Converting SQL window function to Spark Scala: Need help with ROW_NUMBER()

Data and Databases
1

I’m working on moving a SQL query to Spark Scala. The original query uses ROW_NUMBER() with PARTITION BY and ORDER BY. Here’s what I’ve got so far:

val joinedDF = df2.join(df1, $"df2.member_id" === $"df1.senderId")
  .select($"df1.senderId", $"df2.company_id")

But I’m stuck on how to add the ROW_NUMBER() part. The SQL also has a WHERE clause with a date range condition. Any tips on how to complete this in Spark Scala?

I’m pretty new to Spark and window functions. It would be great if someone could explain how to approach this step by step. Thanks!

2

hey Sam_Mischief! Spark’s window functions are super cool! have you tried using Window.partitionBy() and row_number()? they’re like SQL’s ROW_NUMBER(). for the date range, you could use filter() after applying the window function. what specific parts are tripping you up? maybe we could brainstorm some solutions together!

3

yo Sam_Mischief, spark’s got ur back! for ROW_NUMBER(), try this:

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

val windowSpec = Window.partitionBy("company_id").orderBy("date_col")
val result = joinedDF.withColumn("row_num", row_number().over(windowSpec))
  .filter($"date_col".between(startDate, endDate))
  .filter($"row_num" === 1)

hope that helps! lmk if u need more info

4

To implement the ROW_NUMBER() function in Spark Scala, you’ll need to use window functions. Here’s a step-by-step approach:

  1. Import the necessary functions:
    import org.apache.spark.sql.expressions.Window
    import org.apache.spark.sql.functions._

  2. Define your window specification:
    val windowSpec = Window.partitionBy(“company_id”).orderBy(“some_date_column”)

  3. Apply the row_number function:
    val resultDF = joinedDF.withColumn(“row_num”, row_number().over(windowSpec))

  4. Add your date range filter:
    val filteredDF = resultDF.filter($“some_date_column”.between(startDate, endDate))

  5. Select your final columns:
    val finalDF = filteredDF.filter($“row_num” === 1).select(“senderId”, “company_id”)

This should give you equivalent functionality to your SQL query with ROW_NUMBER(). Adjust column names and date range as needed for your specific use case.