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=============== pytest-pipeline

|ci| |coverage| |pypi|

.. |ci| image:: https://travis-ci.org/bow/pytest-pipeline.png?branch=master :target: https://travis-ci.org/bow/pytest-pipeline

.. |coverage| image:: https://codecov.io/gh/bow/pytest-pipeline/branch/master/graph/badge.svg :target: https://codecov.io/gh/bow/pytest-pipeline

.. |pypi| image:: https://badge.fury.io/py/pytest-pipeline.svg :target: http://badge.fury.io/py/pytest-pipeline

pytest-pipeline is a pytest plugin for functional testing of data analysis pipelines. They are usually long-running scripts or executables with multiple input and/or output files + directories. The plugin is meant for end-to-end testing where you test for conditions before the pipeline run and after the pipeline runs (output files, checksums, etc.).

pytest-pipeline is tested against Python versions 3.6, 3.7. and 3.8.

Installation

::

pip install pytest-pipeline

Walkthrough

For our example, we will use a super simple pipeline (an executable, really) that writes a file and prints to stdout:

.. code-block:: python

#!/usr/bin/env python

from __future__ import print_function

if __name__ == "__main__":

    with open("result.txt", "w") as result:
        result.write("42\n")
    print("Result computed")

At this point it's just a simple script, but it is enough to represent a single, long running task to be tested. If you want to follow along, save the above file as run_pipeline.

With the pipeline above, here's how your test would look like with pytest_pipeline:

.. code-block:: python

import os
import shutil
import unittest
from pytest_pipeline import PipelineRun, mark, utils

# we can subclass `PipelineRun` to add custom methods
# using `PipelineRun` as-is is also possible
class MyRun(PipelineRun):

    # before_run-marked functions will be run before the pipeline is executed
    @mark.before_run
    def test_prep_executable(self):
        # copy the executable to the run directory
        shutil.copy2("/path/to/run_pipeline", "run_pipeline")
        # ensure that the file is executable
        assert os.access("run_pipeline", os.X_OK)

# a pipeline run is treated as a test fixture
run = MyRun.class_fixture(cmd="./run_pipeline", stdout="run.stdout")

# the fixture is bound to a unittest.TestCase using the usefixtures mark
@pytest.mark.usefixtures("run")
# tests per-pipeline run are grouped in one unittest.TestCase instance
class TestMyPipeline(unittest.TestCase):

    def test_result_md5(self):
        assert utils.file_md5sum("result.txt") == "50a2fabfdd276f573ff97ace8b11c5f4"

    def test_exit_code(self):
        # the run fixture is stored as the `run_fixture` attribute
        assert self.run_fixture.exit_code == 0

    # we can check the stdout that we capture as well
    def test_stdout(self):
        assert open("run.stdout", "r").read().strip() == "Result computed"

If the test above is saved as test_demo.py, you can then run the test by executing py.test -v test_demo.py. You should see that four passing tests: one that is done prior to the script run and three that are done afterwards.

What just happened?

You just executed your first pipeline test. The plugin itself gives you:

• Test directory creation (one class gets one directory). By default, testdirectories are all created in the /tmp/pipeline_test directory. You can tweak this location by supplying the --base-pipeline-dir command line flag. Need access to the directory name for the command invocation? Use the {run_dir} placeholder in the string command.

• Automatic execution of the pipeline. No need to import subprocess, just define the command via the PipelineRun object. We optionally captured the standard output to a file called run.stdout. Not a fan of doing disk IO? You can also set stdout and/or stderr to True and have their values captured in-memory.

• Timeout control. For long running pipelines, you can also supply a timeout argument which limits how long the pipeline process can run.

• Some simple utility methods for working with files.

And since this is a py.test plugin, test discovery and execution is done via py.test.

Getting + giving help

Please use the issue tracker <https://github.com/bow/pytest-pipeline/issues>_ to report bugs or feature requests. You can always fork and submit a pull request as well.

Local Development

Setting up a local development requires any of the supported Python version. It is ideal if you have support Python 2.x and 3.x versions installed, as that will allow you to run the full tests suite against all versions using tox.

In any case, the following steps can be your guide for setting up your local development environment:

.. code-block:: bash

# Clone the repository and cd into it
$ git clone {repo-url}
$ cd pytest-pipeline

# Create your virtualenv, using pyenv for example (recommended, https://github.com/pyenv/pyenv)
$ pyenv virtualenv 3.7.0 pytest-pipeline-dev
# or using virtualenvwrapper (https://virtualenvwrapper.readthedocs.io/en/latest/)
$ mkvirtualenv -p /usr/bin/python3.7 pytest-pipeline-dev

# From within the root directory and with an active virtualenv, install the dependencies and package itself
$ pip install -e .[dev]

License

See LICENSE.