I created https://issues.apache.org/jira/browse/SPARK-2620 to track this.

Maybe useful to know, this is a regression on Spark 1.0.0. I tested the same sample code on 0.9.1 and it worked (we have several jobs using case classes as key aggregators, so it better does)

-kr, Gerard.


On Tue, Jul 22, 2014 at 5:37 PM, Gerard Maas <gerard.maas@gmail.com> wrote:
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.siegmann@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.maas@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.maas@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.




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