Clojure's future: Friend or Foe at 50k RPS?

Real-world concurrency considerations for your Clojure services.

Alright, fellow Clojurians, gather 'round! Let me tell you about a little adventure I had recently. I hit a point where I need to call a function, let's call it super-cruncher, and I really don't want my main request thread waiting around for it.

"Aha!" thinks my brain, "Async! That's a job for future!"

It felt so right, so Clojure-y. Just wrap the call in
(future (super-cruncher args)) and boom! Instant background task, right? My main thread is free, latency stays low, everyone's happy.

Then came the plot twist.

This super-cruncher function? It's popular. Like, really popular. We're potentially talking 50,000 requests per second needing to call this thing. Suddenly, my simple, elegant future started looking... less simple.

High load of incoming requests.

My Spidey-sense tingled. Could just throwing 50,000 future calls a second into the ether actually work without, you know, causing a digital apocalypse in production? Time for a quick huddle with the collective wisdom (and some helpful LLMs).

And here's the mind-blown moment I had, which might be new to you too:

future isn't magic pixie dust; it's a Thread-Summoning Spell!

Okay, maybe not a spell, but here's the crucial bit: Every time you call future, Clojure dips into its shared, fixed-size agent thread pool and grabs a thread to run your code.

"Shared?" you ask. Yes! "Fixed-size?" Double yes!

Think of it like a popular coffee shop with a limited number of baristas (threads). If one or two people order complex drinks (long-running tasks wrapped in future), it's fine. The other baristas keep the line moving.

future waiting to get a thread from the thread pool

But what happens at 50,000 RPS?

Imagine 50,000 customers per second slamming the counter, each demanding a dedicated barista right now. That cozy coffee shop? It becomes chaos!

1. Thread Pool Exhaustion: That "fixed-size" pool runs out of available threads FAST. New future calls have to wait, potentially blocking other things that might rely on that same shared pool (like agents!). Latency, the very thing I wanted to avoid, could skyrocket.

2. Memory Mania: Every thread needs memory for its stack and other resources. Spinning up potentially thousands (or tens of thousands!) of threads, even if short-lived, puts a strain on memory.

3. Context-Switching Calamity: The CPU (or JVM) has to constantly switch between active threads. With a huge number of threads, it spends more time managing the threads than actually doing the work inside them. It's like our baristas spending all their time bumping into each other instead of making coffee. Performance tanks.

Thread pool exhaustion

Whoa. My simple future plan suddenly looked like paving a highway to Production Hell with good intentions. At 50,000 RPS, relying on future and its shared pool is like trying to drink from a firehose – you're gonna get overwhelmed.

Is future bad? Absolutely not!

It's fantastic for:

• One-off background tasks.

• CPU-bound work you want off the main thread occasionally.

• Things that don't happen at insane frequencies.

But for hyper-frequent, high-throughput async calls like my super-cruncher scenario? It's likely the wrong tool for the job. Using it there risks performance degradation, increased latency, and potential system instability under sustained load.

My Big Takeaway:

Know your tools! future is wonderfully simple, but understanding how it works (shared thread pool!) is critical before deploying it into the high-frequency async battlefield. For scenarios like mine, exploring alternatives like core.async, dedicated thread pools managed more carefully, message queues, or potentially non-blocking patterns might be necessary.

Know your tools and select the right one

So, next time you reach for future, give a little nod to that shared thread pool. Ask yourself: "How busy is this highway going to be?" It might just save you from an unexpected thread tsunami!

Happy (and mindful) coding!