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
analyzers/ <- modules to run analysis on installed packages
build_systems/ <- modules for different build systems
cmd/ <- each file in here is a spack subcommand
compilers/ <- compiler description files
container/ <- module for spack containerize
hooks/ <- hook modules to run at different points
modules/ <- modules for lmod, tcl, etc.
operating_systems/ <- operating system modules
platforms/ <- different spack platforms
reporters/ <- reporters like cdash, junit
schema/ <- schemas to validate data structures
solver/ <- the spack solver
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 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.build_environment
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__.py
to add more unit tests.
Research and Monitoring Modules¶
spack.monitor
Contains
SpackMonitorClient
. This is accessed from thespack install
andspack analyze
commands to send build and package metadata up to a Spack Monitor server.spack.analyzers
A module folder with a
AnalyzerBase
that provides base functions to run, save, and (optionally) upload analysis results to a Spack Monitor server.
Other Modules¶
spack.url
URL parsing, for deducing names and versions of packages from tarball URLs.
spack.error
SpackError
, 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 analyzers¶
To write an analyzer, you should add a new python file to the
analyzers module directory at lib/spack/spack/analyzers
.
Your analyzer should be a subclass of the AnalyzerBase
. For example, if you want
to add an analyzer class Myanalyzer
you would write to
spack/analyzers/myanalyzer.py
and import and
use the base as follows:
from .analyzer_base import AnalyzerBase
class Myanalyzer(AnalyzerBase):
Note that the class name is your module file name, all lowercase except for the first capital letter. You can look at other analyzers in that analyzer directory for examples. The guide here will tell you about the basic functions needed.
Analyzer Output Directory¶
By default, when you run spack analyze run
an analyzer output directory will
be created in your spack user directory in your $HOME
. The reason we output here
is because the install directory might not always be writable.
~/.spack/
analyzers
Result files will be written here, organized in subfolders in the same structure as the package, with each analyzer owning it’s own subfolder. for example:
$ tree ~/.spack/analyzers/
/home/spackuser/.spack/analyzers/
└── linux-ubuntu20.04-skylake
└── gcc-9.3.0
└── zlib-1.2.11-sl7m27mzkbejtkrajigj3a3m37ygv4u2
├── environment_variables
│ └── spack-analyzer-environment-variables.json
├── install_files
│ └── spack-analyzer-install-files.json
└── libabigail
└── lib
└── spack-analyzer-libabigail-libz.so.1.2.11.xml
Notice that for the libabigail analyzer, since results are generated per object, we honor the object’s folder in case there are equivalently named files in different folders. The result files are typically written as json so they can be easily read and uploaded in a future interaction with a monitor.
Analyzer Metadata¶
Your analyzer is required to have the class attributes name
, outfile
,
and description
. These are printed to the user with they use the subcommand
spack analyze list-analyzers
. Here is an example.
As we mentioned above, note that this analyzer would live in a module named
libabigail.py
in the analyzers folder so that the class can be discovered.
class Libabigail(AnalyzerBase):
name = "libabigail"
outfile = "spack-analyzer-libabigail.json"
description = "Application Binary Interface (ABI) features for objects"
This means that the name and output file should be unique for your analyzer. Note that “all” cannot be the name of an analyzer, as this key is used to indicate that the user wants to run all analyzers.
An analyzer run Function¶
The core of an analyzer is its run()
function, which should accept no
arguments. You can assume your analyzer has the package spec of interest at self.spec
and it’s up to the run function to generate whatever analysis data you need,
and then return the object with a key as the analyzer name. The result data
should be a list of objects, each with a name, analyzer_name
, install_file
,
and one of value
or binary_value
. The install file should be for a relative
path, and not the absolute path. For example, let’s say we extract a metric called
metric
for bin/wget
using our analyzer thebest-analyzer
.
We might have data that looks like this:
result = {"name": "metric", "analyzer_name": "thebest-analyzer", "value": "1", "install_file": "bin/wget"}
We’d then return it as follows - note that they key is the analyzer name at self.name
.
return {self.name: result}
This will save the complete result to the analyzer metadata folder, as described previously. If you want support for adding a different kind of metadata (e.g., not associated with an install file) then the monitor server would need to be updated to support this first.
