Years ago, Nadav Rotem related to me this story about why basic block procedures in Swift are not as good as they seem. Nelson Elhage reminded me about this on Twitter and so I thought this should be put into the public record.
Basic block procedures make certain optimizations more difficult. Consider this program:
block j3 (%y1, %y2) { ... }
block j1 () { jump j3(%x1, %x2) }
block j2 () { jump j3(%x3, %x4) }
Is this program easier or more difficult to optimize than the traditional SSA with phi-nodes formulation?
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Greetings from 2024! An official pattern matching PEP has been accepted https://peps.python.org/pep-0636/ and is available in Python 3.10. Class patterns are tested using isinstance, with no inheritance structure necessary, making the pattern described in this post 100% forward compatible to real pattern matching.
One of the features I miss most in non-Haskell programming languages is algebraic data types (ADT). ADTs fulfill a similar role to objects in other languages, but with more restrictions: objects are an open universe, where clients can implement new subclasses that were not known at definition time; ADTs are a closed universe, where the definition of an ADT specifies precisely all the cases that are possible. We often think of restrictions of a bad thing, but in the case of ADTs, the restriction of being a closed universe makes programs easier to understand (a fixed set of cases to understand, as opposed to a potentially infinite set of cases) and allows for new modes of expression (pattern matching). ADTs make it really easy to accurately model your data structures; they encourage you to go for precise types that make illegal states unrepresentable. Still, it is generally not a good idea to try to manually reimplement your favorite Haskell language feature in every other programming language you use, and so for years I’ve suffered in Python under the impression that ADTs were a no go.
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