I’ve noticed that the ideas that I post on my blog are getting much more “well rounded”. That is a problem. It means I’m waiting too long to write about things. So I want to post about something that’s a bit more half-baked – it’s an idea that I’ve been kicking around to create a kind of informal “analysis API” for rustc.

The problem statement

I am interested in finding better ways to support advanced analyses that “layer on” to rustc. I am thinking of projects like Prusti or Facebook’s MIRAI, or even the venerable Clippy. All of these projects are attempts to layer on additional analyses atop Rust’s existing type system that prove useful properties about your code. Prusti, for example, lets you add pre- and post-conditions to your functions, and it will prove that they hold.

In theory, Rust is a great fit for analysis

There has been a trend lately of trying to adapt existing tools build initially for other languages to analyze Rust. Prusti, for example, is adapting an existing project called Viper, which was built to analyze languages like C# or Java. However, actually analyzing programs written in C# or Java in practice is often quite difficult, precisely because of the kinds of pervasive, mutable aliasing that those languages encourage.

Pervasive aliasing means that if you see code like

a . setCount ( 0 );

it can be quite difficult to be sure whether that call might also modify the state of some variable b that happens to be floating around. If you are trying to enforce contracts like “in order to call this method, the count must be greater than zero”, then it’s important to know which variables are affected by calls like setCount .

Rust’s ownership/borrowing system can be really helpful here. The borrow checker rules ensure that it’s fairly easy to see what data a given Rust function might read or mutate. This is of course the key to how Rust is able to steer you away from data races and segmentation faults – but the key insight here is that those same properties can also be used to make higher-level correctness guarantees. Even better, many of the more complex analyses that analysis tools might need – e.g., alias analysis – map fairly well onto what the Rust compile already does.

In practice, analyzing Rust is a pain, but not because of the language

Unfortunately, while Rust ought to be a great fit for analysis tools, it’s a horrible pain to try and implement such a tool in practice. The problem is that there is lots of information that is needed to do this sort of analysis, and that information is not readily accessible. I’m thinking of information like the types of expressions or the kind of aliasing information that the borrow check gathers. Prusti, for example, has to resort to reading the debug output from the borrow checker and trying to reconstitute what is going on.

Ideally, I think what we would want is some way for analyzer tools to leverage the compiler itself. They ought to be able to use the compiler to do the parsing of Rust code, to run the borrow check, and to construct MIR. They should then be able to access the MIR and the accompanying borrow check results and use that to construct their own internal IRs (in practice, virtually all such verifiers would prefer to start from an abstraction level like MIR, and not from a raw Rust AST). They should be able to ask the compiler for information about the layout of data structures in memory and other things they might need, too, or for information about the type signature of other methods.

Enter: on-demand analysis and library-ification

A few years back, the idea of enabling analysis tools to interact with the compiler and request this sort of detailed information would have seemed like a fantasy. But the architectural work that we’ve been doing lately is actually quite a good fit for this use case.

I’m referring to two different trends:

on-demand analysis

library-ification

The first trend: On-demand analysis

On-demand analysis is basically the idea that we should structure the compiler’s internal core into a series of “queries”. Each query is a pure function from some inputs to an output, and it might be something like “parse this file” (yielding an AST) or “type-check this function” (yielding a set of errors). The key idea is that each query can in turn invoke other queries, and thus execution begins from the end state that we want to reach (“give me an executable”) and works its way backwards to the first few steps (“parse this file”). This winds up fitting quite nicely with incremental computation as well as parallel execution. (If you’d like to learn more about this, I gave a talk at PLISS that is available on YouTube.)

On-demand analysis is also a great fit for IDEs, since it allows us to do “just as much work” as we have to” in order to figure out key bits of information (e.g., “what is the type of the expression at the cursor”). The rust-analyzer project is based entirely on on-demand computation, using the salsa library.

On-demand analysis is not only a good fit for IDEs: it’d be a great fit for tools like Prusti. If we had a reasonably stable API, tools like Prusti could use on-demand analysis to ask for just the results they need. For example, if they are analyzing a particular function, they might ask for the borrow check results. In fact, if we did it right, they could also leverage the same incremental compilation caches that the compiler is using, which would mean that they don’t even have to re-parse or recompute results that are already available from a previous build (or, conversly, upcoming builds can re-use results that Prusti computed when doing its analysis).

The second trend: Library-ification

There is a second trend in the compiler, one that’s only just begun, but one that I hope will transform the way rustc development feels by the time it’s done. We call it “library-ification”. The basic idea is to refactor the compiler into a set of independent libraries, all knit together by the query system.

One of the immediate drivers for library-ification is the desire to integrate [rust-analyzer] and rustc into one coherent codebase. Right now, the [rust-analyzer] IDE is basically a re-implementation of the front-end of the Rust compiler. It has its own parser, its own name resolver, and its own type-checker.

