如何在groupBy之后将值聚合到集合中？

Question

我有一个带架构的数据框：

[visitorId: string, trackingIds: array<string>, emailIds: array<string>]

寻找一种方法来分类（或者可能汇总？）由visitorid组成的数据帧，其中的trackingIds和emailIds列会一起追加。例如，如果我的初始df看起来像：

visitorId   |trackingIds|emailIds
+-----------+------------+--------
|a158|      [666b]      |    [12]
|7g21|      [c0b5]      |    [45]
|7g21|      [c0b4]      |    [87]
|a158|      [666b, 777c]|    []

我希望我的输出df看起来像这样

visitorId   |trackingIds|emailIds
+-----------+------------+--------
|a158|      [666b,666b,777c]|      [12,'']
|7g21|      [c0b5,c0b4]     |      [45, 87]

尝试使用groupBy和agg运营商，但运气不佳。

Answer 1

Spark＆gt; = 2.4

您可以使用内置flatten function

替换flatten udf

import org.apache.spark.sql.functions.flatten

将其余部分保留原样。

Spark＆gt; = 2.0，＆lt; 2.4

这可能但非常昂贵。使用您提供的数据：

case class Record(
    visitorId: String, trackingIds: Array[String], emailIds: Array[String])

val df = Seq(
  Record("a158", Array("666b"), Array("12")),
  Record("7g21", Array("c0b5"), Array("45")),
  Record("7g21", Array("c0b4"), Array("87")),
  Record("a158", Array("666b",  "777c"), Array.empty[String])).toDF

和辅助函数：

import org.apache.spark.sql.functions.udf

val flatten = udf((xs: Seq[Seq[String]]) => xs.flatten)

我们可以用占位符填充空白：

import org.apache.spark.sql.functions.{array, lit, when}

val dfWithPlaceholders = df.withColumn(
  "emailIds", 
  when(size($"emailIds") === 0, array(lit(""))).otherwise($"emailIds"))

collect_lists和flatten：

import org.apache.spark.sql.functions.{array, collect_list}

val emailIds = flatten(collect_list($"emailIds")).alias("emailIds")
val trackingIds = flatten(collect_list($"trackingIds")).alias("trackingIds")

df
  .groupBy($"visitorId")
  .agg(trackingIds, emailIds)

// +---------+------------------+--------+
// |visitorId|       trackingIds|emailIds|
// +---------+------------------+--------+
// |     a158|[666b, 666b, 777c]|  [12, ]|
// |     7g21|      [c0b5, c0b4]|[45, 87]|
// +---------+------------------+--------+

使用静态类型Dataset：

df.as[Record]
  .groupByKey(_.visitorId)
  .mapGroups { case (key, vs) => 
    vs.map(v => (v.trackingIds, v.emailIds)).toArray.unzip match {
      case (trackingIds, emailIds) => 
        Record(key, trackingIds.flatten, emailIds.flatten)
  }}

// +---------+------------------+--------+
// |visitorId|       trackingIds|emailIds|
// +---------+------------------+--------+
// |     a158|[666b, 666b, 777c]|  [12, ]|
// |     7g21|      [c0b5, c0b4]|[45, 87]|
// +---------+------------------+--------+

Spark 1.x

您可以转换为RDD和组

import org.apache.spark.sql.Row

dfWithPlaceholders.rdd
  .map {
     case Row(id: String, 
       trcks: Seq[String @ unchecked],
       emails: Seq[String @ unchecked]) => (id, (trcks, emails))
  }
  .groupByKey
  .map {case (key, vs) => vs.toArray.unzip match {
    case (trackingIds, emailIds) => 
      Record(key, trackingIds.flatten, emailIds.flatten)
  }}
  .toDF

// +---------+------------------+--------+
// |visitorId|       trackingIds|emailIds|
// +---------+------------------+--------+
// |     7g21|      [c0b5, c0b4]|[45, 87]|
// |     a158|[666b, 666b, 777c]|  [12, ]|
// +---------+------------------+--------+

Answer 2

@ zero323的答案非常非常完整，但Spark为我们提供了更大的灵活性。以下解决方案怎么样？

import org.apache.spark.sql.functions._
inventory
  .select($"*", explode($"trackingIds") as "tracking_id")
  .select($"*", explode($"emailIds") as "email_id")
  .groupBy("visitorId")
  .agg(
    collect_list("tracking_id") as "trackingIds",
    collect_list("email_id") as "emailIds")

然而，这会遗漏所有空集合（因此还有一些改进空间：））

Answer 3

您可以使用用户定义的聚合函数。

1）使用名为customAggregation的scala类创建自定义UDAF。

package com.package.name

import org.apache.spark.sql.Row
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types._
import scala.collection.JavaConverters._

class CustomAggregation() extends UserDefinedAggregateFunction {

// Input Data Type Schema
def inputSchema: StructType = StructType(Array(StructField("col5", ArrayType(StringType))))

// Intermediate Schema
def bufferSchema = StructType(Array(
StructField("col5_collapsed",  ArrayType(StringType))))

// Returned Data Type .
def dataType: DataType = ArrayType(StringType)

// Self-explaining
def deterministic = true

// This function is called whenever key changes
def initialize(buffer: MutableAggregationBuffer) = {
buffer(0) = Array.empty[String] // initialize array
}

// Iterate over each entry of a group
def update(buffer: MutableAggregationBuffer, input: Row) = {
buffer(0) =
  if(!input.isNullAt(0))
    buffer.getList[String](0).toArray ++ input.getList[String](0).toArray
  else
    buffer.getList[String](0).toArray
}

  // Merge two partial aggregates
 def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
 buffer1(0) = buffer1.getList[String](0).toArray ++ buffer2.getList[String](0).toArray
}

 // Called after all the entries are exhausted.
 def evaluate(buffer: Row) = {
  buffer.getList[String](0).asScala.toList.distinct
 }
}

2）然后在代码中使用UDAF作为

//define UDAF
val CustomAggregation = new CustomAggregation()
DataFrame
    .groupBy(col1,col2,col3)
    .agg(CustomAggregation(DataFrame(col5))).show()