I’m trying to do something kind of unusual with lilos: in addition to almost all the APIs being safe-in-the-Rust sense, I’m also attempting to create an entire system API that is cancel-safe. I’ve written a lot about Rust’s async feature and its notion of cancellation recently, such as my suggestion for reframing how we think about async/await.

My thoughts on this actually stem from my early work on lilos, where I started beating the drum of cancel-safety back in 2020. My notion of what it means to be cancel-safe has gotten more nuanced since then, and I’ve recently made the latest batch of changes to try to help applications built on lilos be more robust by default.

So, wanna nerd out about async API design and robustness? I know you do.

I recently posted about my debugger for async Rust, which can generate what I call “await-traces” for async code that’s suspended and not currently running. I mentioned at the time that it appeared possible to get the source code file name and line number corresponding to the await points, but left that for future work.

This is an update describing that future work.

I recently published an article suggesting a different way of looking at async and await in Rust. In it, I discussed strategies for implementing state machines, and explained why I like async as a tool for building such state machines, even without threads.

In this post I’ll work through an example of why I’m so excited about this technique, by building a real driver for a notoriously tricky bus one piece at a time, using lilos.

I’m a big fan of Rust’s async feature, which lets you write explicit state machines like straight-line code. One of the operating systems I maintain, lilos, is almost entirely based on async, and I think it’s a killer feature for embedded development.

async is also popular when writing webservers and other network services. My colleagues at Oxide use it quite a bit. Watching them work has underscored one of the current issues with async, however: the debugging story is not great. In particular, answering the question “why isn’t my program currently doing anything” is very hard.

I’ve been quietly tinkering on some tools to improve the situation since 2021, and I’ve recently released a prototype debugger for lilos: lildb. lildb can print await traces for uninstrumented lilos programs, which are like stack traces, but for suspended futures. I wrote this to help me debug my own programs, but I’m publishing it to try and move the discussion on async debugging forward. To that end, this post will walk through what it does, how it derives the information it uses, and areas where we could improve things.

Quick links:

• Github
• crates.io
• API docs
• Intro guide

Now that Hubris has gotten some attention, people sometimes ask me if my personal projects are powered by Hubris.

The answer is: no, in general, they are not. My personal projects use my other operating system, lilos, which predates Hubris and takes a fundamentally different approach. It has dramatically lower resource requirements and allows more styles of concurrency.