How to Boost Java 21 Performance with Virtual Threads

💡 𝐉𝐚𝐯𝐚 𝟐𝟏 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐢𝐩 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐓𝐡𝐫𝐞𝐚𝐝𝐬 🔥 Managing thousands of concurrent operations? Platform threads choking your system? Here's the solution: Virtual Threads! 💎This revolutionary feature from Project Loom provides super lightweight threads managed directly by the JVM. While classic threads consume MBs of memory, virtual threads stay at just KB level. ✅ 𝐖𝐡𝐲 𝐚𝐫𝐞 𝐭𝐡𝐞𝐲 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠? • Perfect for I/O-intensive operations. • Enable simple thread-per-request model at scale. • No need for complex reactive programming. • Backward compatible with existing code. ✨The magic happens when your application handles thousands of concurrent requests. ⚡Traditional threads would exhaust system resources, but virtual threads handle this elegantly. 🔥 They automatically yield when blocked, allowing other virtual threads to execute on the same platform thread. So, A REST API that previously handled 1000 concurrent connections can now handle 100,000+ with the same hardware, just by switching to virtual threads. 👍 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐢𝐬 𝐬𝐢𝐦𝐩𝐥𝐞: Use Thread.ofVirtual() or Executors.newVirtualThreadPerTaskExecutor(). Your blocking code becomes scalable without rewriting! #java #springboot #programming #softwareengineering #softwaredevelopment

𝗧𝗼𝗽 𝟱 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄: 1) 𝐓𝐨𝐤𝐞𝐧 𝐁𝐮𝐜𝐤𝐞𝐭 A bucket fills with tokens at a fixed rate. Each request consumes one token. If the bucket is empty (tokens run out) → request is throttled. Great when you want to allow short bursts while enforcing an average request rate. 2) 𝐅𝐢𝐱𝐞𝐝 𝐖𝐢𝐧𝐝𝐨𝐰 𝐂𝐨𝐮𝐧𝐭𝐞𝐫 Divides time into fixed windows (e.g., per minute). Counts requests in the current window. If the count exceeds the limit → block. Simple to implement, but suffers from burst spikes at window edges. 3) 𝐋𝐞𝐚𝐤𝐲 𝐁𝐮𝐜𝐤𝐞𝐭 Think of a queue that leaks at a constant rate. If new requests exceed queue capacity → they are dropped. Produces a smooth and predictable request flow. 4) 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐖𝐢𝐧𝐝𝐨𝐰 𝐋𝐨𝐠 Stores a timestamp for each request. For every new request, removes old timestamps outside the window and count the rest. Highly accurate, but expensive in memory for high-traffic systems. 5) 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐖𝐢𝐧𝐝𝐨𝐰 𝐂𝐨𝐮𝐧𝐭𝐞𝐫 Hybrid of Fixed Window + Log. Splits the window into smaller buckets and uses a weighted sum to estimate the rate. Provides good balance between accuracy and memory efficiency. Which of these do you use most often in production? ♻️ Repost to help more engineers learn this. #java #programming #software

🚀 Day 412 of #500DaysOfCode 🧩 Problem: 3542. Minimum Operations to Convert All Elements to Zero Platform: LeetCode (Medium) 📘 Problem Statement: Given an array of non-negative integers, you can perform operations where you select a subarray and set all occurrences of the minimum number in that subarray to 0. Your goal is to find the minimum number of operations required to convert all elements in the array to 0. 💡 Example: Input: nums = [3,1,2,1] Output: 3 Explanation: 1️⃣ Select [1,3] → set 1s to 0 → [3,0,2,0] 2️⃣ Select [2,2] → set 2 to 0 → [3,0,0,0] 3️⃣ Select [0,0] → set 3 to 0 → [0,0,0,0] ⚙️ Intuition: Each continuous non-zero segment in the array represents one operation. Zeros act as boundaries that divide these segments. Hence, the minimum number of operations equals the count of continuous non-zero segments. 🧠 Approach: Iterate through the array. Count the number of times a new non-zero segment starts → (when nums[i] != 0 and (i == 0 || nums[i-1] == 0)). 🕒 Complexity: Time: O(n) Space: O(1) 🔥 Learned how identifying segment patterns in arrays can lead to simple and efficient solutions! #Day412 #LeetCode #Java #CodingChallenge #ProblemSolving #DSA #Programming #100DaysOfCode #500DaysOfCode #CodeNewbie #Developers

Do you choose a few tools designed to solve modern requirements in a declarative, scalable style — or do you just stick with the old way? Why keep forcing yourself to chase updates on frameworks and tools that were never built for the challenges of modern systems — only to end up waiting for your system to catch up? Have you considered data-oriented reactive programming — the approach that lets your code control the flow, instead of the other way around? #Java #Java21 #ReactiveProgramming #Mutiny #Loom #Quarkus #Vertx #EventDriven #Microservices #CloudNative #AsynchronousProgramming #FunctionalProgramming #ModernJava #SoftwareArchitecture #CleanCode #DevCommunity #Coding #TechLeadership #SoftwareEngineering #Programming

