But to get at whatever the hell I mean by that,
we need to talk about why async Rust exists in the first place.
Let’s talk about:

Modern Concurrency: They’re Green, They’re Mean, & They Ate My Machine

gotta go fast

Suppose we want our code to go fast. We have two big problems to solve:

  1. We want to use the whole computer. Code runs on CPUs, and in 2023,
    even my phone has eight of the damn things. If I want to use more than
    12% of the machine, I need several cores.

  2. We want to keep working while we wait for slow things to complete
    instead of just twiddling our thumbs.
    Sending a message over the Internet, or even opening a file1
    takes eternities in computer time—we could literally do
    millions of other things meanwhile.

And so, we turn to our friends parallelism and concurrency.
It’s a favorite hobby of CS nerds to quibble over distinctions between the two,
so to oversimplify:

Parallelism is about running code in parallel on several CPUs.

Concurrency is about breaking a problem into separate, independent parts.

These are not the same thing—single-core
machines have been running code concurrently for half a century now—but they are related.
So much online well akshually-ing ignores how we often break programs
into concurrent pieces so that those pieces can run in parallel,
and interleave in ways that keep our cores crunching!
(If we didn’t care about performance, why would we bother?)

How do I concurrency?

One of the simplest ways to build a concurrent system is to split code into multiple processes.
After all, the operating system is a lean, mean, concurrency machine,
conspiring with your hardware to make each process think it has the
whole box to itself.
And the OS’s scheduler gives us parallelism for free, running time slices of
any process that’s ready on an available CPU core.
Once upon a time this was
the way,
and we still employ it today whenever we pipe shell commands together.

All hail CubeDrone

But this approach has its limitations. Inter-process communication is not cheap,
since most implementations copy data to OS memory and back.2

Mutex-Based Concurrency Considered Harmful, or, Hoare Was Right

Some people, when confronted with a problem, think, “I know, I’ll use threads,”
and then two they hav erpoblesms.

– Ned Batchelder

We can avoid these overheads using threads—processes that share the same memory.
Common wisdom teaches us to connect them with mysterious beasts,
like mutexes, condition variables, and semaphores. This is a dangerous game!
Simple mistakes will plague you with race conditions
and deadlocks and other terrible diseases that fill your code with bugs,
but only on Tuesdays when it’s raining and the temperature is is a multiple of three.
And god help you if you want to learn how this stuff actually works on modern

There is Another Way.
In his 1978 paper, Communicating Sequential Processes, Tony Hoare
suggested connecting threads with queues, or channels,
which they can use to send each other messages.
This has many advantages:

  • Threads enjoy process-like isolation from the rest of the program,
    since they don’t share memory.
    (Bonus points for memory-safe languages that make it hard to
    accidentally scramble another thread!)

  • Each thread has a very obvious set of inputs (the channels it receives from)
    and outputs (the channels it sends to).
    This is easy to reason about, and easy to debug!
    Instrument the channels for powerful visibility into your system,
    measuring each thread’s throughput.

  • Channels are the synchronization.
    If a channel is empty, the receiver waits until it’s not.
    If a channel is full, the sender waits.
    Threads never sleep while they have work to do,
    and gracefully pause if they outpace the rest of the system.

After decades of mutex madness,
many modern languages heed Hoare’s advice and
provide channels in their standard library.
In Rust, we call them

Most software can stop here, building concurrent systems with threads and channels.4
Combine them with tools to parallelize CPU-intensive loops
(like Rust’s Rayon
or Haskell’s par),
and you’ve got a powerful cocktail.


Ludicrous Speed, go!

going to plaid

Some problems demand a lot of concurrency.
The canonical example, described by Dan Kagel as the
C10K problem
back in 1999, is a web server connected to tens of thousands of concurrent users.
At this scale, threads won’t cut it—while they’re pretty cheap,5
fire up a thread per connection and your computer will grind to a halt.

To solve this, some languages provide a concurrency model where:

  1. Tasks are created and managed in userspace,
    i.e., without the operating system’s help.

  2. A runtime schedules these tasks onto a pool of OS threads,
    usually sized so that each CPU core gets a thread, to maximize parallelism.

This scheme goes by many names—green threads, lightweight threads,
lightweight processes, fibers, coroutines
, and more—complete with pedantic
nerds endlessly debating the subtle differences between them.

