urllib.unquote_to_bytes andurllib.unquote could both potentially generateO(len(string)) intermediatebytes orstr objects while computing the unquoted final result depending on the input provided. As Python objects are relatively large, this could consume a lot of ram.

This switches the implementation to using an expandingbytearray and a generator internally instead of precomputedsplit() style operations.

Microbenchmarks with some antagonistic inputs likemess = "\u0141%%%20a%fe"*1000 show this is 10-20% slower for unquote and unquote_to_bytes and no different for typical inputs that are short or lack much unicode or % escaping. But the functions are already quite fast anyways so not a big deal. The slowdown scales consistently linear with input size as expected.

Memory usage observed manually using/usr/bin/time -v onpython -m timeit runs of larger inputs. Unittesting memory consumption is difficult and does not seem worthwhile.

Observed memory usage is ~1/2 forunquote() and <1/3 forunquote_to_bytes() usingpython -m timeit -s 'from urllib.parse import unquote, unquote_to_bytes; v="\u0141%01\u0161%20"*500_000' 'unquote_to_bytes(v)' as a test.

• Issue:Reduce memory usage of urllib.unquote and unquote_to_bytes #88500

Closes#88500.