Well, I’ve been running them in production on our payment processing service for the last six months. And honestly? It’s mostly great. Mostly.
But if you think you can just flip a switch in your application.properties and magically handle a million concurrent requests on a t4g.nano instance without consequences, you’re going to have a bad time. I learned that the hard way last Tuesday at 3 AM.
The “Free Lunch” Illusion
Here’s the thing though. Just because the threads are cheap doesn’t mean the resources they access are.
Looks clean, right? And locally, on my MacBook M3, it flew. The throughput numbers were ridiculous.
Then we deployed it.
The database connection pool exploded.
Lesson 1: Virtual threads remove the natural backpressure that limited OS threads provided. You now need to explicitly rate-limit your external resources. Semaphores are your friend again.
The Dreaded Pinning Problem
And honestly, I spent four hours debugging this with JFR (Java Flight Recorder). If you aren’t running JFR in production by now, start. The jdk.VirtualThreadPinned event is the only reason I found this.
Structured Concurrency is the Real Winner
Before, if I fired off three async tasks and one failed, the others would keep running, wasting resources. Or I’d have to write complex cancellation logic. Now? It’s declarative.
If userRepo throws an exception, the ordersTask is automatically cancelled. No lingering threads. No resource leaks. It cleans up after itself. It’s boring code, and I love it. Boring code doesn’t wake me up at night.
Performance: A Quick Benchmark
I know, I know. “It depends.” But I wanted to see raw numbers for our specific use case: high-concurrency HTTP calls with 50ms latency.
Final Thoughts
Virtual threads aren’t a magic wand that fixes bad architecture. If your database is the bottleneck, you just get to the bottleneck faster. But they have fundamentally changed how I view concurrency.
Just check for those synchronized blocks. Seriously. Do a grep on your codebase right now. You’ll thank me later.