An analyzer init Function¶
If you don’t need any extra dependencies or checks, you can skip defining an analyzer init function, as the base class will handle it. Typically, it will accept a spec, and an optional output directory (if the user does not want the default metadata folder for analyzer results). The analyzer init function should call it’s parent init, and then do any extra checks or validation that are required to work. For example:
def __init__(self, spec, dirname=None):
super(Myanalyzer, self).__init__(spec, dirname)
# install extra dependencies, do extra preparation and checks here
At the end of the init, you will have available to you:
self.spec: the spec object
self.dirname: an optional directory name the user as provided at init to save
self.output_dir: the analyzer metadata directory, where we save by default
self.meta_dir: the path to the package metadata directory (.spack) if you need it
And can proceed to write your analyzer.
Saving Analyzer Results¶
The analyzer will have save_result
called, with the result object generated
to save it to the filesystem, and if the user has added the --monitor
flag
to upload it to a monitor server. If your result follows an accepted result
format and you don’t need to parse it further, you don’t need to add this
function to your class. However, if your result data is large or otherwise
needs additional parsing, you can define it. If you define the function, it
is useful to know about the output_dir
property, which you can join
with your output file relative path of choice:
outfile = os.path.join(self.output_dir, "my-output-file.txt")
The directory will be provided by the output_dir
property but it won’t exist,
so you should create it:
If you are generating results that match to specific files in the package
install directory, you should try to maintain those paths in the case that
there are equivalently named files in different directories that would
overwrite one another. As an example of an analyzer with a custom save,
the Libabigail analyzer saves *.xml
files to the analyzer metadata
folder in run()
, as they are either binaries, or as xml (text) would
usually be too big to pass in one request. For this reason, the files
are saved during run()
and the filenames added to the result object,
and then when the result object is passed back into save_result()
,
we skip saving to the filesystem, and instead read the file and send
each one (separately) to the monitor:
def save_result(self, result, monitor=None, overwrite=False):
"""ABI results are saved to individual files, so each one needs to be
read and uploaded. Result here should be the lookup generated in run(),
the key is the analyzer name, and each value is the result file.
We currently upload the entire xml as text because libabigail can't
easily read gzipped xml, but this will be updated when it can.
"""
if not monitor:
return
name = self.spec.package.name
for obj, filename in result.get(self.name, {}).items():
# Don't include the prefix
rel_path = obj.replace(self.spec.prefix + os.path.sep, "")
# We've already saved the results to file during run
content = spack.monitor.read_file(filename)
# A result needs an analyzer, value or binary_value, and name
data = {"value": content, "install_file": rel_path, "name": "abidw-xml"}
tty.info("Sending result for %s %s to monitor." % (name, rel_path))
monitor.send_analyze_metadata(self.spec.package, {"libabigail": [data]})
Notice that this function, if you define it, requires a result object (generated by
run()
, a monitor (if you want to send), and a boolean overwrite
to be used
to check if a result exists first, and not write to it if the result exists and
overwrite is False. Also notice that since we already saved these files to the analyzer metadata folder, we return early if a monitor isn’t defined, because this function serves to send results to the monitor. If you haven’t saved anything to the analyzer metadata folder
yet, you might want to do that here. You should also use tty.info
to give
the user a message of “Writing result to $DIRNAME.”
Writing commands¶
Adding a new command to Spack is easy. Simply add a <name>.py
file to
lib/spack/spack/cmd/
, where <name>
is the name of the subcommand.
At the bare minimum, two functions are required in this file:
setup_parser()
¶
Unless your command doesn’t accept any arguments, a setup_parser()
function is required to define what arguments and flags your command takes.
See the Argparse documentation
for more details on how to add arguments.
Some commands have a set of subcommands, like spack compiler find
or
spack module lmod refresh
. You can add subparsers to your parser to handle
this. Check out spack edit --command compiler
for an example of this.
A lot of commands take the same arguments and flags. These arguments should
be defined in lib/spack/spack/cmd/common/arguments.py
so that they don’t
need to be redefined in multiple commands.
<name>()
¶
In order to run your command, Spack searches for a function with the same
name as your command in <name>.py
. This is the main method for your
command, and can call other helper methods to handle common tasks.
