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https://issues.apache.org/jira/browse/ARROW-14959?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joris Van den Bossche updated ARROW-14959:
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Component/s: Python
> Reading Hive-style partitioned parquet files from GCS
> -----------------------------------------------------
>
> Key: ARROW-14959
> URL: https://issues.apache.org/jira/browse/ARROW-14959
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 6.0.1
> Reporter: Garrett Weaver
> Priority: Minor
>
> Trying to read a spark-generated hive-style partitioned parquet dataset with
> *gcsfs* and {*}pyarrow{*}, but getting a *FileNotFoundError* if I try to read
> from the base directory or even if try to read directly from one of the
> partitions. Not sure if I am doing something wrong or it is not supported.
> Note that I have successfully read this hive-style partitioned parquet
> dataset using other methods to rule out any other issues, including:
> * Successful read with pyspark by using spark.read.parquet
> * Successful read of a specific partition by passing list of paths to
> `ParquetDataset`
> * Also, tested reading another spark-generated parquet dataset with no
> Hive-style partitions from GCS and that worked as well.
> Below is what I am trying:
>
> {code:python}
> import gcsfs
> import pyarrow as pa
>
> gcs = gcsfs.GCSFileSystem()
>
> partitions = pa.dataset.partitioning(
> pa.schema([("partition_var", pa.string())]), dictionaries="infer",
> flavor="hive"
> )
>
> partitioned_dataset = pa.dataset.dataset(
> "path/to/partitioned/dataset/base/dir",
> filesystem=gcs,
> format="parquet",
> partitioning=partitions,
> )
>
> partition_of_dataset = pa.dataset.dataset(
> "path/to/partitioned/dataset/base/dir/partition_var=some_value",
> filesystem=gcs,
> format="parquet",
> ){code}
>
> The errors returned for both are below:
> {code:java}
> FileNotFoundError: path/to/partitioned/dataset/base/dir/
> FileNotFoundError:
> path/to/partitioned/dataset/base/dir/partition_var=some_value/{code}
>
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