Spark SQL: apply aggregate functions to a list of columns

问题

Is there a way to apply an aggregate function to all (or a list of) columns of a dataframe, when doing a groupBy? In other words, is there a way to avoid doing this for every column:

df.groupBy(\"col1\")
  .agg(sum(\"col2\").alias(\"col2\"), sum(\"col3\").alias(\"col3\"), ...)

回答1:

There are multiple ways of applying aggregate functions to multiple columns.

GroupedData class provides a number of methods for the most common functions, including count, max, min, mean and sum, which can be used directly as follows:

Python:

df = sqlContext.createDataFrame(
    [(1.0, 0.3, 1.0), (1.0, 0.5, 0.0), (-1.0, 0.6, 0.5), (-1.0, 5.6, 0.2)],
    ("col1", "col2", "col3"))

df.groupBy("col1").sum()

## +----+---------+-----------------+---------+
## |col1|sum(col1)|        sum(col2)|sum(col3)|
## +----+---------+-----------------+---------+
## | 1.0|      2.0|              0.8|      1.0|
## |-1.0|     -2.0|6.199999999999999|      0.7|
## +----+---------+-----------------+---------+

Scala

val df = sc.parallelize(Seq(
  (1.0, 0.3, 1.0), (1.0, 0.5, 0.0),
  (-1.0, 0.6, 0.5), (-1.0, 5.6, 0.2))
).toDF("col1", "col2", "col3")

df.groupBy($"col1").min().show

// +----+---------+---------+---------+
// |col1|min(col1)|min(col2)|min(col3)|
// +----+---------+---------+---------+
// | 1.0|      1.0|      0.3|      0.0|
// |-1.0|     -1.0|      0.6|      0.2|
// +----+---------+---------+---------+

Optionally you can pass a list of columns which should be aggregated

df.groupBy("col1").sum("col2", "col3")

You can also pass dictionary / map with columns a the keys and functions as the values:

Python

exprs = {x: "sum" for x in df.columns}
df.groupBy("col1").agg(exprs).show()

## +----+---------+
## |col1|avg(col3)|
## +----+---------+
## | 1.0|      0.5|
## |-1.0|     0.35|
## +----+---------+

Scala

val exprs = df.columns.map((_ -> "mean")).toMap
df.groupBy($"col1").agg(exprs).show()

// +----+---------+------------------+---------+
// |col1|avg(col1)|         avg(col2)|avg(col3)|
// +----+---------+------------------+---------+
// | 1.0|      1.0|               0.4|      0.5|
// |-1.0|     -1.0|3.0999999999999996|     0.35|
// +----+---------+------------------+---------+

Finally you can use varargs:

Python

from pyspark.sql.functions import min

exprs = [min(x) for x in df.columns]
df.groupBy("col1").agg(*exprs).show()

Scala

import org.apache.spark.sql.functions.sum

val exprs = df.columns.map(sum(_))
df.groupBy($"col1").agg(exprs.head, exprs.tail: _*)

There are some other way to achieve a similar effect but these should more than enough most of the time.

回答2:

Another example of the same concept - but say - you have 2 different columns - and you want to apply different agg functions to each of them i.e

f.groupBy("col1").agg(sum("col2").alias("col2"), avg("col3").alias("col3"), ...)

Here is the way to achieve it - though I do not yet know how to add the alias in this case

See the example below - Using Maps

val Claim1 = StructType(Seq(StructField("pid", StringType, true),StructField("diag1", StringType, true),StructField("diag2", StringType, true), StructField("allowed", IntegerType, true), StructField("allowed1", IntegerType, true)))
val claimsData1 = Seq(("PID1", "diag1", "diag2", 100, 200), ("PID1", "diag2", "diag3", 300, 600), ("PID1", "diag1", "diag5", 340, 680), ("PID2", "diag3", "diag4", 245, 490), ("PID2", "diag2", "diag1", 124, 248))

val claimRDD1 = sc.parallelize(claimsData1)
val claimRDDRow1 = claimRDD1.map(p => Row(p._1, p._2, p._3, p._4, p._5))
val claimRDD2DF1 = sqlContext.createDataFrame(claimRDDRow1, Claim1)

val l = List("allowed", "allowed1")
val exprs = l.map((_ -> "sum")).toMap
claimRDD2DF1.groupBy("pid").agg(exprs) show false
val exprs = Map("allowed" -> "sum", "allowed1" -> "avg")

claimRDD2DF1.groupBy("pid").agg(exprs) show false

来源：https://stackoverflow.com/questions/33882894/spark-sql-apply-aggregate-functions-to-a-list-of-columns

标签

apache-spark

dataframe

apache-spark-sql

aggregate-functions