Spark: How to map Python with Scala or Java User Defined Functions?

Spark 2.1+

You can use SQLContext.registerJavaFunction:

Register a java UDF so it can be used in SQL statements.

which requires a name, fully qualified name of Java class, and optional return type. Unfortunately for now it can be used only in SQL statements (or with expr / selectExpr) and requires a Java org.apache.spark.sql.api.java.UDF*:

scalaVersion := "2.11.8"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-sql" % "2.1.0"
)

package com.example.spark.udfs

import org.apache.spark.sql.api.java.UDF1

class addOne extends UDF1[Integer, Integer] {
  def call(x: Integer) = x + 1
} 

sqlContext.registerJavaFunction("add_one", "com.example.spark.udfs.addOne")
sqlContext.sql("SELECT add_one(1)").show()

## +------+
## |UDF(1)|
## +------+
## |     2|
## +------+

Version indpendent:

I wouldn't go so far as to say it is supported but it is certainly possible. All SQL functions available currently in PySpark are simply a wrappers around Scala API.

Lets assume I want to reuse GroupConcat UDAF I've created as an answer to SPARK SQL replacement for mysql GROUP_CONCAT aggregate function and it is located in a package com.example.udaf:

from pyspark.sql.column import Column, _to_java_column, _to_seq
from pyspark.sql import Row

row = Row("k", "v")
df = sc.parallelize([
    row(1, "foo1"), row(1, "foo2"), row(2, "bar1"), row(2, "bar2")]).toDF()

def groupConcat(col):
    """Group and concatenate values for a given column

    >>> df = sqlContext.createDataFrame([(1, "foo"), (2, "bar")], ("k", "v"))
    >>> df.select(groupConcat("v").alias("vs"))
    [Row(vs=u'foo,bar')]
    """
    sc = SparkContext._active_spark_context
    # It is possible to use java_import to avoid full package path
    _groupConcat = sc._jvm.com.example.udaf.GroupConcat.apply
    # Converting to Seq to match apply(exprs: Column*)
    return Column(_groupConcat(_to_seq(sc, [col], _to_java_column)))

df.groupBy("k").agg(groupConcat("v").alias("vs")).show()

## +---+---------+
## |  k|       vs|
## +---+---------+
## |  1|foo1,foo2|
## |  2|bar1,bar2|
## +---+---------+

There is far too much leading underscores for my taste but as you can see it can be done.

Related to:

• Calling Java/Scala function from a task
• How to use a Scala class inside Pyspark
• Transforming PySpark RDD with Scala