Kamil Szerszeń’s Post

Looking back, one of the most valuable projects I ever worked on was one where we had to break a large, monolithic Java application into microservices. We started with what seemed like a "simple" piece to carve out: the user profile service. We thought it would be a quick win. We were wrong. We quickly discovered a web of hidden dependencies. The "user" object was tangled up with billing, shipping, marketing preferences, and support tickets. What we thought was a simple data model was actually the core of the entire business. 💡 My Key Takeaway: That project taught me a crucial lesson about coupling. The true complexity of a system isn't in its features; it's in the relationships between its features. Before you can split a monolith, you have to spend just as much time mapping its "seams" as you do writing new code. 🤔 What's a project that looked simple on the surface but taught you a deep architectural lesson? #SoftwareArchitecture #SystemDesign #Java #Microservices #Programming

⚙️ Deep Dive: Dependency Injection in Spring Boot If you’ve built any real-world Spring Boot application, you’ve already been leveraging Dependency Injection (DI) — a fundamental concept that drives Spring’s IoC (Inversion of Control) container. At its core, DI is about delegating the responsibility of dependency management to the framework, rather than hardcoding object creation and wiring inside your classes. Here’s a quick refresher 👇 @Service public class OrderService { private final PaymentService paymentService; @Autowired public OrderService(PaymentService paymentService) { this.paymentService = paymentService; } public void processOrder() { paymentService.processPayment(); } } In this setup: Spring’s ApplicationContext scans and instantiates the PaymentService bean. It then injects that bean into the OrderService constructor at runtime. This decouples component creation from component usage, aligning perfectly with the SOLID principles — particularly Dependency Inversion. A few best practices that often get overlooked: 🧩 Prefer constructor injection over field injection — it’s immutable, testable, and compatible with @RequiredArgsConstructor from Lombok. 🔄 Use @Configuration + @Bean when explicit bean creation or customization is needed. 🧠 Remember that DI isn’t just syntactic sugar — it’s what enables Spring to manage scopes, proxies, AOP, and transactions seamlessly. 💭 Question for fellow Spring devs: How do you manage dependency injection in large modular Spring Boot projects — via component scanning, explicit configuration, or a mix of both? #SpringBoot #Java #DependencyInjection #InversionOfControl #CleanArchitecture #SoftwareEngineering

Project Loom is Here: Stop Writing Thread-Blocking Code in Java The arrival of Java Virtual Threads (Project Loom, production-ready since Java (21) is the biggest shift in concurrent design we've seen in years. For a decade, our Spring Boot applications were limited by expensive platform threads, often leading to thread starvation and wasted memory when waiting on I/O (like external REST API calls). This forced us into using complex asynchronous frameworks, trading readability and maintainability for raw throughput. Virtual Threads completely change the game by making threads cheap and abundant and also a million virtual threads can now run on just a few dozen platform threads. The key takeaway: we can now write simple, synchronous, thread-blocking Java code that is both highly readable and incredibly efficient, removing a huge source of backend complexity. This is a major productivity gain for every Full Stack team. This shift has huge implications for our systems. We instantly gain massive increases in concurrency and throughput for I/O-bound microservices without touching complex reactive boilerplate. Our backend latency stabilizes because we eliminate thread starvation, leading to a better user experience on the frontend. The transition is often seamless: you just configure your framework to use a Virtual Thread executor—often requiring zero code changes to your business logic. This simplifies integration: when our Full Stack application calls a slow external API, the Java thread pauses cheaply, not expensively. Have you started testing your existing services with Virtual Threads? Did you see the performance boost right out of the box? #Java #Java21 #ProjectLoom #Concurrency #Springboot #FullStackDeveloper #TechArchitecture #JavaDeveloper #SoftwareDeveloper #SoftwareEngineer #BackendDeveloper #C2C #C2H

🔄 Circular Dependencies in Java: The Silent Architecture Killer Class A needs Class B. Class B needs Class A. You're stuck in a loop. The Problem: Circular dependencies create tight coupling, break unit testing, and can crash your Spring application with BeanCurrentlyInCreationException. 5 Ways to Break the Cycle: 1. Interface Extraction Invert the dependency. Let both classes depend on an abstraction, not each other. 2. Lazy Initialization Use @Lazy in Spring or setter injection to defer object creation. 3. Introduce a Mediator Create a third class to coordinate communication between the two. 4. Event-Driven Design Replace direct calls with events. Decouple through messaging. 5. Refactor Ruthlessly If classes are circular, they're probably doing too much. Split responsibilities. The Bottom Line: Circular dependencies are a code smell. They signal poor separation of concerns. Fix them early, or pay the price in technical debt. Pro tip: Use static analysis tools to detect cycles before they reach production. What's your go-to strategy for handling circular dependencies? #Java #SoftwareEngineering #CleanCode #TechDebt #SpringBoot#Systemdesign

💡 Day 8 of my 𝗝𝗮𝘃𝗮 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘆 𝘀𝗲𝗿𝗶𝗲𝘀: 🧠 Question: In large Spring Boot applications, object creation and dependency management can become complex. Which Design Pattern helps manage object creation efficiently, and how does Spring leverage it? ✅ Answer: The Factory Design Pattern helps centralize and simplify object creation - instead of instantiating classes directly using new, we delegate this responsibility to a factory. 𝐈𝐧 𝐒𝐩𝐫𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: Spring uses the Factory pattern through its IoC Container (ApplicationContext). It acts as a Bean Factory, managing object creation, dependency injection, and lifecycle automatically. 𝐞𝐱𝐚𝐦𝐩𝐥𝐞: You define beans in configuration or annotations like @Component, and Spring’s ApplicationContext (the factory) creates and wires them when needed. 𝐛𝐞𝐧𝐞𝐟𝐢𝐭𝐬: • Loose coupling between components • Easier testing and maintenance • Centralized control of object lifecycle ✅ Factory Pattern = clean, modular, and dependency-managed applications - the core of Spring’s IoC. ⚙️ See you tomorrow for Day 9 👋 #Java #SpringBoot #DesignPatterns #FactoryPattern #IoC #BackendDeveloper #CleanCode #ContinuousLearning #QuestionOfTheDay

Level up your Java projects! I recently came across a fantastic PDF outlining Real-Time Java Coding Standards and Best Practices, and this diagram on Project Structure immediately caught my eye. This standardized, layered approach is critical for clean, maintainable, scalable, and secure code in production-grade applications. Key takeaways from the suggested structure: Microservices Architecture: Clear separation of concerns into distinct layers. controller: For REST controllers. service: The home for all Business Logic. repository: Where the JPA repositories (data access) live. model/dto: Separation for Entities and Data Transfer Objects. config, exception, util, security: Dedicated packages for Spring config, custom errors, helpers, and authentication. Adhering to a strong structure like this significantly reduces technical debt and improves team collaboration. If you're building enterprise-level Java applications, taking the time to standardize your project layout is a must! What are your go-to best practices for Java project structure? Share your thoughts below! 👇 Hashtags: #Java #CodingStandards #BestPractices #SoftwareArchitecture #RealTimeJava #Development #Programming #CleanCode #EnterpriseApplications

🤯 Controversial Take: The Private Field is a Lie, and It's Killing Your Tests. 🧪 If you are a serious C# or Java developer, you were taught to use the private keyword for encapsulation—to hide data and protect the internal state of a class. But here is the brutal truth that separates textbook code from real-world, highly testable code: Blind adherence to private fields is an anti-pattern. The Encapsulation Misinterpretation 🔒 Encapsulation's goal is to protect the object's invariants (its rules and structural integrity), not to hide every single piece of data from the rest of the application. When you make a field private, you gain nothing but pain when it comes time for unit testing: 1. Test Fragility: You are forced to test the internal state by only interacting with public methods. If those public methods are complex, your tests become complex and brittle. 2. Unnecessary Complexity: Developers create awkward, unnecessary public methods (SetInternalStateForTesting()) or use reflection (a hack!) just to check a private field's value after an action. 3. No Isolation: Your test is no longer a simple unit test; it becomes an integration test because you have to run through the entire public API to observe a single internal change. The "Testable" Alternative: Internal or Protected 💡 For logic that must be tested but shouldn't be exposed to the public API of the assembly, internal (or protected for inheritance) is often the superior choice. By exposing fields to your separate Test Assembly (using InternalsVisibleTo in C#), you enable: • Atomic Unit Tests: You can directly assert that a single action correctly modified a single internal field, making your tests precise, fast, and stable. • Faster Development Cycle: You waste zero time writing wrapper methods solely for testing purposes. The GC is your best friend, but you have to write code that allows it to work efficiently. Poorly managed references are the single biggest bottleneck that modern garbage-collected languages face. Agree or Disagree? Is the textbook definition of private ruining developer productivity, or is exposing internal fields a reckless path to chaos? Let the debate begin. 👇 #OOP #Csharp #Java #UnitTesting #CleanCode #SoftwareArchitecture

