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https://issues.apache.org/jira/browse/SPARK-12072?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15035823#comment-15035823
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Rares Mirica commented on SPARK-12072:
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My set is in the millions of parameters. I believe you are right, the schema
should be accessible in a round-about way with minimum serialisation. I realise
this would be one of those "add another layer of abstraction" solutions that
might not be a good idea but the current state means that dataframes combined
with some of the transformers in the pipeline api simply don't scale for python
at least.
> 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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