Async Rust: Cooperative vs Preemptive scheduling

This post contains excerpts from my book Black Hat Rust

Threads were designed to parallelize compute-intensive tasks. However, these days, a lot of applications (such as a network scanner) are I/O (Input / Output) intensive.

Thus, threads have two significant problems:

  • They use a lot of memory (compared to others solutions).
  • Launches and context switches have a cost that can be felt when a lot (in the ten of thousands) threads are running.

In practice, it means that by using threads, our apps would spend a lot of time waiting for network requests to complete and use way more resources than necessary.

Please welcome async-await.

The problem with Threads

From a programmer's perspective, async/await provides the same things as threads: concurrency, better hardware utilization, improved speed, but with dramatically better performance and lower resource usage for I/O bound workloads.

What is an I/O bound workload? Those are tasks that spend most of their time waiting for network or disk operations to complete instead of being limited by the computing power of the processor.

Threads were designed a long time ago, when most of the computing was not network (web) related stuff, and thus are not suitable for too many concurrent I/O tasks.

operation async thread
Creation 0.3 microseconds 17 microseconds
Context switch 0.2 microseconds 1.7 microseconds

As we can see with these measurements made by Jim Blandy, context switching is roughly 8.5 times faster with async than with Linux threads and use approximately 20 times less memory.

In the programming language world, there are mainly 2 ways to deal with I/O tasks: preemptive scheduling and cooperative scheduling.

Preemptive Scheduling

Preemptive scheduling is when the scheduling of the tasks is out of the control of the developer, entirely managed by a runtime. Whether the programmer is launching a sync or an async task, there is no difference in the code.

For example, the Go programming relies on preemptive scheduling.

It has the advantage of being easier to learn: for the developers, there is no difference between sync and async code. Also, it is almost impossible to misuse: the runtime takes care of everything.

Here is an example of making an HTTP request in Go:

resp, err := http.Get("http://kerkour.com")

Just by looking at this snippet, we can't tell if http.Get is I/O intensive or compute intensive.

The disadvantages are:

  • Speed, which is limited by the cleverness of the runtime.
  • Hard to debug bugs: If the runtime has a bug, it may be extremely hard to find it out, as the runtime is treated as dark magic by developers.

Cooperative Scheduling

On the other hand, with cooperative scheduling, the developer is responsible for telling the runtime when a task is expected to spend some time waiting for I/O. Waiting, you said? Yes, you get it. It's the exact purpose of the await keyword. It's an indication for the runtime (and compiler) that the task will take some time waiting for an operation to complete, and thus the computing resources can be used for another task in the meantime.

It has the advantage of being extremely fast. Basically, the developer and the runtime are working together, in harmony, to make the most of the computing power at disposition.

The principal disadvantage of cooperative scheduling is that it's easier to misuse: if a await is forgotten (fortunately, the Rust compiler issues warnings), or if the event loop is blocked (what is an event loop? continue reading to learn about it) for more than a few micro-seconds, it can have a disastrous impact on the performance of the system.

The corollary is that an async program should deal with extreme care with compute-intensive operations.

Here is an example of making an HTTP request in Rust:

let res = reqwest::get("https://www.rust-lang.org").await?;

The .await keyword tells us that the reqwest::get function is expected to take some time to complete.

Runtimes

What is a runtime? How do they work under the hood? Subscribe below not to miss next week's post, where we will dig the inner working of runtimes.

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