Build Tools as Pure Functional Programs

The term "build tool" is used to describe a host of different pieces of software, each with their own features, uses and complexity. It is sometimes hard to see the forest for the trees: in the midst of minifying Javascript, compiling C++, or generating protobuf sources, what is a build tool all about?

In this post, I will argue that fundamentally a build-tool models the same thing as a pure functional program. The correspondence between the two is deep, and in studying it we can get new insights into both build-tooling and functional programming.

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy the Author's book Hands-on Scala Programming

"Build tool" is a bit of a catch-all phrase used to describe software that does a host of different concrete tasks:

Generates source code defining Java classes from your .proto files

files Generating Spritesheets or Mipmaps from images

Downloads dependencies from remote package repositories like Maven, PyPI or NPM

Zipping up a folder full of files for deployment

Deploys your code to a remote service

Runs linters, style-checkers & static analyzers

Compiles your Java into Bytecode

Compiles your Java (GWT) into Javascript

Compiles your Scala into Javascript

Compiles your Typescript into Javascript

Compiles your Javascript (ES6) into Javascript (ES5)

Apart from these concrete tasks (and compiling everything into Javascript these days...) , there are a lot of cross-cutting considerations that build tools tend to take care of: things that are not specific to any one task, but apply to the project as a whole:

Given a task the user wants to run, make sure all tasks it depends on are run first, in the correct order

Cache the output of tasks so they aren't repeated unnecessarily, but flush the caches when necessary and re-build incrementally to make sure the output is up-to-date

Parallelize the tasks that can be run in parallel

Watch the input source files for changes and automatically re-run the downstream tasks that depend on them

DRY up repetitive groups of tasks: every Scala module may have a compile and package task, every Javascript module may have minify lint and test , but I don't want to have to write these tasks over and over for each one

Delegate work to third-party tools: compilers, linters, packagers

While different build tools have different syntaxes, different features or different capabilities, basically all of them manage these same core competencies.

The Build Graph

At it's simplest, a build tool is just runs a single task, taking some input and producing some output, e.g.:

compile Java Source ------> Java Bytecode

Every time you change the Java Source , you want your build tool to compile it into Java Bytecode . Simple.

However, you quickly find you need more than one operation to be performed: perhaps you need to package up your Java Bytecode into .jar files for deployment, after first being put through an obfuscator like Proguard to protect your proprietary IP:

compile proguard package Source ------> Bytecode -------> Obfuscated Bytecode ------> Release Jars

Now, every time you change the Source , your build tool should put it through all of compile , proguard and package it into the final Release Jars ; or perhaps, if you're just testing locally, you can skip the proguard / package steps and run the raw Bytecode . Your build tool must be flexible enough you can tell it exactly which output you need ( Bytecode , Obfuscated Bytecode , or Release Jar s) and only do as much work necessary to get to that step.

As your project grows, you may realize you may need to integrate even more things into the pipeline:

You want to generate some source code from .proto files; these should get compile d together with the normal Source code, but re-generated every time you change the .proto s

You want to have a set of non-obfuscated Test Jars for internal deployments & QA, where you don't need to worry about IP but want the stack traces to be easier to understand. These Test Jars must also contain Source Jars full of source code for better integration with your QA team's IDEs and debuggers

You want to integrate open-source libraries into your project! These may be shipped as .jar files full of bytecode that you download from Maven Central using their Maven Coordinates, which you need to include your final jars (but no need to obfuscate)

Now, your build graph may look like this:

compile proguard package Source ------> Bytecode -------> Obfuscated Bytecode ------> Release Jars | | \ / | | \ / | Generated Source \ Test Jars / | ^ \ ^ / | | protoc \ /| / | | ,-'----' \ / | Proto Files / package \ / | / | / '----------------> Source Jars Libraries package ^ / Maven Coordinates -------------' download

Now, you as-the-programmer still want to do the same things as before: you want to tell your build tool

Give me the Test Jars

And you want your build tool to be able to figure out it needs to

package the Source into Source Jars

the into protoc the Proto Files into Generated Source

the into compile the Source and Generated Source files into Bytecode

the and files into package the Bytecode and include the Source Jars and Libraries into the file Test Jars

Furthermore, next time you change either the Source files, the protoc files, or tweak the Maven Coordinates of the open-source libraries, you want your build tool to do the absolute minimum work required to bring the Test Jars up to date. And you want the tool to do that work in parallel.

