Michael Chirico created SPARK-26331:
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Summary: 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
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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