[jira] [Commented] (SPARK-26331) Allow SQL UDF registration to recognize default function values from Scala

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-26331?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16719769#comment-16719769 ] 

Hyukjin Kwon commented on SPARK-26331:
--------------------------------------

Yea default value doesn't work under the hood in Java side. Workaround is easy - define two methods. I don't think we should support this.

> Allow SQL UDF registration to recognize default function values from Scala
> --------------------------------------------------------------------------
>
>                 Key: SPARK-26331
>                 URL: https://issues.apache.org/jira/browse/SPARK-26331
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark, SQL
>    Affects Versions: 2.4.0
>            Reporter: Michael Chirico
>            Priority: Minor
>
> {code:java}
> As described here:{code}
> [https://stackoverflow.com/q/53702727/3576984]
> I have a UDF I would like to be flexible enough to accept 3 arguments (or in general n+k), but for the most part, only 2 (in general, n) are required. The natural approach to this is to implement the UDF with 3 arguments, one of which has a standard default value.
> Copying a toy example from SO:
> {code:java}
> // Scala
> package myUDFs
> import org.apache.spark.sql.api.java.UDF3
> class my_udf extends UDF3[Int, Int, Int, Int] {
>   override def call(a: Int, b: Int, c: Int = 6): Int = { 
>     c*(a + b)
>   }
> }{code}
> I would prefer the following to give the expected output of 18:
> {code:java}
> # Python
> from pyspark.conf import SparkConf
> from pyspark.sql import SparkSession
> from pyspark.sql.types import IntType
> spark_conf = SparkConf().setAll([('spark.jars', 'myUDFs-assembly-0.1.1.jar')])
> spark = SparkSession.builder.appName('my_app').config(conf = spark_conf).enableHiveSupport().getOrCreate()
> spark.udf.registerJavaFunction("my_udf", "myUDFs.my_udf", IntType())
> spark.sql('select my_udf(1, 2)').collect()
> {code}
> But it seems this is currently impossible.
> It seems like the only current work around is to define two UDFs, one with the default pre-specified; the other with flexible parameters.



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