How Can I Keep Other Columns When Grouping in a Spark DataFrame?

Preserving Other Columns When Grouping in Spark DataFrame

When performing a DataFrame groupby operation, it's common to encounter the issue of only receiving the grouped columns and the aggregate values. This can be inconvenient if you need to preserve other columns from the original DataFrame.

For instance, consider the following groupby:

df.groupBy(df("age")).agg(Map("id" -> "count"))

This will return a DataFrame containing only the "age" and "count(id)" columns. However, if "name" and other columns exist in the original DataFrame, you may want to include them in the result.

In standard SQL, this can be achieved using the following query:

select name, age, count(id) from df group by age

To replicate this behavior in Spark, you can join the aggregated results with the original table. The key column(s) for the join should be the same as the groupby column(s).

Here's an example using Spark SQL:

val groupedDf = df.groupBy($"age").agg(count($"id").as("count"))
val joinedDf = df.join(groupedDf, Seq("age"), "left")

The joinedDf will now contain all the original columns along with the aggregated "count" column.

Another approach is to use arbitrary aggregates like first or last to include additional columns. For example:

val groupedDf = df.groupBy($"age").agg(first($"name").as("name"), count($"id").as("count"))

This will ensure that the "name" column is preserved in the aggregated DataFrame.

While these methods allow you to retrieve other columns during grouping, it's important to note that such queries are generally not well-defined and may exhibit varying behavior across different systems.

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