使用pyspark将Scala类用作UDF [英] Using Scala classes as UDF with pyspark

问题描述

使用Apache Spark时，我正在尝试将一些计算从Python转移到Scala.我想使用Java的类接口来使用持久变量，就像这样(这是基于我更复杂的用例的荒谬的MWE):

I'm trying to offload some computations from Python to Scala when using Apache Spark. I would like to use the class interface from Java to be able to use a persistent variable, like so (this is a nonsensical MWE based on my more complex use case):

package mwe

import org.apache.spark.sql.api.java.UDF1

class SomeFun extends UDF1[Int, Int] {
  private var prop: Int = 0

  override def call(input: Int): Int = {
    if (prop == 0) {
      prop = input
    }
    prop + input
  }
}

现在我正尝试在pyspark内部使用此类:

Now I'm attempting to use this class from within pyspark:

import pyspark
from pyspark.sql import SQLContext
from pyspark import SparkContext

conf = pyspark.SparkConf()
conf.set("spark.jars", "mwe.jar")
sc = SparkContext.getOrCreate(conf)

sqlContext = SQLContext.getOrCreate(sc)
sqlContext.registerJavaFunction("fun", "mwe.SomeFun")

df0 = sc.parallelize((i,) for i in range(6)).toDF(["num"])
df1 = df0.selectExpr("fun(num) + 3 as new_num")
df1.show()

并获得以下异常:

pyspark.sql.utils.AnalysisException: u"cannot resolve '(UDF:fun(num) + 3)' due to data type mismatch: differing types in '(UDF:fun(num) + 3)' (struct<> and int).; line 1 pos 0;\n'Project [(UDF:fun(num#0L) + 3) AS new_num#2]\n+- AnalysisBarrier\n      +- LogicalRDD [num#0L], false\n"

实现此目的的正确方法是什么?我上课需要依靠Java本身吗?我非常感谢提示！

What is the correct way to implement this? Will I have to resort to Java itself for the class? I'd greatly appreciate hints!

推荐答案

异常的来源是使用不兼容的类型:

The source of the exception is usage of incompatible types:

• 首先，o.a.s.sql.api.java.UDF*对象需要外部Java(不是Scala类型)，因此UDF期望整数应采用带框的Integer(java.lang.Integer)而不是Int.

• First of all o.a.s.sql.api.java.UDF* objects require external Java (not Scala types), so UDF expecting integers should take boxed Integer (java.lang.Integer) not Int.

class SomeFun extends UDF1[Integer, Integer] {
  ...
  override def call(input: Integer): Integer = {
    ...

除非您使用LongType而不是IntegerType的旧版Python num列使用:

Unless you use legacy Python num column uses of LongType not IntegerType:

df0.printSchema()
root
 |-- num: long (nullable = true)

所以实际签名应该是

class SomeFun extends UDF1[java.lang.Long, java.lang.Long] {
  ...
  override def call(input: java.lang.Long): java.lang.Long = {
    ...

或者应该在应用UDF之前强制转换数据

or data should be casted before applying UDF

df0.selectExpr("fun(cast(num as integer)) + 3 as new_num")

在UDF中不允许最终可变的状态.它不会引起异常，但是总体行为将是不确定的.

Finally mutable state is not allowed in UDFs. It won't cause an exception but overall behavior will be non-deterministic.

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