[jira] [Commented] (SPARK-7549) Support aggregating over nested fields

["Nicholas Chammas (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-7549?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14538965#comment-14538965 ] 

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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