Rust is King, But Java’s Project Loom Just Changed the Game
So, for what feels like, I don’t know, forever in our super fast-paced software world, Rust has really been the go-to champion. Especially if you’re talking about things like system-level programming, apps where every millisecond counts, or, dare I say it, “fearless concurrency.” Developers, myself included, have totally gravitated towards it. Why? Because it promises stuff like memory safety without a garbage collector — which is, like, a huge deal — plus blazing-fast execution, and this amazing ability to just prevent data races right there when you compile your code.
Honestly, Rust is a language that makes you work for it; it demands a lot of precision. But, oh boy, does it pay off! You get unparalleled reliability and speed. If you ever needed to squeeze every single bit of performance out of your hardware or build super concurrent services that just wouldn’t fall over, Rust often felt like the only real choice, didn’t it?
The whole async-await thing in Rust, especially with powerhouse runtimes like Tokio - which, by the way, is still absolutely thriving as of late 2025, especially after async-std kinda bowed out in March 2025 - really gave us a solid answer to all those historical headaches we had managing concurrent tasks. It actually made building these incredibly complex, high-throughput systems not just possible, but genuinely safe and predictable. That's why Rust has carved out such a cool niche, you know, in areas like WebAssembly, blockchain, and even in parts of the Linux kernel. Pretty wild, right?
But here’s where our story takes a little turn. While Rust was busy making itself perfect and winning over a ton of us, the old, wise giant, Java, was quietly cooking up something truly transformative. It wasn’t some flashy new syntax or, thank goodness, a complete rewrite. Nope. It was a really fundamental shift in how it handles one of the trickiest parts of modern software: concurrency. And this big shift, which we call Project Loom with its Virtual Threads, might actually be the “smarter” move that totally redefines everything. Trust me, it’s a game-changer that even us seasoned Rustaceans should probably pause and really think about.
The Silent Revolution: Enter Project Loom’s Virtual Threads ✨ Okay, let’s just be real for a sec: even with all its enterprise dominance, Java’s traditional way of handling concurrency had its fair share of annoyances. Trying to build highly concurrent, I/O-heavy applications often meant you were wrestling with super complex asynchronous APIs, ending up in what we lovingly call “callback hell,” or just dealing with really resource-hungry operating system threads. I mean, each classic Java thread could easily chew up a good megabyte of stack space. Imagine multiplying that by thousands of concurrent requests! You’d hit resource exhaustion and performance walls so fast it wasn’t even funny. We basically had to invent all these elaborate workarounds, like reactive frameworks, which, while powerful, often meant trading off how readable our code was for better scalability. Ugh.
This is exactly where Project Loom steps in, bringing us Virtual Threads (some folks just call them fibers). And the best part? After being little preview features in JDK 19 and 20, these Virtual Threads were officially finalized and became a permanent, full-fledged feature in JDK 21, which actually landed on September 19, 2023. Structured Concurrency and Scoped Values, which are also part of the Loom family, are still cooking as preview features, with their 5th previews expected around JDK 25. Pretty cool progress, if you ask me.
Now, picture this: you can suddenly spawn millions of threads that are incredibly lightweight- we’re talking kilobytes of memory, not megabytes — and they’re managed directly by the Java Virtual Machine (JVM), not your operating system. That, my friends, is the pure magic Loom delivers. How does it actually work? Well, the core idea is just brilliantly simple: when one of these virtual threads hits a blocking operation (like, say, waiting for a database query to finish or an HTTP response to come back), the JVM just quietly parks it. It doesn’t hog a precious OS thread. Instead, that underlying OS thread (they call it a “carrier thread”) gets freed up to go work on other virtual threads. Then, once your I/O operation is done, that parked virtual thread seamlessly resumes. Poof!
This might seem like a small tweak, but honestly, it has massive implications:
- Mega Scalability: Suddenly, that easy-to-understand “thread-per-request” idea actually becomes totally doable for apps handling hundreds of thousands, or even millions, of concurrent tasks. No more stressing about your thread pool size!
