GSP103

Overview

Managed Service for Apache Spark is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or days take seconds or minutes instead. Create Managed Service for Apache Spark clusters quickly and resize them at any time, so you don't have to worry about your data pipelines outgrowing your clusters.

This lab shows you how to use the Google Cloud console to create a Managed Apache Spark cluster, run a simple Apache Spark job in the cluster, and then modify the number of workers in the cluster.

What you'll do

In this lab, you learn how to:

• Create a Managed Apache Spark cluster in the Google Cloud console
• Run a simple Apache Spark job
• Modify the number of workers in the cluster

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the right is the Lab setup and access panel with the following:

   • The Open Google Cloud console button
   • The temporary credentials (username and password) that you must use for this lab
   • Other information, if needed, to step through this lab

   Note that the lab timer is located near the top of the page, showing the remaining time.

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab setup and access panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab setup and access panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

2. Click through the following windows:

   • Continue through the Cloud Shell information window.
   • Authorize Cloud Shell to use your credentials to make Google Cloud API calls.

When you are connected, you are already authenticated, and the project is set to your Project_ID, . The output contains a line that declares the Project_ID for this session:

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

3. (Optional) You can list the active account name with this command:

gcloud auth list

4. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

5. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Confirm Cloud Dataproc API is enabled

To create a Managed Apache Spark cluster in Google Cloud, the Cloud Dataproc API must be enabled. To confirm the API is enabled:

1. Click Navigation menu > APIs & Services > Library.

2. Type Cloud Dataproc API in the Search for APIs & Services dialog. The console will display the Cloud Dataproc API in the search results.

3. Click on Cloud Dataproc API to display the status of the API. If the API is not already enabled, click the Enable button.

Once the API is enabled, proceed with the lab instructions.

Grant a necessary IAM role to the Compute Engine service account

1. In the Google Cloud console, in the navigation menu (), select IAM & Admin > IAM.

2. Click Edit principal (pencil icon) for the Compute Engine default service account ([project-number]-compute@developer.gserviceaccount.com).

3. Click + Add another role.

4. For Select a role, search for Storage Admin, and select it from the results.

5. Click Save.

Task 1. Create a cluster

1. In Cloud Shell, run the following command to create a cluster called example-cluster with e2-standard-2 VMs and 30 GB standard persistent disks:

gcloud dataproc clusters create example-cluster \ --region={{{ project_0.default_region }}} \ --zone={{{ project_0.default_zone }}} \ --master-machine-type=e2-standard-2 \ --master-boot-disk-type=pd-standard \ --master-boot-disk-size=30GB \ --worker-machine-type=e2-standard-2 \ --worker-boot-disk-type=pd-standard \ --worker-boot-disk-size=30GB \ --num-workers=2

2. If prompted to confirm a zone or authorize Cloud Shell, enter Y or click Authorize.

Your cluster will take a few minutes to create. The command output displays Created [... example-cluster] once it is ready.

Click Check my progress to verify your performed task.

Create a cluster

Task 2. Submit a job

To run a sample Spark job:

1. In the Google Cloud console, select Navigation menu () > View All Products, then under the Analytics section, click Managed Apache Spark > Clusters.

You should see your newly created cluster example-cluster listed with a Running status.

2. Click Jobs in the left pane to switch to Managed Apache Spark's jobs view, then click Submit job.

3. Set the following fields to update Job. Accept the default values for all other fields:

Field	Value
Region
Cluster	example-cluster
Job type	Spark
Main class or jar	org.apache.spark.examples.SparkPi
Jar files	file:///usr/lib/spark/examples/jars/spark-examples.jar
Arguments	1000 (This sets the number of tasks.)

4. Click Submit.

Note: How the job calculates Pi: The Spark job estimates a value of Pi using the Monte Carlo method. It generates x,y points on a coordinate plane that models a circle enclosed by a unit square. The input argument (1000) determines the number of x,y pairs to generate; the more pairs generated, the greater the accuracy of the estimation. This estimation leverages Managed Apache Spark worker nodes to parallelize the computation. For more information, see Estimating Pi using the Monte Carlo Method and see JavaSparkPi.java on GitHub.

Your job should appear in the Jobs list, which shows your project's jobs with its cluster, type, and current status. Job status displays as Running, and then Succeeded after it completes.

Click Check my progress to verify your performed task.

Submit a job

Task 3. View the job output

To see your completed job's output:

1. Click the job ID in the Jobs list.

2. Select LINE WRAP to ON or scroll all the way to the right to see the calculated value of Pi. Your output, with LINE WRAP ON, should look something like this:

Your job has successfully calculated a rough value for pi!

Task 4. Update a cluster to modify the number of workers

To change the number of worker instances in your cluster:

1. Select Clusters in the left navigation pane to return to the Managed Apache Spark Clusters view.

2. Click example-cluster in the Clusters list. By default, the page displays an overview of your cluster's CPU usage.

3. Click Configuration to display your cluster's current settings.

4. Click Edit. The number of worker nodes is now editable.

5. Enter 4 in the Worker nodes field.

6. Click Save.

Your cluster is now updated. Check out the number of VM instances in the cluster.

Click Check my progress to verify your performed task.

Update a cluster

1. To rerun the job with the updated cluster, you would click Jobs in the left pane, then click SUBMIT JOB.

2. Set the same fields you set in the Submit a job section:

Field	Value
Region
Cluster	example-cluster
Job type	Spark
Main class or jar	org.apache.spark.examples.SparkPi
Jar files	file:///usr/lib/spark/examples/jars/spark-examples.jar
Arguments	1000 (This sets the number of tasks.)

3. Click Submit.

Task 5. Test your understanding

Below are multiple-choice questions to reinforce your understanding of this lab's concepts. Answer them to the best of your abilities.

Congratulations!

Now you know how to use the Google Cloud console to create and update a Managed Apache Spark cluster and then submit a job in that cluster.

Next steps / Learn more

This lab is also part of a series of labs called Qwik Starts. These labs are designed to give you a little taste of the many features available with Google Cloud. Review the list of "Qwik Starts" in the lab catalog to find the next lab you'd like to take!

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Manual Last Updated August 27, 2026

Lab Last Tested August 27, 2026

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