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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:
As described here:
[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.
was:
As described here:
[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
>
> As described here:
> [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.
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