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--- spark:using-scala [2014/11/10 15:43]
straka created
+++ spark:using-scala [2018/11/24 22:42]
straka [Usage Examples]
@@ Line 20: / Line 20: @@
 </file>
-  * run interactive shell inside ''spark-qrsh'', or start local Spark cluster using as many threads as there are cores:
+  * run interactive shell using existing Spark cluster (i.e., inside ''spark-qrsh''), 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:
@@ Line 26: / Line 26: @@
   * start Spark cluster (10 machines, 1GB RAM each) on SGE and run interactive shell:
   <file>spark-qrsh 10 1G 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 := "2.11.12"
+libraryDependencies += "org.apache.spark" %% "spark-core" % "2.3.2"
+</file>
+  * compile the application
+  <file>sbt package</file>
+  * run ''word_count'' application inside existing Spark cluster (i.e., inside ''spark-qsub'' or ''spark-qrsh''), or start local Spark cluster using as many threads as there are cores if there is none:
+  <file>spark-submit target/scala-2.11/word_count_2.11-1.0.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-2.11/word_count_2.11-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>
+  * start Spark cluster (10 machines, 1GB RAM each) on SGE and run ''word_count'' application:
+  <file>spark-qsub 10 1G spark-submit target/scala-2.11/word_count_2.11-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>

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