In the AOP workflow, the process involves two key phases: **Development:** - Identify cross-cutting concerns. - Create aspects. - Define pointcuts to specify where logic should be applied. - Implement advice, which constitutes the logic itself. **Runtime:** During runtime, the AOP framework performs the following steps: - Detect aspects. - Create target objects and AOP proxies. - When a client invokes a method, the proxy intercepts the call. - If a pointcut matches, the advice (Before, AfterReturning, AfterThrowing, After) executes around the target method. - The Around advice has the ability to control the execution of the target method. - Finally, the results are returned to the client through the proxy. #SpringBoot #Java #Coding #Developers #SoftwareEngineering #TechLearning

🚀 From Struggling with Complexity to Designing Scalable Systems in C++ When I started working on large-scale projects, System Design felt overwhelming. Threads, memory management, scalability, load balancing — it all seemed like an endless puzzle. But over time, I realized that great system design isn’t about knowing every component — it’s about understanding how they connect, evolve, and perform under pressure. 💡 Why C++ became my system design backbone: C++ gives you unmatched control over memory, performance, and concurrency — three pillars that define system scalability. Here’s what I learned from building real-world systems: 1️⃣ Start with constraints. Before coding, define your latency, throughput, and scalability goals. 2️⃣ Design modularly. Build independent components — caching layers, queues, API handlers — that can evolve without breaking others. 3️⃣ Use STL and smart pointers wisely. Efficiency is key, but safety matters just as much. 4️⃣ Think concurrency. Use std::thread, async patterns, or lock-free queues to boost performance. 5️⃣ Observe everything. Logging, metrics, and profiling are the real debugging tools at scale. Now, when I design a system — whether it’s a microservice, game engine module, or distributed backend — I don’t just think in terms of code, but in terms of architecture, resilience, and data flow. If you’re learning C++ or diving into system design — keep experimenting. Start small, scale gradually, and always measure. Each design iteration teaches you more than any book ever could. #SystemDesign #Cpp #SoftwareEngineering #BackendDevelopment #HighPerformanceComputing #DistributedSystems #TechCommunity #CodingJourney #ScalableArchitecture #DeveloperGrowth #Programming

🚀 𝐇𝐢 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲! Have you ever wondered why we so often use the 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 in our day-to-day development — for things like managing a single 𝐃𝐁 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧, 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐧𝐚𝐠𝐞𝐫, or 𝐥𝐨𝐠𝐠𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦? 🤔 But here’s the real question… 👉 Have you ever looked deep into why the final, 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐫𝐞𝐚𝐝𝐲 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 looks the way it does — and how it evolved through years of optimization? Let’s take a quick journey 👇 ⚡ 𝐄𝐚𝐠𝐞𝐫 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 → Simple but wastes memory 💤 𝐋𝐚𝐳𝐲 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 → Efficient but not thread-safe 🔒 𝐓𝐡𝐫𝐞𝐚𝐝-𝐬𝐚𝐟𝐞 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 → Safe, but slower due to synchronization 🔁 𝐃𝐨𝐮𝐛𝐥𝐞-𝐂𝐡𝐞𝐜𝐤𝐞𝐝 𝐋𝐨𝐜𝐤𝐢𝐧𝐠 → Better, yet vulnerable to reflection & serialization issues 🧩 𝐁𝐢𝐥𝐥 𝐏𝐮𝐠𝐡 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 → Clean & efficient 🏆 𝐄𝐧𝐮𝐦 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 → The final form — thread-safe, reflection-proof & serialization-safe Here’s the beauty of the final optimized code 👇 public enum EnumSingleton {   INSTANCE;       public void showMessage() {     System.out.println("Enum Singleton works!");   } } I broke down the complete evolution of the 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 — from basics to the JVM-level magic behind 𝐄𝐧𝐮𝐦 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 — in my latest article. 📖 Read here: https://lnkd.in/gMfKMJeS If you love 𝐜𝐨𝐝𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬, 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬, and 𝐝𝐞𝐞𝐩 𝐝𝐢𝐯𝐞𝐬 𝐢𝐧𝐭𝐨 𝐉𝐚𝐯𝐚 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥𝐬, don’t forget to follow — more tech breakdowns coming soon! 💡 #Java #DesignPatterns #SoftwareEngineering #BackendDevelopment #SpringBoot #CodeOptimization #Developers #Programming #TechCommunity

