perf: improve ArrowGroupBy.__iter__ performances
#1334
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FBruzzesi
commented
Nov 7, 2024
| *[nw_namespace.col(k) == v for k, v in zip(self._keys, key_value)] | ||
| next( | ||
| ( | ||
| t := self._df._from_native_frame( |
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quite proud of this walrus 😇
FBruzzesi
commented
Nov 7, 2024
| import pyarrow.compute as pc # ignore-banned-import | ||
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| col_token = generate_temporary_column_name(n_bytes=8, columns=self._df.columns) | ||
| null_token = "__null_token_value__" # noqa: S105 |
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S105 is "hardcoded-password-string" - this is definitly not a password, but it is hardcoded 😁
FBruzzesi
commented
Nov 7, 2024
Comment on lines
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| *[pc.cast(table[key], pa.string()) for key in self._keys], | ||
| "", |
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This is because the last *string argument is used as separator
MarcoGorelli
approved these changes
Nov 8, 2024
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According to Marco's performance benchmarking for plotly, the bottleneck for a few functions seems to be the call we do to
ArrowGroupBy.__iter__.Since pyarrow does not natively support iterating over groups, we (actually pointing finger to myself) implemented a (let's say naive) way of still allowing for that - I remember the use case was for scikit-lego to fully support arrow as well.
This PR tries to improve those performances using native arrow methods and no simple shortcuts. Steps are as follow: