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