Github user koertkuipers commented on the issue:
https://github.com/apache/spark/pull/15979
admittedly the result looks weird. it really should be:
+-----------+--------+
| key|count(1)|
+-----------+--------+
| null| 1|
| [1,1]| 1|
+-----------+--------+
is that a separate bug or related? i remember running into this before,
because serializing and then deserializing None comes back out as
Some((null, null)), which causes NPE in codegen. i ran into this with
Aggregator buffers.
On Sun, Dec 4, 2016 at 12:13 PM, Koert Kuipers <[email protected]> wrote:
> spark 2.0.x does not have mapValues. but this works:
>
> scala> Seq(("a", Some((1, 1))), ("a", None)).toDS.groupByKey(_._2).
> count.show
> +-----------+--------+
> | key|count(1)|
> +-----------+--------+
> |[null,null]| 1|
> | [1,1]| 1|
> +-----------+--------+
>
>
>
> On Sun, Dec 4, 2016 at 9:59 AM, Koert Kuipers <[email protected]> wrote:
>
>> Yes it worked before
>>
>> On Dec 4, 2016 02:33, "Wenchen Fan" <[email protected]> wrote:
>>
>>> val x: Dataset[String, Option[(String, String)]] = ...
>>> x.groupByKey(_._1).mapValues(_._2).agg(someAgg)
>>>
>>> Does it work before?
>>>
>>> Please see the discussion in the JIRA: https://issues.apache.org/jira
>>> /browse/SPARK-18251
>>> Ideally we have a map between type T and catalyst schema, and Option[T]
>>> maps to the same catalyst schema with T, with additional null handling.
>>> We shouldn't change this mapping, which means we can't use a single
field
>>> struct type to represent Option[T].
>>>
>>> It's still possible to support Option[T] completely(without breaking
>>> backward compatibility), but that may need a lof of hacky code and
special
>>> handling, I don't think it worth, as we can easy work around it, by
>>> Tuple1.
>>>
>>> â
>>> You are receiving this because you commented.
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>
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