While trivial projects may have a one-step build or a linear "asset pipeline", any non-trivial project ends up with a graph of build-steps and intermediate-results similar to the one shown above. And this example is pretty trivial as far as real-world builds go!

Getting to the Build Graph

Fundamentally, most builds tools end up modeling the same data structure. In [make](https://en.wikipedia.org/wiki/Make_(software)), each rule you specify the name of the output followed by the the names of the inputs, with the action to transform inputs to outputs on the following line:

hello: main.o factorial.o hello.o g++ main.o factorial.o hello.o -o hello main.o: main.cpp g++ -c main.cpp

With rake, you specify the input s and output as the header for each task , followed by a block of Ruby code that transforms the input to the output:

task :build_refact => [:clean] do target = SITE_DIR + 'refact/' mkdir_p target, QUIET require 'refactoringHome' OutputCapturer.new.run {run_refactoring} end file 'build/dev/rake.html' => 'dev/rake.xml' do |t| require 'paper' maker = PaperMaker.new t.prerequisites[0], t.name maker.run end

With SBT, you specify the output but the inputs to a task are automatically inferred by who you call .value on, in the body of a Scala block that transforms the input to the output:

assembly in Test := { val dest = target.value/"amm" IO.copyFile(assembly.value, dest) import sys.process._ Seq("chmod", "+x", dest.getAbsolutePath).! dest }

There are as many ways of defining the build graph as there are build tools. However, at it's core the build graph - a directed, acyclic graph of build steps and intermediate results, is core to basically every tool out there.

Given the wealth of possibilities, it might beg the question: what is the simplest, most straightforward way in which someone may use code to define a directed acyclic graph?

Functional Programs are Directed Acyclic Graphs

It turns out that the simplest way to define a directed acyclic graph data-structure in code is with a pure functional program.

Here is the simple 1-step build from earlier:

compile Java Source ------> Java Bytecode

And here it is as a functional (Python) program:

source = ... bytecode = compile(source)

Ignore for the moment the question of how the program is executed: for now we just want to focus on the structure. The (small) directed graph above tells you that Java Source is passed through the compile function, and results in Bytecode . The two-line Python snippet expresses the same thing.

The multi-stage "pipeline" build can be similarly translated, from a ASCII graph:

compile proguard package Source ------> Bytecode -------> Obfuscated Bytecode ------> Release Jars

To a short Python snippet:

source = ... bytecode = compile(source) obfuscated_bytecode = proguard(bytecode) release_jars = package(obfuscated_bytecode)

Again, we ignore how the program will run, and just focus on how the program is structured. Both the ASCII graph and functional-python-snippet have the same structure: source being put through a number of transformations ( compile , proguard , package ) to create a sequence of named results ( bytecode , obfuscated_bytecode , release_jars ) each dependent on the previous.

Lastly, we can look at the most complex build-graph above, and see how it might be expressed as a functional Python program. From ASCII art:

compile proguard package Source ------> Bytecode -------> Obfuscated Bytecode ------> Release Jars | | \ / | | \ / | Generated Source \ Test Jars / | ^ \ ^ / | | protoc \ /| / | | ,-'----' \ / | Proto Files / package \ / | / | / '----------------> Source Jars Libraries package ^ / Maven Coordinates -------------' download

To functional Python:

source = ... proto_files = ... maven_coordinates = [...] libraries = download(maven_coordinates) generated_source = protoc(proto_files) bytecode = compile(source, generated_source) source_jars = package(source) test_jars = package(source_jars, bytecode, libraries) obfuscated_bytecode = proguard(bytecode) release_jars = package(obfuscated_bytecode, libraries)

This Python snippet is non-trivial, just like the ASCII graph above it. Unlike the earlier snippets, we have some build steps like compile which take multiple inputs, which we model as a function call taking multiple arguments. You also have some intermediate results like Bytecode which are used in multiple build steps, which is reflected by the bytecode value being passed into multiple functions.

