Boost Spring Boot Performance with Java 21 Virtual Threads

🧠 𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝗶𝗻𝗸 (𝗣𝗮𝗿𝘁 𝟭𝟯.𝟱.𝟯): 𝗝𝗮𝘃𝗮 𝗛𝘁𝘁𝗽𝗖𝗹𝗶𝗲𝗻𝘁 - 𝗠𝗼𝗱𝗲𝗿𝗻 𝗦𝘁𝗮𝗰𝗸 𝗡𝗼𝗯𝗼𝗱𝘆 𝗨𝘀𝗲𝘀 Java 11 gave us HttpClient. HTTP/2 built-in. Connection pooling included. Async API. No OkHttp, Apache, or Netty dependency. Five years later, everyone still adds external HTTP libraries. Why? Spring Boot doesn't default to it. Teams don't know it exists. Legacy code stays legacy. But it's there. Java 21 virtual threads change blocking model. Time to reconsider built-in? --- 🔧 𝗪𝗵𝗮𝘁 𝗝𝗮𝘃𝗮 𝗛𝘁𝘁𝗽𝗖𝗹𝗶𝗲𝗻𝘁 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝘀 _HttpClient client = HttpClient.newBuilder()   .version(Version.HTTP_2) // HTTP/2 default   .connectTimeout(...)   .build();_ 𝗕𝘂𝗶𝗹𝘁-𝗶𝗻: - HTTP/2 by default (fallback to HTTP/1.1) - Connection pooling (automatic) - Async and sync APIs (CompletableFuture) - No external dependencies --- 🔧 𝗪𝗵𝘆 𝗡𝗼𝗯𝗼𝗱𝘆 𝗨𝘀𝗲𝘀 𝗜𝘁 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗱𝗲𝗳𝗮𝘂𝗹𝘁𝘀: RestTemplate (13.3), WebClient (13.4). Not Java HttpClient. 𝗟𝗲𝗴𝗮𝗰𝘆 𝗰𝗼𝗱𝗲 𝗨𝗻𝗳𝗮𝗺𝗶𝗹𝗶𝗮𝗿𝗶𝘁𝘆 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Fewer Stack Overflow answers, less community content. --- 🔧 𝗝𝗮𝘃𝗮 𝟮𝟭 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗧𝗵𝗿𝗲𝗮𝗱𝘀 𝗖𝗵𝗮𝗻𝗴𝗲 𝗧𝗵𝗲 𝗚𝗮𝗺𝗲 _HttpClient client = HttpClient.newHttpClient(); // Blocking call on virtual thread = no problem String response = client.send(request, BodyHandlers.ofString()).body();_ 𝗕𝗲𝗳𝗼𝗿𝗲 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝘁𝗵𝗿𝗲𝗮𝗱𝘀: Blocking HTTP = platform thread per request. Doesn't scale (13.4). 𝗪𝗶𝘁𝗵 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝘁𝗵𝗿𝗲𝗮𝗱𝘀: Blocking code performs like async. Simple blocking HttpClient calls scale to thousands of concurrent requests. 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳: WebClient reactive complexity vs HttpClient simplicity + virtual threads. Both scale. Different models. --- 🔧 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗽𝗼𝗼𝗹𝗶𝗻𝗴: Built-in, automatic. 𝗛𝗧𝗧𝗣/𝟮 𝘀𝘁𝗮𝘁𝗲: HPACK overhead same as any HTTP/2 client (13.5.2). 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀: Zero external. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗳𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁: fewer jars. --- ⚖️ 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀 𝗝𝗮𝘃𝗮 𝗛𝘁𝘁𝗽𝗖𝗹𝗶𝗲𝗻𝘁: ✓ HTTP/2 built-in, no dependencies, virtual thread ready ✗ Less Spring integration, smaller ecosystem, less familiar 𝗢𝗸𝗛𝘁𝘁𝗽/𝗔𝗽𝗮𝗰𝗵𝗲: ✓ Mature, large ecosystem, Spring integrated ✗ External dependencies, more complex configuration 𝗪𝗲𝗯𝗖𝗹𝗶𝗲𝗻𝘁: ✓ Reactive, Spring native, high concurrency ✗ Reactor complexity, learning curve (13.4) --- 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Built-in HTTP finally viable. Reconsider dependency bloat. #Java #HttpClient #Java11 #Java21 #VirtualThreads #HTTP2 #Performance #Memory #NoDependencies #JavaPerformance #BackendDevelopment #ProjectLoom #ModernJava #CloudComputing #EnterpriseJava #DevOps #Microservices #MemoryOptimization #SoftwareEngineering #CloudNative #ProductionReady #JavaDevelopment #Async #NonBlocking #HighScale

