pytest-workflow¶
Introduction¶
Writing workflows is hard. Testing if they are correct is even harder. Testing with bash scripts or other code has some flaws. Is this bug in the pipeline or in my test-framework? Pytest-workflow aims to make testing as simple as possible so you can focus on debugging your pipeline.
Installation¶
Pytest-workflow is tested on python 3.5, 3.6, 3.7 and 3.8. Python 2 is not supported.
In a virtual environment¶
- Create a new python3 virtual environment.
- Make sure your virtual environment is activated.
- Install using pip
pip install pytest-workflow
.
On Ubuntu or Debian¶
- This requires the
python3
andpython3-pip
packages to be installed. - Installing
- system-wide:
sudo python3 -m pip install pytest-workflow
. - for your user only (no sudo needed):
python3 -m pip install --user pytest-workflow
- system-wide:
pytest
can now be run withpython3 -m pytest
.
Note
Running plain pytest
on Ubuntu or Debian outside of a virtual
environment will not work with pytest-workflow
because this will start
the python2 version of pytest
. This is because python2 is the default
python on any distribution released before January 1st 2020.
Conda¶
Pytest-workflow is also available as a conda package on conda-forge. To install with conda:
- Set up conda to use the conda-forge channel
- If you want to use pytest-workflow together with bioconda you can follow the instructions here.
conda install pytest-workflow
.
Writing tests with pytest-workflow¶
Getting started¶
In order to write tests that are discoverable by the plugin you need to complete the following steps.
- Create a
tests
directory in the root of your repository. - Create your test yaml files in the
tests
directory. The files need to start withtest
and have a.yml
or.yaml
extension.
Below is an example of a YAML file that defines a test:
- name: Touch a file
command: touch test.file
files:
- path: test.file
This will run touch test.file
and check afterwards if a file with path:
test.file
is present. It will also check if the command
has exited
with exit code 0
, which is the only default test that is run. Testing
workflows that exit with another exit code is also possible.
Test options¶
- name: moo file # The name of the workflow (required)
command: bash moo_workflow.sh # The command to execute the workflow (required)
files: # A list of files to check (optional)
- path: "moo.txt" # File path. (Required for each file)
contains: # A list of strings that should be in the file (optional)
- "moo"
must_not_contain: # A list of strings that should NOT be in the file (optional)
- "Cock a doodle doo"
md5sum: e583af1f8b00b53cda87ae9ead880224 # Md5sum of the file (optional)
- name: simple echo # A second workflow. Notice the starting `-` which means
command: "echo moo" # that workflow items are in a list. You can add as much workflows as you want
files:
- path: "moo.txt"
should_exist: false # Whether a file should be there or not. (optional, if not given defaults to true)
stdout: # Options for testing stdout (optional)
contains: # List of strings which should be in stdout (optional)
- "moo"
must_not_contain: # List of strings that should NOT be in stout (optional)
- "Cock a doodle doo"
- name: mission impossible # Also failing workflows can be tested
tags: # A list of tags that can be used to select which test
- should fail # is run with pytest using the `--tag` flag.
command: bash impossible.sh
exit_code: 2 # What the exit code should be (optional, if not given defaults to 0)
files:
- path: "fail.log" # Multiple files can be tested for each workflow
- path: "TomCruise.txt.gz" # Gzipped files can also be searched, provided their extension is '.gz'
contains:
- "starring"
stderr: # Options for testing stderr (optional)
contains: # A list of strings which should be in stderr (optional)
- "BSOD error, please contact the IT crowd"
must_not_contain: # A list of strings which should NOT be in stderr (optional)
- "Mission accomplished!"
The above YAML file contains all the possible options for a workflow test.
Note
Workflow names must be unique. Pytest workflow will crash when multiple workflows have the same name, even if they are in different files.
Writing custom tests¶
Pytest-workflow provides a way to run custom tests on files produced by a workflow.
import pathlib
import pytest
@pytest.mark.workflow('files containing numbers')
def test_div_by_three(workflow_dir):
number_file = pathlib.Path(workflow_dir, "123.txt")
number_file_content = number_file.read_text()
assert int(number_file_content) % 3 == 0
The @pytest.mark.workflow('files containing numbers')
marks the test
as belonging to a workflow named files containing numbers
. This test will
only run if the workflow ‘files containing numbers’ has run.
