In a previous post, I established the notion of “registers” - code in Rust can be written in different registers, and it’s important to adequately support all registers. I specifically discussed the low-level interface of the AsyncIterator trait, about which there is currently a debate. The interface it currently has is a method called poll_next, which is a “poll” method like Future::poll. Poll methods are very “low-level” and are harder to write correctly than async functions. Some people would like to see AsyncIterator shifted to have an async next method, simply the “asyncified” Iterator trait.

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In the previous post in this series, I wrote about how the core state machine of ringbahn is implemented. In this post I want to talk about another central concept in ringbahn: “drivers”, external libraries which determine how ringbahn schedules IO operations over an io-uring instance.

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Today I made a new release of the iou library, which contains idiomatic Rust bindings to the liburing library. This library allows users to manipulate the new io-uring interface for asynchronous IO on Linux. For more context, you can read my previous post on the first release of iou last year.

This new release greatly expands the API of iou, introduces some valuable improvements, and contains some breakages. I figured I would let this blog post serve as some basic release notes.

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Last time I wrote about ringbahn, a safe API for using io-uring from Rust. I wrote that I would soon write a series of posts about the mechanism that makes ringbahn work. In the first post in that series, I want to look at the core state machine of ringbahn which makes it memory safe. The key types involved are the Ring and Completion types.

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While implementing ringbahn, I introduced at least two bugs that caused memory safety errors, resulting in segfaults, allocator aborts, and bizarre undefined behavior. I’ve fixed both bugs that I could find, and now I have no evidence that there are more memory safety issues in the current codebase (though that doesn’t mean there aren’t, of course). I wanted to write about both of these bugs, because they had an interesting thing in common: they were both caused by destructors.

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This is just a note on getting the best performance out of an async program.

The point of using async IO over blocking IO is that it gives the user program more control over handling IO, on the premise that the user program can use resources more effectively than the kernel can. In part, this is because of the inherent cost of context switching between the userspace and the kernel, but in part it is also because the user program can be written with more specific understanding of its exact requirements.

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In my previous post, I discussed the new io-uring interface for Linux, and how to create a safe API for using io-uring from Rust. In the time since that post, I have implemented a prototype of such an API. The crate is called ringbahn, and it is intended to enable users to perform IO on io-uring without any risk of memory unsafety.

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Last fall I was working on a library to make a safe API for driving futures on top of an an io-uring instance. Though I released bindings to liburing called iou, the futures integration, called ostkreuz, was never released. I don’t know if I will pick this work up again in the future but several different people have started writing other libraries with similar goals, so I wanted to write up some notes on what I learned working with io-uring and Rust’s futures model. This post assumes some level of familiarity with the io-uring API. A high level overview is provided in this document.

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I’ve just released a new crate called waitmap. This is a concurrent hash map (built on top of dashmap) intended for use as a concurrency primitive with async/await. It extends the API of dashmap by having an additional wait method. The wait future looks up an entry in the map and suspends this task if the entry was not present when wait was called. The task will be woken whenever a value is inserted under that key.…

One of the big sources of difficulty on the async ecosystem is spawning tasks. Because there is no API in std for spawning tasks, library authors who want their library to spawn tasks have to depend on one of the multiple executors in the ecosystem to spawn a task, coupling the library to that executor in undesirable ways. Ideally, many of these library authors would not need to spawn tasks at all.…