Remember, before adding a new command, think to yourself whether or not this new command is actually necessary. Sometimes, the functionality you desire can be added to an existing command. Also remember to add unit tests for your command. If it isn’t used very frequently, changes to the rest of Spack can cause your command to break without sufficient unit tests to prevent this from happening.
Whenever you add/remove/rename a command or flags for an existing command, make sure to update Spack’s Bash tab completion script.
Writing Hooks¶
A hook is a callback that makes it easy to design functions that run for different events. We do this by way of defining hook types, and then inserting them at different places in the spack code base. Whenever a hook type triggers by way of a function call, we find all the hooks of that type, and run them.
Spack defines hooks by way of a module at lib/spack/spack/hooks
where we can define
types of hooks in the __init__.py
, and then python files in that folder
can use hook functions. The files are automatically parsed, so if you write
a new file for some integration (e.g., lib/spack/spack/hooks/myintegration.py
you can then write hook functions in that file that will be automatically detected,
and run whenever your hook is called. This section will cover the basic kind
of hooks, and how to write them.
Types of Hooks¶
The following hooks are currently implemented to make it easy for you, the developer, to add hooks at different stages of a spack install or similar. If there is a hook that you would like and is missing, you can propose to add a new one.
pre_install(spec)
¶
A pre_install
hook is run within an install subprocess, directly before
the install starts. It expects a single argument of a spec, and is run in
a multiprocessing subprocess. Note that if you see pre_install
functions associated with packages these are not hooks
as we have defined them here, but rather callback functions associated with
a package install.
post_install(spec)
¶
A post_install
hook is run within an install subprocess, directly after
the install finishes, but before the build stage is removed. If you
write one of these hooks, you should expect it to accept a spec as the only
argument. This is run in a multiprocessing subprocess. This post_install
is
also seen in packages, but in this context not related to the hooks described
here.
on_install_start(spec)
¶
This hook is run at the beginning of lib/spack/spack/installer.py
,
in the install function of a PackageInstaller
,
and importantly is not part of a build process, but before it. This is when
we have just newly grabbed the task, and are preparing to install. If you
write a hook of this type, you should provide the spec to it.
def on_install_start(spec):
"""On start of an install, we want to...
"""
print('on_install_start')
on_install_success(spec)
¶
This hook is run on a successful install, and is also run inside the build
process, akin to post_install
. The main difference is that this hook
is run outside of the context of the stage directory, meaning after the
build stage has been removed and the user is alerted that the install was
successful. If you need to write a hook that is run on success of a particular
phase, you should use on_phase_success
.
on_install_failure(spec)
¶
This hook is run given an install failure that happens outside of the build
subprocess, but somewhere in installer.py
when something else goes wrong.
If you need to write a hook that is relevant to a failure within a build
process, you would want to instead use on_phase_failure
.
on_phase_success(pkg, phase_name, log_file)
¶
This hook is run within the install subprocess, and specifically when a phase successfully finishes. Since we are interested in the package, the name of the phase, and any output from it, we require:
pkg: the package variable, which also has the attached spec at
pkg.spec
phase_name: the name of the phase that was successful (e.g., configure)
log_file: the path to the file with output, in case you need to inspect or otherwise interact with it.
on_phase_error(pkg, phase_name, log_file)
¶
In the case of an error during a phase, we might want to trigger some event
with a hook, and this is the purpose of this particular hook. Akin to
on_phase_success
we require the same variables - the package that failed,
the name of the phase, and the log file where we might find errors.
on_analyzer_save(pkg, result)
¶
After an analyzer has saved some result for a package, this hook is called, and it provides the package that we just ran the analysis for, along with the loaded result. Typically, a result is structured to have the name of the analyzer as key, and the result object that is defined in detail in An analyzer run Function.
def on_analyzer_save(pkg, result):
"""given a package and a result...
"""
print('Do something extra with a package analysis result here')
Adding a New Hook Type¶
Adding a new hook type is very simple! In lib/spack/spack/hooks/__init__.py
you can simply create a new HookRunner
that is named to match your new hook.
For example, let’s say you want to add a new hook called post_log_write
to trigger after anything is written to a logger. You would add it as follows:
# pre/post install and run by the install subprocess
pre_install = HookRunner('pre_install')
post_install = HookRunner('post_install')
# hooks related to logging
post_log_write = HookRunner('post_log_write') # <- here is my new hook!
