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Posted to issues@arrow.apache.org by "Wes McKinney (JIRA)" <ji...@apache.org> on 2019/06/07 19:26:00 UTC

[jira] [Assigned] (ARROW-4723) [Python] Skip _files when reading a directory containing parquet files

     [ https://issues.apache.org/jira/browse/ARROW-4723?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney reassigned ARROW-4723:
-----------------------------------

    Assignee: Hyukjin Kwon

> [Python] Skip _files when reading a directory containing parquet files
> ----------------------------------------------------------------------
>
>                 Key: ARROW-4723
>                 URL: https://issues.apache.org/jira/browse/ARROW-4723
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>            Reporter: Hossein Falaki
>            Assignee: Hyukjin Kwon
>            Priority: Major
>              Labels: parquet, pull-request-available
>             Fix For: 0.14.0
>
>          Time Spent: 1h 20m
>  Remaining Estimate: 0h
>
> It is common for Apache Spark or other big data platforms to save additional meta-data files denoted with _ when saving parquet data.
> When using  {{make_batch_reader}} to load a directory saved by parquet containing such files we encounter the following error:
> {code:java}
> PetastormMetadataError Traceback (most recent call last)
> /databricks/python/lib/python3.6/site-packages/petastorm/etl/dataset_metadata.py in infer_or_load_unischema(dataset)
>     388 try:
> --> 389 return get_schema(dataset) 
>     390 except PetastormMetadataError:
> /databricks/python/lib/python3.6/site-packages/petastorm/etl/dataset_metadata.py in get_schema(dataset)
>     342 raise PetastormMetadataError( 
> --> 343 'Could not find _common_metadata file. Use materialize_dataset(..) in' 
>     344 ' petastorm.etl.dataset_metadata.py to generate this file in your ETL code.'
> PetastormMetadataError: Could not find _common_metadata file. Use materialize_dataset(..) in petastorm.etl.dataset_metadata.py to generate this file in your ETL code. You can generate it on an existing dataset using petastorm-generate-metadata.py{code}
>  
> This is because our Runtime stores the following two files at the end of the job:
> {code:java}
> dbfs:/tmp/petastorm/_committed_4686077819843716563	_committed_4686077819843716563	1965
> dbfs:/tmp/petastorm/_started_4686077819843716563{code}



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