In this lab, we will launch Apache Spark jobs on Could DataProc, to estimate the digits of Pi in a distributed fashion.

From the console on GCP, on the side menu, click on DataProc and Clusters.

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You might be needed to enable API, so just click on the button. Then, you are redirected to a page where you can create clusters. Click on Create Clusters.

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Change the name of the cluster if you want, and leave all the other parameters to default. Then, click on “Create” to create the cluster.

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The cluster will now start.

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Then, move to the “Jobs” tab.

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Click on the “Submit a job” button. You can give your job a specific name, and make sure to change the job’s type to Spark rather than Hadoop.

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We will be using one of the pre-defined jobs in Spark examples. To do so, in the field “Main class or jar”, simply type :

org.apache.spark.examples.SparkPi

The code for this job can be found on Github. What this essentially does is to run a Monte Carlo simulation of pairs of X and Y coordinates in a unit circle and use the definition of the area to retrieve the Pi estimate.

package org.apache.spark.examples;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;

import java.util.ArrayList;
import java.util.List;

public final class SparkPi {

    public static void main(String[] args) throws Exception {
        final SparkConf sparkConf = new SparkConf().setAppName("SparkPi");
        final JavaSparkContext jsc = new JavaSparkContext(sparkConf);

        final int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
        final int n = 100000 * slices;
        final List<Integer> l = new ArrayList<>(n);
        for (int i = 0; i < n; i++) {
            l.add(i);
        }

        final JavaRDD<Integer> dataSet = jsc.parallelize(l, slices);

        final int count = dataSet.map(integer -> {
            double x = Math.random() * 2 - 1;
            double y = Math.random() * 2 - 1;
            return (x * x + y * y < 1) ? 1 : 0;
        }).reduce((a, b) -> a + b);

        System.out.println("Pi is roughly " + 4.0 * count / n);
    }
}

You can set the Jar files field to :

file:///usr/lib/spark/examples/jars/spark-examples.jar

The job takes arguments, which can be set to 1000 here :

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You can then click on “Submit” to submit your job. From the cluster tab, you can click on the name of the cluster and access a cluster monitoring dashboard :

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If you click on “Jobs” in the Cluster tabs, you’ll notice the progress of the job we launched. It took 46 seconds in my case.

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You can click on the name of the job.

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You will directly see the output that states :

19/07/31 21:55:46 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl: Submitted application application_1564609038185_0001

Pi is roughly 3.1418519514185195

19/07/31 21:56:12 INFO org.spark_project.jetty.server.AbstractConnector: Stopped Spark@5288ab42{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}

Job output is complete

If you don’t need it anymore, make sure to DELETE the cluster.


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