Building Python Distributions
Building Python Distributions
Python doesn't really need build scripts, makefiles, anything like that. Until it does.
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In compiled languages, the build script or makefile is pretty important. Java has Maven (and Gradle and Ant) for this job.
Python doesn't really have much for this. Mostly because it's needless.
Some folks like the idea of a build script. I've been asked for suggestions.
First and foremost: Go Slow. A build script is not essential. It's barely even helpful. Python isn't Java. There's no maven/gradle/ant nonsense because it isn't necessary. Make is a poor choice of tools for reasons we'll see below.
For folks new to Python, here's the step that's sometimes important.
python setup.py sdist bdist_wheel upload
This uses the source distribution tools (sdist) to build a "wheel" out of the source code. That's the only thing that's important, and even that's optional. The source is all that really exists, and a Git Pull is the only thing that's truly required.
Really. There's no compilation, and there's no reason to do any processing prior to uploading source.
For folks experienced with Python, this may be obvious. For folks not so experienced, it's difficult to emphasize enough that Python is just source. No "class" files. No "jar" files. No "war" files. No "ear" files. None of that. A wheel is a Zip archive that follows some simple conventions.
Some Preliminary Steps
A modicum of care is a good idea before simply uploading something. There are a few steps that make some sense.
- Run pylint to check for obvious code problems. A low pylint score indicates that the code needs to be cleaned up. There's no magically ideal number, but with a few judicious "disable" comments, it's easy to get to 10.00.
- Run mypy to check the type hints. If mypy complains, you've got potentially serious problems.
- Run py.test and get a coverage report. There's no magically perfect test coverage number: more is better. Even 100% line-of-code coverage doesn't necessarily mean that all of the potential combinations of logic paths have been covered.
- Run sphinx to create documentation.
Only py.test has a simple pass-fail aspect. If the unit tests don't pass: that's a clear problem.
Using make doesn't work out terribly well. It can be used, but it seems to me to be too confusing to set up properly.
Why? Because we don't have the kind of simple file relationships with which make works out so nicely. If we had simple *.c -> *.o -> *.ar kinds of relationships, make would be perfect. We don't have that, and this seems to make make more trouble than it's worth. Both pylint and py.test keep history as well as produce reports. Sphinx is make-like already, which is why I'm leery of layering on the complexity.
My preference is something like this:
import pytest from pylint import epylint as lint import sphinx from mypy.api import api (pylint_stdout, pylint_stderr) = lint.py_run('*.py', return_std=True) print(pylint_stdout.getvalue()) result = mypy.api.run('*.py') pytest.main(["futurize_both/tests"]) sphinx.main(['source', 'build/html', '-b', 'singlehtml'])
The point here is to simply run the four tools and then look at the output to see what needs to be fixed. Circumstances will dictate changes to the parameters being used. New features will need different reports than bug fixes. Some parts of a project will have different focus than other parts. Conversion from Python 2 to Python 3 will indicate a shift in focus, also.
The idea of a one-size-fits-all script seems inappropriate. These tools are sophisticated. Each has a distinctive feature set. Tweaking the parameters by editing the build script seems like a simple, flexible solution. I'm not comfortable defining parameter-parsing options for this, since each project I work on seems to be unique.
Important. Right now, mypy-lang in the PyPI repository and mypy in GitHub differ. The GitHub version includes an api module; the PyPI release does not include this. This script may not work for you, depending on which mypy release you're using. This will change in the future, making things nicer. Until then, you may want to run mypy "the hard way" using subprocess.check_call().
In enterprise software development environments, it can make sense to set some thresholds for pylint and pytest coverage. It is very helpful to include type hints everywhere, also. In this context, it might make sense to parse the output from lint, mypy, and py.test to stop processing if some quality thresholds are met.
As noted above: Go Slow. This kind of tool automation isn't required and might actually be harmful if done badly. Arguing over pylint metrics isn't as helpful as writing unit test cases. I worry about teams developing an inappropriate focus on pylint or coverage reports—and the associated numerology—to the exclusion of sensible automated testing.
I think tools like https://pypi.python.org/pypi/pytest-bdd might be of more value than a simplistic "automated" tool chain. Automation doesn't seem as helpful as clarity in test design. I like the BDD idea with Gherkin test specifications because the Given-When-Then story outline seems to be very helpful for test design.
Published at DZone with permission of Steven Lott , DZone MVB. See the original article here.
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