[jira] [Commented] (SPARK-12072) python dataframe ._jdf.schema().json() breaks on large metadata dataframes

["Ben Teeuwen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12072?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15432459#comment-15432459 ] 

Ben Teeuwen commented on SPARK-12072:
-------------------------------------

[~holdenk] we haven't been able to test the patch above (yet). Workarounds have been created using non-dataframe like operations. But recently I seem to have hit a wall related to the above. The discussion I've started on the spark 'user' mailinglist, topic "OOM with StringIndexer, 800m rows & 56m distinct value column", is that related to this ticket? Do you think your patch addresses it?

> python dataframe ._jdf.schema().json() breaks on large metadata dataframes
> --------------------------------------------------------------------------
>
>                 Key: SPARK-12072
>                 URL: https://issues.apache.org/jira/browse/SPARK-12072
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 1.5.2
>            Reporter: Rares Mirica
>
> When a dataframe contains a column with a large number of values in ml_attr, schema evaluation will routinely fail on getting the schema as json, this will, in turn, cause a bunch of problems with, eg: calling udfs on the schema because calling columns relies on _parse_datatype_json_string(self._jdf.schema().json())



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