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https://issues.apache.org/jira/browse/SPARK-4561?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Michael Armbrust updated SPARK-4561:
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Target Version/s: 1.4.0 (was: 1.3.0)
> PySparkSQL's Row.asDict() should convert nested rows to dictionaries
> --------------------------------------------------------------------
>
> Key: SPARK-4561
> URL: https://issues.apache.org/jira/browse/SPARK-4561
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 1.2.0
> Reporter: Josh Rosen
> Assignee: Davies Liu
>
> In PySpark, you can call {{.asDict
> ()}} on a SparkSQL {{Row}} to convert it to a dictionary. Unfortunately,
> though, this does not convert nested rows to dictionaries. For example:
> {code}
> >>> sqlContext.sql("select results from results").first()
> Row(results=[Row(time=3.762), Row(time=3.47), Row(time=3.559),
> Row(time=3.458), Row(time=3.229), Row(time=3.21), Row(time=3.166),
> Row(time=3.276), Row(time=3.239), Row(time=3.149)])
> >>> sqlContext.sql("select results from results").first().asDict()
> {u'results': [(3.762,),
> (3.47,),
> (3.559,),
> (3.458,),
> (3.229,),
> (3.21,),
> (3.166,),
> (3.276,),
> (3.239,),
> (3.149,)]}
> {code}
> Actually, it looks like the nested fields are just left as Rows (IPython's
> fancy display logic obscured this in my first example):
> {code}
> >>> Row(results=[Row(time=1), Row(time=2)]).asDict()
> {'results': [Row(time=1), Row(time=2)]}
> {code}
> Here's the output I'd expect:
> {code}
> >>> Row(results=[Row(time=1), Row(time=2)])
> {'results' : [{'time': 1}, {'time': 2}]}
> {code}
> I ran into this issue when trying to use Pandas dataframes to display nested
> data that I queried from Spark SQL.
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