Developer Guide¶
This guide is intended for people who want to work on Spack itself. If you just want to develop packages, see the Packaging Guide.
It is assumed that you’ve read the Basic Usage and Packaging Guide sections, and that you’re familiar with the concepts discussed there. If you’re not, we recommend reading those first.
Overview¶
Spack is designed with three separate roles in mind:
- Users, who need to install software without knowing all the details about how it is built.
- Packagers who know how a particular software package is built and encode this information in package files.
- Developers who work on Spack, add new features, and try to make the jobs of packagers and users easier.
Users could be end users installing software in their home directory, or administrators installing software to a shared directory on a shared machine. Packagers could be administrators who want to automate software builds, or application developers who want to make their software more accessible to users.
As you might expect, there are many types of users with different
levels of sophistication, and Spack is designed to accommodate both
simple and complex use cases for packages. A user who only knows that
he needs a certain package should be able to type something simple,
like spack install <package name>, and get the package that he
wants. If a user wants to ask for a specific version, use particular
compilers, or build several versions with different configurations,
then that should be possible with a minimal amount of additional
specification.
This gets us to the two key concepts in Spack’s software design:
- Specs: expressions for describing builds of software, and
- Packages: Python modules that build software according to a spec.
A package is a template for building particular software, and a spec as a descriptor for one or more instances of that template. Users express the configuration they want using a spec, and a package turns the spec into a complete build.
The obvious difficulty with this design is that users under-specify what they want. To build a software package, the package object needs a complete specification. In Spack, if a spec describes only one instance of a package, then we say it is concrete. If a spec could describes many instances, (i.e. it is under-specified in one way or another), then we say it is abstract.
Spack’s job is to take an abstract spec from the user, find a concrete spec that satisfies the constraints, and hand the task of building the software off to the package object. The rest of this document describes all the pieces that come together to make that happen.
Directory Structure¶
So that you can familiarize yourself with the project, we’ll start with a high level view of Spack’s directory structure:
spack/ <- installation root
bin/
spack <- main spack executable
etc/
spack/ <- Spack config files.
Can be overridden by files in ~/.spack.
var/
spack/ <- build & stage directories
repos/ <- contains package repositories
builtin/ <- pkg repository that comes with Spack
repo.yaml <- descriptor for the builtin repository
packages/ <- directories under here contain packages
cache/ <- saves resources downloaded during installs
opt/
spack/ <- packages are installed here
lib/
spack/
docs/ <- source for this documentation
env/ <- compiler wrappers for build environment
external/ <- external libs included in Spack distro
llnl/ <- some general-use libraries
spack/ <- spack module; contains Python code
cmd/ <- each file in here is a spack subcommand
compilers/ <- compiler description files
test/ <- unit test modules
util/ <- common code
Spack is designed so that it could live within a standard UNIX
directory hierarchy, so lib,
var, and opt all contain a spack subdirectory in case
Spack is installed alongside other software. Most of the interesting
parts of Spack live in lib/spack.
Spack has one directory layout and there is no install process.
Most Python programs don’t look like this (they use distutils, setup.py,
etc.) but we wanted to make Spack very easy to use. The simple layout
spares users from the need to install Spack into a Python environment.
Many users don’t have write access to a Python installation, and installing
an entire new instance of Python to bootstrap Spack would be very complicated.
Users should not have to install install a big, complicated package to
use the thing that’s supposed to spare them from the details of big,
complicated packages. The end result is that Spack works out of the
box: clone it and add bin to your PATH and you’re ready to go.
Code Structure¶
This section gives an overview of the various Python modules in Spack, grouped by functionality.
Build environment¶
spack.stage- Handles creating temporary directories for builds.
spack.compilation- This contains utility functions used by the compiler wrapper script,
cc. spack.directory_layout- Classes that control the way an installation directory is laid out.
Create more implementations of this to change the hierarchy and
naming scheme in
$spack_prefix/opt
Spack Subcommands¶
spack.cmd- Each module in this package implements a Spack subcommand. See writing commands for details.
Unit tests¶
spack.test- Implements Spack’s test suite. Add a module and put its name in
the test suite in
__init__.pyto add more unit tests. spack.test.mock_packages- This is a fake package hierarchy used to mock up packages for Spack’s test suite.
Other Modules¶
spack.url- URL parsing, for deducing names and versions of packages from tarball URLs.
spack.errorSpackError, the base class for Spack’s exception hierarchy.llnl.util.tty- Basic output functions for all of the messages Spack writes to the terminal.
llnl.util.tty.color- Implements a color formatting syntax used by
spack.tty. llnl.util- In this package are a number of utility modules for the rest of Spack.
Spec objects¶
Package objects¶
Most spack commands look something like this:
- Parse an abstract spec (or specs) from the command line,
- Normalize the spec based on information in package files,
- Concretize the spec according to some customizable policies,
- Instantiate a package based on the spec, and
- Call methods (e.g.,
install()) on the package object.
The information in Package files is used at all stages in this process.
Conceptually, packages are overloaded. They contain:
Stage objects¶
Writing commands¶
Unit tests¶
Unit testing¶
Profiling¶
Spack has some limited built-in support for profiling, and can report
statistics using standard Python timing tools. To use this feature,
supply --profile to Spack on the command line, before any subcommands.
spack --profile¶
spack --profile output looks like this:
$ spack --profile graph dyninst
==> Error: No available compiler version matches 'None' on operating_system ubuntu14for target x86_64
...
The bottom of the output shows the top most time consuming functions, slowest on top. The profiling support is from Python’s built-in tool, cProfile.