对火花数据帧的同一列进行多个聚合操作

问题描述

我有三个包含以下信息的字符串类型数组:

I have three Arrays of string type containing following information:

• groupBy 数组:包含我想要对数据进行分组的列的名称.
• 聚合数组:包含我要聚合的列的名称.
• operations 数组:包含我想要执行的聚合操作

我正在尝试使用 spark 数据框来实现这一点.Spark 数据帧提供了一个 agg()，您可以在其中传递 Map [String,String](列名和各自的聚合操作)作为输入，但是我想对数据的同一列执行不同的聚合操作.有关如何实现这一目标的任何建议?

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?

推荐答案

Scala:

例如，您可以使用定义的 mapping 从名称到函数映射函数列表:

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|
// +---+------+------+------+

或

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

不幸的是，内部使用的解析器 SQLContext 并未公开，但您始终可以尝试构建纯 SQL 查询:

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)

另见:

• Spark SQL:将聚合函数应用于列列表

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