dickwall/spark-scala3

Spark-Scala3

This repository aims at making the adoption of Scala 3 easier for projects using Apache Spark.

How to get this library

Add the following dependency to your build.sbt:

"io.github.vincenzobaz" %% "spark-scala3-encoders" % "0.2.5"
"io.github.vincenzobaz" %% "spark-scala3-udf" % "0.2.5"

Apache Spark version

As of version 3.2, Spark is published for Scala 2.13, which makes it possible to be used from Scala 3 as a library.

You can add it in your build.sbt with:

val sparkVersion = "3.3.2"

libraryDependencies ++= Seq(
  ("org.apache.spark" %% "spark-core" % sparkVersion).cross(CrossVersion.for3Use2_13),
  ("org.apache.spark" %% "spark-sql" % sparkVersion).cross(CrossVersion.for3Use2_13)
)

Encoders

The Spark sql API provides the Dataset[T] abstraction. Most methods of this type require an implicit (given in Scala 3 parlance) instance of Encoder[T].

These instances are automatically derived when you use:

scala import sqlContext.implicits._

Unfortunately this derivation relies on Scala 2.13 runtime reflection which is not supported in Scala 3.

This library provides you with an alternative compile time encoder derivation which you can enable with the following import:

import scala3encoders.given

Udf

Udf or "user defined functions" enable Spark to use own functions that process rows. The main routine for creating and registering those functions are spark.sql.functions.udf and SparkSession.register. These cannot be replaced as simply as the encoders. The udf call itself is a generic function using TypeTags which are not available in Scala 3. The register is needed to make the calls available inside a spark.sql statement with a string identifier.

To use spark-scala3 udf:

import scala3udf.{Udf => udf} // "old" udf doesn't interfer with new scala3udf.udf when renamed

With the renaming in place, you can use the call udf(lambda) as before without interfering with the udf function in org.apache.spark.sql.functions. Instead of calling spark.register(myFun1), you can call either:

• myFun1.registerWith(spark, "myFun1")

• udf.registerWith(spark, myFun1, myFun2, ...) - this will automatically name the used parameter value names Instead of an explicit Spark session an implicit value could also be used, e.g. using given spark: SparkSession = SparkSession.builder()...getOrCreate. Then the equivalent calls are:

• myFun1.register("myFun1")

• udf.register(myFun1, myFun2, ...)

Type checks will not work with Spark 3.3.x - this could lead to unexpected failures, e.g. of the collect() function. The recommendation is to use spark version at least 3.4.1.