You then need to decide what arguments my hook would expect. Since this is
related to logging, let’s say that you want a message and level. That means
that when you add a python file to the lib/spack/spack/hooks
folder with one or more callbacks intended to be triggered by this hook. You might
use my new hook as follows:
def post_log_write(message, level):
"""Do something custom with the messsage and level every time we write
to the log
"""
print('running post_log_write!')
To use the hook, we would call it as follows somewhere in the logic to do logging. In this example, we use it outside of a logger that is already defined:
import spack.hooks
# We do something here to generate a logger and message
spack.hooks.post_log_write(message, logger.level)
This is not to say that this would be the best way to implement an integration with the logger (you’d probably want to write a custom logger, or you could have the hook defined within the logger) but serves as an example of writing a hook.
Unit tests¶
Unit testing¶
Developer environment¶
Warning
This is an experimental feature. It is expected to change and you should not use it in a production environment.
When installing a package, we currently have support to export environment variables to specify adding debug flags to the build. By default, a package install will build without any debug flag. However, if you want to add them, you can export:
export SPACK_ADD_DEBUG_FLAGS=true
spack install zlib
If you want to add custom flags, you should export an additional variable:
export SPACK_ADD_DEBUG_FLAGS=true
export SPACK_DEBUG_FLAGS="-g"
spack install zlib
These environment variables will eventually be integrated into spack so they are set from the command line.
Developer commands¶
spack doc
¶
spack style
¶
spack style exists to help the developer user to check imports and style with mypy, flake8, isort, and (soon) black. To run all style checks, simply do:
$ spack style
To run automatic fixes for isort you can do:
$ spack style --fix
You do not need any of these Python packages installed on your system for the checks to work! Spack will bootstrap install them from packages for your use.
spack unit-test
¶
See the contributor guide section on
spack unit-test
.
spack python
¶
spack python
is a command that lets you import and debug things as if
you were in a Spack interactive shell. Without any arguments, it is similar
to a normal interactive Python shell, except you can import spack and any
other Spack modules:
$ spack python
Spack version 0.10.0
Python 2.7.13, Linux x86_64
>>> from spack.version import Version
>>> a = Version('1.2.3')
>>> b = Version('1_2_3')
>>> a == b
True
>>> c = Version('1.2.3b')
>>> c > a
True
>>>
If you prefer using an IPython interpreter, given that IPython is installed
you can specify the interpreter with -i
:
$ spack python -i ipython
Python 3.8.3 (default, May 19 2020, 18:47:26)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.17.0 -- An enhanced Interactive Python. Type '?' for help.
Spack version 0.16.0
Python 3.8.3, Linux x86_64
In [1]:
With either interpreter you can run a single command:
$ spack python -c 'import distro; distro.linux_distribution()'
('Ubuntu', '18.04', 'Bionic Beaver')
$ spack python -i ipython -c 'import distro; distro.linux_distribution()'
Out[1]: ('Ubuntu', '18.04', 'Bionic Beaver')
or a file:
$ spack python ~/test_fetching.py
$ spack python -i ipython ~/test_fetching.py
just like you would with the normal python
command.
spack blame
¶
Spack blame is a way to quickly see contributors to packages or files in the spack repository. You should provide a target package name or file name to the command. Here is an example asking to see contributions for the package “python”:
$ spack blame python
LAST_COMMIT LINES % AUTHOR EMAIL
2 weeks ago 3 0.3 Mickey Mouse <cheddar@gmouse.org>
a month ago 927 99.7 Minnie Mouse <swiss@mouse.org>
2 weeks ago 930 100.0
By default, you will get a table view (shown above) sorted by date of contribution,
with the most recent contribution at the top. If you want to sort instead
by percentage of code contribution, then add -p
:
$ spack blame -p python
And to see the git blame view, add -g
instead:
$ spack blame -g python
Finally, to get a json export of the data, add --json
:
$ spack blame --json python
spack url
¶
A package containing a single URL can be used to download several different
versions of the package. If you’ve ever wondered how this works, all of the
magic is in spack.url
. This module contains methods for extracting
the name and version of a package from its URL. The name is used by
spack create
to guess the name of the package. By determining the version
from the URL, Spack can replace it with other versions to determine where to
download them from.
