Abstract Algorithms’ Post

Does Java really use too much memory? This is a common concern we hear from developers. Igor Souza takes a fact-based look at Java's memory usage, examining specific JEPs (JDK Enhancement Proposals) that have significantly improved memory efficiency over the years. The article covers: - How modern Java has changed compared to older versions - Concrete JEPs that reduced memory footprint - Real-world implications for your applications If you've been avoiding Java because of memory concerns, or if you're working with legacy assumptions about Java's resource usage, this article provides the data you need. Read the full analysis here: https://lnkd.in/e9RrhpSQ #Java #JVM #Performance #Memory

💡 JVM Memory in 1 Minute – Where Your Java Code Actually Lives As Java developers, we often hear about Heap, Stack, and Metaspace—but what do they actually do at runtime? 🤔 Here’s a simple breakdown 👇 When your Java program runs, the JVM divides memory into different areas, each with a specific responsibility. ➡️ Heap • Stores all objects and runtime data • Shared across all threads • Managed by Garbage Collector How it works: • New objects are created in Young Generation (Eden) • Surviving objects move to Survivor spaces • Long-lived objects move to Old Generation GC behavior: • Minor GC → cleans Young Generation (fast) • Major/Full GC → cleans Old Generation (slower) ➡️ Metaspace (Java 8+) • Stores class metadata (class structure, methods, constants) • Uses native memory (outside heap) • Grows dynamically Important: • Does NOT store objects or actual data • Cleaned when classloaders are removed ➡️ Stack • Each thread has its own stack • Used for method execution Stores: • Local variables • Primitive values • Object references (not actual objects) Working: • Method call → push frame • Method ends → pop frame ➡️ PC Register • Tracks current instruction being executed • Each thread has its own Purpose: • Helps JVM know what to execute next • Important for multi-threading ➡️ Native Method Stack • Used for native (C/C++) calls • Accessed via JNI Class → Metaspace Object → Heap Execution → Stack Next step → PC Register Native calls → Native Stack #Java #JVM #MemoryManagement #SoftwareEngineering #BackendDevelopment

Modern Java quietly made the Visitor pattern relevant again. Sealed classes changed the tradeoffs. It might be time to revisit Visitor. I wrote about why Visitor still makes sense if you're using Java 17–20: https://lnkd.in/dPkpgqtb

Day 7 of #100DaysOfCode — Java is getting interesting ☕ Today I explored the Java Collections Framework. Before this, I was using arrays for everything. But arrays have one limitation — fixed size. 👉 What if we need to add more data later? That’s where Collections come in. 🔹 Key Learnings: ArrayList grows dynamically — no size worries Easy operations: add(), remove(), get(), size() More flexible than arrays 🔹 Iterator (Game changer) A clean way to loop through collections: hasNext() → checks next element next() → returns next element remove() → safely removes element 🔹 Concept that clicked today: Iterable → Collection → List → ArrayList This small hierarchy made everything much clearer. ⚡ Array vs ArrayList Array → fixed size ArrayList → dynamic size Array → stores primitives ArrayList → stores objects Still exploring: Set, Map, Queue next 🔥 Consistency is the only plan. Showing up every day 💪 If you’re also learning Java or working with Collections — let’s connect 🤝 #Java #Collections #ArrayList #100DaysOfCode #JavaDeveloper #LearningInPublic

Are you still creating threads manually in Java? Previously I covered Thread, Runnable, and Callable, now I have dived deeper into ExecutorService, Thread Pools, and CompletableFuture—the tools we actually use in real-world systems. In this blog, I’ve explained: ✓ What Thread Pools are and why they matter ✓ How to use ExecutorService for better performance & control ✓ How CompletableFuture enables clean, non-blocking async code ✓ Practical Java examples you can apply immediately → Check it out and let me know your thoughts! #Java #Multithreading #Concurrency #BackendDevelopment #SpringBoot #SoftwareEngineering

Most explanations of Multithreading in Java barely scratch the surface. You’ll often see people talk about "Thread" or "Runnable", and stop there. But in real-world systems, that’s just the starting point—not the actual practice. At its core, multithreading is about running multiple tasks concurrently—leveraging the operating system to execute work across CPU time slices or multiple cores. Think of it like cooking while attending a stand-up meeting. Different tasks, progressing at the same time. In Java, beginners are introduced to: - Extending the "Thread" class - Implementing the "Runnable" interface But here’s the reality: 👉 This is NOT how production systems are built. In company-grade applications, developers rely on the "java.util.concurrent" package and more advanced patterns: 🔹 Thread Pools (Executor Framework) Creating threads manually is expensive. Thread pools reuse a fixed number of threads to efficiently handle many tasks using "ExecutorService". 🔹 Synchronization When multiple threads access shared resources, you must control access to prevent inconsistent data. This is where "synchronized" comes in. 🔹 Locks & ReentrantLock For more control than "synchronized", developers use "ReentrantLock"—allowing manual lock/unlock, try-lock, and better flexibility. 🔹 Race Conditions One of the biggest problems in multithreading. When multiple threads modify shared data at the same time, results become unpredictable. 🔹 Thread Communication (Condition) Threads don’t just run—they coordinate. Using "Condition", "wait()", and "notify()", threads can signal each other and work together. --- 💡 Bottom line: Multithreading is not just about creating threads. It’s about managing concurrency safely, efficiently, and predictably. That’s the difference between writing code… and building scalable systems. #Java #Multithreading #BackendEngineering #SoftwareEngineering #Concurrency #Tech