🚀 I spent hours condensing SOLID Principles into one ultimate Java guide — so you don't have to! Most people can NAME the SOLID principles. Very few can APPLY them in real code. That changes today. 👇 ━━━━━━━━━━━━━━━━━ 🔵 S — Single Responsibility Principle "One class. One job. One reason to change." Your UserManager shouldn't be sending emails AND generating PDFs AND querying databases. Split it. Your future self will thank you. ━━━━━━━━━━━━━━━━━ 🟢 O — Open/Closed Principle "Open for extension. Closed for modification." Stop rewriting your AreaCalculator every time a new Shape appears. Use abstract classes. Add new behavior by EXTENDING — never by touching tested code. ━━━━━━━━━━━━━━━━━ 🟠 L — Liskov Substitution Principle "A Square is NOT a Rectangle. Don't lie to your compiler." If your subclass breaks the parent's contract, you don't have inheritance — you have a bug factory. Design your hierarchy honestly. ━━━━━━━━━━━━━━━━━ 🔴 I — Interface Segregation Principle "Don't force a Robot to implement eat() and sleep()." Fat interfaces are a code smell. Split them into focused, lean contracts. Implement only what makes sense — nothing more. ━━━━━━━━━━━━━━━━━ 🟣 D — Dependency Inversion Principle "Depend on DatabaseInterface — not MySQLDatabase." This is the foundation of Spring Boot's entire DI system. Program to abstractions. Swap implementations without touching business logic. ━━━━━━━━━━━━━━━━━ 📌 What's inside the full guide: ✅ Full form of every principle ✅ Real-world analogies (restaurants, iPhones, power sockets) ✅ Side-by-side ❌ Bad vs ✅ Good Java code ✅ Benefits for each principle ✅ One-page cheat sheet to keep forever ━━━━━━━━━━━━━━━━━ 💬 Which SOLID principle do you find hardest to apply in real projects? Drop your answer below — I read every comment. 👇 ♻️ Repost this to help a fellow developer level up. It takes 2 seconds and might change someone's career. 🔔 Follow me → Pondurai Madheswaran for daily Java, Clean Code & System Design content. ━━━━━━━━━━━━━━━━━ #Java #SOLID #CleanCode #SoftwareEngineering #OOP #DesignPrinciples #CodingBestPractices #SpringBoot #SoftwareDevelopment #TechLinkedIn #Programming #JavaDeveloper #BackendDevelopment #CodeQuality #PonduraiWrites

🚀 Spring Boot Annotations – Explained 🔹Core Spring Annotation @Component / @Service / @Repository 👉 Mark classes as managed by Spring Real world: @Service → Business logic (payment processing, order validation) @Repository → Database layer (fetch users, save transactions) @Component → Generic helper utilities 🔹 Dependency Injection @Autowired / @Qualifier / @Primary 👉 Automatically connects objects together Real world: @Autowired injects your PaymentService into a controller @Qualifier chooses between StripePayment vs PayPalPayment @Primary sets the default implementation 🔹 REST API Annotations @RestController / @GetMapping / @PostMapping / @PutMapping / @DeleteMapping 👉 Expose APIs to frontend & clients Real world: @GetMapping("/users") → Fetch users @PostMapping("/orders") → Create new order @PutMapping("/profile") → Update profile @DeleteMapping("/cart/{id}") → Remove item 🔹 JPA & Database @Entity / @Id / @GeneratedValue / @OneToMany / @ManyToOne 👉 Map Java objects to database tables Real world: User → Users table Order → Orders table One user can have many orders Each order belongs to one user 🔹 Exception Handling & Validation @ControllerAdvice / @ExceptionHandler / @Valid / @NotNull 👉 Handle errors & validate requests cleanly Real world: @Valid ensures request data is correct @NotNull prevents empty fields @ControllerAdvice returns clean error responses @ExceptionHandler handles failures gracefully Comment if this helps in a better understanding of these Spring Boot Annotations. 