Mypyc: Compile type-annotated Python to C

https://itnext.io/why-the-kotlin-native-memory-model-cannot-...

I believe I did answer this in my original comment, so I will just refer you back to that.

I disagree that there was any conflating going on. Type system designs enforced with static typing are a hallmark aspect of most of these design patterns around things like type classes, interfaces and inheritance. Of course similar things can exist in dynamically typed languages but they are not the same. For example “interfaces” in Python are just duck typing conventions (apart from built-in CPython data model properties). That duck typing interface is not at all similar to interfaces as a type system design pattern in a statically types language. Any similarity is purely semantic.

As for examples, one example that I worked on heavily involved a Scala system for managing DAG dependencies in task execution. The system was set up using phantom typing and a bunch of sealed case classes such that for any logical type of Task that could exist in a DAG, the task had an “Active” and “Passive” variant, where the “Active” variant could only be obtained through a monadic validator processing a “Passive” variant.

The goal was to use the type system itself to encode the concept of “this task has passed through validation and it’s allowed to be processed.”

Because this was designed at the type system level, it created huge problems and never added any real value in the sense of making it “logically impossible” to create invalid Tasks. Number one, it led to huge, painful boiler plate to create the case classes for every type of Task and specialize the type class with a “validator” function. Number two, we eventually realized there were many different aspects to “validation” that did not map well to the concept of “passing through a validator.” For example, some tasks depended on data that didn’t exist in the required location at a certain time, and hence needed retry logic to validate. Some situations involved re-running an already complete task (usually for resource usage observability reasons, or because an external data dependency changed). At any rate, baking validation status into a static type via the phantom type design was nothing but a headache. For all the beautiful code supposedly protecting us from processing invalid jobs, all that we got was difficult constant refactors.

Eventually we abandoned it and just used Luigi instead, and wrote all DAG management code in Python. It was the best decision we made. We lost zero safety and our defect rate did not get worse. Testing caught all the same bugs that compilation would have caught in Scala, and more, with less total code. And because the nature of the tasks in Luigi was just “whatever arbitrary Python you want” it was super easy to write effective validators, accommodate new use cases on the fly, and keep the code clean without dogmatic adherence to a precommitted type system design. Luigi happening to use some lightweight inheritance patterns was forgivable, given the dynamic typing flexibility.