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Before you begin
- Labs create a Google Cloud project and resources for a fixed time
- Labs have a time limit and no pause feature. If you end the lab, you'll have to restart from the beginning.
- On the top left of your screen, click Start lab to begin
Create a Dataproc cluster
/ 50
Submit a job
/ 30
Update a cluster
/ 20
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 Google Cloud console to create a Dataproc cluster, run a simple Apache Spark job in the cluster, and then modify the number of workers in the cluster.
In this lab, you learn how to:
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:
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 left is the Lab Details pane with the following:
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.
If necessary, copy the Username below and paste it into the Sign in dialog.
You can also find the Username in the Lab Details pane.
Click Next.
Copy the Password below and paste it into the Welcome dialog.
You can also find the Password in the Lab Details pane.
Click Next.
Click through the subsequent pages:
After a few moments, the Google Cloud console opens in this tab.
To create a Dataproc cluster in Google Cloud, the Cloud Dataproc API must be enabled. To confirm the API is enabled:
Click Navigation menu > APIs & Services > Library:
Type Cloud Dataproc in the Search for APIs & Services dialog. The console will display the Cloud Dataproc API in the search results.
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.
To assign storage permission to the service account, which is required for creating a cluster:
Go to Navigation menu > IAM & Admin > IAM.
Click the pencil icon on the compute@developer.gserviceaccount.com
service account.
click on the + ADD ANOTHER ROLE button. select role Storage Admin
Once you've selected the Storage Admin role, click on Save
In the Cloud Platform Console, select Navigation menu > Dataproc > Clusters, then click Create cluster.
Click Create for Cluster on Compute Engine.
Set the following fields for your cluster and accept the default values for all other fields:
Field | Value |
---|---|
Name | example-cluster |
Region | |
Zone | |
Machine Series (Manager Node) | E2 |
Machine Type (Manager Node) | e2-standard-2 |
Primary disk size (Manager Nodes) | 30 GB |
Number of Worker Nodes | 2 |
Machine Series (Worker Nodes) | E2 |
Machine Type (Worker Nodes) | e2-standard-2 |
Primary disk size (Worker Nodes) | 30 GB |
Internal IP only | Deselect "Configure all instances to have only internal IP addresses" |
us-central1
or europe-west1
, to isolate resources (including VM instances and Cloud Storage) and metadata storage locations utilized by Cloud Dataproc within the user-specified region.
Your new cluster will appear in the Clusters list. It may take a few minutes to create, the cluster Status shows as Provisioning until the cluster is ready to use, then changes to Running.
Test completed task
Click Check my progress to verify your performed task.
To run a sample Spark job:
Click Jobs in the left pane to switch to Dataproc's jobs view, then click Submit job.
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.) |
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.
Test completed task
Click Check my progress to verify your performed task.
To see your completed job's output:
Click the job ID in the Jobs list.
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!
To change the number of worker instances in your cluster:
Select Clusters in the left navigation pane to return to the Dataproc Clusters view.
Click example-cluster in the Clusters list. By default, the page displays an overview of your cluster's CPU usage.
Click Configuration to display your cluster's current settings.
Click Edit. The number of worker nodes is now editable.
Enter 4 in the Worker nodes field.
Click Save.
Your cluster is now updated. Check out the number of VM instances in the cluster.
Test completed task
Click Check my progress to verify your performed task.
To rerun the job with the updated cluster, you would click Jobs in the left pane, then click SUBMIT JOB.
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.) |
Below are multiple-choice questions to reinforce your understanding of this lab's concepts. Answer them to the best of your abilities.
Now you know how to use the Google Cloud console to create and update a Dataproc cluster and then submit a job in that cluster.
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 July 2, 2024
Lab Last Tested July 2, 2024
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