Ayoub Taouam’s Post

Java records are one of my favorite modern additions to the language because they make simple data modeling much cleaner and more explicit. They were introduced as a preview feature in Java 14 and became a standard feature in Java 16. In practice, they let us declare immutable, data‑carrier types in a single line, while the compiler generates constructor, accessors, `equals`, `hashCode`, and `toString` for us. This pushes us to design small, focused value objects instead of bloated POJOs. What I really like is how records express intent: when you see `record OrderId(String value) {}`, you immediately know it is a small, immutable value type. That clarity improves readability in large codebases and makes modeling domain concepts more straightforward. Immutability by default also helps with concurrency and functional style, since we do not need to worry about unexpected state changes spread across the code. The community reception has been largely positive. Many Java developers see records as long‑awaited “built‑in Lombok `@Data` / Kotlin data classes / Scala case classes” for the Java world. Framework support (for example for JSON DTOs, HTTP APIs, and projections) has grown fast, which encourages using records for DTOs, value objects, and other data‑centric parts of the application. This also aligns nicely with pattern matching improvements, making deconstruction of records more expressive and safe. Of course, records are not a silver bullet. They are a great default for immutable data, but they are not ideal for entities that require rich lifecycle behavior or heavy mutability, and changing record components is a breaking change for public APIs. Still, for most modern Java applications, using records for simple, immutable data structures feels like a clear step forward in clarity, safety, and conciseness. #java #javaprogramming #javarecords #softwareengineering #cleanarchitecture #immutability #backenddevelopment #codingbestpractices #dtos #domainmodeling

Have you ever thought about how Java stores the classes, methods, and variables you write? What happens inside the JVM after you run your Java program? In the Medium blog post below, I have discussed how the JVM manages all of these. If you found this helpful, please give it a like and follow me to stay tuned for my next blog on Garbage Collection.

Great blog post from Mark Paluch about the next big Spring Data feature that will allow better safety, DevXP and refactoring by using type-safe property references instead of Strings. https://lnkd.in/eEenb7AJ #spring #java

🧵 Thread: Understanding Threads in Java – Simplified After all these years in Java development, one concept that repeatedly appears in real-world systems is Threads & Concurrency. Let’s break it down simply 👇 --- 🔹 1️⃣ What is a Thread? A Thread is the smallest unit of execution inside a process. Think of a program like a restaurant kitchen. 👩🍳 One chef cooking everything = Slow 👨🍳👩🍳 Multiple chefs cooking different dishes = Faster That’s exactly what threads do in a program. Process (Application) | +-------------------+ | | | Thread1 Thread2 Thread3 (Task) (Task) (Task) Multiple tasks run simultaneously inside the same application. --- 🔹 2️⃣ Why do we need Threads? Threads help us: ✅ Improve performance ✅ Utilize CPU efficiently ✅ Handle multiple users in web applications ✅ Run background tasks Example in a backend system: User Request → Thread Pool | +---------+----------+ | | Thread A Thread B Process Payment Send Notification Instead of waiting for one task to finish, the system handles tasks concurrently. --- 🔹 3️⃣ How Java Creates Threads Two common ways: 1. Extending Thread class MyThread extends Thread { public void run() { System.out.println("Thread running"); } } 2. Implementing Runnable (Preferred) class MyTask implements Runnable { public void run() { System.out.println("Task running"); } } --- 🔹 4️⃣ Real-world Example Imagine an e-commerce system Customer Order Placed | +---------------------------+ | | | Thread 1 Thread 2 Thread 3 Process Order Send Email Update Inventory Without threads → Everything runs one by one With threads → Tasks run in parallel --- 🔹 5️⃣ But there's a catch ⚠️ Multiple threads accessing the same data can cause problems like: ❌ Race Conditions ❌ Data inconsistency ❌ Deadlocks That’s why Java provides tools like: 🔐 synchronized 🔐 Locks 🔐 Concurrent Collections 🔐 Executor Framework --- 💡 Key takeaway Threads improve performance — but proper synchronization is the key to safe concurrency. --- If you're preparing for Java interviews or building scalable systems, understanding threads is fundamental. In my next post I’ll explain: 🧵 How Thread Pools work internally (Executor Framework) --- #Java #Multithreading #BackendEngineering #SoftwareArchitecture #JavaDeveloper #LearningInPublic

