问题
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
}
}
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()
And get the following exception:
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"
What is the correct way to implement this? Will I have to resort to Java itself for the class? I'd greatly appreciate hints!
回答1:
The source of the exception is usage of incompatible types:
First of all
o.a.s.sql.api.java.UDF*
objects require external Java (not Scala types), so UDF expecting integers should take boxedInteger
(java.lang.Integer
) notInt
.class SomeFun extends UDF1[Integer, Integer] { ... override def call(input: Integer): Integer = { ...
Unless you use legacy Python
num
column uses ofLongType
notIntegerType
:df0.printSchema() root |-- num: long (nullable = true)
So the actual signature should be
class SomeFun extends UDF1[java.lang.Long, java.lang.Long] { ... override def call(input: java.lang.Long): java.lang.Long = { ...
or data should be casted before applying UDF
df0.selectExpr("fun(cast(num as integer)) + 3 as new_num")
Finally mutable state is not allowed in UDFs. It won't cause an exception but overall behavior will be non-deterministic.
来源:https://stackoverflow.com/questions/49632117/using-scala-classes-as-udf-with-pyspark