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`urllib.unquote_to_bytes` and `urllib.unquote` could both potentially generate `O(len(string))` intermediate `bytes` or `str` 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 expanding `bytearray` and a generator internally instead of precomputed `split()` style operations. Microbenchmarks with some antagonistic inputs like `mess = "\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` on `python -m timeit` runs of larger inputs. Unittesting memory consumption is difficult and does not seem worthwhile. Observed memory usage is ~1/2 for `unquote()` and <1/3 for `unquote_to_bytes()` using `python -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. |
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| robotparser.py | ||