A lot of Mill's design principles are intended to fix SBT's flaws, as described in the blog post What's wrong with SBT, building on the best ideas from tools like CBT and Bazel, and the ideas from my blog post Build Tools as Pure Functional Programs. Before working on Mill, read through that post to understand where it is coming from!
Mill's most important abstraction is the dependency graph of Task
s. Constructed using the T {...}
T.task {...}
T.command {...}
syntax, these track the dependencies between steps of a build, so those steps can be executed in the correct order, queried, or parallelized.
While Mill provides helpers like ScalaModule
and other things you can use to quickly instantiate a bunch of related tasks (resolve dependencies, find sources, compile, package into jar, ...) these are secondary. When Mill executes, the dependency graph is what matters: any other mode of organization (hierarchies, modules, inheritance, etc.) is only important to create this dependency graph of Task
s.
The syntax for running targets from the command line mill Foo.bar.baz
is the same as referencing a target in Scala code, Foo.bar.baz
Everything that you can run from the command line lives in an object hierarchy in your build.sc
file. Different parts of the hierarchy can have different Target
s available: just add a new def foo = T {...}
somewhere and you'll be able to run it.
Cross builds, using the Cross
data structure, are just another kind of node in the object hierarchy. The only difference is syntax: from the command line you'd run something via mill core.cross[a].printIt
while from code you use core.cross("a").printIt
due to different restrictions in Scala/Bash syntax.
Every Target
in a build, defined by def foo = T {...}
, is cached by default. Currently this is done using a foo/meta.json
file in the out/
folder. The Target
is also provided a foo/
path on the filesystem dedicated to it, for it to store output files etc.
This happens whether you want it to or not. Every Target
is cached, not just the "slow" ones like compile
or assembly
.
Caching is keyed on the .hashCode
of the returned value. For Target
s returning the contents of a file/folder on disk, they return PathRef
instances whose hashcode is based on the hash of the disk contents. Serialization of the returned values is tentatively done using uPickle.
The Mill build process is meant to be run over and over, not only as a long-lived daemon/console. That means we must minimize the startup time of the process, and that a new process must be able to re-construct the in-memory data structures where a previous process left off, in order to continue the build.
Re-construction is done via the hierarchical nature of the build: each Target
foo.bar.baz
has a fixed position in the build hierarchy, and thus a fixed position on disk out/foo/bar/baz/meta.json
. When the old process dies and a new process starts, there will be a new instance of Target
with the same implementation code and same position in the build hierarchy: this new Target
can then load the out/foo/bar/baz/meta.json
file and pick up where the previous process left off.
Minimizing startup time means aggressive caching, as well as minimizing the total amount of bytecode used: Mill's current 1-2s startup time is dominated by JVM classloading. In future, we may have a long lived console or nailgun/drip-based server/client models to speed up interactive usage, but we should always keep "cold" startup as fast as possible.
Task
s are Applicative, not Monadic. There is .map
, .zip
, but no .flatMap
operation. That means that we can know the structure of the entire dependency graph before we start executing Task
s. This lets us perform all sorts of useful operations on the graph before running it:
Given a Target the user wants to run, pre-compute and display what targets will be evaluated ("dry run"), without running them
Automatically parallelize different parts of the dependency graph that do not depend on each other, perhaps even distributing it to different worker machines like Bazel/Pants can
Visualize the dependency graph easily, e.g. by dumping to a DOT file
Query the graph, e.g. "why does this thing depend on that other thing?"
Avoid running tasks "halfway": if a Target's upstream Targets fail, we can skip the Target completely rather than running halfway and then bailing out with an exception
In order to avoid making people using .map
and .zip
all over the place when defining their Task
s, we use the T {...}
/T.task {...}
/T.command {...}
macros which allow you to use Task#apply()
within the block to "extract" a value.
def test() = T.command {
TestRunner.apply(
"mill.UTestFramework",
runDepClasspath().map(_.path) :+ compile().path,
Seq(compile().path)
}
This is roughly equivalent to the following:
def test() = T.command { T.zipMap(runDepClasspath, compile, compile) {
(runDepClasspath1, compile2, compile3) =>
TestRunner.apply(
"mill.UTestFramework",
runDepClasspath1.map(_.path) :+ compile2.path,
Seq(compile3.path)
)
}
This is similar to SBT's :=
/.value
macros, or scala-async
's async
/await
. Like those, the T {...}
macro should let users program most of their code in a "direct" style and have it "automatically" lifted into a graph of Task
s.
Why should you expect that the Mill build tool can achieve simple, easy & flexible, where other build tools in the past have failed?
Build tools inherently encompass a huge number of different concepts:
These are a lot of questions to answer, and we haven't even started talking about the actually compiling/running any code yet! If each such facet of a build was modelled separately, it's easy to have an explosion of different concepts that would make a build tool hard to understand.
Before you continue, take a moment to think: how would you answer to each of those questions using an existing build tool you are familiar with? Different tools like SBT, Fake, Gradle or Grunt have very different answers.
Mill aims to provide the answer to these questions using as few, as familiar core concepts as possible. The entire Mill build is oriented around a few concepts:
These concepts are already familiar to anyone experienced in Scala (or any other programming language...), but are enough to answer all of the complicated build-related questions listed above.
The module hierarchy is the graph of objects, starting from the root of the build.sc
file, that extend mill.Module
. At the leaves of the hierarchy are the Target
s you can run.