Multiple workflows can use the same custom test like this:
import pathlib
import pytest
@pytest.mark.workflow('my_workflow', 'another_workflow',
'yet_another_workflow')
def test_ensure_long_logs_are_written(workflow_dir):
log = pathlib.Path(workflow_dir, "log.out")
assert len(log.readtext()) > 10000
workflow_dir
is a fixture. It does not work without a
pytest.mark.workflow('workflow_name')
mark. This is a
pathlib.Path object that
points to the folder where the named workflow was executed. This allows writing
of advanced python tests for each file produced by the workflow.
Note
stdout and stderr are available as files in the root of the
workflow_dir
as log.out
and log.err
respectively.
Running pytest-workflow¶
Usage¶
Run pytest
from an environment with pytest-workflow installed or
python3 -m pytest
if using a system-wide or user-wide installation.
Pytest will automatically gather files in the tests
directory starting with
test
and ending in .yaml
or .yml
.
The workflows are run automatically. Each workflow gets its own temporary
directory to run. The stdout
and stderr
of the workflow command are
also saved to this directory to log.out
and log.err
respectively.
To check the progress of a workflow while it is running you can use tail -f
on the stdout
or stderr
file of the workflow. The locations of these
files are reported in the log as soon as a workflow is started.
Specific pytest options for pytest workflow¶
usage: pytest [-h] [--kwd] [--kwdof] [--wt WORKFLOW_THREADS] [--symlink]
[--tag WORKFLOW_TAGS]
Named Arguments¶
--kwd, --keep-workflow-wd | |
Keep temporary directories where workflows are run for debugging purposes. This also triggers saving of stdout and stderr in the workflow directory. Default: False | |
--kwdof, --keep-workflow-wd-on-fail | |
Similar to –keep-workflow-wd, but only keeps the temporary directories if there are test failures. On success all directories are deleted. Default: False | |
--wt, --workflow-threads | |
The number of workflows to run simultaneously. Default: 1 | |
--symlink | Instead of copying the current working directory, create a similar directory structure where all files are replaced with symbolic links. This saves disk space, but should only be used for tests that do use these files read-only. Default: False |
--tag | Run workflows with this name or tag. Default: [] |
Temporary directory cleanup and creation¶
The temporary directories are cleaned up after the tests are completed.
If you wish to inspect the output of a failing
workflow you can use the --keep-workflow-wd
or --kwd
flag to disable
cleanup. This will also make sure the logs of the pipeline are not deleted.
If you only want to keep directories when one or more tests fail you can use
the --keep-workflow-wd-on-fail
or --kwdof
flag.
--keep-workflow-wd-on-fail
will keep all temporary directories, even from
workflows that have succeeded.
If you wish to change the temporary directory in which the workflows are run
use --basetemp <dir>
to change pytest’s base temp directory.
Warning
When a directory is passed to --basetemp
some of the directory
contents will be deleted. For example: if your workflow is named
"my workflow"
then any file or directory named my_workflow
will be
deleted. This makes sure you start with a clean slate if you run pytest
again with the same basetemp
directory.
DO NOT use --basetemp
on directories where none of the
contents should be deleted.
The temporary directories created are copies of pytest’s root directory, the
directory from which it runs the tests. If you have lots of tests, and if you
have a large repository, this may take a lot of disk space. To alleviate this
you can use the --symlink
flag which will create the same directory layout
but instead symlinks the files instead of copying them. This is slower for
lots of small files, and it carries with it the risk that the tests may alter
files from your work directory. If there are a lot of large files and files are
used read-only in tests, then it will use a lot less disk space and be faster
as well.
Running multiple workflows simultaneously¶
To run multiple workflows simultaneously you can use
--workflow-threads <int>
or --wt <int>
flag. This defines the number
of workflows that can be run simultaneously. This will speed up things if
you have enough resources to process these workflows simultaneously.
