name := "SparkMe Project"
version := "1.0"
organization := "pl.japila"
scalaVersion := "2.11.7"
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.6.0-SNAPSHOT" (1)
resolvers += Resolver.mavenLocal
Your first Spark application (using Scala and sbt)
This page gives you the exact steps to develop and run a complete Spark application using Scala programming language and sbt as the build tool.
Tip
|
Refer to Quick Start’s Self-Contained Applications in the official documentation. |
The sample application called SparkMe App is…FIXME
Overview
You’re going to use sbt as the project build tool. It uses build.sbt
for the project’s description as well as the dependencies, i.e. the version of Apache Spark and others.
The application’s main code is under src/main/scala
directory, in SparkMeApp.scala
file.
With the files in a directory, executing sbt package
results in a package that can be deployed onto a Spark cluster using spark-submit
.
In this example, you’re going to use Spark’s local mode.
Project’s build - build.sbt
Any Scala project managed by sbt uses build.sbt
as the central place for configuration, including project dependencies denoted as libraryDependencies
.
build.sbt
-
Use the development version of Spark 1.6.0-SNAPSHOT
SparkMe Application
The application uses a single command-line parameter (as args(0)
) that is the file to process. The file is read and the number of lines printed out.
package pl.japila.spark
import org.apache.spark.{SparkContext, SparkConf}
object SparkMeApp {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("SparkMe Application")
val sc = new SparkContext(conf)
val fileName = args(0)
val lines = sc.textFile(fileName).cache
val c = lines.count
println(s"There are $c lines in $fileName")
}
}
sbt version - project/build.properties
sbt (launcher) uses project/build.properties
file to set (the real) sbt up
sbt.version=0.13.9
Tip
|
With the file the build is more predictable as the version of sbt doesn’t depend on the sbt launcher. |
Packaging Application
Execute sbt package
to package the application.
➜ sparkme-app sbt package
[info] Loading global plugins from /Users/jacek/.sbt/0.13/plugins
[info] Loading project definition from /Users/jacek/dev/sandbox/sparkme-app/project
[info] Set current project to SparkMe Project (in build file:/Users/jacek/dev/sandbox/sparkme-app/)
[info] Compiling 1 Scala source to /Users/jacek/dev/sandbox/sparkme-app/target/scala-2.11/classes...
[info] Packaging /Users/jacek/dev/sandbox/sparkme-app/target/scala-2.11/sparkme-project_2.11-1.0.jar ...
[info] Done packaging.
[success] Total time: 3 s, completed Sep 23, 2015 12:47:52 AM
The application uses only classes that comes with Spark so package
is enough.
In target/scala-2.11/sparkme-project_2.11-1.0.jar
there is the final application ready for deployment.
Submitting Application to Spark (local)
Note
|
The application is going to be deployed to local[*] . Change it to whatever cluster you have available (refer to Running Spark in cluster).
|
spark-submit
the SparkMe application and specify the file to process (as it is the only and required input parameter to the application), e.g. build.sbt
of the project.
Note
|
build.sbt is sbt’s build definition and is only used as an input file for demonstration purposes. Any file is going to work fine.
|
➜ sparkme-app ~/dev/oss/spark/bin/spark-submit --master "local[*]" --class pl.japila.spark.SparkMeApp target/scala-2.11/sparkme-project_2.11-1.0.jar build.sbt
Using Spark's repl log4j profile: org/apache/spark/log4j-defaults-repl.properties
To adjust logging level use sc.setLogLevel("INFO")
15/09/23 01:06:02 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/09/23 01:06:04 WARN MetricsSystem: Using default name DAGScheduler for source because spark.app.id is not set.
There are 8 lines in build.sbt
Note
|
Disregard the two above WARN log messages. |
You’re done. Sincere congratulations!