====== Using Scala ======

In order to use Spark in Scala, environment has to bee set up according to [[:spark#using-spark-in-ufal-environment|Using Spark in UFAL Environment]] (including the ''sbt'').

===== Starting Interactive Shell =====

Interactive shell can be started using:
<file>spark-shell</file>

As described in [[running-spark-on-single-machine-or-on-cluster|Running Spark on Single Machine or on Cluster]], environmental variable ''MASTER'' specifies which Spark master to use (or whether to start a local one).

==== Usage Examples ====
Consider the following simple script computing 10 most frequent words of Czech Wikipedia:
<file scala>
(sc.textFile("/net/projects/spark-example-data/wiki-cs", 3*sc.defaultParallelism)
   .flatMap(_.split("\\s"))
   .map((_,1)).reduceByKey(_+_)
   .sortBy(_._2, ascending=false)
   .take(10))
</file>

  * run interactive shell using existing Spark cluster (i.e., inside ''spark-srun''), or start local Spark cluster using as many threads as there are cores if there is none:
  <file>spark-shell</file>
  * run interactive shell with local Spark cluster using one thread:
  <file>MASTER=local spark-shell</file>
  * start Spark cluster (10 machines, 2GB RAM each) via Slurm and run interactive shell:
  <file>spark-srun 10 2G spark-shell</file>


===== Running Scala Spark Applications =====

Compiled Scala Spark program (JAR) can be started using:
<file>spark-submit</file>

As described in [[running-spark-on-single-machine-or-on-cluster|Running Spark on Single Machine or on Cluster]], environmental variable ''MASTER'' specifies which Spark master to use (or whether to start a local one).

==== Compilation of Scala Spark Programs ====

If you do not know how to compile Scala programs, you can use the following directions:
  - create a directory for your project
  - copy ''/net/projects/spark/sbt/spark-template.sbt'' to your project directory and rename it to your project name (i.e., ''my-best-project.sbt'')
  - replace the ''spark-template'' by your project name in the first line (i.e., ''name := "my-best-project"'')
  - run ''sbt package'' to create JAR (note that first run of ''sbt'' will take several minutes)
The resulting JAR can be found in ''target/scala-2.11'' subdirectory, named after your project.

==== Usage Examples ====
Consider the following simple word-count application ''word_count.scala'':
<file scala>
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._

object Main {
  def main(args: Array[String]) {
    if (args.length < 2) sys.error("Usage: input output")
    val (input, output) = (args(0), args(1))

    val sc = new SparkContext()
    sc.textFile(input, 3*sc.defaultParallelism)
      .flatMap(_.split("\\s"))
      .map((_,1)).reduceByKey(_+_)
      .sortBy(_._2, ascending=false)
      .saveAsTextFile(output)
    sc.stop()
  }
}
</file>

The ''sbt'' project file ''word_count.sbt'':
<file>
name := "word_count"

version := "1.0"

scalaVersion := "2.12.17"
 
libraryDependencies += "org.apache.spark" %% "spark-core" % "3.3.1"
</file>

  * compile the application
  <file>sbt package</file>

  * run ''word_count'' application inside existing Spark cluster (i.e., inside ''spark-sbatch'' or ''spark-srun''), or start local Spark cluster using as many threads as there are cores if there is none:
  <file>spark-submit target/scala-2.12/word_count_2.12-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>
  * run ''word_count'' application with local Spark cluster using one thread:
  <file>MASTER=local spark-submit target/scala-2.12/word_count_2.12-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>
  * start Spark cluster (10 machines, 2GB RAM each) on Slurm and run ''word_count'' application:
  <file>spark-sbatch 10 2G spark-submit target/scala-2.12/word_count_2.12-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>