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https://issues.apache.org/jira/browse/ARROW-8888?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17129062#comment-17129062
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Joris Van den Bossche commented on ARROW-8888:
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Trying with strings, I don't see much speedup in that case by using
multithreading, but also not a significant slowdown:
{code}
In [10]: df = pd.DataFrame({key: ['a'] * 1_000_000 for key in range(10)})
In [11]: %timeit table = pa.Table.from_pandas(df)
3.43 s ± 12.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [12]: %timeit table = pa.Table.from_pandas(df, nthreads=1)
3.79 s ± 162 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
{code}
Strings are stored in pandas as Python objects. I am not fully sure how the
conversion in arrow is implemented, but so it might be this requires the GIL,
and then multithreading won't help.
Such a big slowdown as you mention is still strange though, so if you can try
to look further into it, that's certainly welcome.
> [Python] Heuristic in dataframe_to_arrays that decides to multithread convert
> cause slow conversions
> ----------------------------------------------------------------------------------------------------
>
> Key: ARROW-8888
> URL: https://issues.apache.org/jira/browse/ARROW-8888
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.16.0
> Environment: MacOS: 10.15.4 (Also happening on windows 10)
> Python: 3.7.3
> Pyarrow: 0.16.0
> Pandas: 0.25.3
> Reporter: Kevin Glasson
> Priority: Minor
>
> When calling pa.Table.from_pandas() the code path that uses the
> ThreadPoolExecutor in dataframe_to_arrays (called by Table.from_pandas) the
> conversion is much much slower.
>
> I have a simple example - but the time difference is much worse with a real
> table.
>
> {code:java}
> Python 3.7.3 | packaged by conda-forge | (default, Dec 6 2019, 08:54:18)
> Type 'copyright', 'credits' or 'license' for more information
> IPython 7.13.0 – An enhanced Interactive Python. Type '?' for help.
> In [1]: import pyarrow as pa
> In [2]: import pandas as pd
> In [3]: df = pd.DataFrame({"A": [0] * 10000000})
> In [4]: %timeit table = pa.Table.from_pandas(df)
> 577 µs ± 15.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
> In [5]: %timeit table = pa.Table.from_pandas(df, nthreads=1)
> 106 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
> {code}
>
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