🔍 Deep Dive: Internals of HashMap in Java HashMap is one of the most widely used data structures in Java, but have you ever wondered how it works under the hood? Let’s break it down. 🧩 Core Structure - Internally, a HashMap is backed by an array of buckets. - Each bucket stores entries as Nodes (key, value, hash, next). - Before Java 8: collisions were handled using linked lists. - From Java 8 onwards: if collisions in a bucket exceed a threshold (default 8), the list is converted into a balanced Red-Black Tree for faster lookups. ⚙️ Hashing & Indexing - When you insert a key-value pair, the hashCode() of the key is computed. - This ensures uniform distribution of keys across buckets. 🚀 Performance - Average time complexity for put() and get() is O(1), assuming a good hash function. - Worst case (all keys collide into one bucket): - Before Java 8 → O(n) due to linked list traversal. - After Java 8 → O(log n) thanks to tree-based buckets. 🛠️ Key Design Choices - Load Factor (default 0.75): controls when the HashMap resizes. - Rehashing: when capacity × load factor is exceeded, the array doubles in size and entries are redistributed. - Null Handling: HashMap allows one null key and multiple null values. 💡 Takeaway HashMap is a brilliant example of balancing speed, memory efficiency, and collision handling. Its evolution from linked lists to trees highlights how Java adapts to real-world performance needs. What’s your favorite use case of HashMap in production systems? #Java #Collections #ScalableSystems

🚀 **Java Records – Writing Less Code, Doing More** One of the most useful features introduced in Java is **Records**. They help developers create immutable data classes with minimal boilerplate. 🔹 **What is a Java Record?** A *record* is a special type of class designed to hold immutable data. Java automatically generates common methods like: * `constructor` * `getters` * `toString()` * `equals()` * `hashCode()` 📌 **Example:** ```java public record User(String name, int age) {} ``` That's it! Java automatically creates: * `name()` and `age()` accessor methods * `equals()` and `hashCode()` * `toString()` * constructor 🔹 **Why use Records?** ✅ Less boilerplate code ✅ Immutable by default ✅ Cleaner and more readable models ✅ Perfect for DTOs and data carriers 🔹 **Behind the scenes** The above record behaves roughly like writing a full class with fields, constructor, getters, equals, hashCode, and toString — but with just one line. 💡 Records are a great example of how **Java continues to evolve to make developers more productive.** Are you using Records in your projects yet? #Java #JavaDeveloper #Programming #SoftwareDevelopment #Coding #Tech

Why I Still Use Java in 2026 Every few months: "Java is dead," "Java is legacy." Meanwhile, I keep building systems with it. Here's why. 1. The JVM Is a Marvel Twenty-five years of optimization matter. The JVM profiles, inlines, and optimizes at runtime. That 99th percentile latency stability? JVM. Months without restarts? JVM. Heap dumps that pinpoint memory leaks? JVM. 2. Modern Java Is Actually Pleasant If you haven't looked since Java 8, you're missing something. Records kill boilerplate. Switch expressions create readable flows. Text blocks make embedded JSON painless. Pattern matching reduces instanceof noise. Java 21 with virtual threads means simple blocking code that scales like reactive. No more CompletableFuture chains. No more 40-line stack traces. 3. The Ecosystem Is Unmatched Web frameworks? Spring Boot, Quarkus, Micronaut. Database drivers? Every DB has a mature JDBC driver. Queues? Kafka, RabbitMQ, AWS SDKs - all first-class. Profiling? JFR, JMC, Async Profiler - free and excellent. Whatever problem you have, someone solved it in Java years ago. The library is stable, documented, and production-hardened. 4. Tooling That Respects My Time IntelliJ understands Java deeply. Refactoring across 200 files? Done. Debugging with hot-swap? Works. Maven and Gradle give reproducible builds I trust. Build a five-year-old project today? It works. 5. Performance You Don't Think About Performance is the default. The JIT handles micro-optimizations. When I need more, Flight Recorder and Async Profiler show exactly where time goes. Most days, I don't need them. The code just runs fast. 6. The Community of Grown-Ups Java developers are boring - in the best way. We've seen hype cycles. We value stability. We write code others will maintain. PR reviews focus on error handling, thread safety, edge cases. Not trendy syntax. 7. Backward Compatibility That's Real Code I wrote for Java 8 runs on Java 21. Not using new features, but running. Enterprises with decade-old projects? Java lets them. 8. The "Dead" Language That Won't Die Top three on TIOBE, GitHub, every survey. Powers Android, Fortune 500 backends, financial, healthcare, government. "Dead" languages don't have quarterly releases, active conferences, and more jobs than candidates. The Honest Trade-Offs Startup time is real. Memory footprint higher than Go or Rust. Verbosity still there compared to Python. But for backend services, long-running processes, systems needing reliability at scale? Worth it. What I Tell Juniors Learn Java. Not because it's trendy. Because it teaches types, memory, concurrency, the JVM model. All of it transfers. And you'll have a career. The Bottom Line I use Java in 2026 because it works. Stable, fast, well-tooled, constantly improving. It builds foundations, not hype. Every time I hear "Java is dead," I look at running systems, job postings, the Java roadmap, and smile. We'll be here a while. #Java #Programming #SoftwareDevelopment #JVM #Coding

