How to Efficiently Perform Grouped Top-N Aggregation in Spark DataFrames?

How to Perform Grouped TopN Aggregation Using Spark DataFrame

In Spark SQL, you can leverage SQL-like syntax to perform complex data manipulations. One common task is to group data and retrieve the top N values from each group. Here's how you can achieve this using a Spark DataFrame:

To group data by a specific column, specify the column name in the GROUP BY clause:

<br>df.groupBy("user")<br>

To sort the results within each group, you can use the orderBy function:

<br>df.groupBy("user").orderBy(desc("rating"))<br>

This will sort the data in descending order of the rating column for each user group.

To retrieve only the top N records from each group, you can use the limit function:

<br>df.groupBy("user").orderBy(desc("rating")).limit(n)<br>

Where n is the desired number of top records to retrieve.

Alternatively, you can use window functions to rank the records within each group and then filter based on the rank:

<br>import org.apache.spark.sql.expressions.Window<br>import org.apache.spark.sql.functions.{rank, desc}</p>
<p>// Window definition<br>val w = Window.partitionBy($"user").orderBy(desc("rating"))</p>
<p>// Filter<br>df.withColumn("rank", rank.over(w)).where($"rank" <= n)<br>

Note that if you don't care about ties, you can replace the rank function with the row_number function.

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