🔥 If you're serious about Java Backend / Spring Boot interviews, mastering these annotations will cover 80% of real project use-cases. #SpringBoot #Java #BackendDevelopment #Microservices #APIs #SpringFramework #SoftwareEngineering #JavaDeveloper

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

𝑫𝒂𝒚 1 𝒐𝒇 𝑴𝒚 𝑱𝒂𝒗𝒂 𝑰𝒏𝒕𝒆𝒓𝒗𝒊𝒆𝒘 𝑷𝒓𝒆𝒑𝒂𝒓𝒂𝒕𝒊𝒐𝒏 𝑱𝒐𝒖𝒓𝒏𝒆𝒚 – 45 𝑫𝒂𝒚𝒔 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆 Started a 45-day Java revision plan, and today focused on Core Java basics to strengthen fundamentals for real-world backend development and interviews. 🔹 𝙅𝙖𝙫𝙖 𝙁𝙪𝙣𝙙𝙖𝙢𝙚𝙣𝙩𝙖𝙡𝙨 𝙍𝙚𝙛𝙧𝙚𝙨𝙝𝙚𝙙 ✔ Object-Oriented Programming Language – Supports encapsulation, inheritance, polymorphism, and abstraction ✔ Developed by Sun Microsystems (now Oracle) ✔ Write Once, Run Anywhere (WORA) via JVM bytecode execution ✔ Heavily used in Enterprise Systems, Backend APIs, Microservices, and Android Development 💻 𝘿𝙚𝙚𝙥 𝘿𝙞𝙫𝙚: 𝙅𝘿𝙆 𝙫𝙨 𝙅𝙍𝙀 𝙫𝙨 𝙅𝙑𝙈 (𝙄𝙣𝙩𝙚𝙧𝙫𝙞𝙚𝙬 + 𝙋𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡 𝙑𝙞𝙚𝙬) ✅ JDK (Java Development Kit) • Complete development toolkit • Contains JRE + Development tools (javac, debugger, tools.jar, etc.) • Used during development & compilation stage ✅ JRE (Java Runtime Environment) • Provides runtime libraries + JVM • Used only to run Java applications • No compilation capability ✅ JVM (Java Virtual Machine) • Executes bytecode → converts into machine-level instructions • Handles Garbage Collection, Memory Management, Thread Handling, Security • Makes Java platform independent 📊 𝙅𝙖𝙫𝙖 𝘿𝙖𝙩𝙖 𝙏𝙮𝙥𝙚𝙨 – 𝙒𝙝𝙮 𝙄𝙩 𝙈𝙖𝙩𝙩𝙚𝙧𝙨 𝙞𝙣 𝙍𝙚𝙖𝙡 𝙎𝙮𝙨𝙩𝙚𝙢𝙨 Java is strongly typed → prevents runtime surprises → improves code safety. 👉 Primitive Types (Performance Critical) Used in high-performance logic → less memory overhead 👉 Non-Primitive Types (Object Handling) Used in Collections, APIs, Database Entities, DTOs 📦 Variables – Memory & Scope Understanding ✔ Local Variables → Stack memory → method-level lifecycle ✔ Instance Variables → Heap memory → object-level state ✔ Static Variables → Class-level shared memory → useful in caching, counters, configs 🔐 𝘼𝙘𝙘𝙚𝙨𝙨 𝙈𝙤𝙙𝙞𝙛𝙞𝙚𝙧𝙨 – 𝙐𝙨𝙚𝙙 𝙞𝙣 𝙍𝙚𝙖𝙡 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 • public → API exposure • private → Encapsulation & data protection • default → Package-level module design • protected → Inheritance-based architecture ⚙️ 𝙈𝙚𝙩𝙝𝙤𝙙𝙨 – 𝙍𝙚𝙪𝙨𝙖𝙗𝙞𝙡𝙞𝙩𝙮 + 𝘾𝙡𝙚𝙖𝙣 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 A well-designed method improves: ✔ Code reusability ✔ Testability ✔ Maintainability ✔ Readability Includes: Access Modifier + Return Type + Method Name + Parameters + Business Logic 🖥️ 𝙋𝙧𝙖𝙘𝙩𝙞𝙘𝙚 𝙁𝙤𝙘𝙪𝙨 – 𝘾𝙤𝙢𝙢𝙖𝙣𝙙 𝙇𝙞𝙣𝙚 𝘼𝙧𝙜𝙪𝙢𝙚𝙣𝙩𝙨 Revisited Command Line Arguments → Useful for: • Passing runtime configs • Batch job execution • Automation scripts • Microservice startup parameters 💡 𝙆𝙚𝙮 𝙍𝙚𝙛𝙡𝙚𝙘𝙩𝙞𝙤𝙣 Revisiting basics always highlights how clean fundamentals lead to better system design, faster debugging, and more scalable code. Consistency > Speed. #Java #InterviewPreparation #BackendDevelopment #45DaysChallenge #JavaDeveloper #Programming #LearningJourney #SoftwareEngineering

