[
https://issues.apache.org/jira/browse/SPARK-12072?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15034564#comment-15034564
]
holdenk commented on SPARK-12072:
---------------------------------
Around what # of attrs are you seeing failures at? Do you think we should
instead pass the schema as native Java objects and add some utils for Python to
work with them?
> 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())
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
