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https://issues.apache.org/jira/browse/SPARK-26331?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Michael Chirico updated SPARK-26331:
------------------------------------
Description:
{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.
was:
{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:
{\{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) }}\{{ }}
{\\{ }
}}
{{}}}
I would prefer the following to give the expected output of 18:
{\{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()}}
But it seems this is currently impossible.
> 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.
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