Spark Dataframe groupBy以序列作为键参数 [英] Spark Dataframe groupBy with sequence as keys arguments

问题描述

我有一个spark dataFrame，我想通过多个键来聚合值

I have a spark dataFrame and I want to aggregate values by multiple keys

正如Spark文档所建议的:

As spark documentation suggests:

def groupBy(col1:String，cols:String *):GroupedData将 DataFrame使用指定的列，因此我们可以对其进行汇总

def groupBy(col1: String, cols: String*): GroupedData Groups the DataFrame using the specified columns, so we can run aggregation on them

所以我要做以下

 val keys = Seq("a", "b", "c")
 dataframe.groupBy(keys:_*).agg(...)

Intellij Idea引发以下错误:

Intellij Idea throws me following errors:

1. 扩展非重复参数
2. 类型不匹配:预期的Seq [Column]，实际的Seq [String]

但是，我可以手动传递多个参数而不会出现错误:

However, I can pass multiple arguments manually without errors:

dataframe.groupBy("a", "b", "c").agg(...)

所以，我的问题是:如何以编程方式做到这一点?

So, my question is: How can I do this programmatically?

推荐答案

将列与groupBy(cols: Column*)

import org.apache.spark.sql.functions.col

val keys = Seq("a", "b", "c").map(col(_))
dataframe.groupBy(keys:_*).agg(...)

或head/tail与groupBy(col1: String, cols: String*):

val keys = Seq("a", "b", "c") 
dataframe.groupBy(keys.head, keys.tail: _*).agg(...)  

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