Spark: Efficient mass lookup in pair RDD's

Question

In Apache Spark I have two RDD's. The first data : RDD[(K,V)] containing data in key-value form. The second pairs : RDD[(K,K)] contains a set of interesting key-pairs of this data.

How can I efficiently construct an RDD pairsWithData : RDD[((K,K)),(V,V))], such that it contains all the elements from pairs as the key-tuple and their corresponding values (from data) as the value-tuple?

Some properties of the data:

• The keys in data are unique
• All entries in pairs are unique
• For all pairs (k1,k2) in pairs it is guaranteed that k1 <= k2
• The size of 'pairs' is only a constant the size of data |pairs| = O(|data|)
• Current data sizes (expected to grow): |data| ~ 10^8, |pairs| ~ 10^10

Current attempts

Here is some example code in Scala:

import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._

// This kind of show the idea, but fails at runtime.
def massPairLookup1(keyPairs : RDD[(Int, Int)], data : RDD[(Int, String)]) = {
  keyPairs map {case (k1,k2) =>
    val v1 : String = data lookup k1 head;
    val v2 : String = data lookup k2 head;
    ((k1, k2), (v1,v2))
  }
}

// Works but is O(|data|^2)
def massPairLookup2(keyPairs : RDD[(Int, Int)], data : RDD[(Int, String)]) = {
  // Construct all possible pairs of values
  val cartesianData = data cartesian data map {case((k1,v1),(k2,v2)) => ((k1,k2),(v1,v2))}
  // Select only the values who's keys are in keyPairs
  keyPairs map {(_,0)} join cartesianData mapValues {_._2}
}

// Example function that find pairs of keys
// Runs in O(|data|) in real life, but cannot maintain the values
def relevantPairs(data : RDD[(Int, String)]) = {
  val keys = data map (_._1)
  keys cartesian keys filter {case (x,y) => x*y == 12 && x < y}
}

// Example run
val data = sc parallelize(1 to 12) map (x => (x, "Number " + x))
val pairs = relevantPairs(data)
val pairsWithData = massPairLookup2(pairs, data) 


// Print: 
// ((1,12),(Number1,Number12))
// ((2,6),(Number2,Number6))
// ((3,4),(Number3,Number4))
pairsWithData.foreach(println)

Attempt 1

First I tried just using the lookup function on data, but that throws an runtime error when executed. It seems like self is null in the PairRDDFunctions trait.

In addition I am not sure about the performance of lookup. The documentation says This operation is done efficiently if the RDD has a known partitioner by only searching the partition that the key maps to. This sounds like n lookups takes O(n*|partition|) time at best, which I suspect could be optimized.

Attempt 2

This attempt works, but I create |data|^2 pairs which will kill performance. I do not expect Spark to be able to optimize that away.

Answer 1

Your lookup 1 doesn't work because you cannot perform RDD transformations inside workers (inside another transformation).

In the lookup 2, I don't think it's necessary to perform full cartesian...

You can do it like this:

val firstjoin = pairs.map({case (k1,k2) => (k1, (k1,k2))})
    .join(data)
    .map({case (_, ((k1, k2), v1)) => ((k1, k2), v1)})
val result = firstjoin.map({case ((k1,k2),v1) => (k2, ((k1,k2),v1))})
    .join(data)
    .map({case(_, (((k1,k2), v1), v2))=>((k1, k2), (v1, v2))})

Or in a more dense form:

    val firstjoin = pairs.map(x => (x._1, x)).join(data).map(_._2)
    val result = firstjoin.map({case (x,y) => (x._2, (x,y))})
        .join(data).map({case(x, (y, z))=>(y._1, (y._2, z))})

I don't think you can do it more efficiently, but I might be wrong...