Pranav Ghorpade’s Post

Most concurrency bugs are not because of threads… they’re because of shared mutable state. 🚨 One thing that has saved me multiple times in Java: Immutability. In multi-threaded applications, multiple threads accessing the same object can easily lead to: → Race conditions → Inconsistent data → Hard-to-debug production issues The simplest way to avoid this? Don’t allow the data to change. What I follow 👇 ✔ Use immutable objects wherever possible ✔ Make fields final ✔ Avoid setters for shared data ✔ Prefer returning new objects instead of modifying existing ones Why this works: Immutable objects are inherently thread-safe. No locks. No synchronization. No surprises. In real systems, this reduces a huge class of bugs without adding complexity. Sometimes the best concurrency solution… is not writing concurrent code at all. Do you actively use immutability in your designs? 👇 #Java #Multithreading #Concurrency #BackendDevelopment #SoftwareEngineering #CleanCode

☕ Java hasn't just aged — it's evolved. Here's a quick developer's timeline of every major LTS release that changed how we write Java: 𝗝𝗮𝘃𝗮 𝟴 (𝟮𝟬𝟭𝟰) — The revolution. Lambdas + Streams completely changed how we think about functional-style in Java. Still running half the enterprise world. 𝗝𝗮𝘃𝗮 𝟭𝟭 (𝟮𝟬𝟭𝟴) — The cleanup. var made local types less verbose, the new HTTP Client API replaced the clunky old one, and legacy dead weight (Java EE, CORBA) finally got removed. 𝗝𝗮𝘃𝗮 𝟭𝟳 (𝟮𝟬𝟮𝟭) — The moderniser. Sealed classes, Records, Pattern Matching, and Text Blocks made Java feel genuinely expressive again. This is where teams started seriously migrating. 𝗝𝗮𝘃𝗮 𝟮𝟭 (𝟮𝟬𝟮𝟯) — The game changer. Virtual Threads (Project Loom) brought lightweight concurrency without reactive framework complexity. Huge for high-throughput apps. 𝗝𝗮𝘃𝗮 𝟮𝟱 (𝟮𝟬𝟮𝟱) — The powerhouse. Project Panama brings seamless native interop. Project Valhalla finally delivers value types — smaller heap, better cache performance. Follow me for more. comment down which version are you using ? #Java #SoftwareDevelopment #BackendDevelopment #Programming #JVM #Tech

While building a recent Spring Boot application, I realized... The Hook: Stop defaulting to .parallelStream() to make your Java code "faster." 🛑 The Insight: It’s a common misconception that parallel streams always improve performance. Under the hood, parallelStream() uses the common ForkJoinPool. If you are executing CPU-intensive tasks on a massive dataset, it’s great. But if you are doing I/O operations (like database calls or network requests) inside that stream, you will exhaust the thread pool and bottleneck your entire application. The Pro Tip: Always benchmark. For I/O bound tasks, look into asynchronous programming (like CompletableFuture) or Java 21's Virtual Threads instead of parallel streams. #Java #PerformanceOptimization #SoftwareEngineering #CleanCode

🚀Java 26 Released: Massive Impact. Have You Started Using It? 🤔 The latest release of Java focuses on what truly matters in production - performance, reliability, and scalability. 👇 🌐 HTTP/3 SUPPORT Use modern transport for faster service-to-service calls in microservices. e.g., HttpClient.newHttpClient().send(request, BodyHandlers.ofString()); BENEFIT 👉 Lower Latency APIs 🔒 STRONGER IMMUTABILITY Prevents unsafe modification of final fields, so avoids hidden bugs in large systems. e.g., final User user = new User("prod-safe"); // cannot be altered via reflection easily BENEFIT 👉 Safer Data Models ⚡ G1 GC OPTIMIZATION Improved garbage collection reduces pause times under high load. e.g., java -XX:+UseG1GC -Xms2g -Xmx2g App BENEFIT 👉 Better Throughput 🚀 AOT CACHING Preloads objects to reduce startup time & ideal for Kubernetes, autoscaling. e.g., java -XX:+UseAOTCache App BENEFIT 👉 Faster Startup in Containers 💬What do you think about Java 26 features? #Java26 #Java #JVM #BackendDevelopment #Performance #Microservices

