Virtual threads Traditional thread-per-request models were expensive. Virtual threads make concurrency cheap, scalable, and easier to reason about. Every new Spring Boot service has spring.threads.virtual.enabled=true set by default. The era of reactive-by-default for I/O-bound work is fading fast — teams are writing straightforward, blocking-style code and still achieving WebFlux-level concurrency. Before Virtual Threads: → You needed reactive programming (WebFlux, RxJava) to handle high concurrency → Reactive code is hard to read, hard to debug, hard to onboard → Context switching between threads was expensive at scale After Virtual Threads: → Write simple, imperative code → JVM handles millions of lightweight threads natively → Same or better throughput — zero reactive complexity Why this matters: → Reactive code was powerful but painful to write and debug → Virtual threads give you the same performance with half the complexity #Java #SpringBoot #BackendDevelopment #Microservices #VirtualThreads

🔥 Hot take: Thread pools are becoming legacy thinking in Java. For years, we’ve been juggling: → Limited thread pools → High memory usage → Complex async code And calling it “scalable” 😅 Then comes Java 21 + Virtual Threads (Project Loom)… ⚡ Thousands (even millions) of lightweight threads ⚡ Near-zero overhead ⚡ Write simple, blocking code — still scale like crazy No more over-engineering with reactive frameworks just to handle concurrency. Sometimes, the best innovation isn’t adding complexity… it’s removing it. If you haven’t explored Virtual Threads yet, now is the time. #Java21 #ProjectLoom #VirtualThreads #Concurrency #BackendDevelopment #SoftwareEngineering #TechTrends

"Architecting Knowledge" - Java Wisdom Series Post #17: Virtual Threads - Rethinking Concurrency 👇 Million threads. One JVM. Welcome to Project Loom. Why This Matters: Platform threads map 1:1 to OS threads - each consumes ~1MB stack memory. You can create maybe 4000-10000 before your JVM dies. Virtual threads are JVM-managed and stack memory is allocated dynamically on heap - you can create millions. When a virtual thread blocks on I/O, the JVM unmounts it from its carrier thread (platform thread), letting that carrier run other virtual threads. This makes blocking I/O efficient again - no more callback hell. BUT beware thread pinning: synchronized blocks prevent unmounting in Java 21-23 (fixed in 24). Use ReentrantLock for long blocking operations. Key Takeaway: Virtual threads aren't faster - they're cheaper and more scalable. Perfect for I/O-bound workloads (web servers, microservices, API calls). Don't pool them, don't cache in ThreadLocal aggressively. Write simple blocking code, let Loom handle concurrency. #Java #JavaWisdom #VirtualThreads #ProjectLoom #Concurrency #Java21 Are you still using thread pools for I/O-bound tasks? Time to go virtual! All code examples on GitHub - bookmark for quick reference: https://lnkd.in/dJUx3Rd3

I used to think synchronized was enough for handling multithreading. Until I needed more control. 👉 Problem: In real-world backend systems, basic locking isn’t always flexible enough. ❌ With synchronized: No control over lock acquisition Threads can block indefinitely No way to interrupt waiting threads 👉 Then I discovered ReentrantLock ✅ Why it’s more powerful: ✔️ tryLock() → avoids waiting forever ✔️ lockInterruptibly() → can stop waiting threads ✔️ Fairness option → prevents thread starvation ✔️ More control over locking/unlocking 🧠 Real-world use: High-concurrency systems Complex locking scenarios When you need timeout-based locking 💡 Simple thought: synchronized = simple & automatic ReentrantLock = flexible & powerful 💬 Curious: Have you ever needed more control than synchronized provides? #Java #Multithreading #Backend #SystemDesign #Concurrency #LearningInPublic

🚀 Java isn’t just surviving in 2026—it’s thriving. While people still talk about Java 8, the real action is in Java 21/25+. If you are still handling concurrency using traditional threads, you are missing out. Virtual Threads (Project Loom) have fundamentally changed how I approach backend engineering. Handling thousands of blocking I/O tasks? It’s now lightweight and readable. Here is what I’m focusing on to keep my skills sharp in 2026: 🔹 Virtual Threads: Handling concurrency without complexity. 🔹 Pattern Matching & Records: Cleaner, immutable data modeling. 🔹 Spring AI: Bridging enterprise Java with Generative AI. Modern Java is engineered for responsibility, performance, and scalability. #Java #ModernJava #SpringBoot #VirtualThreads #CloudNative #BackendEngineering #Java25