- Way Simpler Code: You get to write code that looks straightforward, synchronous, and even kinda “blocking” on the surface, but it actually achieves asynchronous performance underneath. This just dramatically boosts readability and seriously cuts down on how much brainpower developers need to use.
- Better Debugging: Oh, my goodness, debugging async code used to be a total nightmare, with stack traces jumping all over the place. With Virtual Threads, debugging feels refreshingly old-school — in a good way! Each virtual thread gives you a clean, predictable stack trace, making it so much easier to figure out what went wrong.
- Potential Cost Savings: If your app uses resources way more efficiently, you can handle more stuff with less infrastructure. That’s a win, right? And it could mean some real savings on your cloud bill.
And here’s a super important update for us, sitting here in November 2025: An annoying limitation in those earlier JDK 21 releases was something called “pinning.” That’s when a virtual thread could still block its underlying carrier thread if it did a blocking operation inside a synchronized block or method. That kinda took away some of the benefits, you know? But guess what? JDK 24 (which dropped in March 2024) actually rolled out enhancements (JEP 491) that mostly fix the pinning issue when you use synchronized blocks! This is huge. It means even older, legacy Java code that relies on synchronized can now often get the benefits of virtual threads without a massive refactoring effort. How cool is that?
So yeah, Virtual Threads aren’t just some theory anymore; they’re fully supported in JDK 21 and all the versions after that. And frameworks have jumped on board super fast:
- Spring Boot 3.2+ (that came out in November 2023) has built-in support. You can literally just flip a switch with spring.threads.virtual.enabled=true in your application.properties file, and boom! Your web containers like Tomcat and Jetty, your @Async methods, and even your messaging listeners all start using Virtual Threads automatically.
- Quarkus also plays nicely, often letting you use annotations like @RunOnVirtualThread.
- Micronaut Framework 4.0.0 (from July 2023) automatically knows if Virtual Threads are available and uses them for blocking executors. And hold on, Micronaut 4.9.0 (June 2025) even introduced an experimental “loom carrier mode” for Netty event loops. They’re all in!
import java.util.concurrent.Executors; import java.util.concurrent.Callable; import java.util.List; import java.util.ArrayList; import java.util.concurrent.Future; import java.time.Duration; // For that nice, modern Java time API public class VirtualThreadExample {
public static void main(String[] args) throws Exception {
// Just some made-up URLs to fetch, imagine real API calls here
List<String> urls = List.of(
"https://api.example.com/data/user/123",
"https://api.example.com/data/product/456",
"https://api.example.com/data/order/789"
);
System.out.println("Starting some I/O-bound tasks using awesome Virtual Threads!");
// We're using a virtual thread for each task here, which is perfect for high concurrency.
try (var executor = Executors.newVirtualThreadPerTaskExecutor()) {
List<Callable<String>> tasks = new ArrayList<>();
for (String url : urls) {
tasks.add(() -> {
String threadName = Thread.currentThread().getName();
System.out.println("⏳ Getting data from " + url + " on thread: " + threadName);
// This 'sleep' is just pretending to be a slow network call or database query.
Thread.sleep(Duration.ofSeconds(2));
System.out.println("✅ Done getting " + url + " on thread: " + threadName);
return "Here's the data we got from " + url;
});
}
// Fire off all the tasks and wait for the results.
List<Future<String>> results = executor.invokeAll(tasks);
System.out.println("\nAlright, all tasks are finished! Here's what we got:");
for (Future<String> result : results) {
System.out.println("⭐ Got this back: " + result.get());
}
} // This 'try-with-resources' bit is super handy; the executor shuts itself down here.
System.out.println("And just like that, the application is done. Easy peasy!");
}
} In this little Java example, see how each Callable task gets its own virtual thread? That Thread.sleep(Duration.ofSeconds(2)) is just simulating a blocking I/O call, like a network request taking a bit. If we were using old-school platform threads, a small thread pool would get choked up really fast. But with virtual threads, the JVM is super smart about managing those underlying OS threads, letting tons of these "blocking" tasks run at the same time without breaking a sweat. And notice, the code still looks so clean and synchronous, yet it scales like a beast! Pretty neat, huh? The Question: Does Java Just Have a Smarter Story for Concurrency? 🤔
Rust’s way of doing concurrency is, undeniably, incredibly powerful. I mean, its ownership model and that borrow checker are pure linguistic genius. They literally guarantee memory safety and stop data races right there at compile time, giving us that famous “fearless concurrency.” And Rust’s async runtimes? Super efficient, leveraging zero-cost abstractions to get amazing performance with hardly any overhead. If you’re building truly low-level systems where every single byte and CPU cycle matters, Rust is still absolutely the gold standard. No arguments there.