Running specific workflows¶
To run a specific workflow use the --tag
flag. Each workflow is tagged with
its own name and additional tags in the tags
key of the yaml.
- name: moo
tags:
- animal
command: echo moo
- name: cock-a-doodle-doo
tags:
- rooster sound
- animal
command: echo cock-a-doodle-doo
- name: vroom vroom
tags:
- car
command: echo vroom vroom
With the command pytest --tag moo
only the workflow named ‘moo’ will be
run. With pytest --tag 'rooster sound'
only the ‘cock-a-doodle-doo’
workflow will run. Multiple tags can be used like this:
pytest --tag 'rooster sound' --tag animal
This will run all workflows that
have both ‘rooster sound’ and ‘animal’.
Internally names and tags are handled the same so if the following tests:
- name: hello
command: echo 'hello'
- name: hello2
command: echo 'hello2'
tags:
- hello
are run with pytest --tag hello
then both hello
and hello2
are run.
Examples¶
Snakemake example¶
An example yaml file that could be used to test a snakemake pipeline is listed below.
- name: test-dry-run
command: snakemake -n -r -p -s Snakefile
- name: test-full-run
command: snakemake -r -p -s Snakefile
files:
- "my_output.txt"
stderr:
contains:
- "(100%) done"
WDL with Cromwell example¶
Below an example yaml file is explained which can be used to test a WDL pipeline run through Cromwell.
By default Cromwell outputs its files in the execution folder in a deeply-nested folder structure. Cromwell can output to a separate workflow-outputs folder and since Cromwell version 40 it can also output the files in a structure that is not nested. For more information check the Cromwell documentation on global workflow options.
In order to run Cromwell for CI tests an options file should be present in the repository with the following contents:
{
"final_workflow_outputs_dir": "test-output",
"use_relative_output_paths": true,
"default_runtime_attributes": {
"docker_user": "$EUID"
}
}
final_workflow_outputs_dir
will make sure all the files produced in the
workflow will be copied to the final_workflow_outputs_dir
.
use_relative_output_paths
will get rid of all the Cromwell specific folders
such as call-myTask
etc. The default_runtime_attributes
are only
necessary when using docker containers. It will make sure all the files are
created by the same user that runs the test (docker containers run as root by
default). This will ensure that files can be deleted by pytest-workflow
afterwards.
The following yaml file tests a WDL pipeline run with Cromwell. In this case Cromwell is installed via conda. The conda installation adds a wrapper to Cromwell so it can be used as a command, instead of having to use the jar.
- name: My pipeline
command: cromwell run -i inputs.json -o options.json moo.wdl
files:
- path: test-output/moo.txt.gz
md5sum: 173fd8023240a8016033b33f42db14a2
stdout:
contains:
- "WorkflowSucceededState"
WDL with miniwdl example¶
For miniwdl please consult the runner reference for more information on the localization of output files as well as options to modify the running of miniwdl from the environment.
Miniwdl will localize all the output files to an output_links
directory
inside the test output directory. If you have a workflow with the output:
Inside the output_links
directory the directories moo_file
and
stats
will be created. Inside these directories will be the produced files.
The following yaml file tests a WDL pipeline run with miniwdl.
- name: My pipeline
command: miniwdl run -i inputs.json -d test-output/ moo.wdl
files:
- path: test-output/output_links/moo_file/moo.txt.gz
md5sum: 173fd8023240a8016033b33f42db14a2
- path: test-output/output_links/stats/number_of_moos_per_cow.tsv
contains:
- 42
- path: test-output/output_links/stats/joy_invoking_moos.tsv
must_not_contain:
- 0
Please note that the trailing slash in -d test-output/
is important. It
will ensure the files end up in the test-output
directory.
Known issues¶
pytest-workflow
does not work well together withpytest-cov
. This is due to the temporary directory creating nature ofpytest-workflow
. This can be solved by using:coverage run --source=<your_source_here> -m py.test <your_test_dir>
This will work as expected.
Reporting bugs and feature requests¶
Bugs can be reported and features can be requested on our Github issue tracker.
