How to make spark partition sticky, i.e. stay with node? - streaming

Question

I am trying to use Spark Streaming 1.2.0. At some point, I grouped streaming data by key and then applied some operation on them.
The following is a segment of the test code:
...
JavaPairDStream<Integer, Iterable<Integer>> grouped = mapped.groupByKey();
JavaPairDStream<Integer, Integer> results = grouped.mapToPair(
new PairFunction<Tuple2<Integer, Iterable<Integer>>, Integer, Integer>() {
#Override
public Tuple2<Integer, Integer> call(Tuple2<Integer, Iterable<Integer>> tp) throws Exception {
TaskContext tc = TaskContext.get();
String ip = InetAddress.getLocalHost().getHostAddress();
int key = tp._1();
System.out.println(ip + ": Partition: " + tc.partitionId() + "\tKey: " + key);
return new Tuple2<>(key, 1);
}
});
results.print();
mapped is an JavaPairDStream wrapping a dummy receiver that stores an array of integers every second.
I ran this app on a cluster with two slaves, each has 2 cores.
When I checked out the printout, it seems that partitions were not assigned to nodes permanently (or in a "sticky" fashion). They moved between the two nodes frequently. This creates a problem for me.
In my real application, I need to load fairly large amount of geo data per partition. These geo data will be used to process the data in the streams. I can only afford to load part of the geo data set per partition. If the partition moves between nodes, I will have to move the geo data too, which can be very expensive.
Is there a way to make the partitions sticky, i.e. partition 0,1,2,3 stay with node 0, partition 4,5,6,7 stay with node 1?
I have tried setting spark.locality.wait to a large number, say, 1000000. And it did not work.
Thanks.

Answer 1

I found a workaround.
I can make my auxiliary data a RDD. Partition it and cache it.
Later, I can cogroup it with other RDDs and Spark will try to keep the cached RDD partitions where they are and not shuffle them. E.g.
...
JavaPairRDD<Integer, GeoData> geoRDD =
geoRDD1.partitionBy(new HashPartitioner(num)).cache();
Later, do this
JavaPairRDD<Integer, Integer> someOtherRDD = ...
JavaPairRDD<Integer, Tuple2<Iterator<GeoData>>, Iterator<Integer>>> grp =
geoRDD.cogroup(someOtherRDD);
Then, you can use foreach on the cogroupped rdd to process the input data with geo data.