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https://issues.apache.org/jira/browse/ARROW-11857?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joris Van den Bossche reassigned ARROW-11857:
---------------------------------------------
Assignee: Weston Pace
> [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
>
> 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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