The regular expressions in parse_name_offset
and parse_version_offset
are used to extract the name and version, but they aren’t perfect. In order
to debug Spack’s URL parsing support, the spack url
command can be used.
spack url parse
¶
If you need to debug a single URL, you can use the following command:
$ spack url parse http://cache.ruby-lang.org/pub/ruby/2.2/ruby-2.2.0.tar.gz
==> Parsing URL: http://cache.ruby-lang.org/pub/ruby/2.2/ruby-2.2.0.tar.gz
==> Matched version regex 0: r'^[a-zA-Z+._-]+[._-]v?(\\d[\\d._-]*)$'
==> Matched name regex 10: r'^([A-Za-z\\d+\\._-]+)$'
==> Detected:
http://cache.ruby-lang.org/pub/ruby/2.2/ruby-2.2.0.tar.gz
---- ~~~~~
name: ruby
version: 2.2.0
==> Substituting version 9.9.9b:
http://cache.ruby-lang.org/pub/ruby/2.2/ruby-9.9.9b.tar.gz
---- ~~~~~~
You’ll notice that the name and version of this URL are correctly detected,
and you can even see which regular expressions it was matched to. However,
you’ll notice that when it substitutes the version number in, it doesn’t
replace the 2.2
with 9.9
where we would expect 9.9.9b
to live.
This particular package may require a list_url
or url_for_version
function.
This command also accepts a --spider
flag. If provided, Spack searches
for other versions of the package and prints the matching URLs.
spack url list
¶
This command lists every URL in every package in Spack. If given the
--color
and --extrapolation
flags, it also colors the part of
the string that it detected to be the name and version. The
--incorrect-name
and --incorrect-version
flags can be used to
print URLs that were not being parsed correctly.
spack url summary
¶
This command attempts to parse every URL for every package in Spack and prints a summary of how many of them are being correctly parsed. It also prints a histogram showing which regular expressions are being matched and how frequently:
$ spack url summary
==> Generating a summary of URL parsing in Spack...
Total URLs found: 5619
Names correctly parsed: 5003/5619 (89.04%)
Versions correctly parsed: 5141/5619 (91.49%)
==> Statistics on name regular expressions:
Index Right Wrong Total Regular Expression
0 1456 248 1704 r'github\\.com/[^/]+/([^/]+)'
1 6 0 6 r'gitlab[^/]+/api/v4/projects/[^/]+%2F([^/]+)'
2 28 7 35 r'gitlab[^/]+/(?!api/v4/projects)[^/]+/([^/]+)'
3 17 6 23 r'bitbucket\\.org/[^/]+/([^/]+)'
4 8 0 8 r'pypi\\.(?:python\\.org|io)/packages/source/[A-Za-z\\d]/([^/]+)'
6 11 2 13 r'\\?f=([A-Za-z\\d+-]+)$'
7 16 0 16 r'\\?package=([A-Za-z\\d+-]+)'
9 2 2 4 r'([^/]+)/download.php$'
10 3459 341 3800 r'^([A-Za-z\\d+\\._-]+)$'
==> Statistics on version regular expressions:
Index Right Wrong Total Regular Expression
0 3548 109 3657 r'^[a-zA-Z+._-]+[._-]v?(\\d[\\d._-]*)$'
1 1135 18 1153 r'^v?(\\d[\\d._-]*)$'
2 11 21 32 r'^[a-zA-Z+]*(\\d[\\da-zA-Z]*)$'
3 6 18 24 r'^[a-zA-Z+-]*(\\d[\\da-zA-Z-]*)$'
4 4 112 116 r'^[a-zA-Z+_]*(\\d[\\da-zA-Z_]*)$'
5 57 23 80 r'^[a-zA-Z+.]*(\\d[\\da-zA-Z.]*)$'
6 217 6 223 r'^[a-zA-Z\\d+-]+-v?(\\d[\\da-zA-Z.]*)$'
7 1 0 1 r'^[a-zA-Z\\d+-]+-v?(\\d[\\da-zA-Z_]*)$'
8 31 1 32 r'^[a-zA-Z\\d+_]+_v?(\\d[\\da-zA-Z.]*)$'
9 0 1 1 r'^[a-zA-Z\\d+_]+\\.v?(\\d[\\da-zA-Z.]*)$'
