I suppose `reduce` as built-in is the footgun because it's too easy to reach for. Now if someone doesn't know about `join` perhaps they look up how to do it because they think 'surely there's a better way than a loop without an import'.
replyDoesn't reduce force the accumulator to be shared though? Both the reduce and the lambda are holding onto references to acc, which defeats any "single reference" optimizations.
replyThe problem with:
reply ret = ""
for s in strings:
ret += s
If the s are small the usual geometric buffer growth mitigates that. Of course you can compute the final buffer size in this case, but often you have a bunch of dynamically-generated strings of different sizes.
reply def reduce(acc, f):
for v in self:
acc = f(acc, v)
return acc
The binding for acc in the reduce call is still active during the f call, which means there are at least two references to acc.
replyWhy is it still active? Even an interpreter with no lookahead could see that it goes out of scope immediately when f returns (it gets shadowed on that line), so as long as there's no guarantee about when finalizers get called, it should be able to mark it dead inside of reduce as soon as it's passed to f. Like move semantics here should be a general pattern for optimization, no?
replyDoes Python actually do that? If the f call throws, you can still observe the (unchanged) binding of acc in reduce.
replyFair, I suppose there's no end to the level of insanity that a programmer can do in a dynamic language. I'd think it could perhaps still look to see there's no catch, but maybe eval makes even that impossible.
replyIt might - let's assume it does. My point is that it's better to use the explicit optimized method for joining strings in a performance-sensitive context than to try to meet the conditions for an implicit optimization.
reply