfaces “just for testing” or built layers that don’t actually simplify anything …this is for you. Read the full breakdown here: https://lnkd.in/dmBz-hYJ Curious—what’s the worst interface overengineering you’ve seen in a Go codebase?

Are you still using @Autowired on private fields? It might be time to refactor. 🛑 While Field Injection is short and easy to write, it hides dependencies and makes your code harder to maintain. As your application grows, Constructor Injection becomes the superior choice. Why the shift? ✅ Immutability: You can define your dependencies as final, ensuring they aren't changed after initialization. ✅ Testability: No need for Reflection or Mockito's @InjectMocks magic just to run a simple Unit Test. You can just pass mocks through the constructor. ✅ Object Integrity: It prevents the "NullPointerException" trap. Your object is never in an inconsistent state; it either has all its dependencies or it doesn't compile. Tip: If your constructor has more than 5 dependencies, it's a "Code Smell." It’s telling you that your class is doing too much and needs to be split (SRP violation). Do you use @Autowired for speed, or do you stick to Constructor Injection for safety? Let's debate! 👇 #Java #SpringBoot #CleanCode #SoftwareArchitecture #Testing #BackendDevelopment #CodingTips

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗽𝗿𝗶𝗻𝗴 𝗔𝗢𝗣 𝗮𝗻𝗱 𝗦𝘁𝗼𝗽 𝗥𝗲𝗽𝗲𝗮𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 Repetitive "boilerplate" code is the silent killer of clean architectures. In Spring Boot development, we often see service layers drowning in code that has nothing to do with the actual business logic. Things like:  • 📝 Logging method entry and exit.  • 🛡️ Security/Permission checks.  • ⏱️ Performance monitoring (measuring execution time).  • 🔄 Cache eviction management. If you are manually adding this logic to every service method, you’re creating a maintenance nightmare. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Spring Aspect-Oriented Programming (AOP). 𝗔𝗢𝗣 lets you separate these "Cross-Cutting Concerns" from your business logic. You write the logic once in an 𝗔𝘀𝗽𝗲𝗰𝘁, and Spring automatically applies it whenever specific methods are called. Your Service class remains clean, readable, and focused on one responsibility. How It Works (Example): Instead of copying 𝗹𝗼𝗴𝗴𝗲𝗿.𝗶𝗻𝗳𝗼(...) into every method, you create a single Logging 𝗔𝘀𝗽𝗲𝗰𝘁 like the one below. Using @𝗔𝗿𝗼𝘂𝗻𝗱 advice, you can intercept the method call, start a timer, execute the actual method, and then log the result. The Benefits: ✅ 𝗗𝗥𝗬 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: Write logic once, use it everywhere. ✅ 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴: Business logic doesn’t know (or care) about logging/monitoring. ✅ 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Enable or disable cross-cutting features easily. 🛑 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗧𝗶𝗽: 𝗧𝗵𝗲 𝗣𝗿𝗼𝘅𝘆 𝗧𝗿𝗮𝗽! When using Spring AOP (by default), Spring creates a dynamic proxy object around your target class. The AOP logic only executes when you call the method through that proxy. 𝗧𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀: If 𝙈𝙚𝙩𝙝𝙤𝙙𝘼() inside your Service class calls 𝙈𝙚𝙩𝙝𝙤𝙙𝘽() (which is also inside the same class), the call is internal and bypasses the proxy. Your AOP advice for 𝙈𝙚𝙩𝙝𝙤𝙙𝘽() will NOT run. Knowing this subtle nuance is what separates Spring experts from beginners! How are you using AOP in your Spring Boot applications? Share your best use cases below! 👇 #SpringBoot #Java #SoftwareArchitecture #CodingBestPractices #BackendDev