But if you draw the line from identifier to identifier, you'll find that this results in exactly the same directed acyclic graph data structure we saw earlier!

I call these Python snippets functional because each snippet is made of pure functions with no side effects: the only inputs they take are their arguments, and their result is their return value. We're assuming compile ing the source code won't sneakily mutate the source and cause it to behave differently the second time, or delete it so that trying to package it into source_jars fails. We assume that running protoc to make generated_sources from proto_files won't cause our maven_coordinates to change.

The code is structured as functions, whose only inputs are passed as arguments, and only output is their return value, with no side-effects. That is what makes it functional, regardless if it's written in Python or Scala or Haskell.

I have showed above how any build, from simple to complex, can be generally modeled as a directed acyclic graph. We have also seen how the directed acyclic graph happens to be the exact data structure that underlies pure-functional code. If that is the case, why don't people write their builds in pure-functions, whether in Python or something else?

Why not a Build-Tool of Pure Functions?

It turns out that you can write a build tool with pure functions. In fact, all you need is the appropriate set of implementations: a download function that downloads things from Maven Central. A protoc function that invokes the Protobuf code generator. A package function that zips things into jars. And then you run python script.py :

source = ... proto_files = ... maven_coordinates = [...] libraries = download(maven_coordinates) generated_source = protoc(proto_files) bytecode = compile(source, generated_source) source_jars = package(source) test_jars = package(source_jars, bytecode, libraries) obfuscated_bytecode = proguard(bytecode) release_jars = package(obfuscated_bytecode, libraries)

And it will generate your test_jars , your release_jars , and everything else, invoking the individual build steps in the correct order to create the results. Many people who have worked on large projects would have no doubt encounted jury-rigged Python scripts that do exactly that!

It turns out that you do not need to give your build-steps strings to serve as unique IDs, or explicitly specify lists of dependencies so the tool knows what each step. In the structure of this pure-functional Python snippet, we already have all the information necessary for all the build tools in the world to work with!

If you can write a Python script using pure functions to serve as a build tool, why doesn't everyone do that? That certainly is simpler than learning a whole new language/syntax/semantics/ecosystem just to invoke some functions in the correct order.

Downsides of a Naive Pure-Function Build Tool

It turns out, while the above Python script with naive download / protoc / package /etc. implementations can work, it isn't a very good build tool because it misses out on some of the things we've come to expect:

I want my build tool to cache intermediate results and re-use them; the above snippet re-evaluates everything each time, which is slow and unnecessary

Python (and most other languages!) typically run sequentially, whereas I know that some steps do not affect each other and can be run in parallel

I can't ask the Python script which files it's interested in, so I can watch just those; the only thing I can do with the script is run it, not ask questions!

These three points, while simple to state, are enormously important. Nobody likes waiting minutes each time to re-download the same libraries over and over, or re-compile code that hasn't changed. That would be a deal-breaker for any build tool, regardless of simplicity: I'll happily put up with awkward Makefile syntax or magic Ruby/Rake incantations if it will save me time in my build-test-debug iteration cycle.

Even traditional "functional programming" languages like Haskell or OCaml default to a single-threaded, un-cached batch-oriented, non-queryable programming model that's manifestly unsuitable for build tooling.

However, if those three points above are the only reason not to use a build-tool which models your build as pure-functions, then perhaps there is hope.

The most crucial part of any piece of software is the user-facing data-model and internal data-model. Algorithms can be adjusted, implementations can be optimized or tweaks, but changing either data-model is enormously difficult. The data-model of your program permeates every decision, every API, and every user interface.

