spark expression rename the column list after aggregation

问题

I have written below code to group and aggregate the columns

 val gmList = List("gc1","gc2","gc3")
 val aList = List("val1","val2","val3","val4","val5")

 val cype = "first"

 val exprs = aList.map((_ -> cype )).toMap

 dfgroupBy(gmList.map (col): _*).agg (exprs).show

but this create a columns with appending aggregation name in all column as shown below

so I want to alias that name first(val1) -> val1, I want to make this code generic as part of exprs

  +----------+----------+-------------+-------------------------+------------------+---------------------------+------------------------+-------------------+
 |    gc1   |  gc2     | gc3         |        first(val1)      |      first(val2)|       first(val3)          |       first(val4)      |       first(val5) |
 +----------+----------+-------------+-------------------------+------------------+---------------------------+------------------------+-------------------+

回答1:

One approach would be to alias the aggregated columns to the original column names in a subsequent select. I would also suggest generalizing the single aggregate function (i.e. first) to a list of functions, as shown below:

import org.apache.spark.sql.functions._

val df = Seq(
  (1, 10, "a1", "a2", "a3"),
  (1, 10, "b1", "b2", "b3"),
  (2, 20, "c1", "c2", "c3"),
  (2, 30, "d1", "d2", "d3"),
  (2, 30, "e1", "e2", "e3")
).toDF("gc1", "gc2", "val1", "val2", "val3")

val gmList = List("gc1", "gc2")
val aList = List("val1", "val2", "val3")

// Populate with different aggregate methods for individual columns if necessary
val fList = List.fill(aList.size)("first")

val afPairs = aList.zip(fList)
// afPairs: List[(String, String)] = List((val1,first), (val2,first), (val3,first))

df.
  groupBy(gmList.map(col): _*).agg(afPairs.toMap).
  select(gmList.map(col) ::: afPairs.map{ case (v, f) => col(s"$f($v)").as(v) }: _*).
  show
// +---+---+----+----+----+
// |gc1|gc2|val1|val2|val3|
// +---+---+----+----+----+
// |  2| 20|  c1|  c2|  c3|
// |  1| 10|  a1|  a2|  a3|
// |  2| 30|  d1|  d2|  d3|
// +---+---+----+----+----+

回答2:

You can slightly change the way you are generating the expression and use the function alias in there:

import org.apache.spark.sql.functions.col
val aList = List("val1","val2","val3","val4","val5")
val exprs = aList.map(c => first(col(c)).alias(c) )
dfgroupBy( gmList.map(col) : _*).agg(exprs.head , exprs.tail: _*).show

回答3:

Here's a more generic version that will work with any aggregate functions and doesn't require naming your aggregate columns up front. Build your grouped df as you normally would, then use:

val colRegex = raw"^.+\((.*?)\)".r
val newCols = df.columns.map(c => col(c).as(colRegex.replaceAllIn(c, m => m.group(1))))
df.select(newCols: _*)

This will extract out only what is inside the parentheses, regardless of what aggregate function is called (e.g. first(val) -> val, sum(val) -> val, count(val) -> val, etc.).

来源：https://stackoverflow.com/questions/53002360/spark-expression-rename-the-column-list-after-aggregation

标签

scala

apache-spark

apache-spark-sql