🚀 Experimenting with Multithreading in Java – Real Performance Impact Recently, I built a multi-threaded web crawler in Java to understand the real-world impact of concurrency. The crawler scrapes product data (title + price) from a paginated website and stores it in a CSV file. 🧪 The Experiment: I ran the same crawler with different thread pool sizes. Case 1: Single Thread Execution time: ~678 seconds Tasks executed sequentially. Each HTTP request completed before the next one started. Case 2: 20 Threads (FixedThreadPool(20)) Execution time dropped dramatically. Multiple product pages were fetched in parallel, significantly reducing total runtime. 💡 Key Insight: The crawler is I/O-bound, not CPU-bound. Most of the time is spent waiting on network calls and server responses. While one thread waits for a response, other threads can continue working. That’s where multithreading creates massive performance gains. 📌 What I Learned: Thread pools drastically improve throughput for I/O-heavy systems. Too many threads can hurt performance due to context switching, memory overhead, and potential server throttling. Optimal thread count depends on CPU cores and the ratio of wait time to compute time. There’s even a formula: Optimal Threads ≈ CPU Cores × (1 + Wait Time / Compute Time) 🏗 Technical Takeaways Used ExecutorService with FixedThreadPool Implemented synchronized CSV storage for thread safety Used awaitTermination() to measure actual execution time Learned the importance of safe resource sharing in concurrent systems This experiment reinforced one key lesson: Multithreading isn’t just about parallelism — it’s about understanding where your system actually waits. #Java #Multithreading #BackendDevelopment #PerformanceEngineering #Concurrency

You can't instantiate an abstract class. Everyone knows that. But there are two ways to work with one — and knowing the difference makes you a better Java developer. 🔁 Part 2: How to "instantiate" an abstract class ✅ Option 1: Concrete Subclass (the standard way) Create a class that extends it and implements all abstract methods. abstract class Animal {   abstract void speak(); } class Dog extends Animal {   void speak() { System.out.println("Woof"); } } Animal a = new Dog(); // polymorphism ✅ This is the most common pattern. It's what Spring does under the hood with AbstractController, and what JUnit does with TestCase. ✅ Option 2: Anonymous Class (the quick way) No need to create a separate class. Java lets you implement it inline, on the spot. Animal a = new Animal() {   void speak() { System.out.println("..."); } }; // ✅ Technically, Java creates an unnamed subclass behind the scenes. Useful for: one-off implementations, quick testing, callbacks. ⚡ Anonymous Class vs Lambda — what changed in Java 8? Before Java 8: anonymous classes were the go-to for passing behavior. After Java 8: if the abstract class has only ONE abstract method (functional interface), a lambda is cleaner. // Anonymous class (verbose) Runnable r = new Runnable() {   public void run() { System.out.println("Running"); } }; // Lambda (clean) Runnable r = () -> System.out.println("Running"); Rule of thumb: → One abstract method + no shared state = use a lambda or functional interface → Multiple abstract methods + shared state = use abstract class + subclass 🎯 Real-world usage: Spring: AbstractController, AbstractMessageConverter JUnit: TestCase uses Template Method — setUp() and tearDown() are hooks you override Java Collections: AbstractList, AbstractMap give you most of the implementation for free The key insight: abstract classes aren't just restrictions. They're extension points — carefully designed places where the framework says "you decide what happens here." 📊 Oracle Java Docs — Anonymous Classes: https://lnkd.in/eVdJts4v 📊 Effective Java, Item 42 — Prefer lambdas to anonymous classes: https://lnkd.in/exH-cA-j #Java #OOP #SpringBoot #CleanCode #BackendDevelopment #SoftwareEngineering #TechLeadership

🛑 A Quick Java Memory Guide Understanding the Java Memory Model is non-negotiable for writing performant, bug-free code. If you don’t know where your data lives, you don’t really know Java. Here is the breakdown of Stack vs Heap: 🧠 The Stack (LIFO - Last In, First Out) · What lives here: Primitive values (int, double) and references to objects (pointers). · Scope: Each thread has its own private stack. Variables exist only as long as their method is running. · Access Speed: Very fast. · Management: Automatically freed when methods finish. 🗄️ The Heap (The Common Storage) · What lives here: The actual objects themselves (all instances of classes). · Scope: Shared across the entire application. · Access Speed: Slower than the stack due to global access. · Management: Managed by the Garbage Collector (GC). 💡 The Golden Rule: The reference is on the Stack, but the object it points to is on the Heap. Map<String, String> myMap = new HashMap<>(); (Stack: myMap) --> (Heap: HashMap object) 👇 Q1: If I declare int id = 5; inside a method, where is this value stored? A1: Stack. It's a local primitive variable, so it lives directly in the stack frame. Q2: I created an array: int[] data = new int[100];. The array holds primitives. Is the data stored on the Stack or Heap? A2: Heap. The array itself is an object in Java. The reference data lives on the Stack, but the 100 integers are stored contiguously on the Heap. Q3: What happens to memory if I pass this object reference to another method? A3: A copy of the reference is passed (passed by value). Both methods now have a pointer (on their respective stacks) to the same single object on the Heap. ♻️ Repost if you found this helpful! Follow me for more Java wisdom. #Java #Programming #SoftwareEngineering #MemoryManagement #Coding