How to Simulate SQL's `ROW_NUMBER()` Function in Spark RDD?

SQL Row Number Equivalent in Spark RDD

In Spark, obtaining a row number equivalent to SQL's row_number() over (partition by ... order by ...) for an RDD can be achieved using Spark 1.4's enhanced functionality.

Solution:

1. Create a Test RDD:

val sample_data = Seq(((3, 4), 5, 5, 5),
((3, 4), 5, 5, 9),
((3, 4), 7, 5, 5),
((1, 2), 1, 2, 3),
((1, 2), 1, 4, 7),
((1, 2), 2, 2, 3))

val temp1 = sc.parallelize(sample_data)

2. Partition by Key and Order:

Utilize the rowNumber() function introduced in Spark 1.4 to create a partitioned window:

import org.apache.spark.sql.expressions.Window

val partitionedRdd = temp1
  .map(x => (x._1, x._2._1, x._2._2, x._2._3))
  .groupBy(_._1)
  .mapGroups((_, entries) =>
    entries.toList
      .sortBy(x => (x._2, -x._3, x._4))
      .zipWithIndex
      .map(x => (x._1._1, x._1._2, x._1._3, x._1._4, x._2 + 1))
  )

3. Output the Result:

partitionedRdd.foreach(println)

// Example output:
// ((1,2),1,4,7,1)
// ((1,2),1,2,3,2)
// ((1,2),2,2,3,3)
// ((3,4),5,5,5,4)
// ((3,4),5,5,9,5)
// ((3,4),7,5,5,6)

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