The vision: shared components

So we saw that, presently, rust-analyzer is effectively a re-implementation of many parts of the the Rust compiler. But it’s also interesting to look at what rust-analyzer does not have – its own trait system. rust-analyzer uses the [chalk] library to handle its trait system. And, of course, work is also underway to integrate chalk into rustc.

At the moment, chalk is a promising but incomplete project. But if it works as well as I hope, it points to a promising possibility. We can have the “trait solver” as a coherent block of functionality that is shared by multiple projects. And we could go further, so that we wind up with rustc and rust-analyzer being just two “small shims” over top the same core packages that make up the compiler. One shim would export those packages in a “batch compilation” format suitable for use by cargo, and one as a LSP server suitable for use by IDEs.

The vision: Clean APIs defined in terms of Rust concepts

Chalk is interesting for another reason, too. The API that Chalk offers is based around core concepts and should, I think, be fairly stable. For example, it communicates with the compiler via a trait, the RustIrDatabase , that allows it to query for specific bits of information about the Rust source (e.g., “tell me about this impl”), and doesn’t require a full AST or lots of specifics from its host. One of the benefits of this is that we can have a relatively simple testing harness that lets us write chalk unit tests in a simplified form of Rust syntax.

The fact that chalk’s unit tests are “mini Rust programs” is nice because they’re readable, but it’s important a deeper reason, too. I’ve many times experienced problems when using unit tests where the tests wind up tied very tightly to the structure of the code, and hence big swaths of tests get invalidated when doing refactoring, and it’s often quite hard to port them to the new interface. We don’t generally have to worry about this with rustc, since its tests are just example programs – and the same is true for Chalk, by and large. My sense is that one of the ways that we will know where good library boundaries lie will be our ability to write unit tests in a clear way.

Library-ification can help make rustc more accessible

Right now, many folks have told me that the rustc code base can be quite intimidating. There’s a lot of code. It takes a while to build and requires some custom setup to get things going (not to mention gobs of RAM). Although, like any large code-base, it is factored into several relatively independent modules, it’s not always obvious where the boundaries between those modules are, so it’s hard to learn it a piece at a time.

But imagine instead that rustc was composed of a relatively small number of well-defined libraries, with clear and well-documented APIs that separated them. Those libraries might be in separate repositories and they might not, but regardless you could jump into a single library and start working. It would have a clear API that connects it to the rest of the compiler, and a testing harness that lets you run unit tests that exercise that API (along of course with our existing suite of example programs, which serve as integration tests).

The benefits of course aren’t limited to new contributors. I really enjoy hacking on chalk because it’s a relatively narrow and pliable code base. It’s easy to jump from place to place and find what I’m looking for. In contrast, working on rustc feels much more difficult, even though I know the codebase quite well.

Library-ification will work best if APIs aren’t changing

One thing I want to emphasize. I think that this whole scheme will work best if we can find interfaces between components that are not changing all the time. Frequently changing interfaces would indicate that the modules between the compiler are coupled in ways we’d prefer to avoid, and it will make it harder for people to work within one library without having to learn the details of the others.

Now we come to the final step. If we imagine that we are able to subdivide rustc into coherent libraries, and that those libraries have relatively clean, stable APIs betwen them, then it is also plausible that we can start publishing those libraries on crates.io (or perhaps wrappers around them, with simplified and more limited APIs). This then starts to look sort of like the .NET Roslyn compiler – we are exporting the tools to help people analyze and understand Rust code for themselves. So, for example, Prusti could invoke rustc’s borrow checker and read its results directly, without having to resort to elaborate hacks.

On stability and semver

I’ve tossed out the term “stable” a few times throughout this post, so it’s worth putting in a few words for how I think stability would work if we went down this direction. I absolutely do not think we would want to commit to some kind of fixed, unchanging API for rustc or libraries used by rustc. In fact, in the early days, I imagine we’d just publish a new major version of each library with each Rust release, which would imply that you’d have to do frequent updates.

But once the APIs settle down – and, as I wrote, I really hope that they do – I think we would simply want to have meaningful semver, like any other library. In other words, we should always feel free to make breaking changes to our APIs, but we should announce when we do so, and I hope that we don’t have to do so frequently.

If this all really works out, I imagine we’d start to think about scheduling breaking changes in APIs, or finding alternatives that let us keep tooling working. I think that’d be a fine price to pay in exchange for having a host of powerful tooling available, but in any case it’s quite far away.

Conclusion

This post sketches out my vision for how Rust compiler development in the long term. I’d like to see a rustc based on a relatively small number of well-defined components that encapsulate major chunks of functionality, like “the trait system”, “the borrow checker”, or “the parser”. In the short term, these components should allow us to share code between rustc and rust-analyzer, and to make rustc more understandable. In the longer term, these components could even enable us to support a broad ecosystem of compiler tools and analyses.