Tried working with virtual threads recently and the difference is actually interesting. With normal threads: Each request takes up a thread More users means more threads and higher memory usage Scaling can become expensive over time With virtual threads: Much lighter compared to traditional threads Can handle a large number of requests without heavy system load Makes concurrency much simpler for I/O heavy tasks It doesn’t replace normal threads completely, but for backend services dealing with high I/O, it feels really useful. Still exploring it and learning more. #java #multithreading #backend

The Java Virtual Machine (JVM) is a masterpiece of complex engineering. It’s not just an interpreter; it’s a runtime ecosystem managing execution, memory, and performance optimizations dynamically. If you want to debug advanced performance bottlenecks or optimize high-scale backend services, you must understand how the JVM processes your code. We break it down into four core pillars: 1️⃣ The ClassLoader: How .class files are verified and initialized into the system. 2️⃣ The Runtime Data Areas: The "Phantom Zones"—Stack (thread-safe operations), Heap (object storage), and the Metaspace. 3️⃣ The Execution Engine: Where the magic happens (Interpreter + JIT Compiler + Garbage Collector). 4️⃣ Native Interface: How Java communicates with the underlying Operating System. Master the machine. Control the code. [Log_Level: Deep_Dive] #TheBytecodePhantom #Java #JVM #SystemArchitecture #SoftwareEngineering #BackendDeveloper #TechDeepDive

Multithreading bugs love “almost correct” code. Especially check-then-act logic. We ran into this: if (cache.contains(key)) { return cache.get(key); } Looks fine. Until multiple threads hit it at the same time. Result? Duplicate work. Race conditions. Inconsistent state. The fix wasn’t adding more checks. It was atomicity: • Use concurrent data structures • Prefer atomic operations (computeIfAbsent, etc.) • Eliminate check-then-act patterns In concurrency, “almost safe” is unsafe. Always. #Multithreading #Concurrency #Java #BackendEngineering #SystemDesign #ScalableSystems

Day 65/100: Today I took another topic of LLD i.e. #Concurrency. Concurrency is nothing but dealing with multiple things at the same time. Every modern software system deals with concurrency. Your phone runs dozens of apps simultaneously. A web server handles thousands of requests at once. In Golang we can achieve concurrency using goroutines whereas in other programming languages like java we have multithreading concept for concurrency. When we talking about concurrency there is always confusion between concurrency and parallelism. Concurrency is about organizing code to handle many tasks in overlapping time periods. Parallelism involves performing multiple tasks at the same exact time, usually on different CPU cores. In concurrency we have to discover some important terms related to it. Race condition: A race condition occurs when the behavior of a program depends on the relative timing of events, such as the order in which threads are scheduled. When two or more threads access shared data concurrently, and at least one modifies it, the final result depends on who "wins the race" to access the data first. Deadlock: A deadlock is a state where a set of threads is blocked because each thread is holding a resource and waiting for a resource held by another thread in the set. Mutex: Mutex is a synchronization primitive that provides mutual exclusion. When a thread acquires (locks) a mutex, any other thread that tries to acquire the same mutex will block until the first thread releases (unlocks) it. The thread that locks must be the one to unlock. Concurrency Patterns: Thread Pool Pattern Producer-Consumer Pattern Fork-Join Pattern FanIn/out Pattern Time to do it again tomorrow :) #systemdesign #100daysofcode #softwareengineering #consistency

Go has no threads. Yet it handles 10x more concurrent requests than Java. Here is why that should change how you think about concurrency. When thousands of requests hit a server simultaneously, the biggest bottleneck is always the thread. Traditional languages like Java create one OS thread per request. Threads are heavy, kernel managed, and expensive to context switch. Go solved this differently with Goroutines. → A Goroutine's stack is dynamic. It only grows when it actually needs to, not upfront → Creating a Goroutine involves zero system calls. The kernel has no idea it exists → Context switching happens entirely in user space. No kernel involvement whatsoever → The Go scheduler handles everything. OS threads only see what Go exposes to them This is powered by the GMP model: → G: Goroutines, can run in the millions → M: Machine, the actual OS threads, just a handful → P: Processor, the logical CPU that schedules G onto M Millions of Goroutines multiplex across just a few OS threads. When a Goroutine blocks, Go detaches that thread, spins up work elsewhere, and keeps everything moving. The program never stalls. A Goroutine starts at just 2KB because Go's runtime manages memory dynamically instead of fixed provisioning like the OS does. This is not a language feature. It is an architectural decision. Minimize kernel involvement. Maximize work in user space. Let the runtime do what the OS was doing badly. That is the real reason Go scales the way it does. What architecture decision in your stack has had the biggest impact on performance? #GoLang #SystemDesign #BackendEngineering #Concurrency #BuildingInPublic #TechFounders #SoftwareArchitecture #Engineering #Programming #DevOps