How to Retrieve Top N Items per User Group in a Spark SQL DataFrame using Scala?

Generating TopN for Grouped Data in Spark SQL DataFrame

Problem:

Given a Spark SQL DataFrame with columns representing users, items, and user ratings, how can we group by user and then retrieve the top N items for each group using Scala?

Answer:

To achieve this, we can utilize the rank window function as follows:

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.{rank, desc}

val n: Int = ???

// Define the window specification
val w = Window.partitionBy($"user").orderBy(desc("rating"))

// Calculate the rank for each item
val withRank = df.withColumn("rank", rank.over(w))

// Filter to retain only the top N items
val topNPerUser = withRank.where($"rank" <p><strong>Further Details:</strong></p>

• The rank function assigns a rank to each item within each user group, with the highest-rated item receiving a rank of 1.
• The w window specification defines the scope of the ranking by partitioning the DataFrame by user and ordering the data descending by rating.
• The withRank DataFrame now includes a "rank" column, which can be used for filtering.
• The topNPerUser DataFrame contains only the top N items for each user, based on their rating.

If you prefer to use the row_number function, which assigns sequential row numbers rather than ranks (ignoring ties), you can replace rank with row_number in the window definition:

val w = Window.partitionBy($"user").orderBy(desc("rating"))

val withRowNumber = df.withColumn("row_number", row_number.over(w))

val topNPerUser = withRowNumber.where($"row_number" 

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