4 attributes of scary code: 1. Feels needlessly complex. 2. Unclear intent. 3. No tests. 4. Buggy. 😬 This is a scary combo. It often means we're hesitant to touch it. Whoever does takes a big risk. Solution: 1. Create tests to cover the current working behavior. 2. Write a test that fails due to the buggy behavior. 3. Refactor to improve clarity, decomposing the code if necessary to improve the feedback loop and isolate the buggy section. 4. Make the test pass. How do you confront scary code in your projects? Drop your thoughts below! 👇 --- 👋 Join 28,000+ software engineers learning JavaScript, Software Design, and Architecture: https://thetshaped.dev/ ----- ♻ Repost to help others find it. #softwareengineering #programming #thetshapeddev

✅ Subtyping: There are two classes A and B. Here class B is inherited from class A. class A is called super type and class B is called sub type. As per subtyping rule wherever super type A is required you can replace with its sub type B. ✅Super Keyword: super is used to refer super class and parent class object. 1)super. : ▪️ super. is used to call super class variables/methods in subclass. 2)super() /super method: ▪️ super() is used to call super class constructor in sub class constructor. 👉 This should be first statement in constructor.This process is called constructor chaining. ✅ Method Overriding: Method overriding occurs when a subclass provides its own implementation of a method defined in the parent class. Why it's useful? ✔ Enhances or customizes existing functionality ✔ Enables runtime polymorphism 🔹 Same method name, same parameters, but defined differently in the subclass. ✅ Dynamic Method Dispatch: Dynamic method dispatch is the mechanism by which the overridden method is selected at runtime, not compile time. Thanks to our mentor Anand Kumar Buddarapu Sir for explaining these concepts clearly. #Java #OOPS #Polymorphism #Subtyping #MethodOverriding #DynamicDispatch #Superkeyword

💡 𝐄𝐚𝐫𝐥𝐲 𝐩𝐞𝐞𝐤 𝐚𝐭 𝐂# 𝟏𝟒 𝐚𝐧𝐝 .𝐍𝐄𝐓 𝟏𝟎 🔥 💎 𝐄𝐱𝐭𝐞𝐧𝐝𝐞𝐝 𝐧𝐚𝐦𝐞𝐨𝐟 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 🎉 𝐓𝐨𝐝𝐚𝐲 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐚𝐲! .𝐍𝐄𝐓 𝟏𝟎 𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐝 𝐭𝐨𝐝𝐚𝐲! Let's take a closer look at 𝐄𝐱𝐭𝐞𝐧𝐝𝐞𝐝 𝐧𝐚𝐦𝐞𝐨𝐟 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬, one of the most anticipated features; 🔥 C# 14 introduces support for 𝐮𝐧𝐛𝐨𝐮𝐧𝐝 𝐠𝐞𝐧𝐞𝐫𝐢𝐜 𝐭𝐲𝐩𝐞𝐬 in nameof expressions, eliminating the need for placeholder type arguments when working with generic types. 📖 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐄𝐱𝐭𝐞𝐧𝐝𝐞𝐝 𝐧𝐚𝐦𝐞𝐨𝐟 ?  Extends the familiar nameof operator to unbound generic types (e.g., List<>, Dictionary<,>), so you can get a type’s name without magic strings or reflection hacks. ✅ 𝐖𝐡𝐚𝐭'𝐬 𝐍𝐞𝐰:  🔸 Use nameof(List<>) instead of nameof(List<object>)  🔸 Support for multi-parameter generics: nameof(Dictionary<,>)  🔸 Cleaner reflection and metadata code  🔸 Better refactoring safety 🌟 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬:  💎 𝐂𝐥𝐞𝐚𝐧𝐞𝐫 𝐒𝐲𝐧𝐭𝐚𝐱: No more arbitrary placeholder types. 💎 𝐁𝐞𝐭𝐭𝐞𝐫 𝐑𝐞𝐚𝐝𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Intent is crystal clear. 💎 𝐑𝐞𝐟𝐚𝐜𝐭𝐨𝐫-𝐒𝐚𝐟𝐞: Compiler ensures type names stay in sync. 💎 𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐟𝐨𝐫 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚: Logging, DI containers, code generatio. 📝 𝐁𝐞𝐟𝐨𝐫𝐞 𝐂# 𝟏𝟒: nameof(List<object>) // Ugly placeholder required nameof(Dictionary<object, object>) // Even worse! 🎉 𝐖𝐢𝐭𝐡 𝐂# 𝟏𝟒: nameof(List<>) // Clean and intuitive! nameof(Dictionary<,>) // Perfect! 🏆 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬:  🔸 Dependency injection registration  🔸 Logging and diagnostics  🔸 Code generation tools  🔸 API documentation generation  🔸 Generic service registration 🎯 This small but powerful enhancement makes generic type handling more elegant and maintainable. Another step toward cleaner, more expressive C# code! 💭 How will you leverage this in your code? #csharp #dotnet #programming #softwareengineering #softwaredevelopment #csharp14 #dotnet10