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:

  1. Users, who need to install software without knowing all the details about how it is built.

  2. Packagers who know how a particular software package is built and encode this information in package files.

  3. 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:

  1. Specs: expressions for describing builds of software, and

  2. 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 the spack install and spack 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:

  1. Parse an abstract spec (or specs) from the command line,

  2. Normalize the spec based on information in package files,

  3. Concretize the spec according to some customizable policies,

  4. Instantiate a package based on the spec, and

  5. 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:          5579
    Names correctly parsed:    4964/5579 (88.98%)
    Versions correctly parsed: 5105/5579 (91.50%)

==> Statistics on name regular expressions:

    Index   Right   Wrong   Total   Regular Expression
        0    1446     246    1692   r'github\\.com/[^/]+/([^/]+)'
        1       6       0       6   r'gitlab[^/]+/api/v4/projects/[^/]+%2F([^/]+)'
        2      28       7      35   r'gitlab[^/]+/(?!api/v4/projects)[^/]+/([^/]+)'
        3      16       6      22   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+-]+)$'
        7      14       0      14   r'\\?package=([A-Za-z\\d+-]+)'
        9       2       2       4   r'([^/]+)/download.php$'
       10    3433     342    3775   r'^([A-Za-z\\d+\\._-]+)$'

==> Statistics on version regular expressions:

    Index   Right   Wrong   Total   Regular Expression
        0    3528     104    3632   r'^[a-zA-Z+._-]+[._-]v?(\\d[\\d._-]*)$'
        1    1126      18    1144   r'^v?(\\d[\\d._-]*)$'
        2      11      21      32   r'^[a-zA-Z+]*(\\d[\\da-zA-Z]*)$'
        3       6      19      25   r'^[a-zA-Z+-]*(\\d[\\da-zA-Z-]*)$'
        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     214       6     220   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      30       1      31   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      26      36      62   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      27       0      27   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):

_images/projects.png

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:

  1. Create two new project boards:

    • One for the next major release

    • One for the next point release

  2. 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.

  3. 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 be releases/vX.Y. That is, you should create a releases/vX.Y branch if you are preparing the X.Y.0 release.

  4. Bump the version in lib/spack/spack/__init__.py.

    See this example from 0.13.0

  5. Update CHANGELOG.md with major highlights in bullet form.

    Use proper markdown formatting, like this example from 0.15.0.

  6. Push the release branch to GitHub.

  7. Make sure CI passes on the release branch, including:

    If CI is not passing, submit pull requests to develop as normal and keep rebasing the release branch on develop until CI passes.

  8. Follow the steps in Publishing a release on GitHub.

  9. Follow the steps in Updating releases/latest and develop.

  10. 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:

  1. Create a new project board for the next point release.

  2. Move any optional tasks that are not done to the next project board.

  3. Check out the release branch (it should already exist).

    For the X.Y.Z release, the release branch is called releases/vX.Y. For v0.15.1, you would check out releases/v0.15:

    $ git checkout releases/v0.15
    
  4. 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:

    _images/pr-commit.png

    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.

    1. If the dependency changes are small, you might just cherry-pick it, too. If you do this, add the task to the release board.

    2. 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.

    3. 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.

  5. Bump the version in lib/spack/spack/__init__.py.

    See this example from 0.14.1.

  6. 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.

  7. Push the release branch to GitHub.

  8. Make sure CI passes on the release branch, including:

    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.

  9. Follow the steps in Publishing a release on GitHub.

  10. Follow the steps in Updating releases/latest and develop.

  11. Follow the steps in Announcing a release.

Publishing a release on GitHub

  1. 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 or v0.15.1).

    • Set Target to the releases/vX.Y branch (e.g., releases/v0.15).

    • Set Release title to vX.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.

  2. When you are ready to finalize the release, click Publish release.

  3. Immediately after publishing, go back to github.com/spack/spack/releases and download the auto-generated .tar.gz file for the release. It’s the Source code (tar.gz) link.

  4. Click Edit on the release you just made and attach the downloaded release tarball as a binary. This does two things:

    1. 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.

    2. 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.

  5. 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 or 0.16.0, then you should tag it with releases/latest, as these are higher than 0.15.3.

  • 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 as releases/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 on develop; and

  • ensures 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:

  1. Announce the release on Twitter.

    • Compose the tweet on the @spackpm account per the spack-twitter slack channel.

    • Be sure to include a link to the release’s page on GitHub.

      You can base the tweet on this example.

  2. 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.

  3. 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!