Topic of the day Java Memory Management? 💡 Java Memory Management Understanding JVM memory becomes easy when you connect it with real code 🔹 1. Heap Memory (Objects Storage) This is where all objects are created and stored. 👉 Example: Student s = new Student(); ✔ new Student() → object is created in Heap ✔ s (reference) → stored in Stack 📌 Real-time: Like storing data in a database, Heap holds actual objects. 🔹 2. Stack Memory (Method Execution) Each thread has its own stack which stores method calls and local variables. 👉 Example: public void display() { int x = 10; } ✔ display() method → pushed into Stack ✔ x → stored in Stack ✔ After method ends → removed automatically 📌 Real-time: Like a call stack in your mobile – recent calls come and go. 🔹 3. Method Area / Metaspace (Class-Level Data) Stores class metadata, static variables, and constant pool. 👉 Example: class Test { static int count = 100; } ✔ count → stored in Method Area ✔ Class structure → also stored here 📌 Real-time: Like a blueprint shared across the entire application. 🔹 4. PC Register (Program Counter) Keeps track of the current instruction of a thread. 👉 Example: System.out.println("Hello"); System.out.println("Java"); ✔ PC Register tracks which line is currently executing 📌 Real-time: Like a cursor pointing to the current line in your code editor. 🔹 5. Native Method Stack (JNI Execution) Used when Java interacts with native (C/C++) code. 👉 Example: System.loadLibrary("nativeLib"); ✔ Native methods execution handled here 📌 Real-time: Like calling an external system/service from your application. 🧠 Quick Revision Trick: Objects → Heap Variables & Methods → Stack Static & Class Info → Method Area Execution Line → PC Register External Code → Native Stack #Java #JVM #MemoryManagement #JavaDeveloper #Backend #Coding #Programming #SpringBoot #Coding

💡 Most Java developers still think thread creation is just: new Thread().start(); But that mindset is already outdated. Java has quietly evolved… and with Virtual Threads (Project Loom), the entire concurrency model has changed 🚀 Here’s the real shift: 👉 Traditional Threads → Heavy, 1:1 OS mapping 👉 ExecutorService → Controlled, pooled execution 👉 Virtual Threads → Lightweight, scalable, millions possible 🔥 And the most powerful pattern today? 👉 Executors.newVirtualThreadPerTaskExecutor() You get: ✔ Clean architecture (task vs execution separation) ✔ Massive scalability ✔ Simple, readable code ✔ No thread pool tuning headaches 🧠 The biggest mindset change? 👉 Don’t change your business logic 👉 Just change how it executes Same Runnable. Different execution model. That’s how modern Java systems are built. 📌 I’ve broken this down with: ✔ Complete runnable examples ✔ Traditional vs Executor vs Virtual Threads ✔ Internal working (what JVM actually does) ✔ Real-world scenarios & pitfalls 👇 Read here: https://lnkd.in/e-vdH8Vc ⚠️ If you're building backend systems and not using virtual threads yet… you’re leaving serious performance on the table. #Java #VirtualThreads #ProjectLoom #Concurrency #BackendDevelopment #Multithreading

🔥 Java Records — Cleaner code, but with important trade-offs I used to write a lot of boilerplate in Java just to represent simple data: Fields… getters… equals()… hashCode()… toString() 😅 Then I started using Records—and things became much cleaner. 👉 Records are designed for one purpose: Representing immutable data in a concise way. What makes them powerful: 🔹 Built-in immutability (fields are final) 🔹 No boilerplate for getters or utility methods 🔹 Compact and highly readable 🔹 Perfect for DTOs and API responses But here’s what many people overlook 👇 ⚠️ Important limitations of Records: 🔸 Cannot extend other classes (they already extend java.lang.Record) 🔸 All fields must be defined in the canonical constructor header 🔸 Not suitable for entities with complex behavior or inheritance 🔸 Limited flexibility compared to traditional classes So while Records reduce a lot of noise, they are not a universal replacement. 👉 They work best when your class is truly just data, not behavior. 💡 My takeaway: Good developers don’t just adopt new features—they understand where not to use them. ❓ Question for you: Where do you prefer using Records—only for DTOs, or have you explored broader use cases? #Java #AdvancedJava #JavaRecords #CleanCode #BackendDevelopment #SoftwareEngineering

In Java versions below 24 virtual threads can pin their carrier platform threads during synchronized blocks or native calls. This prevents the carrier from executing other tasks leading to thread starvation and sub-optimal resource usage. Interesting article on differences between Java 21 and Java 24 in terms how they handle virtual thread pinning. https://lnkd.in/dPpsDYHH