So the core concept is that when you validate some property about input you should also transform the input into a new form that represents the new guarantee in the type system.
This is very, closely related to the “make invalid states unrepresentable” concept. If we have validated our list to be non empty, we should return a non empty list - after all an empty list is now invalid and as such the type system should exclude that possibility.
Yes! The concepts are intertwined.
I think the key take away, for me, is to lean heavily into your type system and allow that to do some of the heavy lifting. Accept that something like a username is not a string, but a subtype of a string (this has to be true if any validation is required, otherwise you’d just accept any valid string).
Honestly, for any large scale project in Python, Pydantic makes it bearable. We use Python heavily at work (and I’d argue we shouldn’t be for the projects we’re working on…), and Pydantic is the one library we’re using that I wouldn’t be without. Precisely because it allows us to inject some of these static typing concepts and keeps us honest, and our code understandable.
I subconsciously knew this, I currently am making a simple data exchange format to use with a program, and I am using PEG to create a parser. Chances of errors happning in this DXF is really low, but if the parser can’t parse it, it’s invalid.
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So the core concept is that when you validate some property about input you should also transform the input into a new form that represents the new guarantee in the type system.
This is very, closely related to the “make invalid states unrepresentable” concept. If we have validated our list to be non empty, we should return a non empty list - after all an empty list is now invalid and as such the type system should exclude that possibility.
Yes! The concepts are intertwined. I think the key take away, for me, is to lean heavily into your type system and allow that to do some of the heavy lifting. Accept that something like a
usernameis not a string, but a subtype of a string (this has to be true if any validation is required, otherwise you’d just accept any valid string).Hmm, I guess, this is why even a loosey goosey language like Python would have Pydantic…
Honestly, for any large scale project in Python, Pydantic makes it bearable. We use Python heavily at work (and I’d argue we shouldn’t be for the projects we’re working on…), and Pydantic is the one library we’re using that I wouldn’t be without. Precisely because it allows us to inject some of these static typing concepts and keeps us honest, and our code understandable.
I remember this article and it’s quite good, especially for Haskellers.
It’s one of my favourites. Something I revisit every couple of years.
I subconsciously knew this, I currently am making a simple data exchange format to use with a program, and I am using PEG to create a parser. Chances of errors happning in this DXF is really low, but if the parser can’t parse it, it’s invalid.