11 27 36 63 r'^(?:[a-zA-Z\\d+-]+-)?v?(\\d[\\da-zA-Z.-]*)$'
12 3 0 3 r'^[a-zA-Z+]+v?(\\d[\\da-zA-Z.-]*)$'
13 5 2 7 r'^[a-zA-Z\\d+_]+-v?(\\d[\\da-zA-Z.]*)$'
14 28 4 32 r'^[a-zA-Z\\d+.]+_v?(\\d[\\da-zA-Z.-]*)$'
15 1 0 1 r'^[a-zA-Z\\d+-]+-v?(\\d[\\da-zA-Z._]*)$'
16 2 1 3 r'^[a-zA-Z\\d+._]+-v?(\\d[\\da-zA-Z.]*)$'
17 5 0 5 r'^[a-zA-Z+-]+(\\d[\\da-zA-Z._]*)$'
18 1 2 3 r'^[a-zA-Z\\d+_-]+-v?(\\d[\\da-zA-Z.]*)$'
19 0 1 1 r'bzr(\\d[\\da-zA-Z._-]*)$'
20 10 0 10 r'[?&](?:sha|ref|version)=[a-zA-Z\\d+-]*[_-]?v?(\\d[\\da-zA-Z._-]*)$'
21 29 0 29 r'[?&](?:filename|f|get)=[a-zA-Z\\d+-]+[_-]v?(\\d[\\da-zA-Z.]*)'
22 8 1 9 r'github\\.com/[^/]+/[^/]+/releases/download/[a-zA-Z+._-]*v?(\\d[\\da-zA-Z._-]*)/'
23 12 59 71 r'(\\d[\\da-zA-Z._-]*)/[^/]+$'
This command is essential for anyone adding or changing the regular expressions that parse names and versions. By running this command before and after the change, you can make sure that your regular expression fixes more packages than it breaks.
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 hdf5
==> Bootstrapping clingo from pre-built binaries
o hdf5
|\
| |\
| | |\
| | o | openmpi
| |/| |
|/|/| |
| | |\ \
| | | |\ \
| | | | |\ \
| | | | | |\ \
| | | o | | | | openssh
| |_|/| | | | |
|/| | | | | | |
| | | |\ \ \ \ \
| | | | |\ \ \ \ \
| | | | | | | o | | libevent
| | | | |_|_|/ / /
| | | |/| | | | |
| | | | | | | | o cmake
| | | | |_|_|_|/|
| | | |/| |_|_|/
| | | | |/| | |
| | | o | | | | openssl
...
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.
Releases¶
This section documents Spack’s release process. It is intended for project maintainers, as the tasks described here require maintainer privileges on the Spack repository. For others, we hope this section at least provides some insight into how the Spack project works.
Release branches¶
There are currently two types of Spack releases: major releases (0.13.0
, 0.14.0
, etc.) and point releases (0.13.1
, 0.13.2
, 0.13.3
, etc.). Here is a
diagram of how Spack release branches work:
o branch: develop (latest version)
|
o merge v0.14.1 into develop
|\
| o branch: releases/v0.14, tag: v0.14.1
o | merge v0.14.0 into develop
|\|
| o tag: v0.14.0
|/
o merge v0.13.2 into develop
|\
| o branch: releases/v0.13, tag: v0.13.2
o | merge v0.13.1 into develop
|\|
| o tag: v0.13.1
o | merge v0.13.0 into develop
|\|
| o tag: v0.13.0
o |
| o
|/
o
The develop
branch has the latest contributions, and nearly all pull
requests target develop
.
Each Spack release series also has a corresponding branch, e.g.
releases/v0.14
has 0.14.x
versions of Spack, and
releases/v0.13
has 0.13.x
versions. A major release is the first
tagged version on a release branch. Minor releases are back-ported from
develop onto release branches. This is typically done by cherry-picking
bugfix commits off of develop
.
To avoid version churn for users of a release series, minor releases should not make changes that would change the concretization of packages. They should generally only contain fixes to the Spack core. However, sometimes priorities are such that new functionality needs to be added to a minor release.
