Spark: Best practice for retrieving big data from RDD to local machine

Question

I've got big RDD(1gb) in yarn cluster. On local machine, which use this cluster I have only 512 mb. I'd like to iterate over values in RDD on my local machine. I can't use collect(), because it would create too big array locally which more then my heap. I need some iterative way. There is method iterator(), but it requires some additional information, I can't provide.

UDP: commited toLocalIterator method

question from:https://stackoverflow.com/questions/21698443/spark-best-practice-for-retrieving-big-data-from-rdd-to-local-machine

Answer 1

Update: RDD.toLocalIterator method that appeared after the original answer has been written is a more efficient way to do the job. It uses runJob to evaluate only a single partition on each step.

TL;DR And the original answer might give a rough idea how it works:

First of all, get the array of partition indexes:

val parts = rdd.partitions

Then create smaller rdds filtering out everything but a single partition. Collect the data from smaller rdds and iterate over values of a single partition:

for (p <- parts) {
    val idx = p.index
    val partRdd = rdd.mapPartitionsWithIndex(a => if (a._1 == idx) a._2 else Iterator(), true)
    //The second argument is true to avoid rdd reshuffling
    val data = partRdd.collect //data contains all values from a single partition 
                               //in the form of array
    //Now you can do with the data whatever you want: iterate, save to a file, etc.
}

I didn't try this code, but it should work. Please write a comment if it won't compile. Of cause, it will work only if the partitions are small enough. If they aren't, you can always increase the number of partitions with rdd.coalesce(numParts, true).