⚙️🧹 [JAVA26] JEP 522: G1 GC BOOSTS THROUGHPUT BY REDUCING SYNCHRONIZATION G1 is the default GC in HotSpot, designed to balance latency + throughput. But that balance has a cost: application threads (mutators) and GC threads must coordinate, and that coordination adds synchronization overhead. JEP 522 targets exactly that — without changing how you use G1. 🔸 TLDR ▪️ Java 26 ships JEP 522: faster G1 by reducing synchronization + shrinking write barrier code. ▪️ No new flags, no migration pain — just better throughput (and slightly better latency) for many workloads. 🔸 WHAT JEP 522 CHANGES (IN HUMAN TERMS) ▪️ Less synchronization between app threads and GC threads → less contention → better throughput ▪️ Smaller / simpler write barrier code injected by the JVM (the code that runs on every reference write) → lower overhead and better JIT optimization opportunities ▪️ Same G1 architecture + same knobs (no new tuning model, no new UX) 🔸 QUICK BACKGROUND: WHY WRITE BARRIERS MATTER Every time your Java code updates an object reference (e.g., obj.field = other), G1 runs a post-write barrier to keep track of which heap “cards” became dirty and need later processing. That barrier can be surprisingly “heavy” in instruction count vs throughput-oriented collectors — and it can limit compiler optimizations. 🔸 EXPECTED IMPACT (WHAT YOU FEEL IN PROD) ▪️ Throughput improvement: the simplification can yield up to ~5% broadly, and up to ~15% on workloads that heavily modify object-reference fields (think allocation-heavy / pointer-churny apps). ▪️ Latency: pause times can decrease slightly as a side effect of reduced contention. 🔸 WHO SHOULD CARE ▪️ Services doing lots of object graph mutations (ORM-heavy workloads, caching layers, event enrichment pipelines) ▪️ High-throughput apps where G1 is already “good”, but you still want free performance with no config change 🚀 ▪️ Teams standardizing on Java 26 and wanting perf wins “for default settings” 🔸 TAKEAWAYS ▪️ If you run G1 (default), Java 26 can bring a real perf bump “for free”. ▪️ The biggest wins are for apps with lots of reference writes (high mutation rate). ▪️ This is a reminder that sometimes the best performance work is removing coordination, not adding cleverness 🧠✨ #Java #OpenJDK #JDK26 #G1GC #GarbageCollection #JVM #Performance #HotSpot #Engineering #BackendEngineering Go further with Java certification: Java👇 https://lnkd.in/eZKYX5hP Spring👇 https://lnkd.in/eADWYpfx SpringBook👇 https://bit.ly/springtify JavaBook👇 https://bit.ly/jroadmap

🧵 𝗝𝗮𝘃𝗮 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 - 𝗣𝗮𝗿𝘁 𝟮: 𝗪𝗵𝗮𝘁 𝗟𝗶𝘃𝗲𝘀 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗝𝗩𝗠? In Part 1, we saw that running a Java program creates a process with its own memory layout: code, data, heap, and stack. The JVM runs inside that process. It requests memory from the OS, organizes it into its own runtime areas, and the Java code executes entirely within that structure. Everything below lives inside that JVM-managed memory. 1️⃣ 𝗝𝗮𝘃𝗮 𝗛𝗲𝗮𝗽 The JVM allocates the Java Heap inside its process memory. This is where runtime data lives: • All objects • Instance fields • Static variables There is only one heap per JVM. All threads share it. If two threads modify the same object, they are modifying the same memory location. 