Multiple Aggregate operations on the same column o

问题:

I have three Arrays of string type containing following information:

• groupBy array: containing names of the columns I want to group my data by.
• aggregate array: containing names of columns I want to aggregate.
• operations array: containing the aggregate operations I want to perform

I am trying to use spark data frames to achieve this. Spark data frames provide an agg() where you can pass a Map [String,String] (of column name and respective aggregate operation ) as input, however I want to perform different aggregation operations on the same column of the data. Any suggestions on how to achieve this?

回答1:

Scala:

You can for example map over a list of functions with a defined mapping from name to function:

import org.apache.spark.sql.functions.{col, min, max, mean}
import org.apache.spark.sql.Column

val df = Seq((1L, 3.0), (1L, 3.0), (2L, -5.0)).toDF("k", "v")
val mapping: Map[String, Column => Column] = Map(
  "min" -> min, "max" -> max, "mean" -> avg)

val groupBy = Seq("k")
val aggregate = Seq("v")
val operations = Seq("min", "max", "mean")
val exprs = aggregate.flatMap(c => operations .map(f => mapping(f)(col(c))))

df.groupBy(groupBy.map(col): _*).agg(exprs.head, exprs.tail: _*).show
// +---+------+------+------+
// |  k|min(v)|max(v)|avg(v)|
// +---+------+------+------+
// |  1|   3.0|   3.0|   3.0|
// |  2|  -5.0|  -5.0|  -5.0|
// +---+------+------+------+

or

df.groupBy(groupBy.head, groupBy.tail: _*).agg(exprs.head, exprs.tail: _*).show

Unfortunately parser which is used internally SQLContext is not exposed publicly but you can always try to build plain SQL queries:

df.registerTempTable("df")
val groupExprs = groupBy.mkString(",")
val aggExprs = aggregate.flatMap(c => operations.map(
  f => s"$f($c) AS ${c}_${f}")
).mkString(",")

sqlContext.sql(s"SELECT $groupExprs, $aggExprs FROM df GROUP BY $groupExprs")

Python:

from pyspark.sql.functions import mean, sum, max, col

df = sc.parallelize([(1, 3.0), (1, 3.0), (2, -5.0)]).toDF(["k", "v"])
groupBy = ["k"]
aggregate = ["v"] 
funs = [mean, sum, max]

exprs = [f(col(c)) for f in funs for c in aggregate]

# or equivalent df.groupby(groupBy).agg(*exprs)
df.groupby(*groupBy).agg(*exprs)

See also:

• Spark SQL: apply aggregate functions to a list of column

回答2:

For those that wonder, how @zero323 answer can be written without a list comprehension in python:

from pyspark.sql.functions import min, max, col
# init your spark dataframe

expr = [min(col("valueName")),max(col("valueName"))]
df.groupBy("keyName").agg(*expr)

回答3:

case class soExample(firstName: String, lastName: String, Amount: Int)
val df =  Seq(soExample("me", "zack", 100)).toDF

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

val groupped = df.groupBy("firstName", "lastName").agg(
     sum("Amount"),
     mean("Amount"), 
     stddev("Amount"),
     count(lit(1)).alias("numOfRecords")
   ).toDF()

display(groupped)

// Courtesy Zach ..

Zach simplified answer for a post Marked Duplicate Spark Scala Data Frame to have multiple aggregation of single Group By