GSP104

Overview

Dataproc 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 Dataproc 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 command line to create a Dataproc 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 Dataproc cluster using the command line
• 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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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 or private browser window to run this lab. This prevents any 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: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your 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 pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

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 Details 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 Details 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 view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

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.

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.

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

gcloud auth list

3. Click Authorize.

Output:

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

4. (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.

Task 1. Create a cluster

1. In Cloud Shell, run the following command to set the Region:

gcloud config set dataproc/region {{{project_0.default_region | Region}}}

2. Dataproc creates staging and temp buckets that are shared among clusters in the same region. Since we're not specifying an account for Dataproc to use, it will use the Compute Engine default service account, which doesn't have storage bucket permissions by default. Let's add those.

• First, run the following commands to grab the PROJECT_ID and PROJECT_NUMBER:

PROJECT_ID=$(gcloud config get-value project) && \ gcloud config set project $PROJECT_ID PROJECT_NUMBER=$(gcloud projects describe $PROJECT_ID --format='value(projectNumber)')

• Now run the following command to give the Storage Admin role to the Compute Engine default service account:

gcloud projects add-iam-policy-binding $PROJECT_ID \ --member=serviceAccount:$PROJECT_NUMBER-compute@developer.gserviceaccount.com \ --role=roles/storage.admin

3. Run the following command to create a cluster called example-cluster with e2-standard-4 VMs and default Cloud Dataproc settings:

gcloud dataproc clusters create example-cluster --worker-boot-disk-size 500 --worker-machine-type=e2-standard-4 --master-machine-type=e2-standard-4

4. If asked to confirm a zone for your cluster. Enter Y.

Your cluster will build for a couple of minutes.

Waiting for cluster creation operation...done. Created [... example-cluster]

When you see a "Created" message, you're ready to move on.

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a Dataproc cluster, you will see an assessment score.

Create a Dataproc cluster

Task 2. Submit a job

• Run this command to submit a sample Spark job that calculates a rough value for pi:

gcloud dataproc jobs submit spark --cluster example-cluster \ --class org.apache.spark.examples.SparkPi \ --jars file:///usr/lib/spark/examples/jars/spark-examples.jar -- 1000

The command specifies:

• That you want to run a spark job on the example-cluster cluster
• The class containing the main method for the job's pi-calculating application
• The location of the jar file containing your job's code
• The parameters you want to pass to the job—in this case, the number of tasks, which is 1000

Note: Parameters passed to the job must follow a double dash (--). See the gcloud documentation for more information.

The job's running and final output is displayed in the terminal window:

Waiting for job output... ... Pi is roughly 3.14118528 ... state: FINISHED

Test completed task

Click Check my progress to verify your performed task. If you have successfully submitted a job, you will see an assessment score.

Submit a job

Task 3. Update a cluster

1. To change the number of workers in the cluster to four, run the following command:

gcloud dataproc clusters update example-cluster --num-workers 4

Your cluster's updated details are displayed in the command's output:

Waiting on operation [projects/qwiklabs-gcp-7f7aa0829e65200f/regions/global/operations/b86892cc-e71d-4e7b-aa5e-6030c945ea67]. Waiting for cluster update operation...done.

2. You can use the same command to decrease the number of worker nodes:

gcloud dataproc clusters update example-cluster --num-workers 2

Now you can create a Dataproc cluster and adjust the number of workers from the gcloud command line on the Google Cloud.

Task 4. 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!

You learned how to use the command line to create and modify a Dataproc cluster and submit jobs.

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. Search for "Qwik Starts" in the lab catalog to find the next lab you'd like to take!

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Manual Last Updated February 04, 2024

Lab Last Tested October 23, 2023

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