The aim of this project is to be as user-friendly as possible for writing workflow tests, so all suggestions and bug reports are welcome!
Contributing¶
If you feel like this project is missing a certain something, feel free to make a pull request. You can find our Github page here.
Changelog¶
version 1.3.0¶
Python 3.6 and pytest 5.4.0.0 are now minimum requirements for pytest-workflow. This was necessary for fixing the deprecation warning issue and the issue with the subdirectory evaluation. This also gave the opportunity to simplify the source code using new python 3.6 syntax.
- Using the
name
keyword argument in workflow marks will be deprecated from 1.4.0 onwards. A warning will be given if this is used. For example:pytest.mark.workflow(name="my_workflow")
. Use the name as argument instead:pytest.mark.workflow("my_workflow")
. - Allow running custom tests on multiple workflows. You can now use
pytest.mark.workflow("worflow name 1", "workflow name 2", ...)
. (Issue #75) - Add a miniwdl example to the documentation.
- Added a
--symlink
flag to the CLI that changes the copying behavior. Instead of copying, it creates a similar directory structure where all files are linked to with symbolic links. (Issue #96) - Refactored the code base. Python 3.6’s f-strings and type annotation were used consistently throughout the project. Some code was rewritten to be more concise and readable.
- Improved speed for searching string content in files. This was achieved by removing intermediate functions and simplifying the search function.
- Improved speed for calculating md5sums by increasing the read buffer size from 8k to 64k.
- Solve issue where pytest would display a lot of deprecation warnings when running pytest-workflow. (Issue #98)
- Fix issues with later versions of Cromwell and Snakemake in CI testing.
- Add correct subdirectory evaluation to fix issue where
/parent-dir/child
was evaluated as a subdirectory of/parent
due to starting with the same string. (Issue #95) - Fix error in cromwell example which did not allow it to remove folders correctly.
version 1.2.3¶
- Added missing
help
section for--tag
on the CLI. - Documentation: added usage chapter for pytest-workflow specific options.
- Documentation: updated Cromwell example.
- Removed redundant references to pylint in code comments and CI.
- Remove Codacy from the CI.
version 1.2.2¶
- Test against python3.8
- Do not test on python3.5 snakemake as it crashes. Added test for python3.7 snakemake.
- Fix a typo in the documentation.
- Add tags ‘wdl’, ‘cromwell’ and ‘snakemake’ to the package to increase discoverability.
- Remove pylint from the lint procedure as it was very strict and got stricter with every update, causing tests that previously succeeded to fail on a regular basis.
- Make sure pytest-workflow crashes when multiple workflows have the same name, even when they are in different files.
- Added setup.cfg to include license in source distributions on PyPI for future versions
version 1.2.1¶
- Since pytest 4.5.0 unknown markers give a warning.
@pytest.mark.workflow
markers are now added to the configuration. Information on usage shows up withpytest --mark
. - Updated documentation to reflect the move to conda-forge as requested on this github issue.
- Updated documentation on how to test Cromwell + WDL pipelines.
version 1.2.0¶
- Giving a
--basetemp
directory that is within pytest’s current working directory will now raise an exception to prevent infinite recursive directory copying. - The cleanup message is only displayed when pytest-workflow is used.
- Added a
--keep-workflow-wd-on-fail
or--kwdof
flag. Setting this flag will make sure temporary directories are only deleted when all tests succeed.
version 1.1.2¶
- Fixed a bug where the program would hang indefinitely after a user input error.
version 1.1.1¶
- Added
--kwd
as alias for--keep-workflow-wd
. Notify the user of deletion of temporary directories and logs. - Released pytest-workflow as a conda package on bioconda.
version 1.1.0¶
- Enabled custom tests on workflow files.
Version 1.0.0¶
Lots of small fixes that improve the usability of pytest-workflow are included in version 1.0.0.
