Question

我想知道是否有某种方法可以为多列上的spark数据帧指定自定义聚合函数。

我有一个类似这样的表（名称，项目，价格）：

john | tomato | 1.99
john | carrot | 0.45
bill | apple  | 0.99
john | banana | 1.29
bill | taco   | 2.59

为：

我想将每个人的项目和费用汇总到这样的列表中：

john | (tomato, 1.99), (carrot, 0.45), (banana, 1.29)
bill | (apple, 0.99), (taco, 2.59)

这在数据帧中是否可行？我最近了解了collect_list，但似乎只适用于一列。

Answer 1

在收集列表之前，请考虑使用struct函数将列分组在一起：

import org.apache.spark.sql.functions.{collect_list, struct}
import sqlContext.implicits._

val df = Seq(
  ("john", "tomato", 1.99),
  ("john", "carrot", 0.45),
  ("bill", "apple", 0.99),
  ("john", "banana", 1.29),
  ("bill", "taco", 2.59)
).toDF("name", "food", "price")

df.groupBy($"name")
  .agg(collect_list(struct($"food", $"price")).as("foods"))
  .show(false)

输出：

+----+---------------------------------------------+
|name|foods                                        |
+----+---------------------------------------------+
|john|[[tomato,1.99], [carrot,0.45], [banana,1.29]]|
|bill|[[apple,0.99], [taco,2.59]]                  |
+----+---------------------------------------------+

Answer 2

以DataFrame执行此操作的最简单方法是首先收集两个列表，然后将UDF zip两个列表一起使用。类似的东西：

import org.apache.spark.sql.functions.{collect_list, udf}
import sqlContext.implicits._

val zipper = udf[Seq[(String, Double)], Seq[String], Seq[Double]](_.zip(_))

val df = Seq(
  ("john", "tomato", 1.99),
  ("john", "carrot", 0.45),
  ("bill", "apple", 0.99),
  ("john", "banana", 1.29),
  ("bill", "taco", 2.59)
).toDF("name", "food", "price")

val df2 = df.groupBy("name").agg(
  collect_list(col("food")) as "food",
  collect_list(col("price")) as "price" 
).withColumn("food", zipper(col("food"), col("price"))).drop("price")

df2.show(false)
# +----+---------------------------------------------+
# |name|food                                         |
# +----+---------------------------------------------+
# |john|[[tomato,1.99], [carrot,0.45], [banana,1.29]]|
# |bill|[[apple,0.99], [taco,2.59]]                  |
# +----+---------------------------------------------+

Answer 3

也许比zip函数更好的方法（由于UDF和UDAF对性能非常不利，因此）是将两列包装到Struct中。

这可能也可以工作：

df.select('name, struct('food, 'price).as("tuple"))
  .groupBy('name)
  .agg(collect_list('tuple).as("tuples"))

Answer 4

这是一个选项，可以将数据帧转换为Map的RDD，然后在其上调用groupByKey。结果将是键值对列表，其中value是元组列表。

df.show
+----+------+----+
|  _1|    _2|  _3|
+----+------+----+
|john|tomato|1.99|
|john|carrot|0.45|
|bill| apple|0.99|
|john|banana|1.29|
|bill|  taco|2.59|
+----+------+----+


val tuples = df.map(row => row(0) -> (row(1), row(2)))
tuples: org.apache.spark.rdd.RDD[(Any, (Any, Any))] = MapPartitionsRDD[102] at map at <console>:43

tuples.groupByKey().map{ case(x, y) => (x, y.toList) }.collect
res76: Array[(Any, List[(Any, Any)])] = Array((bill,List((apple,0.99), (taco,2.59))), (john,List((tomato,1.99), (carrot,0.45), (banana,1.29))))

Answer 5

collect_list似乎仅适用于一列：要使collect_list用于多个列，您必须将要聚合的列包装在结构中。例如：

val aggregatedData = df.groupBy("name").agg(collect_list(struct("item", "price")) as("food")) aggregatedData.show +----+------------------------------------------------+ |name|foods | +----+------------------------------------------------+ |john|[[tomato, 1.99], [carrot, 0.45], [banana, 1.29]]| |bill|[[apple, 0.99], [taco, 2.59]] | +----+------------------------------------------------+

使用Spark中的自定义函数聚合多个列

5 个答案: