Debugging Java Backend Issue with Data Model Mismatch

While working on backend systems, I revisited some features from Java 17… and honestly, they make code much cleaner. One feature I find really useful is Records. 👉 Earlier: We used to write a lot of boilerplate just to create a simple data class. Getters Constructors toString(), equals(), hashCode() ✅ With Java 17 — Records: You can define a data class in one line: public record User(String name, int age) {} That’s it. Java automatically provides: ✔️ Constructor ✔️ Getters ✔️ equals() & hashCode() ✔️ toString() 💡 Practical usage: User user = new User("Dipesh", 25); System.out.println(user.name()); // Dipesh System.out.println(user.age()); // 25 🧠 Where this helps: DTOs in APIs Response objects Immutable data models What I like most is how it reduces boilerplate and keeps the code focused. Would love to know — are you using records in your projects? #Java #Java17 #Backend #SoftwareEngineering #Programming #Microservices #LearningInPublic

Built an HTTP server from scratch in Java. No frameworks. No Netty. No Spring Boot. Just raw sockets, manual byte parsing, and a thread pool wired from the ground up. The goal was never to reinvent the wheel. It was to understand what the wheel is actually made of. What was built: → TCP socket listener handing connections to a fixed thread pool of 500 workers → HTTP/1.1 parser written byte-by-byte - request line, headers, body (JSON + form-urlencoded) → Router handling HEAD, GET and POST with static file serving and query param injection → Structured error responses for 400, 404, 500, 501, and 505 → WebRootHandler with path traversal protection → Full JUnit test suite, Maven build, SLF4J logging, and a Dockerized deployment What it reinforced: Networking - HTTP is just bytes on a wire. The kernel speaks TCP, not HTTP. The parser is what gives those bytes meaning. Operating systems - the boundary between user space and kernel space stopped being abstract. accept(), read(), write() are syscalls. Everything else lives in the JVM. Concurrency - 500 threads sharing a single handler. Statelessness stops being a design preference and becomes a correctness requirement. Docker - the container wraps the JVM, not the kernel. Syscalls go to the host kernel. There is no container kernel. Building something from scratch is one of the most honest ways to learn. Every assumption gets tested, every abstraction gets earned. A recommendation from Kashif Sohail turned into weeks of going deep on networking, OS internals, and concurrency. Grateful for that push. GitHub repo :https://lnkd.in/dxxjXxpt #Java #Networking #OperatingSystems #Backend #SystemsEngineering #Docker #OpenSource

🚨 Can a Thread call start() twice in Java? Short answer — No. And we learned this the hard way in production. 😬 😓 The real story We had a payment retry system. When a payment failed, our code called thread.start() again on the same thread to retry. Seemed logical... until we saw IllegalThreadStateException crashing the entire service at midnight. 💀 🔍 Why does this happen? Once a thread finishes, it moves to a TERMINATED state. Java does not allow restarting a dead thread — ever. ❌ Wrong: t.start(); t.start(); → 💥 CRASH ✅ Right: Create a new Thread each time, or use ExecutorService 💡 How we fixed it Replaced raw threads with ExecutorService. Every retry = a new task submitted to the pool. No crashes. No headaches. 🧠 Remember: 🔁 Thread lifecycle → New → Runnable → Running → Terminated 🚫 Once terminated — cannot restart ✅ Always use a new Thread or ExecutorService One line of mistake. One midnight crash. One lesson for life. 🙂 Have you ever hit this bug? Drop a comment 👇 #Java #Multithreading #Threading #JavaDeveloper #BackendDevelopment #CodingTips #SoftwareEngineering

