How to replicate SQL's `row_number()` function using a Spark RDD?

How to Replicate SQL's row_number() for a Spark RDD?

In SQL, the row_number() function assigns a unique row number to each record in a table, partitioned by a given set of columns and ordered by another set. Replicating this functionality for a Spark RDD can be done using the following steps:

1. Create a PairRDD of Key-Value Pairs

Convert the RDD of tuples (K, V) into a PairRDD of (K, (V, 1)) pairs. This will allow sorting to be applied based on both the key K and the placeholder value 1.

2. Sort the RDD

Use sortByKey() to sort the PairRDD based on the key K. This will group all records with the same key together.

3. Zip With Index

Call zipWithIndex() on the sorted RDD to add an extra column representing the row number within each key partition.

4. Combine Key, Values, and Row Numbers

Extract the key, values, and row numbers from the PairRDD using a mapping function and combine them into a new tuple (K, V, rowNum).

This process allows you to assign row numbers to each record in the RDD, effectively replicating the functionality of SQL's row_number() function with partitioning.

Example:

val temp1 = sc.parallelize(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 temp2 = temp1
  .map(x => (x, 1))
  .sortByKey()
  .zipWithIndex()
  .map(a => (a._1._1, a._1._2._1, a._1._2._2, a._1._2._3, a._2 + 1))

temp2.collect().foreach(println)

Output:

((1,2),1,2,3,2)
((1,2),1,4,7,1)
((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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