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Pytest plugin for functional testing of data analysispipelines

Project Description


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 2.7, 3.3, 3.4, 3.5, and 3.6.


pip install pytest-pipeline


For our example, we will use a super simple pipeline that writes a file and prints to stdout:

#!/usr/bin/env python

from __future__ import print_function

if __name__ == "__main__":

    with open("result.txt", "w") as result:
    print("Result computed")

At this point it’s just a simple script, but it should be enough to illustrate the plugin. Also, 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:

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
    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
# 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, you can then run the test by executing py.test -v You should see that three tests were executed and all four passed.

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.
  • 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.

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 to report bugs or feature requests. You can always fork and submit a pull request as well.




Version 0.3

Release 0.3.0

Release date: 24 January 2017

  • Allow stdout and/or stderr capture in-memory. This can be done by setting their respective keyword arguments to True when creating the run fixture.

Version 0.2

Release 0.2.0

Release date: 31 March 2015

  • Pipeline runs are now modelled differently. Instead of a class attribute, they are now created as pytest fixtures. This allows the pipeline runs to be used in non-unittest.TestCase tests.
  • The after_run decorator is deprecated.
  • The command line flags –xfail-pipeline and –skip-run are deprecated.

Version 0.1

Release 0.1.0

Release date: 25 August 2014

  • First release on PyPI.
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