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

A lot of Java devs solve thread-safety issues with one keyword: synchronized. And honestly, sometimes that’s the right call. Simple, readable, gets the job done. But I’ve seen codebases where synchronized is used everywhere, and once traffic grows, performance starts falling apart. What happens? Only one thread can enter that block/method at a time. If 50 requests hit it together: 1 executes 49 wait So even if your app server has resources, requests are still lining up behind one lock. Where it gets ugly When slow work is inside the lock: -DB queries -External API calls -File operations -Heavy loops / processing Now threads aren’t waiting for CPU. They’re waiting because one thread is holding a lock during slow operations. That’s where response times spike. Better approach depends on the case Use ReentrantLock if you need more control: -tryLock() -timeout -fairness -interruptible waits Use concurrent collections like ConcurrentHashMap instead of manually synchronizing shared maps/lists. Don’t lock the whole method if only one small state update needs protection. Use AtomicInteger / AtomicLong for counters instead of full locks. Real takeaway Thread safety matters. But making everything synchronized is not a concurrency strategy. First make it correct. Then make it scale. #Java #Concurrency #Multithreading #BackendDevelopment #Performance #SoftwareEngineering

Your Spring Boot app has 200 users waiting. Your CPU is at 10%. No errors. No crashes. The culprit? 20 threads — all blocked, all doing nothing. I built a benchmark project to see exactly what happens inside the JVM when a classic thread pool hits this wall, and what changes when Java 21 virtual threads take over. Same app. Same business logic. One executor swap. Results with 200 concurrent users over 30 seconds: → Throughput: 1.95 req/s (classic) vs 252.89 req/s (loom) → p(95) latency: 56 seconds (classic) vs 0.81 seconds (loom) → Requests completed: 117 (classic) vs 7,784 (loom) → 140 users never got a response in classic mode. In Loom, all 200 finished cleanly with zero interruptions. What surprised me most was not the throughput number. It was looking inside the JVM mid-load and seeing 1,846 virtual threads active while the OS thread count barely moved. Loom does not add OS threads — it parks tasks as objects on the heap and frees the carrier thread instantly. One non-obvious gotcha for anyone adopting this in production: Thread.getAllStackTraces() intentionally excludes virtual threads in Java 21. Your existing monitoring tools may already be blind to them. Full benchmark with thread snapshots, heap data, GC numbers, and code walkthrough in the article below. #Java #Java21 #ProjectLoom #SpringBoot #BackendDevelopment #SoftwareEngineering #Programming

thanks a lot Ragasudha R really fantastic work on Performance benchmarking for Classic Platform / OS Threads vs Virtual Threads loved reading this blog working as a Performance Engineer

Continuing my recent posts on JVM internals and performance, today I’m sharing a look at Java 21 Virtual Threads (from Project Loom). For a long time, Java handled concurrency using platform threads (OS threads)—which are powerful but expensive, especially for I/O-heavy applications. This led to complex patterns like thread pools, async programming, and reactive frameworks to achieve scalability. With Virtual Threads, Java introduces a lightweight threading model where thousands (even millions) of threads can be managed efficiently. 👉 When a virtual thread performs a blocking I/O operation, the underlying carrier (platform) thread is released to do other work. This brings Java closer to the efficiency of event-loop models (like in Node.js), while still allowing developers to write simple, synchronous code without callback-heavy complexity. However, in real-world scenarios, especially when teams migrate from Java 8/11 to Java 21, it’s important to keep a few things in mind: • Virtual Threads are not a silver bullet—they primarily improve I/O-bound workloads, not CPU-bound ones • If the architecture is not aligned, you may not see significant latency improvements • Legacy codebases often contain synchronized blocks or locking, which can lead to thread pinning and reduce the benefits of Virtual Threads Project Loom took years to evolve because it required deep changes to the JVM, scheduling, and thread management—while preserving backward compatibility and Java’s simplicity. Sharing a diagram that illustrates: • Platform threads vs Virtual Threads • Carrier thread behavior • Pinning scenarios Curious to hear—are you exploring Virtual Threads in your applications, or still evaluating? 👇 #Java #Java21 #VirtualThreads #ProjectLoom #Concurrency #Performance #SoftwareEngineering

The hardest part of concurrency is not running things in parallel. It is deciding what to do while things are still running. We had a flow where some results came back early, some were slow, and some were optional. But everything was treated like one flat batch. That was the mistake. What we missed was that not all concurrent work has the same shape. - Some work is critical. - Some can fail quietly. - Some should finish early and unblock the rest. Structured concurrency helped me think about this differently. Not just as "run these tasks together", but as “give this work a clearer lifecycle and clearer ownership.” One principle that stuck with me: if work has different responsibilities, it probably should not live in the same scope. https://lnkd.in/gBTdjKeV #Java #SystemDesign #Concurrency