A Target
's position in the module hierarchy tells you many things. For example, a Target
at position core.test.compile
would:
Cache output metadata at out/core/test/compile/meta.json
Output files to the folder out/core/test/compile/dest/
Source files default to a folder in core/test/
, core/test/src/
Be runnable from the command-line via mill core.test.compile
Be referenced programmatically (from other Target
s) via core.test.compile
From the position of any Target
within the object hierarchy, you immediately know how to run it, find its output files, find any caches, or refer to it from other Target
s. You know up-front where the Target
's data "lives" on disk, and are sure that it will never clash with any other Target
's data.
The Scala call graph of "which target references which other target" is core to how Mill operates. This graph is reified via the T {...}
macro to make it available to the Mill execution engine at runtime. The call graph tells you:
Which Target
s depend on which other Target
s
For a given Target
to be built, what other Target
s need to be run and in what order
Which Target
s can be evaluated in parallel
What source files need to be watched when using --watch
on a given target (by tracing the call graph up to the Source
s)
What a given Target
makes available for other Target
s to depend on (via its return value)
Defining your own task that depends on others is as simple as def foo =
T {...}
The call graph within your Scala code is essentially a data-flow graph: by defining a snippet of code:
val b = ...
val c = ...
val d = ...
val a = f(b, c, d)
you are telling everyone that the value a
depends on the values of b
c
and d
, processed by f
. A build tool needs exactly the same data structure: knowing what Target
depends on what other Target
s, and what processing it does on its inputs!
With Mill, you can take the Scala call graph, wrap everything in the T {...}
macro, and get a Target
-dependency graph that matches exactly the call-graph you already had:
val b = T { ... }
val c = T { ... }
val d = T { ... }
val a = T { f(b(), c(), d()) }
Thus, if you are familiar with how data flows through a normal Scala program, you already know how data flows through a Mill build! The Mill build evaluation may be incremental, it may cache things, it may read and write from disk, but the fundamental syntax, and the data-flow that syntax represents, is unchanged from your normal Scala code.
Classes and traits are a common way of re-using common data structures in Scala: if you have a bunch of fields which are related and you want to make multiple copies of those fields, you put them in a class/trait and instantiate it over and over.
In Mill, inheriting from traits is the primary way for re-using common parts of a build:
Scala "project"s with multiple related Target
s within them, are just a Trait
you instantiate
Replacing the default Target
s within a project, making them do new things or depend on new Target
s, is simply override
-ing them during inheritance
Modifying the default Target
s within a project, making use of the old value to compute the new value, is simply override
ing them and using super.foo()
Required configuration parameters within a project
are abstract
members
Cross-builds are modelled as instantiating a (possibly anonymous) class multiple times, each instance with its own distinct set of Target
s
In normal Scala, you bundle up common fields & functionality into a class
you can instantiate over and over, and you can override the things you want to customize. Similarly, in Mill, you bundle up common parts of a build into trait
s you can instantiate over and over, and you can override the things you want to customize. "Subprojects", "cross-builds", and many other concepts are reduced to simply instantiating a trait
over and over, with tweaks.
Mill is built as a substitute for SBT, whose problems are described here. Nevertheless, Mill takes on some parts of SBT (builds written in Scala, Task graph with an Applicative "idiom bracket" macro) where it makes sense.
Mill is largely inspired by Bazel. In particular, the single-build-hierarchy, where every Target has an on-disk-cache/output-folder according to their position in the hierarchy, comes from Bazel.
Bazel is a bit odd in its own right. The underlying data model is good (hierarchy + cached dependency graph) but getting there is hell. It (like SBT) is also a 3-layer interpretation model, but layers 1 & 2 are almost exactly the same: mutable python which performs global side effects (layer 3 is the same dependency-graph evaluator as SBT/mill).
You end up having to deal with a non-trivial python codebase where everything happens via:
do_something(name="blah")
or
do_other_thing(dependencies=["blah"])
where "blah"
is a global identifier that is often constructed programmatically via string concatenation and passed around. This is quite challenging.
Having the two layers be “just python” is great since people know python, but I think it's unnecessary to have two layers ("evaluating macros" and "evaluating rule impls") that are almost exactly the same, and I think making them interact via return values rather than via a global namespace of programmatically-constructed strings would make it easier to follow.
With Mill, I’m trying to collapse Bazel’s Python layer 1 & 2 into just 1 layer of Scala, and have it define its dependency graph/hierarchy by returning values, rather than by calling global-side-effecting APIs. I've had trouble trying to teach people how-to-bazel at work, and am pretty sure we can make something that's easier to use.
Mill's "direct-style" applicative syntax is inspired by my old Scala.Rx project. While there are differences (Mill captures the dependency graph lexically using Macros, Scala.Rx captures it at runtime), they are pretty similar.
The end-goal is the same: to write code in a "direct style" and have it automatically "lifted" into a dependency graph, which you can introspect and use for incremental updates at runtime.
Scala.Rx is itself build upon the 2010 paper Deprecating the Observer Pattern.
Mill looks a lot like CBT. The inheritance based model for customizing Module
s/ScalaModule
s comes straight from there, as does the "command line path matches Scala selector path" idea. Most other things are different though: the reified dependency graph, the execution model, the caching module all follow Bazel more than they do CBT
This documentation was build from mill master branch.
About Mill: Mill is an Open Source Project created by Li Haoyi. It is actively maintained and the git repository has more that 100 individual contributors around the world.
About Mills Creator: Li Haoyi is a software engineer, an early contributor to Scala.js, and the author of many open-source Scala tools such as Mill, the Ammonite REPL and FastParse. If you've enjoyed using Mill, or enjoyed using Haoyi's other open source libraries, please chip in (or get your Company to chip in!) via Patreon so he can continue his open-source work.