Oh I see, I think you're trying to do something like (in SQL):
SELECT order, mean(price) FROM orders GROUP BY order
In this case, I'm not aware of a way to use the DoubleRDDFunctions, since
you have a single RDD of pairs where each pair is of type (KeyType,
Iterable[Double]).
It seems to me that you want to write a function:
def stats(numList: Iterable[Double]): org.apache.spark.util.StatCounter
and then use
pairRdd.mapValues( value => stats(value) )
On Fri, Sep 12, 2014 at 5:05 PM, rzykov <rzy...@gmail.com> wrote:
> Tried this:
>
> ordersRDD.join(ordersRDD).map{case((partnerid, itemid),((matchedida,
> pricea), (matchedidb, priceb))) => ((matchedida, matchedidb), (if(priceb >
> 0) (pricea/priceb).toDouble else 0.toDouble))}
> .groupByKey
> .values.stats
> .first
>
> Error:
> <console>:37: error: could not find implicit value for parameter num:
> Numeric[Iterable[Double]]
> .values.stats
>
>
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/Computing-mean-and-standard-deviation-by-key-tp11192p14065.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
> For additional commands, e-mail: user-h...@spark.apache.org
>
>
