[jira] [Commented] (ARROW-11857) [Python] Resource temporarily unavailable when using the new Dataset API with Pandas

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-11857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17315654#comment-17315654 ] 

Joris Van den Bossche commented on ARROW-11857:
-----------------------------------------------

[~AntonFriberg] sorry for the slow reply, but thanks for the report!

[~westonpace] should this already be fixed in master with all the threadpool-related work? 

> [Python] Resource temporarily unavailable when using the new Dataset API with Pandas
> ------------------------------------------------------------------------------------
>
>                 Key: ARROW-11857
>                 URL: https://issues.apache.org/jira/browse/ARROW-11857
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 3.0.0
>         Environment: OS: Debian GNU/Linux 10 (buster) x86_64 
> Kernel: 4.19.0-14-amd64 
> CPU: Intel i7-6700K (8) @ 4.200GHz 
> Memory: 32122MiB
> Python: v3.7.3
>            Reporter: Anton Friberg
>            Assignee: Weston Pace
>            Priority: Critical
>             Fix For: 4.0.0
>
>
> When using the new Dataset API under v3.0.0 it instantly crashes with
> {code:java}
>  terminate called after throwing an instance of 'std::system_error'
>  what(): Resource temporarily unavailable{code}
> This does not happen in an earlier version. The error message leads me to believe that the issue is not on the Python side but might be in the C++ libraries.
> As background, I am using the new Dataset API by calling the following
> {code:java}
> s3_fs = fs.S3FileSystem(<minio credentials>)
> dataset = pq.ParquetDataset(
>         f"{bucket}/{base_path}",
>         filesystem=s3_fs,
>         partitioning="hive",
>         use_legacy_dataset=False,
>         filters=filters
> )
> dataframe = dataset.read_pandas(columns=columns).to_pandas(){code}
> The dataset itself contains 10,000s of files around 100 MB in size and is created using incremental bulk processing from pandas and pyarrow v1.0.1. With the filters I am limiting the amount of files that are fetch to around 20.
> I am suspecting an issue with a limit in the total amount of threads that are spawning but I have been unable to resolve it by calling
> {code:java}
> pyarrow.set_cpu_count(1) {code}



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