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23 May 2017 — by Théophane Hufschmitt
Typing Nix

Théophane is a summer intern here at Tweag I/O. Thanks also in part to the financial help from the Nix community, he’s spending six months on devising a type system for the Nix language.

Nix, the purely functional package manager

Nix is a cross platform tool for managing the configuration (set of packages installed, /etc/ config files etc) of your system. It currently supports Linux and macOS and support for more platforms are in the works.

Conventional package managers (APT, Yum, pacman and friends) treat the file system as one giant shared mutable data structure. The problem with this approach is that mutating global state is hard. Ever ended up with a hosed system because you lost power in the middle of a system upgrade? This is because package managers never figured out how to make mutation of the global state atomic. Wished you could easily rollback your system to a previous state and know for sure that this state is identical to how your system really was at some point in the past? Traditional package managers can’t cope with this because tracking state changes precisely and exhaustively is again an unsolved problem when package update scripts are allowed to make arbitrary changes to arbitrary parts of such a huge piece of mutable state as your entire root filesystem.

Configuration management tools (Chef, Puppet, Salt and friends) were invented to ensure that the global state of your filesystem converges to some intended state that you can precisely and declaratively define. But in practice applying e.g. the same Salt configuration on two different machines with a priori divergent starting states seldom leaves both machines in exactly the same state. This also means that rolling back the configuration after an unfortunate change won’t always restore the system to its exact original state.

Nix tries to solve this very problem by drastically reducing the amount of mutable state it has to manage. Less state and fewer ways to change that state means atomic updates and more reliable rollbacks. Drastically reducing mutable state… sound familiar? If you’re a functional programmer, it should! In fact, this is just one page that Nix took from functional programming books. Good abstraction facilities for writing configurations is another one. Today we’ll talk about borrowing yet another page: using powerful static type checking to provide guidance when configurations get refactored.

Nix today

Users typically specify the configuration of their system by reusing large amounts of configuration modules already written by the community. The largest collection of such modules, Nixpkgs, is today an incredibly active and diverse project. In fact that repository is now one of most active repositories on all of Github. It has also fallen victim of its own success. Clocking in at over 1 million lines of code, it’s becoming increasingly difficult to perform global refactorings of all configuration modules at once. Indeed any such refactoring might silently break these modules. This is where type checking can help a great deal: the checker can tell you half way through a refactoring what other code needs to change and how.

Nix in the future, with types

Configurations in Nix are written in a full-fledged programming language, featuring all manner of primitive datatypes (numbers, strings, file paths, etc), anonymous records and first-class functions. This is a very big deal for writing configurations in the large. Functions allow code reuse and abstraction, two crucial ingredients without which writing out configurations by hand would become unwieldy.

This language is untyped. Lack of types have created challenges in the past. A recent example was the integration of multiple-output derivation into Nixpkgs. Without diving into the details of the change (have a look at this if you want some details), this was a modification which needed a lot of refactoring in Nixpkgs.

Thanks to intensive testing, most of the problems were fixed before the change reach the mainstream branch. This testing required a lot of effort, and yet despite that some bugs still slipped through the tests. Some of these bugs could have been caught by a type system.

Design principles

A first and really important remark is that we don’t want the typed Nix to be incompatible with the legacy one. Our goal is not to invent a new language that could one day replace Nix. Given the size of Nixpkgs and the efforts invested in it, backward compatibility is primordial.

The problem of course, is that the current code has not been designed with typing in mind. This code as it stands will probably never type check in any reasonable static type system. But thanks to the wonders of gradual typing, this isn’t a real problem: we just have to gradually type the untypeable part. Furthermore, we had the chance of having Jeremy Siek − the inventor of gradual typing − in Paris for a month, and he provided us some substantial help in designing this.

The gist of gradual types is the that in addition to static types, you got a special “gradual” type (often noted ”?” or ”☆”) that means “I don’t know how to type this, let’s just assume it is well typed”.

Some examples

This is really nice, but no really concrete. So here are some examples to show how this could look like in practice.

Simple ML-like type inference

Here is a sample Nix expression (a simplified version of the stdenv.mkDerivation function which is used in Nixpkgs to build packages):

{ buildInputs, meta ? {}, outputs ? [ "out" ], ... }@attrs:
  derivation (attrs //
      builder = attrs.realBuilder or "/bin/sh";
      args = [ "-e" (atrrs.builder or ./ ];

Let’s show how this would be typed:

  • First, this is a function definition, so with a type of the form τ → σ.

  • By looking at the pattern, we can deduce that τ must be a record type, which must contain the field buildInputs, may contain the fields meta and outputs, and may also contain anything else (because of the ”…”).

  • Furthermore, by looking at the default values for meta and outputs, we can see that the meta field can have a record type and outputs the type of a list of strings.

  • Knowing that the derivation builtins expects its builder argument to be a string, we can deduce that if the argument of our function contains a field realBuilder, then it is of type string. Likewise, a field builder should be of type path, and the field buildInputs of type [derivation*] (a list of derivations)

  • The result of the function is the result of the derivation built-in, which is of type derivation (which in reality is just a special record type), so the return type σ will be derivation.

All this put together, we got that the expression has type:

{ buildInputs = [derivation*];
  meta =? Any;
  outputs =? [string*];
  realBuilder =? string;
  builder =? path; ... }
  → derivation

Where =? means that the field is optional, and Any is the super type of all types (as we don’t know anything about how meta is used, we can’t say much about his type).

Introducing a type error…

Now, assume we got almost exactly the same function, but with another default value for outputs:

{ buildInputs, meta ? {}, outputs ? "out", ... }@attrs:
  derivation (attrs //
      builder = attrs.realBuilder or "/bin/sh";
      args = [ "-e" (atrrs.builder or ./ ];

Here the type system should make the same deductions, but will notice that outputs can have type string while derivation expects an output argument of type [string*]. So this won’t type check.

… And fixing it

Now, we may write the (correct) function:

{ buildInputs, meta ? {}, outputs ? "out", ... }@attrs:
  let realOutputs =
    if isList outputs then
    else [ outputs ];
  derivation (attrs //
      builder = attrs.realBuilder or "/bin/sh";
      args = [ "-e" (atrrs.builder or ./ ];
      outputs = realOutputs;

Here, the type system should be able to see that outputs can have type string or [string*], and that in both cases, realOutputs will have type [string*], so the call to derivation will be well-typed. The type of the function would then be:

{ buildInputs = [derivation*];
  meta =? {...};
  outputs =? [string*]|string;
  realBuilder =? string;
  builder =? path; ... }
  → derivation

In reality, this is probably not inferable without annotating the type of the function − at least not according to the state of the art in type inference. If we write the function ourselves, no problem, we can just add type annotations, but if we use a function from Nixpkgs that we don’t want to mess with, we can circumvent the problem using gradual types. In this case, the type-checker would say “OK, I don’t understand what’s going on here, so I’ll just give up and say that this has the type ? → ?” − or probably something slightly more precise because it is should always be possible to deduce for example that the argument must contain a field buildInputs and that the return type should be a derivation, just not an exact static type.

Long is the road

Of course, this is still at a very early stage: the theory is still a work in progress (thanks to the help of Giuseppe Castagna) and the implementation has just started.

In the meantime, you can follow the progress on the github pages of the project, on for the theoretical part and for the implementation.

About the authors
Théophane HufschmittThéophane is a Software Engineer and self-proclaimed Nix guru. He lives in a small house surrounded by awesome castles in the Loire Valley. When he’s not taking care of his four sons or playing some music, you might find him working.
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