def filter(rule, whatever):
if rule.x in whatever.x:
return True

rules = get_rules()
whatevers = get_whatevers()
for rule in rules:
for whatever in whatevers:
if filter(rule, whatever):
cnt = cnt + 1

return cnt

It's true that they didn't optimize the code, but
they did not for any language having for all of them the same cost in
terms of iterations.

sure, but I would argue that you do need to write code in a clean style appropriate for the language at hand.

For instance, the above creates a function that is a simple one-liner -- there is no reason to do that, and the fact that function calls to have significant overhead in Python is going to bite you.

for rule in rules:
x = rule.x
for whatever in whatevers:
if x in whatever.x:
cnt += 1

The performance of the CPython boosted x3/x4 just doing these "silly" things.

"inlining" the filter call is making the code more pythonic and readable -- a no brainer. I wouldn't call that a optimization.

making rule.x local is an optimization -- that is, the only reason you'd do it to to make the code go faster. how much difference did that really make?

I also don't know what type your "whatevers" are, but "x in something" can be order (n) if they re sequences, and using a dict or set would be a much better performance.

and perhaps collections.Counter would help here, too.

In short, it is a non-goal to get python to run as fast as static langues for simple nested loop code like this :-)

The case of the rule cache IMHO is very striking, we have plenty
examples in many repositories where the caching of none local
variables is a widely used pattern, why hasn't been considered a way
to do it implicitly and by default?

you can bet it's been considered -- the Python core devs are a pretty smart bunch :-)

The fundamental reason is that rule.x could change inside that loop -- so you can't cache it unless you know for sure it won't. -- Again, dynamic language.

The case of the slowness to call functions in CPython is quite
recurrent and looks like its an unsolved problem at all.

dynamic language again ...

If the default code that you
can write in a language is by default slow and exists an alternative
to make it faster, this language is doing something wrong.

yes, that's true -- but your example shouldn't be the default code you write in Python.

BTW: pypy looks like is immunized [1]

[1] https://gist.github.com/pfreixes/d60d00761093c3bdaf29da025a004582

PyPy uses a JIT -- which is the way to make a dynamic language run faster -- That's kind of why it exists....

-CHB

Christopher Barker, Ph.D.
Oceanographer

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