定义一个接受 Spark DataFrame 中的对象数组的 UDF? [英] Defining a UDF that accepts an Array of objects in a Spark DataFrame?

问题描述

使用 Spark 的 DataFrame 时，需要用户定义函数 (UDF) 来映射列中的数据.UDF 要求显式指定参数类型.就我而言，我需要操作由对象数组组成的列，但我不知道要使用什么类型.举个例子:

When working with Spark's DataFrames, User Defined Functions (UDFs) are required for mapping data in columns. UDFs require that argument types are explicitly specified. In my case, I need to manipulate a column that is made up of arrays of objects, and I do not know what type to use. Here's an example:

import sqlContext.implicits._

// Start with some data. Each row (here, there's only one row) 
// is a topic and a bunch of subjects
val data = sqlContext.read.json(sc.parallelize(Seq(
  """
  |{
  |  "topic" : "pets",
  |  "subjects" : [
  |    {"type" : "cat", "score" : 10},
  |    {"type" : "dog", "score" : 1}
  |  ]
  |}
  """)))

使用内置的org.apache.spark.sql.functions对列中的数据进行基本操作相对简单

It's relatively straightforward to use the built-in org.apache.spark.sql.functions to perform basic operations on the data in the columns

import org.apache.spark.sql.functions.size
data.select($"topic", size($"subjects")).show

+-----+--------------+
|topic|size(subjects)|
+-----+--------------+
| pets|             2|
+-----+--------------+

编写自定义 UDF 来执行任意操作通常很容易

and it's generally easy to write custom UDFs to perform arbitrary operations

import org.apache.spark.sql.functions.udf
val enhance = udf { topic : String => topic.toUpperCase() }
data.select(enhance($"topic"), size($"subjects")).show 

+----------+--------------+
|UDF(topic)|size(subjects)|
+----------+--------------+
|      PETS|             2|
+----------+--------------+

但是如果我想使用 UDF 来操作主题"列中的对象数组怎么办?我对 UDF 中的参数使用什么类型?例如，如果我想重新实现 size 函数，而不是使用 spark 提供的函数:

But what if I want to use a UDF to manipulate the array of objects in the "subjects" column? What type do I use for the argument in the UDF? For example, if I want to reimplement the size function, instead of using the one provided by spark:

val my_size = udf { subjects: Array[Something] => subjects.size }
data.select($"topic", my_size($"subjects")).show

显然 Array[Something] 不起作用...我应该使用什么类型！?我应该完全放弃 Array[] 吗?四处看看告诉我 scala.collection.mutable.WrappedArray 可能与它有关，但我仍然需要提供另一种类型.

Clearly Array[Something] does not work... what type should I use!? Should I ditch Array[] altogether? Poking around tells me scala.collection.mutable.WrappedArray may have something to do with it, but still there's another type I need to provide.

推荐答案

您要找的是 Seq[o.a.s.sql.Row]:

import org.apache.spark.sql.Row

val my_size = udf { subjects: Seq[Row] => subjects.size }

说明:

• ArrayType 的当前表示，正如您已经知道的，WrappedArray 所以 Array 将不起作用，最好留在安全的一面.
• 根据官方规范，StructType 的本地(外部)类型是 Row.不幸的是，这意味着对各个字段的访问不是类型安全的.

• Current representation of ArrayType is, as you already know, WrappedArray so Array won't work and it is better to stay on the safe side.
• According to the official specification, the local (external) type for StructType is Row. Unfortunately it means that access to the individual fields is not type safe.

注意事项:

• 在 Spark 中创建 struct2.3、传递给udf的函数必须返回Product类型(Tuple*或case class)，而不是行.那是因为对应的 udf 变体 依赖 Scala 反射:

• To create struct in Spark < 2.3, function passed to udf has to return Product type (Tuple* or case class), not Row. That's because corresponding udf variants depend on Scala reflection:

将 n 个参数的 Scala 闭包定义为用户定义函数 (UDF).数据类型是根据 Scala 闭包的签名自动推断的.

Defines a Scala closure of n arguments as user-defined function (UDF). The data types are automatically inferred based on the Scala closure's signature.

在 Spark >= 2.3 中可以直接返回 Row，只要提供了架构.

def udf(f: AnyRef, dataType: DataType): UserDefinedFunction使用 Scala 闭包定义确定性用户定义函数 (UDF).对于这个变体，调用者必须指定输出数据类型，并且没有自动输入类型强制.

def udf(f: AnyRef, dataType: DataType): UserDefinedFunction Defines a deterministic user-defined function (UDF) using a Scala closure. For this variant, the caller must specify the output data type, and there is no automatic input type coercion.

参见示例如何在 Java/Kotlin 中创建返回复杂类型的 Spark UDF?.

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