2️⃣ 𝗠𝗲𝘁𝗮𝘀𝗽𝗮𝗰𝗲 Metaspace stores class metadata, method bytecode, and runtime constant pool. It defines the structure of your program. It does not store changing variable values. 𝗠𝗲𝘁𝗮𝘀𝗽𝗮𝗰𝗲 𝗶𝘀 𝘀𝗵𝗮𝗿𝗲𝗱, 𝗯𝘂𝘁 𝗶𝘁 𝗵𝗼𝗹𝗱𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗻𝗼𝘁 𝗺𝘂𝘁𝗮𝗯𝗹𝗲 𝘀𝘁𝗮𝘁𝗲. 3️⃣ 𝗝𝗩𝗠 𝗦𝘁𝗮𝗰𝗸 Each thread gets its own JVM stack. The stack stores: • Method call frames • Local variables • Method parameters • References to heap objects This memory is private to the thread. If Thread A declares a local variable, Thread B cannot access it. But remember: A reference to an object lives on the stack. The object it points to lives on the heap. So the stack can be private while still pointing to shared memory. That distinction is critical. 4️⃣ 𝗣𝗖 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 Each thread has its own Program Counter register. It keeps track of the current bytecode instruction being executed. 5️⃣ 𝗡𝗮𝘁𝗶𝘃𝗲 𝗦𝘁𝗮𝗰𝗸 Each thread also has a native stack. So when your Java code calls a native method (for example, something written in C/C++ through JNI), execution temporarily leaves the JVM and runs native code. Like the JVM stack, it is private to the thread. So, When a thread works with local variables, it is operating on its own private memory. But when it reads or modifies an object or a static variable, it is operating on shared heap memory. Two threads can execute completely different stack frames, yet still read and update the same heap object at the same time. And that is where unexpected behavior begins. This is 𝗣𝗮𝗿𝘁 𝟮 of the Java Concurrency series. Follow along and feel free to refine or add anything I miss.

zio-openfeature v0.6.2 is out! zio-openfeature (https://lnkd.in/dapfciW6 ) is an open-source Scala library that lets you manage feature flags in a safe, functional way — built on top of the OpenFeature (https://openfeature.dev/) standard so it works with any compatible provider (Optimizely, LaunchDarkly, Flagd, and more). A few highlights from the latest release: ✅ Safer evaluations — flag checks now refuse to run when the provider is in an error state, not just when it's not yet ready. This prevents silent misbehavior in production. 🔁 Correct hook ordering — hooks that clean up or handle errors now run in the right order (last registered, first to run), which is what the OpenFeature spec requires. 🔒 Type-safe internals — a new TypedKey construct makes passing data through the hook pipeline strongly typed, catching mistakes at compile time instead of runtime. 🧹 Major cleanup — v0.6.0 brought a large internal refactor: cleaner Java interop, better concurrency primitives, and less boilerplate. No breaking changes to the public API. #Scala #ZIO #OpenFeature #FeatureFlags #OpenSource #Optimizely