JVM Architecture - what actually runs your Java code ⚙️ While working with Java and Spring Boot, I realized something: We spend a lot of time writing code, but not enough time understanding what executes it. That’s where the JVM (Java Virtual Machine) comes in. A simple breakdown: • Class Loader Loads compiled `.class` files into memory. • Runtime Data Areas * Heap → stores objects (shared across threads) 🧠 * Stack → stores method calls and local variables (per thread) * Method Area → stores class metadata and constants * PC Register → tracks current instruction * Native Method Stack → handles native calls • Execution Engine * Interpreter - runs bytecode line by line * JIT Compiler - optimizes frequently used code into native machine code ⚡ • Garbage Collector Automatically removes unused objects from memory --- Why this matters: Understanding JVM helps in: * Debugging memory issues (like OutOfMemoryError) * Improving performance * Writing more efficient backend systems --- The more I learn, the more I see this pattern: Good developers write code. Better developers understand how it runs. #Java #JVM #BackendDevelopment #SpringBoot #SystemDesign

𝐖𝐡𝐲 𝐢𝐬 𝐦𝐲 𝐂𝐮𝐬𝐭𝐨𝐦 𝐀𝐧𝐧𝐨𝐭𝐚𝐭𝐢𝐨𝐧 𝐫𝐞𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐧𝐮𝐥𝐥? 🤯 Every Java developer eventually tries to build a custom validation or logging engine, only to get stuck when method.getAnnotation() returns null. The secret lies in the @Retention meta-annotation. If you don't understand these three levels, your reflection-based engine will never work: 1️⃣ SOURCE (e.g., @Override, @SuppressWarnings) Where? Only in your .java files. Why? It’s for the compiler. Once the code is compiled to .class, these annotations are GONE. You cannot find them at runtime. 2️⃣ CLASS (The default!) Where? Stored in the .class file. Why? Used by bytecode analysis tools (like SonarLint or AspectJ). But here's the kicker: the JVM ignores them at runtime. If you try to read them via Reflection — you get null. 3️⃣ RUNTIME (e.g., @Service, @Transactional) Where? Stored in the bytecode AND loaded into memory by the JVM. Why? This is the "Magic Zone." Only these can be accessed by your code while the app is running. In my latest deep dive, I built a custom Geometry Engine using Reflection. I showed exactly how to use @Retention(RUNTIME) to create a declarative validator that replaces messy if-else checks. If you’re still confused about why your custom metadata isn't "visible," this breakdown is for you. 👇 Link to the full build and source code in the first comment! #Java #Backend #SoftwareArchitecture #ReflectionAPI #CleanCode #ProgrammingTips

☕ Ever Wondered How JVM Actually Works? Let’s Break It Down. 🚀 Many Java developers use JVM daily, but few truly understand what happens behind the scenes. Let’s simplify it 👇 🔹 Step 1: Write Java Code Create your file like Hello.java 🔹 Step 2: Compile the Code Use javac Hello.java This converts source code into bytecode (.class) 🔹 Step 3: Class Loader Starts Work JVM loads required classes into memory when needed. 🔹 Step 4: Memory Areas Created JVM manages different memory sections: ✔ Heap (objects) ✔ Stack (method calls) ✔ Method Area (class metadata) ✔ PC Register 🔹 Step 5: Execution Engine Runs Code Bytecode is executed using: ✔ Interpreter ✔ JIT Compiler (improves speed) 🔹 Step 6: Garbage Collector Cleans Memory Unused objects are removed automatically. 🔹 Simple Flow Java Code → Bytecode → JVM → Machine Execution 💡 Strong Java developers don’t just write code. They understand what happens under the hood. 🚀 Master fundamentals, and performance tuning becomes easier. #Java #JVM #Programming #SoftwareEngineering #BackendDevelopment #Developers #Coding #JavaDeveloper #TechLearning #SpringBoot