Gzipped files can now also be checked for contents. Files with ‘.gz’ as extension are automatically decompressed.
stdout
andstderr
of workflows are now streamed to a file instead of being kept in memory. This means you can check the progress of a workflow by runningtail -f <stdout or stderr>
. The location ofstdout
andstderr
is now reported at the start of each worflow. If the--keep-workflow-wd
is not set thestdout
andstderr
files will be deleted with the rest of the workflow files.The log reports now when a workflow is starting, instead of when it is added to the queue. This makes it easier to see which workflows are currently running and if you forgot to use the
--workflow-threads
or--wt
flag.Workflow exit code failures now mention the name of the workflow. Previously the generic name “Workflow” was used, which made it harder to figure out which workflows failed.
When tests of file content fail because the file does not exist, a different error message is given compared to when the file exist, but the content is not there, which makes debugging easier. Also the accompanying “FileNotFound” error stacktrace is now suppressed, which keeps the test output more pleasant.
When tests of stdout/stderr content or file content fail a more informative error message is given to allow for easier debugging.
All workflows now get their own folder within the same temporary directory. This fixes a bug where if
basetemp
was not set, each workflow would get its own folder in a separate temp directory. For example running workflows ‘workflow1’ and ‘workflow2’ would create two temporary folders:‘/tmp/pytest_workflow_33mrz5a5/workflow1’ and ‘/tmp/pytest_workflow_b8m1wzuf/workflow2’
This is now changed to have all workflows in one temporary directory per pytest run:
‘/tmp/pytest_workflow_33mrz5a5/workflow1’ and ‘/tmp/pytest_workflow_33mrz5a5/workflow2’
Disallow empty
command
andname
keys. An emptycommand
caused pytest-workflow to hang. Empty names are also disallowed.
Version 0.4.0¶
- Added more information to the manual on how to debug pipelines and use
pytest-workflow
outside a virtual environment. - Reworked code to use
tempfile.mkdtemp
to create a truly unique temporary working directory if the--basetemp
flag is not used. This replaces the old code which dependeded on pytest internal code which was flagged as deprecated. Also more information was added to the manual about the use of--basetemp
. - Added a test case for WDL pipelines run with Cromwell and wrote an example for using WDL+Cromwell in the manual.
- Added
--tag
flag to allow for easier selection of workflows during testing. - Added a test case for snakemake pipelines and wrote an example for using pytest-workflow with snakemake in the manual.
Version 0.3.0¶
- Improved the log output to look nicer and make workflow log paths easier to find in the test output.
- Fixed an error that polluted the log message with a pytest stacktrace when running more than one workflow. Measures are taken in our test framework to detect such issues in the future.
- Added the possibility to run multiple workflows simultaneously with the
--workflow-threads
or--wt
flag. - Made code easier to maintain by using stdlib instead of pytest’s
py
lib in all of the code. - Added a schema check to ensure that tests have unique names when whitespace is removed.
Version 0.2.0¶
- Cleanup the readme and move advanced usage documentation to our readthedocs page.
- Start using sphinx and readthedocs.org for creating project documentation.
- The temporary directories in which workflows are run are automatically
cleaned up at the end of each workflow test. You can disable this behaviour
by using the
--keep-workflow-wd
flag, which allows you to inspect the working directory after the workflow tests have run. This is useful for debugging workflows. - The temporary directories in which workflows are run can now be
changed by using the
--basetemp
flag. This is because pytest-workflow now uses the built-in tmpdir capabilities of pytest. - Save stdout and stderr of each workflow to a file and report their locations
to stdout when running
pytest
. - Comprehensible failure messages were added to make debugging workflows easier.
Version 0.1.0¶
- A full set of examples is now provided in the README.
- Our code base is now checked by pylint and bandit as part of our test procedure to ensure that our code adheres to python and security best practices.
- Add functionality to test whether certain strings exist in files, stdout and stderr.
- Enable easy to understand output when using pytest verbose mode
(
pytest -v
). The required code refactoring has simplified the code base and made it easier to maintain. - Enable the checking of non-existing files
- Enable the checking of file md5sums
- Use a schema structure that is easy to use and understand.
- Pytest-workflow now has continuous integration and coverage reporting, so we can detect regressions quickly and only publish well-tested versions.
- Fully parametrized tests enabled by changing code structure.
- Initialized pytest-workflow with option to test if files exist.