spark scala-按数组分组 [英] spark scala - Group by Array column

问题描述

我是火花scala的新手。感谢您的帮助。
我有一个数据框

I am very new to spark scala. Appreciate your help.. I have a dataframe

val df = Seq(
  ("a", "a1", Array("x1","x2")), 
  ("a", "b1", Array("x1")),
  ("a", "c1", Array("x2")),
  ("c", "c3", Array("x2")),
  ("a", "d1", Array("x3")),
  ("a", "e1", Array("x2","x1"))
).toDF("k1", "k2", "k3")

我正在寻找一种按k1和k3对其进行分组并将k2收集到数组中的方法。
但是，k3是一个数组，我需要对分组应用包含（而不是精确的
匹配）。换句话说，我正在寻找类似这样的结果

I am looking for a way to group it by k1 and k3 and collect k2 in an array. However, k3 is an array and I need to apply contains (rather than exact match) for the grouping. In other words, I am looking for a result something like this

k1   k3       k2                count
a   (x1,x2)   (a1,b1,c1,e1)     4
a    (x3)      (d1)             1
c    (x2)      (c3)             1

有人可以建议如何实现吗？

Can somebody advise how to achieve this?

提前感谢！

推荐答案

我建议您按k1列分组，收集k2和k3结构的列表， 将收集的列表传递给udf函数，以计算k3中的数组何时包含在另一个k3数组中并添加k2元素。

I would suggest you to group by k1 column, collect list of structs of k2 and k3, pass the collected list to a udf function for counting when an array in k3 is contained in another array of k3 and adding elements of k2.

然后您可以使用 explode 和 select 表达式来得到所需的输出

Then you can use explode and select expressions to get the desired output

以下是完整的工作解决方案

Following is the complete working solution

val df = Seq(
  ("a", "a1", Array("x1","x2")),
  ("a", "b1", Array("x1")),
  ("a", "c1", Array("x2")),
  ("c", "c3", Array("x2")),
  ("a", "d1", Array("x3")),
  ("a", "e1", Array("x2","x1"))
  ).toDF("k1", "k2", "k3")

import org.apache.spark.sql.functions._
def containsGoupingUdf = udf((arr: Seq[Row]) => {
  val firstStruct =  arr.head
  val tailStructs =  arr.tail
  var result = Array((collection.mutable.Set(firstStruct.getAs[String]("k2")), firstStruct.getAs[scala.collection.mutable.WrappedArray[String]]("k3").toSet, 1))
  for(str <- tailStructs){
    var added = false
    for((res, index) <- result.zipWithIndex) {
      if (str.getAs[scala.collection.mutable.WrappedArray[String]]("k3").exists(res._2) || res._2.exists(x => str.getAs[scala.collection.mutable.WrappedArray[String]]("k3").contains(x))) {
        result(index) = (res._1 + str.getAs[String]("k2"), res._2 ++ str.getAs[scala.collection.mutable.WrappedArray[String]]("k3").toSet, res._3 + 1)
        added = true
      }
    }
    if(!added){
      result = result ++ Array((collection.mutable.Set(str.getAs[String]("k2")), str.getAs[scala.collection.mutable.WrappedArray[String]]("k3").toSet, 1))
    }
  }
  result.map(tuple => (tuple._1.toArray, tuple._2.toArray, tuple._3))
})

df.groupBy("k1").agg(containsGoupingUdf(collect_list(struct(col("k2"), col("k3")))).as("aggregated"))
    .select(col("k1"), explode(col("aggregated")).as("aggregated"))
    .select(col("k1"), col("aggregated._2").as("k3"), col("aggregated._1").as("k2"), col("aggregated._3").as("count"))
  .show(false)

应该给您

+---+--------+----------------+-----+
|k1 |k3      |k2              |count|
+---+--------+----------------+-----+
|c  |[x2]    |[c3]            |1    |
|a  |[x1, x2]|[b1, e1, c1, a1]|4    |
|a  |[x3]    |[d1]            |1    |
+---+--------+----------------+-----+ 

我希望答案是有帮助的，您可以根据需要进行修改。

I hope the answer is helpful and you can modify it according to your needs.

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