How to Efficiently Select the Top Row for Each Group in Spark?

Efficiently select the first row of each group

This article aims to extract the row of data with the highest "total value" in each "hour" and "category" grouping. There are several ways to do this:

Use window functions:

Window functions provide an efficient way to perform calculations within each grouping. Here’s one way to do it:

<code>import org.apache.spark.sql.functions.{row_number, max, broadcast}
import org.apache.spark.sql.expressions.Window

val w = Window.partitionBy($"Hour").orderBy($"TotalValue".desc)

val dfTop = df.withColumn("rn", row_number.over(w)).where($"rn" === 1).drop("rn")</code>

Using SQL aggregations and joins:

Another approach is to utilize SQL aggregation and subsequent joins:

<code>val dfMax = df.groupBy($"Hour".as("max_hour")).agg(max($"TotalValue").as("max_value"))

val dfTopByJoin = df.join(broadcast(dfMax),
    ($"Hour" === $"max_hour") && ($"TotalValue" === $"max_value"))
  .drop("max_hour")
  .drop("max_value")</code>

Use structure sorting:

A clever way is to sort a struct containing "Total Value" and "Category":

<code>val dfTop = df.select($"Hour", struct($"TotalValue", $"Category").alias("vs"))
  .groupBy($"Hour")
  .agg(max("vs").alias("vs"))
  .select($"Hour", $"vs.Category", $"vs.TotalValue")</code>

Using DataSet API (Spark 1.6):

The DataSet API provides a concise way to achieve the same result:

<code>case class Record(Hour: Integer, Category: String, TotalValue: Double)

df.as[Record]
  .groupBy($"Hour")
  .reduce((x, y) => if (x.TotalValue > y.TotalValue) x else y)</code>

How to avoid mistakes:

The following methods may produce unreliable results and should be avoided:

• df.orderBy(...).groupBy(...).agg(first(...), ...)
• df.orderBy(...).dropDuplicates(...)

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