If we think that pure-functions is the simplest way of modeling the essential complexity of a build's directed acyclic graph, then an interpreter/evaluator could be written to take the existing pure-functional code, and execute it with the caching, parallelism and queryability necessary to make a good build tool out of it.

Alternately, you could leverage an existing programming-language/interpreter, and try to capture enough of the program's call-graph so that at run-time you can re-construct it and evaluate it in parallel.

Mill is an open-source Scala build tool using the latter strategy to try and follow these principles.

Mill

Mill is a work-in-progress build tool using the Scala programming language. It is a general-purpose Task runner: Scala compilation is delegated to the Scala compiler, Maven dependency resolution is delegated to Coursier, and you can of course delegate to any number of external processes. What Mill aims to do, is to let you define your build graph as pure-functional program, while still achieving the caching, parallelism, and queryability needed to make a good build tool.

The above Python snippet translated to Mill's Scala syntax would look something like this:

def source = T.sources{ ... } def proto_files = T.sources{ ... } def maven_coordinates = T{ ... } def libraries = T{ download(maven_coordinates()) } def generated_source = T{ protoc(proto_files()) } def bytecode = T{ compile(source(), generated_source()) } def source_jars = T{ packageJar(source()) } def test_jars = T{ packageJar(source_jars(), bytecode(), libraries()) } def obfuscated_bytecode = T{ proguard(bytecode()) } def release_jars = T{ packageJar(obfuscated_bytecode(), libraries()) }

Immediately, you will notice some extraneous syntax not seen in the Python version earlier: the Scala language needs the def keyword for local definitions, every definition's right-hand-side is wrapped in a T{...} block, and every call-site has a trailing () , e.g. proto_files() and source() . This extra syntax is the cost of trying to leverage an existing language, rather than building a whole new interpreter.

Despite the additional syntax, the structure of this mill build is exactly the same as the pure-functional Python snippet we saw earlier. And it works:

You can run mill libraries to resolve the given maven_coordinates and download the necessary jars, which will then be cached until the maven_coordinates change. mill bytecode similarly will make the generated_sources from proto_files , and compile them together with the input source , and only re-compiling if one of them changes.

Cached intermediate results are persisted to disk at easy-to-find paths, in a easy-to-read and easy-to-use format.

You can query the build graph to find out what sources are used (directly or indirectly) by a certain task, and re-run only the affected tasks when any of them change.

All of this works today. Automatically parallelizing the build isn't in there yet, but it will be soon.

There is a lot of machinery that goes on behind the scenes in order to make Mill work. Mill needs to automatically infer a unique label for every named task, to ensure it has a place on disk to cache it's data. Mill uses as sort of Free Applicative to reify and model the build graph, and uses Scala AST Macros to let you write code in a straightforward way without using the Applicative zipMap all over the place. Mill's modules are just object s with bundles of related Task s, and Mill uses implicits to propagate context such that every Task knows it's name and place on disk.

But to a user of Mill, all that can be ignored: what matters to a user is that Mill allows you to define a build graph via the call-graph of a pure-functional program.

While virtually every other build tool models the build as a directed acyclic graph in some way, I'm not aware of any which leverage the call-graph of a pure-functional program to model the build. Mill, while still a work in progress, is my attempt at doing so.

Mill is a work in progress, and not quite ready for prime time. But if you are tired of Scala's venerable SBT build tool and are interested in a Scala build tool that simplifies the complexities of a build graph using the call-graph of a pure-functional program, come by our Gitter Room and let's collaborate!

First Class Functions in a Build

One interesting intersection between the worlds of build-tooling and pure-functional programming is the idea of a first-class function. For example, given the smallest Python snippet we had earlier:

source = ... bytecode = compile(source)

A first-class function means that compile is not some magic built-in, but can itself be assigned to a variable, or come from somewhere else, perhaps returned from somewhere else:

source = ... compile = local_file("bin/compiler.jar") bytecode = compile(source)

In functional programming, first-class functions allow for things like higher-order functions and lots of other cool things. However, their application in a pure-functional build tool is even more interesting!

