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Play-Spark2: A Simple Application

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Play-Spark2: A Simple Application

Learn to create a simple application using the lightweight Play framework and Apache Spark cluster computing system in this tutorial.

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In this post, we will create a very simple application with the Play framework and Spark. Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Scala, Java, and Python that make parallel jobs easy to write, and an optimized engine that supports general computation graphs. It also supports a rich set of higher-level tools including Shark and Spark Streaming. Play, on the other hand, is a lightweight, stateless, web-friendly architecture. It is built on Akka and provides predictable and minimal resource consumption (CPU, memory, threads) for highly-scalable applications.

Now we will start building an application using the above technologies. We will try to create a Spark application built in Play 2.5. We can build it in any Play version.

First of all, download the latest activator version using these steps and create a new Play-Scala project.

knoldus@knoldus:~/Desktop/activator/activator-1.3.12-minimal$ activator new
Fetching the latest list of templates...

Browse the list of templates: http://lightbend.com/activator/templates
Choose from these featured templates or enter a template name:
1) minimal-akka-java-seed
2) minimal-akka-scala-seed
3) minimal-java
4) minimal-scala
5) play-java
6) play-scala
(hit tab to see a list of all templates)
> 6
Enter a name for your application (just press enter for 'play-scala')
> play-spark
OK, application "play-spark" is being created using the "play-scala" template

Import your project into IntelliJ inside your built.sbt file and add the following dependencies:

libraryDependencies ++= Seq(
  "org.scalatestplus.play" %% "scalatestplus-play" % "1.5.1" % Test,
  "com.fasterxml.jackson.core" % "jackson-core" % "2.8.7",
  "com.fasterxml.jackson.core" % "jackson-databind" % "2.8.7",
  "com.fasterxml.jackson.core" % "jackson-annotations" % "2.8.7",
  "com.fasterxml.jackson.module" %% "jackson-module-scala" % "2.8.7",
  "org.apache.spark" % "spark-core_2.11" % "2.1.1",
  "org.webjars" %% "webjars-play" % "2.5.0-1",
  "org.webjars" % "bootstrap" % "3.3.6",
  "org.apache.spark" % "spark-sql_2.11" % "2.1.1",
  "com.adrianhurt" %% "play-bootstrap" % "1.0-P25-B3"
libraryDependencies ~= { _.map(_.exclude("org.slf4j", "slf4j-log4j12")) }

Add these routes inside your routes file in your conf package:

# Routes
# This file defines all application routes (Higher priority routes first)
# ~~~~

# Home page

# Map static resources from the /public folder to the /assets URL path
GET     /assets/*file               controllers.Assets.versioned(path="/public", file: Asset)

GET     /webjars/*file                    controllers.WebJarAssets.at(file)

GET     /locations/json       controllers.ApplicationController.index

Next, you need to create a bootstrap package inside your app folder and create a class inside it, which will be the first class to be loaded in the project and will initialize the SparkSession.

package bootstrap

import org.apache.spark.sql.SparkSession
import play.api._

object Init extends GlobalSettings {

  var sparkSession: SparkSession = _

   * On start load the json data from conf/data.json into in-memory Spark
  override def onStart(app: Application) {
    sparkSession = SparkSession.builder

    val dataFrame = sparkSession.read.json("conf/data.json")

   * On stop clear the sparksession
  override def onStop(app: Application) {

  def getSparkSessionInstance = {

To make sure that this class is global in the root package inside your application.conf, add these lines:

# This is the main configuration file for the application.
# ~~~~~

# Secret key
# ~~~~~
# The secret key is used to secure cryptographics functions.
# If you deploy your application to several instances be sure to use the same key!

# The application languages
# ~~~~~

# Global object class
# ~~~~~
# Define the Global object class for this application.
# Default to Global in the root package.
application.global= bootstrap.Init

Inside your controller package, add a new controller and name it as ApplicationController; this controller will read data from a JSON file using SparkSession and query the data, then later convert that data to JSON and send it to the view.

package controllers

import javax.inject.Inject

import scala.concurrent.Future

import org.apache.spark.sql.{DataFrame, SparkSession}
import play.api.i18n.MessagesApi
import play.api.libs.json.Json
import play.api.mvc.{Action, AnyContent, Controller}
import bootstrap.Init

class ApplicationController @Inject()(implicit webJarAssets: WebJarAssets,
    val messagesApi: MessagesApi) extends Controller {

  def index: Action[AnyContent] = { Action.async {
    val query1 =
        SELECT * FROM godzilla WHERE date='2000-02-05' limit 8

     val sparkSession = Init. getSparkSessionInstance
      val result: DataFrame = sparkSession.sql(query1)
      val rawJson = result.toJSON.collect().mkString


Now we are all set to start with our Play application. Using activator run  , you can run your application, and to query the data, you can use the below curl request:


You can find the working demo available here.

Happy coding- I hope this blog will help.

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