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From "holdenk (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-12072) python dataframe ._jdf.schema().json() breaks on large metadata dataframes
Date Tue, 01 Dec 2015 21:01:11 GMT

    [ 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())



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