Up until this point, we have assumed that the build-steps like protoc , compile or proguard are sort of magic: they are just "there", implemented as part of the build tool. However, this is unsatisfying for a number of reasons:

A build tool can't come built-in with every function someone will need. At some point, they'll need custom functions

If your "custom functions" are non-trivial, as things like the Proguard Obfuscator are non-trivial, you probably want all the same properties that apply to your "main build" to also apply to building your "custom functions": parallel builds, caching, etc.

If the implementation of your "custom function" changes, how do you know which caches to invalidate? If they are just functions living in your Python/Java/etc. program, there is no way to analyze which functions a change to your program affects. The only safe thing to do is to invalidate all caches when the "build program" changes: a frustrating operation for large projects which may take a while to re-build from clean!

However, if "custom functions" were simply executable build artifacts like any other, then all these problems are solved:

If the user wants some "custom function" that's not built in, such as a Kotlin compiler? Build it, and then execute it.

The build of your "custom function" is slow? It happens in parallel and is cached, so hopefully won't bother you too much

If the implementation of your "custom function" changes, you know exactly which build-steps it is used in, and can invalidate only-those without invalidating all the others!

The Bazel Build tool is the single build tool I am aware of that gets this particular point right. At it's core, Bazel's build steps aren't defined as builtin-functions that operate on the build artifacts, as we have done above:

bytecode = compile(source, generated_source)

Instead, Bazel's build steps (called actions) are usually defined as running an executable , which you may have built earlier, on some inputs , which you also may have built earlier:

ctx.run( executable = ctx.executable.compile inputs = [ctx.files.source, ctx.files.generated_source], ... )

As a result, any executable that a user can build can be itself used in a build step: extending a Bazel build with a newly-built executable is no different from building anything else: you get all the same parallelism, caching and queryability when building such extensions as you do when building your "main" project. compile / proguard / package , rather than being builtin operations to your "build program", become just another intermediate result to build in the process of building your project.

While the syntax may differ in arbitrary ways (e.g. with all the ctx.blah prefixes), you may recognize the introduction of ctx.run "builtin" function as exactly the same transformation you often see in Lisp textbooks, from:

(operation input1 input2)

To

(apply operation input1 input2)

The good old apply / eval duality, code as data, data as code. But in the land of build-tools, this provides a special elegance and brand-new set of benefits from what you may be familiar with from Lisp/functional-programming land!

Conclusion

Build tools are diverse and varied, but once you dig through all the cruft, whether Bazel's meta-meta pseudo-python interpreter or SBT's four-dimensional two-layer execution model they all look the same underneath: a directed acyclic graph of build-steps and intermediate results, which is executed in parallel, cached and analyzed to provide the efficient, minimally-flaky, maximally-snappy experience people associate with "good" build tools.

It turns out that this directed acyclic graph is exactly the same data model as is represented by pure-functional code, regardless of language!

I think there is a missing piece in the land of build tools: a build tool that really leverages this isomorphism between builds and pure-functional programming to cut through unnecessary ceremony and maximize familiarity.

A build tool that does away with assigning IDs via strings, or specifying lists of dependencies per-task, to instead leverage the identifiers and dependencies already evident in the structure of the pure-functional program.

A build tool that let's people write the same foo = func(bar, baz, qux) that they've been writing for years, but let them execute that code in parallel, caching intermediate results, and query the code (without running it!) to know exactly who foo depends on.

A build tool where the pure-function call-graph and the build-dependency-graph are one and the same, and a programmer can use the same "jump to definition" and "find usages" shortcuts they know and love to navigate their way through the structure of the build.

Mill is one attempt at doing so. It's still somewhat rough and incomplete, but due to the principles laid out in this post, I have reason to believe we'll be finally able to create a build tool that makes intuitive sense to any programmer.

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy the Author's book Hands-on Scala Programming