How to Keep Non-Aggregated Columns After a Spark DataFrame GroupBy?

How to Preserve Non-Aggregated Columns in Spark DataFrame GroupBy

When aggregating data using DataFrame's groupBy method, the resulting DataFrame only contains the group-by key and the aggregated values. However, in some cases, it may be desirable to also include non-aggregated columns from the original DataFrame in the result.

Limitation of Spark SQL

Spark SQL follows the convention of pre-1999 SQL, which does not allow additional columns in aggregation queries. Aggregations like count produce results that are not well-defined when applied to multiple columns, so different systems handling such queries exhibit varying behaviors.

Solution:

To preserve non-aggregated columns in a Spark DataFrame groupBy, there are several options:

1. Join Original DataFrame: Join the aggregated DataFrame with the original DataFrame to add the missing columns.

val aggregatedDf = df.groupBy(df("age")).agg(Map("id" -> "count"))
val joinedDf = aggregatedDf.join(df, Seq("age"), "left")

2. Use Window Functions: Use window functions like first or last to include additional columns in the aggregation query. However, this approach can be computationally expensive in certain scenarios.

import org.apache.spark.sql.expressions.Window
val windowSpec = Window.partitionBy(df("age"))
val aggregatedDf = df.withColumn("name", first(df("name")).over(windowSpec))
  .groupBy(df("age")).agg(Map("id" -> "count"))

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