𝐉𝐚𝐯𝐚 𝐃𝐚𝐢𝐥𝐲 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 – 𝐃𝐚𝐲 2: 𝐂𝐥𝐚𝐬𝐬𝐋𝐨𝐚𝐝𝐞𝐫𝐬 & 𝐌𝐞𝐦𝐨𝐫𝐲 — 𝐉𝐚𝐯𝐚’𝐬 𝐒𝐦𝐚𝐫𝐭 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐒𝐲𝐬𝐭𝐞𝐦: Think of the JVM as a fully automated smart warehouse.Before any product (Java class) can be used, it must be located, verified, stored correctly, and tracked. That’s exactly what happens before your Java code even starts executing. 1. 𝐂𝐥𝐚𝐬𝐬𝐋𝐨𝐚𝐝𝐞𝐫 — 𝐓𝐡𝐞 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐑𝐞𝐜𝐞𝐢𝐯𝐢𝐧𝐠 𝐓𝐞𝐚𝐦 Before Java can use a class, it must be loaded into the JVM. ClassLoaders: ✔️ Locate .class files ✔️ Verify bytecode ✔️ Load them into memory Types of ClassLoaders (Receiving Zones): 🔹 𝐁𝐨𝐨𝐭𝐬𝐭𝐫𝐚𝐩 𝐂𝐥𝐚𝐬𝐬𝐋𝐨𝐚𝐝𝐞𝐫 • Loads core Java inventory (java.lang.*, java.util.*) • This is the foundation stock — always available 🔹 𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧 𝐂𝐥𝐚𝐬𝐬𝐋𝐨𝐚𝐝𝐞𝐫 • Loads optional, extended packages • Think of specialized add-on supplies 🔹 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐥𝐚𝐬𝐬𝐋𝐨𝐚𝐝𝐞𝐫 • Loads your application’s classes • Custom products you bring into the warehouse 🔁 Delegation Model: Each loader checks with its parent first before loading. ➡️ Prevents duplicate inventory and ensures system stability. 2. 𝐉𝐕𝐌 𝐌𝐞𝐦𝐨𝐫𝐲 — 𝐓𝐡𝐞 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐙𝐨𝐧𝐞𝐬 Once classes arrive, JVM memory organizes where everything lives: 📘 𝐌𝐞𝐭𝐡𝐨𝐝 𝐀𝐫𝐞𝐚 • Blueprint records: class structure, methods, static data • Shared reference catalog 📦 𝐇𝐞𝐚𝐩 • Where actual products (objects) are stored • Shared across all workers (threads) 🗂️ 𝐒𝐭𝐚𝐜𝐤 • Personal workbench per worker (thread) • Holds method calls & local variables 📮 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐂𝐨𝐮𝐧𝐭𝐞𝐫 (𝐏𝐂) • Tracks the current instruction each worker is handling ⚙️ 𝐍𝐚𝐭𝐢𝐯𝐞 𝐌𝐞𝐭𝐡𝐨𝐝 𝐒𝐭𝐚𝐜𝐤 • Manages external tools (native C/C++ operations) ✅ 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 ✔️ ClassLoaders find and load Java classes ✔️ JVM Memory organizes and manages them ✔️ This entire process completes before execution begins #Java #JavaDailyUpdates #JavaDeveloper #JVM #ClassLoader #JavaMemory #JavaArchitecture #CoreJava #LearnJava #JavaConcepts #BackendDevelopment #SoftwareEngineering #JavaInterview #SystemDesign #ProgrammingConcepts #CodingTip #DevelopersOfLinkedIn #JavaCommunity #DailyLearning #DeveloperJourney #JavaCommunity #SoftwareDevelopment #SoftwareArchitecture #C2C #WebDevelopment #Developer #C2H #BackendEngineering #Microservices #JavaScript #Data #Deployment #FullStackDevelopment #TechTalk #SoftwareEngineering #Python #Java #AI #FullStack #Frontend #Backend #Cloud #Testing #OpentoWork #Jobs #Jobsearch #C2C #Data #Dataengineering #Automotive #Linkedin #Tips #DevOps #Remote #Hybrid #Senior #UST #Brooksource #ProwessSoft #KYYBA Inc #Experis #SRS Consulting Inc #TEKsystems #The Judge Group #Beacon Hill #BayOne Solutions #Randstad USA #Insightglobal #JavaCommunity .

🔍 Go beyond heap dumps Whether you're a JVM enthusiast, performance engineer, or backend developer, JOL helps you peek under the hood of Java object memory layout, including object headers, field ordering, padding, footprint, and more, using low-level JVM internals. JOL uses Unsafe, JVMTI, and the Serviceability Agent for unparalleled accuracy. Useful for optimizing data structures, reducing memory footprint, and improving cache behavior. 🛠️ JOL can be used as: - A library in your project - A CLI tool for quick analysis - A guide for deep JVM performance tuning and research If you care about performance, memory efficiency, or just love understanding how Java really works behind the scenes. https://lnkd.in/dtC_5B5n #Java #OpenSource #JVM #PerformanceEngineering #Developers