Level up your Java projects! I recently came across a fantastic PDF outlining Real-Time Java Coding Standards and Best Practices, and this diagram on Project Structure immediately caught my eye. This standardized, layered approach is critical for clean, maintainable, scalable, and secure code in production-grade applications. Key takeaways from the suggested structure: Microservices Architecture: Clear separation of concerns into distinct layers. controller: For REST controllers. service: The home for all Business Logic. repository: Where the JPA repositories (data access) live. model/dto: Separation for Entities and Data Transfer Objects. config, exception, util, security: Dedicated packages for Spring config, custom errors, helpers, and authentication. Adhering to a strong structure like this significantly reduces technical debt and improves team collaboration. If you're building enterprise-level Java applications, taking the time to standardize your project layout is a must! What are your go-to best practices for Java project structure? Share your thoughts below! 👇 #Java #CodingStandards #BestPractices #SoftwareArchitecture #RealTimeJava #Development #Programming #CleanCode #EnterpriseApplications

👑 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 — 𝐎𝐧𝐞 𝐎𝐛𝐣𝐞𝐜𝐭 𝐭𝐨 𝐑𝐮𝐥𝐞 𝐓𝐡𝐞𝐦 𝐀𝐥𝐥 In the world of software design, some classes should exist only once throughout the entire application — that’s where the Singleton Design Pattern shines! It ensures that only one instance of a class is created and provides a global point of access to it. 🧩 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐚 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧? A Singleton is a creational design pattern that restricts class instantiation to one single object. For example: A single Database Connection Manager A centralized Logger A Configuration Loader ⚙️ 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 (𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩) 1️⃣ Make the constructor private, so no one can create an instance using new. 2️⃣ Create a private static instance variable of the same class. 3️⃣ Provide a public static method (like getInstance()) that returns the same object every time. 💻 𝐂𝐨𝐝𝐞 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 (𝐓𝐡𝐫𝐞𝐚𝐝-𝐒𝐚𝐟𝐞 𝐕𝐞𝐫𝐬𝐢𝐨𝐧) public class Singleton { private static volatile Singleton instance; private Singleton() { // private constructor prevents instantiation } public static Singleton getInstance() { if (instance == null) { synchronized (Singleton.class) { if (instance == null) { instance = new Singleton(); } } } return instance; } } 🧠 Why volatile and synchronized? To prevent multiple threads from creating different instances during concurrent access. This is called Double-Checked Locking — efficient and thread-safe! ✨ 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 ✅ Ensures only one instance of a class exists ✅ Saves memory and improves performance ✅ Centralized control for shared resources ✅ Thread-safe and globally accessible ⚠️ 𝐂𝐨𝐦𝐦𝐨𝐧 𝐌𝐢𝐬𝐭𝐚𝐤𝐞 ❌ Using Singleton in places where you actually need multiple instances (like per user/session data) can lead to unexpected bugs. 📘 𝐏𝐫𝐨 𝐓𝐢𝐩: In Spring Boot, you already get Singleton behavior by default for Beans — every @Service, @Component, or @Repository is a Singleton unless you specify otherwise. 😉 #DesignPatterns #Java #SpringBoot #SoftwareEngineering #OOP #BackendDevelopment #CleanCode #Microservices #CodingBestPractices #JavaDeveloper #SystemDesign #ProgrammingTips #TechCommunity #CodeWithShivam

I wasted 4 hours debugging a NullPointerException in a DTO last year. Never again. 🤦♂️ The biggest win in modern Java is eliminating the verbosity that used to haunt us. If you are still writing manual getters, setters, and constructors for simple data carriers in your Spring Boot application, you are leaving productivity on the table. Embrace Java Records (since Java 16). They are immutable, concise, and perfect for Data Transfer Objects (DTOs) in a Microservices architecture. They drastically cut boilerplate, making your code cleaner and safer for concurrent operations. This single feature drastically improves developer experience and reduces the surface area for common bugs. When your Microservice goes to Docker and Kubernetes, configuration must be dynamic. Don't hardcode variables! Spring Boot's Externalized Configuration is a foundational feature. The ability to pull configuration from sources like environment variables, Config Maps, or `application.yml` ensures your service adheres to the 12-Factor App principles. This is how scalable, production-ready Java apps are built and integrated into automated CI/CD pipelines 🚀. Finally, master the Java Stream API. It simplifies complex collection processing, making heavy data operations declarative instead of imperative. Paired with `var` (Local-Variable Type Inference), your internal business logic becomes easier to reason about and maintain. Cleaner code is easier to scale, which is the heart of good system design and maintaining low technical debt over time. What is the single most underrated Java or Spring Boot feature that has saved your team the most time and headache? Share your breakthrough moment! #Java #SpringBoot #DevOps #Microservices #SystemDesign #CodingTips