How to Replicate SQL's row_number() Function in Spark Using RDDs?

Getting a SQL Row_Number Equivalent for a Spark RDD

In SQL, the row_number() function allows for the generation of a unique row number for each row in a partitioned and ordered table. This functionality can be replicated in Spark using RDDs, and this article outlines how to achieve this.

Consider an RDD with the schema (K, V), where V represents a tuple (col1, col2, col3). The goal is to obtain a new RDD with an additional column representing the row number for each tuple, organized by a partition on key K.

First Attempt

One common approach is to collect the RDD and sort it using functions like sortBy(), sortWith(), or sortByKey(). However, this method does not maintain the partitioning aspect of the row_number() function.

Partition-Aware Ordering

To achieve partitioned row numbers, you can leverage Window functions in Spark. However, Window functions are primarily designed for use with DataFrames, not RDDs.

Using DataFrames

Fortunately, in Spark 1.4 onwards, row_number() functionality is available for DataFrames. Following this example:

# Create a test DataFrame
testDF = sc.parallelize(
    (Row(k="key1", v=(1,2,3)),
     Row(k="key1", v=(1,4,7)),
     Row(k="key1", v=(2,2,3)),
     Row(k="key2", v=(5,5,5)),
     Row(k="key2", v=(5,5,9)),
     Row(k="key2", v=(7,5,5))
    )
).toDF()

# Add the partitioned row number
(testDF
 .select("k", "v",
         F.rowNumber()
         .over(Window
               .partitionBy("k")
               .orderBy("k")
              )
         .alias("rowNum")
        )
 .show()
)

This will generate a DataFrame with the partitioned row numbers.

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