working with two RDDs apache spark - scala

Question

I am using calliope i.e. spark plugin to connect with cassandra. I have created 2 RDDs which looks like
class A
val persistLevel = org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK
val cas1 = CasBuilder.cql3.withColumnFamily("cassandra_keyspace", "cassandra_coulmn_family 1")
val sc1 = new SparkContext("local", "name it any thing ")
var rdd1 = sc.cql3Cassandra[SCALACLASS_1](cas1)
var rddResult1 = rdd1.persist(persistLevel)
class B
val cas2 = CasBuilder.cql3.withColumnFamily("cassandra_keyspace", "cassandra_coulmn_family 2")
var rdd2 = sc1.cql3Cassandra[SCALACLASS_2](cas2)
var rddResult2 = rdd2.persist(persistLevel)
somehow following code base which creates a new RDD using the other 2 is not working. Is it possible that we cannot iterate with 2 RDDs together?
Here is the code snippet which is not working -
case class Report(id: Long, anotherId: Long)
var reportRDD = rddResult2.flatMap(f => {
val buf = List[Report]()
**rddResult1.collect().toList**.foldLeft(buf)((k, v) => {
val buf1 = new ListBuffer[Report]
buf ++ v.INSTANCE_VAR_FROM_SCALACLASS_1.foldLeft(buf1)((ik, iv) => {
buf1 += Report(f.INSTANCE_VAR_FROM_SCALACLASS_1, iv.INSTANCE_VAR_FROM_SCALACLASS_2)
})
})
})
while if I replace the bold thing and initialize a val for it like -
val collection = rddResult1.collect().toList
var reportRDD = rddResult2.flatMap(f => {
val buf = List[Report]()
**collection**.foldLeft(buf)((k, v) => {
val buf1 = new ListBuffer[Report]
buf ++ v.INSTANCE_VAR_FROM_SCALACLASS_1.foldLeft(buf1)((ik, iv) => {
buf1 += Report(f.INSTANCE_VAR_FROM_SCALACLASS_1, iv.INSTANCE_VAR_FROM_SCALACLASS_2)
})
})
})
it works, is there any explaination?

Answer 1

You are mixing a transformation with an action. The closure of the rdd2.flatMap is executed on the workers, while rdd1.collect is an 'action' in Spark lingo and delivers data back to the driver. So, informally, you could say that the data is not there when you try to flatMap over it. (I don't know enough of the internals -yet- to pin-point the exact root-cause)
If you want to operate on both RDDs distributedly, you should join them using one of the join functions (join, leftOuterJoin, rightOuterJoin, cogroup).
E.g.
val mappedRdd1 = rdd1.map(x=> (x.id,x))
val mappedRdd2 = rdd2.map(x=> (x.customerId, x))
val joined = mappedRdd1.join(mappedRdd2)
joined.flatMap(...reporting logic..).collect

Answer 2

You can operate on RDDs in the application. But you cannot operate on RDDs in the executors (the worker nodes). The executors cannot give commands to drive the cluster. The code inside flatMap runs on the executors.
In the first case, you try to operate on an RDD in the executor. I reckon you would get a NotSerializableException as you cannot even send the RDD object to the executors. In the second case, you pull the RDD contents to the application, and then send this simple List to the executors. (Lambda captures are automatically serialized.)