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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/10/08 05:44:14 UTC
[jira] [Resolved] (SPARK-23833) Incorrect primitive type check for
input arguments of udf
[ https://issues.apache.org/jira/browse/SPARK-23833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-23833.
----------------------------------
Resolution: Incomplete
> Incorrect primitive type check for input arguments of udf
> ---------------------------------------------------------
>
> Key: SPARK-23833
> URL: https://issues.apache.org/jira/browse/SPARK-23833
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 2.2.0, 2.3.0
> Reporter: Valentin Nikotin
> Priority: Major
> Labels: bulk-closed
>
> There is claimed behavior for scala UDFs with primitive type arguments:
> {quote}Note that if you use primitive parameters, you are not able to check if it is null or not, and the UDF will return null for you if the primitive input is null.
> {quote}
> This is initial issue - SPARK-11725
> Correspondent pr - [PR|https://github.com/apache/spark/pull/9770/commits/a8a30674ce531c9cd10107200a3f72f9539cd8f6]
> The problem is that {{ScalaReflection.getParameterTypes}} doesn't work correctly due to type erasure.
> The correct check "if type is primitive" should be based on typeTag something like this:
> {code:java}
> typeTag[T].tpe.typeSymbol.asClass.isPrimitive
> {code}
>
> The problem appears if we have high order functions:
> {code:java}
> val f = (x: Long) => x
> def identity[T, U](f: T => U): T => U = (t: T) => f(t)
> val udf0 = udf(f)
> val udf1 = udf(identity(f))
> val getNull = udf(() => null.asInstanceOf[java.lang.Long])
> spark.range(5).toDF().
> withColumn("udf0", udf0(getNull())).
> withColumn("udf1", udf1(getNull())).
> show()
> spark.range(5).toDF().
> withColumn("udf0", udf0(getNull())).
> withColumn("udf1", udf1(getNull())).
> explain()
> {code}
> Test execution on Spark 2.2 spark-shell:
> {code:java}
> scala> val f = (x: Long) => x
> f: Long => Long = <function1>
> scala> def identity[T, U](f: T => U): T => U = (t: T) => f(t)
> identity: [T, U](f: T => U)T => U
> scala> val udf0 = udf(f)
> udf0: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,LongType,Some(List(LongType)))
> scala> val udf1 = udf(identity(f))
> udf1: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,LongType,Some(List(LongType)))
> scala> val getNull = udf(() => null.asInstanceOf[java.lang.Long])
> getNull: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function0>,LongType,Some(List()))
> scala> spark.range(5).toDF().
> | withColumn("udf0", udf0(getNull())).
> | withColumn("udf1", udf1(getNull())).
> | show()
> +---+----+----+
> | id|udf0|udf1|
> +---+----+----+
> | 0|null| 0|
> | 1|null| 0|
> | 2|null| 0|
> | 3|null| 0|
> | 4|null| 0|
> +---+----+----+
> scala> spark.range(5).toDF().
> | withColumn("udf0", udf0(getNull())).
> | withColumn("udf1", udf1(getNull())).
> | explain()
> == Physical Plan ==
> *Project [id#19L, if (isnull(UDF())) null else UDF(UDF()) AS udf0#24L, UDF(UDF()) AS udf1#28L]
> +- *Range (0, 5, step=1, splits=6)
> {code}
>
> The typeTag information about input parameters is available in udf function but only used to get schema, it should be added to ScalaUDF too so that we can used it later:
> {code:java}
> def udf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = {
> val inputTypes = Try(ScalaReflection.schemaFor(typeTag[A1]).dataType :: ScalaReflection.schemaFor(typeTag[A2]).dataType :: Nil).toOption
> UserDefinedFunction(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, inputTypes)
> }
> {code}
>
> Here is current vs desired version:
> {code:java}
> scala> import org.apache.spark.sql.catalyst.ScalaReflection
> import org.apache.spark.sql.catalyst.ScalaReflection
> scala> ScalaReflection.getParameterTypes(identity(f))
> res2: Seq[Class[_]] = WrappedArray(class java.lang.Object)
> scala> ScalaReflection.getParameterTypes(identity(f)).map(_.isPrimitive)
> res7: Seq[Boolean] = ArrayBuffer(false)
> {code}
> versus
> {code:java}
> scala> import scala.reflect.runtime.universe.{typeTag, TypeTag}
> import scala.reflect.runtime.universe.{typeTag, TypeTag}
> scala> def myGetParameterTypes[T : TypeTag, U](func: T => U) = {
> | typeTag[T].tpe.typeSymbol.asClass
> | }
> myGetParameterTypes: [T, U](func: T => U)(implicit evidence$1: reflect.runtime.universe.TypeTag[T])reflect.runtime.universe.ClassSymbol
> scala> myGetParameterTypes(f)
> res3: reflect.runtime.universe.ClassSymbol = class Long
> scala> myGetParameterTypes(f).isPrimitive
> res4: Boolean = true
> {code}
> Although for this case there is workaround with using {{@specialized(Long)}}
> {code:scala}
> scala> def identity2[@specialized(Long) T, U](f: T => U): T => U = (t: T) => f(t)
> identity2: [T, U](f: T => U)T => U
> scala> ScalaReflection.getParameterTypes(identity2(f))
> res10: Seq[Class[_]] = WrappedArray(long)
> {code}
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