Rust comes at this problem with an “async/await” model,
seen previously in places like C# and Node.js.6
Here, functions marked async don’t block, but immediately return
a future or promise that can be awaited to produce the result.

fn foo() -> i32 { /* returns an int when called */ }

async fn bar() -> i32 { /* returns a future we can .await to get an int */ }


On one hand, futures in Rust are exceedingly small and fast,
thanks to their cooperatively scheduled, stackless design.
But unlike other languages with userspace concurrency,
Rust tries to offer this abstraction while also promising the programmer
total low-level control.

There’s a fundamental tension between the two, and the poor async Rust programmer
is perpetually caught in the middle, torn between the language’s design goals
and the massively-concurrent world they’re trying to build.
Rust attempts to statically verify the lifetime of every object and reference
in your program, all at compile time.
Futures promise the opposite: that we can break code
and the data it references into thousands of little pieces,
runnable at any time, on any thread,
based on conditions we can only know once we’ve started!
A future that reads data from a client should only run when that client’s socket
has data to read, and no lifetime annotation will tells us when that might be.

Send help

Assuring the compiler that everything will be okay runs into the same challenges
you see when working with raw threads.
Data must either be marked Send and moved,
or passed through references with 'static lifetimes.
Both are easier said than done.
Moving (at least without cloning)
is often a non-starter, since it’s common in async code to spawn many
tasks that share common state.
And references are a pain too—there’s no
thread::scope equivalent to help us
bound futures’ lifetimes to anything short of “forever”.

is out,

async fn foo(&BIG_GLOBAL_STATIC_REF_OR_SIMILAR_HORROR, sendable_chungus.clone())

is in.

And unlike launching raw threads, where you might have to deal with these annoyances
in a handful of functions,
this happens constantly due to
async’s viral nature.
Since any function that calls an async function must itself be async,7
you need to solve this problem everywhere, all the time.

Just Arc my shit up

A seasoned Rust developer will respond by saying that Rust gives us simple tools
for dynamic lifetimes spanning multiple threads.
We call them “atomic reference counts”,
or Arc.
While it’s true that they solve the immediate problem—borrows check and our
code compiles—they’re far from a silver bullet.
Used pervasively, Arc gives you the world’s worst garbage collector.
Like a GC, the lifetime of objects and the resources they represent
(memory, files, sockets) is unknowable.
But you take this loss without the wins you’d get from an actual GC!

Don’t buy the “GC is slow” FUD—the claim is a misunderstanding of
latency vs. throughput at best and a bizarre psyop at worst.
A modern, moving garbage collector gets you more allocation throughput,
less fragmentation, and means you don’t have to play Mickey Mouse games with
weak pointers to avoid cycle leaks.
You can even trick systems programmers into leveraging GC in one of the world’s
most important software projects by calling it
“deferred destruction”.
More on that another day.

Other random nonsense

  • Because Rust coroutines are stackless, the compiler turns each one into
    a state machine that advances to the next .await.8
    But this makes any recursive async function a recursively-defined type!
    A user just trying to call a function from itself is met with
    inscrutable errors until they manually box it or use a
    crate that does the same.

  • There’s an important distinction between a future—which does nothing
    until awaited—and a task,
    which spawns work in the runtime’s thread pool… returning a future that
    marks its completion.

  • There’s nothing keeping you from calling blocking code inside a future,
    and there’s nothing keeping that call from blocking the runtime thread it’s on.
    You know, the entire thing we’re trying to avoid with all this async business.

Running away

Mixed together, this all gives async Rust a much different flavor than
“normal” Rust. One with many more gotchas,
that is harder to understand and teach,
and that pushes users to either:

  • Develop a deep understanding of how these abstractions actually work,9
    writing complicated code to handle them, or

  • Sprinkle Arc, Pin, 'static, and other sacred runes throughout their
    code and hope for the best.

Rust proponents (I’d consider myself one!) might call these criticisms overblown.
But I’ve seen whole teams of experienced developers,
trying to use Rust for some new project, mired in this minutia.
To whatever challenges teaching Rust has, async adds a whole new set.

The degree to which these problems just aren’t a thing in other languages
can’t be overstated either.
In Haskell or Go, “async code” is just normal code.
You might say this isn’t a fair comparison—after all,
those languages hide the difference between blocking and non-blocking
code behind fat runtimes, and lifetimes are handwaved with garbage collection.
But that’s exactly the point!
These are pure wins when we’re doing this sort of programming.

Maybe Rust isn’t a good tool for massively concurrent, userspace software.
We can save it for the 99% of our projects that
don’t have to be.

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