But, and there’s always a “but,” right? Rust’s power often comes with quite a steep learning curve. That compiler can sometimes feel like a really strict professor, and trying to wrap your head around lifetimes and borrowing can feel like mental gymnastics for new folks. While it’s incredibly rewarding once you get it, it does demand a pretty big upfront investment in developer time and brainpower. Plus, you know, the async ecosystem saw a bit of a shake-up with async-std discontinuing, putting even more focus on Tokio and smol.
Now, Java, with Project Loom, isn’t really trying to out-Rust Rust on raw low-level performance or promise compile-time memory safety. Instead, it’s taking a really pragmatic, actually smarter approach for, let’s be honest, the vast majority of applications out there. It’s all about making high-scale concurrency easy and accessible for its absolutely massive developer community. It directly tackles Java’s historical weak spots, letting developers scale their applications without ditching their familiar synchronous coding style or having to adopt super complex reactive paradigms.
This isn’t about one language “winning” or anything. It’s more about different philosophies tackling different needs. Rust built its async model right into the language from day one, giving you incredible control and guarantees. Java, on the other hand, is basically adding a modern concurrency model onto a really mature runtime. It’s leveraging its huge existing ecosystem and what developers already know to get similar scalability benefits, but with way less friction. And they keep making it better, too, like with those pinning fixes in JDK 24. It’s an evolving story, for sure.
The Answer: It’s About the Right Tool, and Java Just Got a Sharper One 🛠️ So, is Java “smarter”? Well, for a ton of common backend and enterprise stuff — where developer productivity, easy maintenance, and getting apps out fast are super important — I honestly think Virtual Threads are a smarter move. They seriously lower the bar for building highly concurrent systems, letting developers focus on the actual business logic instead of getting bogged down in tricky concurrency patterns. And the fact that JDK 24 smoothed out that synchronized pinning issue? That just makes it even easier to drop into existing codebases.
Rust’s strengths, though, are still totally unchallenged in areas where you need absolute control, minimal runtime overhead, and those guaranteed compile-time memory safety checks. Think operating systems, tiny embedded systems, game engines, or really critical infrastructure. Rust lets you build systems where absolutely nothing is hidden; you’re in charge of everything. And that’s a truly amazing superpower.
But for the enormous world of web services, microservices, and all those I/O-heavy applications that keep our modern businesses running, Java’s Virtual Threads are, like, a brand-new, super sharp arrow in its quiver. They offer a path to scale that just feels natural to Java developers, fitting seamlessly with code and tools they already use. This means Java can now really go head-to-head in areas where languages like Go and Rust used to have a clear advantage when it came to handling high concurrency easily.
As we look at things here in November 2025, the chat isn’t really about whether Rust is cool — it totally is, and it keeps growing, which is awesome. It’s more about acknowledging that Java, through Project Loom, and especially with Virtual Threads landing in JDK 21 and getting even better in JDK 24, has made a truly significant, practical, and, dare I say, smart leap forward. It’s giving us a really solid, low-headache solution to one of its biggest historical challenges, which just goes to show that innovation can pop up from anywhere and in all sorts of surprising ways.
Ultimately, the truly smartest developers among us in 2025 won’t just pick a team. No, they’ll understand the amazing strengths and new powers both these languages bring. They’ll grab Rust when those unique guarantees and ultimate control are absolutely non-negotiable, and they’ll happily embrace Java’s Loom for its newfound ability to simplify high-scale concurrency without messing with developer experience or all the benefits of its huge, mature ecosystem. It’s about having the right tool for the job, you know?