How to Replicate SQL's row_number() Functionality with Spark RDDs for Partitioned Data?

Spark RDD Equivalent of SQL Row_Number for Partitioned Data

In SQL, row_number() generates a sequential number for rows within partitioned datasets. This feature is not directly available in Spark RDDs. However, there are workarounds to achieve similar functionality.

Partitioning the RDD

Partitioning is crucial for generating row numbers within groups. In your case, you need to partition the RDD by the key_value (K) before sorting. Consider the updated code:

val temp2 = temp1
  .map(x => (x._1, (x._2, x._3, x._4)))
  .sortBy(a => (a._1, -a._2._2, -a._2._3))
  .zipWithIndex
  .map(a => (a._1._1, a._1._2._1, a._1._2._2, a._1._2._3, a._2 + 1))

By applying sortBy to (a._1, -a._2._2, -a._2._3), you're sorting based on key_value, then descending col2, and finally descending col3, mimicking the SQL row_number() behavior.

Adding Row Numbers

After partitioning and sorting, you can add the row numbers using zipWithIndex:

val rowNums = temp2.map(a => (a._1, a._2, a._3, a._4, a._5)).cache()

Note: The DataFrame Implementation Provided in the Response Is a Solution for DataFrames, but Not for RDDs.

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