Both major and minor releases are tagged. After each release, we merge
the release branch back into develop
so that the version bump and any
other release-specific changes are visible in the mainline. As a
convenience, we also tag the latest release as releases/latest
,
so that users can easily check it out to get the latest
stable version. See Updating releases/latest and develop for more details.
Scheduling work for releases¶
We schedule work for releases by creating GitHub projects. At any time, there may be several open release projects. For example, below are two releases (from some past version of the page linked above):
This image shows one release in progress for 0.15.1
and another for
0.16.0
. Each of these releases has a project board containing issues
and pull requests. GitHub shows a status bar with completed work in
green, work in progress in purple, and work not started yet in gray, so
it’s fairly easy to see progress.
Spack’s project boards are not firm commitments so we move work between releases frequently. If we need to make a release and some tasks are not yet done, we will simply move them to the next minor or major release, rather than delaying the release to complete them.
For more on using GitHub project boards, see GitHub’s documentation.
Making major releases¶
Assuming a project board has already been created and all required work completed, the steps to make the major release are:
Create two new project boards:
One for the next major release
One for the next point release
Move any optional tasks that are not done to one of the new project boards.
In general, small bugfixes should go to the next point release. Major features, refactors, and changes that could affect concretization should go in the next major release.
Create a branch for the release, based on
develop
:$ git checkout -b releases/v0.15 develop
For a version
vX.Y.Z
, the branch’s name should bereleases/vX.Y
. That is, you should create areleases/vX.Y
branch if you are preparing theX.Y.0
release.Bump the version in
lib/spack/spack/__init__.py
.Update
CHANGELOG.md
with major highlights in bullet form.Use proper markdown formatting, like this example from 0.15.0.
Push the release branch to GitHub.
Make sure CI passes on the release branch, including:
Regular unit tests
Build tests
The E4S pipeline at gitlab.spack.io
If CI is not passing, submit pull requests to
develop
as normal and keep rebasing the release branch ondevelop
until CI passes.Follow the steps in Publishing a release on GitHub.
Follow the steps in Updating releases/latest and develop.
Follow the steps in Announcing a release.
Making point releases¶
Assuming a project board has already been created and all required work completed, the steps to make the point release are:
Create a new project board for the next point release.
Move any optional tasks that are not done to the next project board.
Check out the release branch (it should already exist).
For the
X.Y.Z
release, the release branch is calledreleases/vX.Y
. Forv0.15.1
, you would check outreleases/v0.15
:$ git checkout releases/v0.15
Cherry-pick each pull request in the
Done
column of the release project board onto the release branch.This is usually fairly simple since we squash the commits from the vast majority of pull requests. That means there is only one commit per pull request to cherry-pick. For example, this pull request has three commits, but they were squashed into a single commit on merge. You can see the commit that was created here:
You can easily cherry pick it like this (assuming you already have the release branch checked out):
$ git cherry-pick 7e46da7
For pull requests that were rebased (or not squashed), you’ll need to cherry-pick each associated commit individually.
Warning
It is important to cherry-pick commits in the order they happened, otherwise you can get conflicts while cherry-picking. When cherry-picking onto a point release, look at the merge date, not the number of the pull request or the date it was opened.
Sometimes you may still get merge conflicts even if you have cherry-picked all the commits in order. This generally means there is some other intervening pull request that the one you’re trying to pick depends on. In these cases, you’ll need to make a judgment call regarding those pull requests. Consider the number of affected files and or the resulting differences.
If the dependency changes are small, you might just cherry-pick it, too. If you do this, add the task to the release board.
If the changes are large, then you may decide that this fix is not worth including in a point release, in which case you should remove the task from the release project.
You can always decide to manually back-port the fix to the release branch if neither of the above options makes sense, but this can require a lot of work. It’s seldom the right choice.
Bump the version in
lib/spack/spack/__init__.py
.Update
CHANGELOG.md
with a list of the changes.This is typically a summary of the commits you cherry-picked onto the release branch. See the changelog from 0.14.1.
Push the release branch to GitHub.
