This is just a deficiency of the api, imo. I agree: mapValues could definitely be a function (K, V)=>V1. The option isn't set by the function, it's on the RDD. So you could look at the code and do this. https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/RDD.scala
def mapValues[U](f: V => U): RDD[(K, U)] = { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) }, preservesPartitioning = true) } What you want: def mapValues[U](f: (K, V) => U): RDD[(K, U)] = { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.map { case t@(k, _) => (k, cleanF(t)) }, preservesPartitioning = true) } One of the nice things about spark is that making such new operators is very easy :) 2015-03-26 17:54 GMT-04:00 Zhan Zhang <zzh...@hortonworks.com>: > Thanks Jonathan. You are right regarding rewrite the example. > > I mean providing such option to developer so that it is controllable. > The example may seems silly, and I don’t know the use cases. > > But for example, if I also want to operate both the key and value part to > generate some new value with keeping key part untouched. Then mapValues may > not be able to do this. > > Changing the code to allow this is trivial, but I don’t know whether > there is some special reason behind this. > > Thanks. > > Zhan Zhang > > > > > On Mar 26, 2015, at 2:49 PM, Jonathan Coveney <jcove...@gmail.com> wrote: > > I believe if you do the following: > > > sc.parallelize(List(1,2,3,4,5,5,6,6,7,8,9,10,2,4)).map((_,1)).reduceByKey(_+_).mapValues(_+1).reduceByKey(_+_).toDebugString > > (8) MapPartitionsRDD[34] at reduceByKey at <console>:23 [] > | MapPartitionsRDD[33] at mapValues at <console>:23 [] > | ShuffledRDD[32] at reduceByKey at <console>:23 [] > +-(8) MapPartitionsRDD[31] at map at <console>:23 [] > | ParallelCollectionRDD[30] at parallelize at <console>:23 [] > > The difference is that spark has no way to know that your map closure > doesn't change the key. if you only use mapValues, it does. Pretty cool > that they optimized that :) > > 2015-03-26 17:44 GMT-04:00 Zhan Zhang <zzh...@hortonworks.com>: > >> Hi Folks, >> >> Does anybody know what is the reason not allowing preserverPartitioning >> in RDD.map? Do I miss something here? >> >> Following example involves two shuffles. I think if preservePartitioning >> is allowed, we can avoid the second one, right? >> >> val r1 = sc.parallelize(List(1,2,3,4,5,5,6,6,7,8,9,10,2,4)) >> val r2 = r1.map((_, 1)) >> val r3 = r2.reduceByKey(_+_) >> val r4 = r3.map(x=>(x._1, x._2 + 1)) >> val r5 = r4.reduceByKey(_+_) >> r5.collect.foreach(println) >> >> scala> r5.toDebugString >> res2: String = >> (8) ShuffledRDD[4] at reduceByKey at <console>:29 [] >> +-(8) MapPartitionsRDD[3] at map at <console>:27 [] >> | ShuffledRDD[2] at reduceByKey at <console>:25 [] >> +-(8) MapPartitionsRDD[1] at map at <console>:23 [] >> | ParallelCollectionRDD[0] at parallelize at <console>:21 [] >> >> Thanks. >> >> Zhan Zhang >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >> > >