Yes, right. 'sc.parallelize(ps).map(x=> (**x.name**,1)).groupByKey().collect
'
An oversight from my side.
Thanks!, Gerard.
On Tue, Jul 22, 2014 at 5:24 PM, Daniel Siegmann <daniel.siegm...@velos.io>
wrote:
> I can confirm this bug. The behavior for groupByKey is the same as
> reduceByKey - your example is actually grouping on just the name. Try
> this:
>
> sc.parallelize(ps).map(x=> (x,1)).groupByKey().collect
> res1: Array[(P, Iterable[Int])] = Array((P(bob),ArrayBuffer(1)),
> (P(bob),ArrayBuffer(1)), (P(alice),ArrayBuffer(1)),
> (P(charly),ArrayBuffer(1)))
>
>
> On Tue, Jul 22, 2014 at 10:30 AM, Gerard Maas <gerard.m...@gmail.com>
> wrote:
>
>> Just to narrow down the issue, it looks like the issue is in
>> 'reduceByKey' and derivates like 'distinct'.
>>
>> groupByKey() seems to work
>>
>> sc.parallelize(ps).map(x=> (x.name,1)).groupByKey().collect
>> res: Array[(String, Iterable[Int])] = Array((charly,ArrayBuffer(1)),
>> (abe,ArrayBuffer(1)), (bob,ArrayBuffer(1, 1)))
>>
>>
>>
>> On Tue, Jul 22, 2014 at 4:20 PM, Gerard Maas <gerard.m...@gmail.com>
>> wrote:
>>
>>> Using a case class as a key doesn't seem to work properly. [Spark 1.0.0]
>>>
>>> A minimal example:
>>>
>>> case class P(name:String)
>>> val ps = Array(P("alice"), P("bob"), P("charly"), P("bob"))
>>> sc.parallelize(ps).map(x=> (x,1)).reduceByKey((x,y) => x+y).collect
>>> [Spark shell local mode] res : Array[(P, Int)] = Array((P(bob),1),
>>> (P(bob),1), (P(abe),1), (P(charly),1))
>>>
>>> In contrast to the expected behavior, that should be equivalent to:
>>> sc.parallelize(ps).map(x=> (x.name,1)).reduceByKey((x,y) => x+y).collect
>>> Array[(String, Int)] = Array((charly,1), (abe,1), (bob,2))
>>>
>>> Any ideas why this doesn't work?
>>>
>>> -kr, Gerard.
>>>
>>
>>
>
>
> --
> Daniel Siegmann, Software Developer
> Velos
> Accelerating Machine Learning
>
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