如何在PySpark中使用Scala UDF？

Question

I want to be able to use a Scala function as a UDF in PySpark

package com.test

object ScalaPySparkUDFs extends Serializable {
    def testFunction1(x: Int): Int = { x * 2 }
    def testUDFFunction1 = udf { x: Int => testFunction1(x) }
} 

I can access testFunction1 in PySpark and have it return values:

functions = sc._jvm.com.test.ScalaPySparkUDFs 
functions.testFunction1(10)

What I want to be able to do is use this function as a UDF, ideally in a withColumn call:

row = Row("Value")
numbers = sc.parallelize([1,2,3,4]).map(row).toDF()
numbers.withColumn("Result", testUDFFunction1(numbers['Value']))

I think a promising approach is as found here: Spark: How to map Python with Scala or Java User Defined Functions?

However, when making the changes to code found there to use testUDFFunction1 instead:

def udf_test(col):
    sc = SparkContext._active_spark_context
    _f = sc._jvm.com.test.ScalaPySparkUDFs.testUDFFunction1.apply
    return Column(_f(_to_seq(sc, [col], _to_java_column)))

I get:

 AttributeError: 'JavaMember' object has no attribute 'apply' 

I don't understand this because I believe testUDFFunction1 does have an apply method?

I do not want to use expressions of the type found here: Register UDF to SqlContext from Scala to use in PySpark

Any suggestions as to how to make this work would be appreciated!

Answer 1

The question you've linked is using a Scala object . Scala object is a singleton and you can use apply method directly.

Here you use a nullary function which returns an object of UserDefinedFunction class co you have to call the function first:

_f = sc._jvm.com.test.ScalaPySparkUDFs.testUDFFunction1() # Note () at the end
Column(_f.apply(_to_seq(sc, [col], _to_java_column)))

Answer 2

Agree with @user6910411, you have to call apply method directly on the function. So, your code will be.

UDF in Scala:

import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.functions._


object ScalaPySparkUDFs {

    def testFunction1(x: Int): Int = { x * 2 }

    def getFun(): UserDefinedFunction = udf(testFunction1 _ )
}

PySpark code:

def test_udf(col):
    sc = spark.sparkContext
    _test_udf = sc._jvm.com.test.ScalaPySparkUDFs.getFun()
    return Column(_test_udf.apply(_to_seq(sc, [col], _to_java_column)))


row = Row("Value")
numbers = sc.parallelize([1,2,3,4]).map(row).toDF()
numbers.withColumn("Result", test_udf(numbers['Value']))