Still confused about how Java actually runs your code? 🤔 Here’s a simple breakdown of how the JVM works 👇 👉 1. Build Phase ✔️ Java code (.java) is compiled using javac ✔️ Converted into bytecode (.class files) 👉 2. Class Loading ✔️ JVM loads classes using: Bootstrap Class Loader Platform Class Loader System Class Loader 👉 3. Linking ✔️ Verify → Prepare → Resolve ✔️ Ensures code is safe and ready to run 👉 4. Initialization ✔️ Static variables & blocks are initialized 👉 5. Runtime Data Areas ✔️ Method Area & Heap (shared) ✔️ Stack, PC Register, Native Stack (per thread) 👉 6. Execution Engine ✔️ Interpreter executes bytecode line by line ✔️ JIT Compiler converts hot code into machine code for speed 👉 7. Native Interface (JNI) ✔️ Interacts with native libraries when needed 💡 JVM is the reason behind Java’s “Write Once, Run Anywhere” power. 📌 Save this post 🔁 Repost to help others 👨💻 Follow Abhishek Sharma for more such content #Java #JVM #SystemDesign #BackendDevelopment #SoftwareEngineer #Developers #TechJobs #Programming #LearnJava

📖 New Post: Java Memory Model Demystified: Stack vs. Heap Where do your variables live? We explain the Stack, the Heap, and the Garbage Collector in simple terms. #java #jvm #memorymanagement

🌊 Java Streams changed how I write code forever. Here's what 9 years taught me. When Java 8 landed, Streams felt like magic. After years of using them in production, here's the real truth: What Streams do BRILLIANTLY: ✅ Filter → map → collect pipelines = clean, readable, expressive ✅ Method references make code self-documenting ✅ Parallel streams can speed up CPU-bound tasks (with caveats) ✅ flatMap is one of the most powerful tools in functional Java What Streams do POORLY: ❌ Checked exceptions inside lambdas = ugly workarounds ❌ Parallel streams on small datasets = overhead, not gains ❌ Complex stateful operations get messy fast ❌ Stack traces become unreadable — debugging is harder My 9-year rule of thumb: Use streams when the INTENT is clear. Fall back to loops when the LOGIC is complex. Streams are about readability. Never sacrifice clarity for cleverness. Favorite advanced trick: Collectors.groupingBy() for powerful data transformations in one line. What's your favorite Java Stream operation? 👇 #Java #Java8 #Streams #FunctionalProgramming #JavaDeveloper

#Post11 In the previous post(https://lnkd.in/dynAvNrN), we saw how to create threads in Java. Now let’s talk about a problem. If creating threads is so simple… why don’t we just create a new thread every time we need one? Let’s say we are building a backend system. For every incoming request/task, we create a new thread: new Thread(() -> { // process request }).start(); This looks simple. But this approach breaks very quickly in real systems because of below mentioned problems. Problem 1: Thread creation is expensive Creating a thread is not just creating an object. It involves: • Allocating memory (stack) • Registering with OS • Scheduling overhead Creating thousands of threads = performance degradation Problem 2: Too many threads → too much context switching We already saw this earlier(https://lnkd.in/dYG3v-vb). More threads does NOT mean more performance. Instead: • CPU spends more time switching • Less time doing actual work Problem 3: No control over thread lifecycle When you create threads manually: • No limit on number of threads • No reuse • Hard to manage failures This quickly becomes difficult to manage as the system grows. So what’s the solution? Instead of creating threads manually: we use something called the Executor Framework. In simple words consider the framework to be like: Earlier, we were manually hiring a worker (thread) for every task. With Executor, we have a team of workers (thread pool), and we just assign tasks to them. Key idea Instead of: Creating a new thread for every task We do: Submit tasks to a pool of reusable threads This is exactly what Java provides using: Executor Framework Key takeaway Manual thread creation works for learning, but does not scale in real-world systems. Thread pools help: • Control number of threads • Reduce overhead • Improve performance We no longer manage threads directly — we delegate that responsibility to the Executor Framework. In the next post, we’ll see how Executor Framework works and how to use it in Java. #Java #Multithreading #Concurrency #BackendDevelopment #SoftwareEngineering