Make sure CI passes on the release branch, including:
Regular unit tests
Build tests
The E4S pipeline at gitlab.spack.io
If CI does not pass, you’ll need to figure out why, and make changes to the release branch until it does. You can make more commits, modify or remove cherry-picked commits, or cherry-pick more from
develop
to make this happen.Follow the steps in Publishing a release on GitHub.
Follow the steps in Updating releases/latest and develop.
Follow the steps in Announcing a release.
Publishing a release on GitHub¶
Create the release in GitHub.
Go to github.com/spack/spack/releases and click
Draft a new release
.Set
Tag version
to the name of the tag that will be created.The name should start with
v
and contain all three parts of the version (e.g.v0.15.0
orv0.15.1
).Set
Target
to thereleases/vX.Y
branch (e.g.,releases/v0.15
).Set
Release title
tovX.Y.Z
to match the tag (e.g.,v0.15.1
).Paste the latest release markdown from your
CHANGELOG.md
file as the text.Save the draft so you can keep coming back to it as you prepare the release.
When you are ready to finalize the release, click
Publish release
.Immediately after publishing, go back to github.com/spack/spack/releases and download the auto-generated
.tar.gz
file for the release. It’s theSource code (tar.gz)
link.Click
Edit
on the release you just made and attach the downloaded release tarball as a binary. This does two things:Makes sure that the hash of our releases does not change over time.
GitHub sometimes annoyingly changes the way they generate tarballs that can result in the hashes changing if you rely on the auto-generated tarball links.
Gets download counts on releases visible through the GitHub API.
GitHub tracks downloads of artifacts, but not the source links. See the releases page and search for
download_count
to see this.
Go to readthedocs.org and activate the release tag.
This builds the documentation and makes the released version selectable in the versions menu.
Updating releases/latest and develop¶
If the new release is the highest Spack release yet, you should
also tag it as releases/latest
. For example, suppose the highest
release is currently 0.15.3
:
If you are releasing
0.15.4
or0.16.0
, then you should tag it withreleases/latest
, as these are higher than0.15.3
.If you are making a new release of an older major version of Spack, e.g.
0.14.4
, then you should not tag it asreleases/latest
(as there are newer major versions).
To tag releases/latest
, do this:
$ git checkout releases/vX.Y # vX.Y is the new release's branch
$ git tag --force releases/latest
$ git push --force --tags
The --force
argument to git tag
makes git
overwrite the existing
releases/latest
tag with the new one.
We also merge each release that we tag as releases/latest
into develop
.
Make sure to do this with a merge commit:
$ git checkout develop
$ git merge --no-ff -s ours vX.Y.Z # vX.Y.Z is the new release's tag
$ git push
We merge back to develop
because it:
updates the version and
CHANGELOG.md
ondevelop
; andensures that your release tag is reachable from the head of
develop
.
We must use a real merge commit (via the --no-ff
option) to
ensure that the release tag is reachable from the tip of develop
.
This is necessary for spack -V
to work properly – it uses git
describe --tags
to find the last reachable tag in the repository and
reports how far we are from it. For example:
$ spack -V
0.14.2-1486-b80d5e74e5
This says that we are at commit b80d5e74e5
, which is 1,486 commits
ahead of the 0.14.2
release.
We put this step last in the process because it’s best to do it only once the release is complete and tagged. If you do it before you’ve tagged the release and later decide you want to tag some later commit, you’ll need to merge again.
Announcing a release¶
We announce releases in all of the major Spack communication channels. Publishing the release takes care of GitHub. The remaining channels are Twitter, Slack, and the mailing list. Here are the steps:
Announce the release on Twitter.
Compose the tweet on the
@spackpm
account per thespack-twitter
slack channel.Be sure to include a link to the release’s page on GitHub.
You can base the tweet on this example.
Announce the release on Slack.
Compose a message in the
#general
Slack channel (spackpm.slack.com).Preface the message with
@channel
to notify even those people not currently logged in.Be sure to include a link to the tweet above.
The tweet will be shown inline so that you do not have to retype your release announcement.
Announce the release on the Spack mailing list.
Compose an email to the Spack mailing list.
Be sure to include a link to the release’s page on GitHub.
It is also helpful to include some information directly in the email.
You can base your announcement on this example email.
Once you’ve completed the above steps, congratulations, you’re done! You’ve finished making the release!