Querying Impurities

Posted on by Chris Warburton

If a computation is “pure”, it has no side-effects: the only thing it does is to create its output, and that output depends only on the computation’s input. Pure computations can’t, for example, read data from a network, format a hard drive or alter their behaviour based on some internal state.

One reason pure computations are nice is that they’re easy to reason about, both for humans and for algorithms. The prevalence of pure computations in Haskell is a major reason why the Glasgow Haskell Compiler is able to perform many heavy-duty optimisations: re-ordering, combining and even throwing away large amounts of code, whilst preserving the original’s behaviour.

Many languages aren’t so pure; any expression can make important globally-visible, behaviour-determining effects at any time. It is important that the implementation of such languages don’t interfere with the order of these effects, since in general effects don’t commute (i.e. “X then Y” isn’t always equivalent to “Y then X”). Hence most languages can’t make such sweeping optimisations that GHC is able to.

Such implementations are conservative: since two expressions (e.g. procedure calls) could be performing arbitrary effects which could be non-commuting, such changes aren’t attempted. By avoiding an optimisation completely, we definitely avoid all problematic cases; but we unfortunately avoid all non-problematic cases too.

It would be interesting if we could query such language implementations to ask what the potential impurities are in some piece of code. For example, we might ask a Python implementation what the impurities are in x() + y(), and it would tell us that looking up x and y in the environment may not be pure; that (attempting to) call their values as procedures may not be pure (e.g. if they’re not callable, an exception will be thrown, and the handler may be impure); that if they are procedures, those procedures may not be pure; that looking up the __add__ method on the first result may not be pure and that calling such an __add__ method may not be pure. Whew!

This looks intimidating, but is mostly a matter of syntax: knowing which language constructs can lead to effectful code being executed. In the above example, we know that + is syntactic sugar for calling an __add__ method, and we know that looking up methods can be effectful. There’s actually no need to look through abstractions, e.g. if an expression contains a procedure call, we can assume it’s effectful without looking at the procedure.

What would such an ability buy us? For a typical codebase, probably not much. However, we could combine this with purity annotations: if we annotate a construct as being pure, it won’t appear in our impurity queries. Pure constructs might include e.g. simple arithmetic, but may also code with unobservable effects; for example, a procedure which includes a memo table to avoid recalculating outputs. We can’t mark the pieces as pure, since they involve mutating a persistent state, but the construct as a whole is pure.

That would be an interesting ability to have, since it lets us take information about code out of a developer’s head and put it into the machine. One immediate benefit would be providing extra knowledge to those reading the code at a later date: a reassurance that no matter what crazy, dynamic stuff is going on inside a piece of code, that it shouldn’t leak that nastiness to the outside. Perhaps another would be to test, prove, disprove or infer such annotations, to aid the developer in understanding their code.

Going back to the idea of optimisation, it would give those same reassurances to the machine as well, allowing it to reason more deeply about the code, replacing its “just in case” wariness with the confidence to make more invasive changes, such as rearranging, supercompiling, etc.

One place that might benefit from such “impurity queries” and “pure from the outside” annotations is build/packaging systems like Nix. Many Nix packages use shell scripts extensively, although they’re run in an isolated environment, filesystem references are hard-coded and the results are put in a read-only filesystem. For example, a script may look up various binaries in its PATH, which is impure and subject to change; however, Nix may hard-code the PATH during installation, that path itself may be read-only and be derived from a content-based hash. Such measures turn the lookup into a pure construct, since it will always find the same file. An optimiser could utilise this, e.g. to inline the file, or to collapse pipelines based on their producer/consumer behaviour.

As it stands, shells are far too dynamic for such optimisations to be sound; for example, all sorts of dynamic hacks can be used to execute arbitrary code at various points in a script’s execution. If we could query for which ones affect our code, we could take measures to disable them, annotate the result as pure, and build a new optimised package out of the old one.