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https://issues.apache.org/jira/browse/SPARK-7549?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14538965#comment-14538965
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Nicholas Chammas commented on SPARK-7549:
-----------------------------------------
To provide a motivating example for the record (copied from my comment on
SPARK-7548), consider a DataFrame that looks like this:
{code}
>>> a = {
'test_name': 'abracadabra',
'results': [{'time': 14.7}, {'time': 22.3}]
}
>>> df = sqlContext.jsonRDD(sc.parallelize([json.dumps(a)]))
>>> df.printSchema()
root
|-- results: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- time: double (nullable = true)
|-- test_name: string (nullable = true)
>>> print df.select('results.time').collect()
[Row(time=[14.7, 22.3])]
{code}
It is currently not possible to aggregate over the nested {{time}} field as
follows:
{code}
df.groupBy('test_name').avg('results.time')
{code}
An alternative to supporting this kind of aggregation would be to offer some
way to "promote" the nested column to a top-level column, like
[{{explode()}}|SPARK-7548], but the more straightforward solution is to enable
aggregations on nested columns directly.
> Support aggregating over nested fields
> --------------------------------------
>
> Key: SPARK-7549
> URL: https://issues.apache.org/jira/browse/SPARK-7549
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Reynold Xin
>
> Would be nice to be able to run sum, avg, min, max (and other numeric
> aggregate expressions) on arrays.
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