Different Styles of Dependency Management

Recently I was hacking out a quick Python script. Everything was fine until I needed to import one common third party library and then Boom! I was dropped head first into the messy chain of Python dependencies, weak typing, virtual environments, and conflicts. I couldn’t even install the necessary library with pip. I’d just get an unintelligible hash of error messages and stack traces.

So off to DuckDuckGo I went to remind myself how one actually builds and packages real world Python programs that do more than print Hello World! It wasn’t pretty, and it took me quite a while to understand it. This was actually harder for me than it might have been for a newbie because I was so invested in the way Java manages dependencies. I’ve spent years working with dependency management in Java at a depth most developers never sink to. I’m an Apache Maven committer. I’ve rewritten most of their documentation about dependencies. I’ve written tools to analyze Java jars, dependency trees, and classpaths. I wrote or edited most of Google’s Java Library Best Practices. I’ve debugged many problems caused by Java class loaders. So I’ve got a pretty good understanding of how Java dynamically links third party dependencies.

But Python? Python doesn’t work like that. And neither does everything else.

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Assume vs. Assert

Assumptions are an underused feature of modern testing frameworks that should be more widely known. Briefly, an assumption verifies that conditions are right to execute the test. An assertion verifies that the test passes. If an assertion fails, the test failed, and we know the code is broken. If an assumption fails, the test was not run and the code may or may not be broken.

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