Verify Prometheus service has been deployed
Verify the application has been deployed
Verify the Dashboard has been created in Cloud Monitoring
Using Prometheus for Monitoring on Google Cloud: Qwik Start
In this lab, you'll set up a Google Kubernetes Engine cluster, then deploy the Managed Service for Prometheus to ingest metrics from a simple application.
Managed Service for Prometheus is Google Cloud's fully managed storage and query service for Prometheus metrics. This service is built on top of Monarch, the same globally scalable data store as Cloud Monitoring.
A thin fork of Prometheus replaces existing Prometheus deployments and sends data to the managed service with no user intervention. This data can then be queried by using PromQL through the Prometheus Query API supported by the managed service and by using the existing Cloud Monitoring query mechanisms.
Deploy the Managed Service for Prometheus to a GKE cluster
Deploy a Python application to monitor
Create a Cloud Monitoring dashboard to view metrics collected
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).
- Time to complete the lab---remember, once you start, you cannot pause a lab.
How to start your lab and sign in to the Google Cloud Console
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 Console button
- Time remaining
- The temporary credentials that you must use for this lab
- Other information, if needed, to step through this lab
Click Open Google Console. 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.
If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.
Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.
Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
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 Cloud Console opens in this tab.
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.
Click Activate Cloud Shell at the top of the Google Cloud console.
It takes a few moments to provision and connect to the environment. 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:
gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
(Optional) You can list the active account name with this command:
(Optional) You can list the project ID with this command:
gcloud, in Google Cloud, refer to the gcloud CLI overview guide.
Setup a Google Kubernetes Engine cluster
Run the following command to deploy a standard GKE cluster, which will prompt you to authorize and enable the GKE API. The cluster will take a couple of minutes to create. Wait for the creation to finish then proceed to the next step.
Run the following command to authenticate to the cluster.
Deploy the Prometheus service
Run the following command to create a namespace to do the work in.
Deploy the application
Deploy a simple application which emits metrics at the
Verify that this simple Python Flask app is serving metrics with the following command:
Output will look like below:
Tell Prometheus where to begin scraping the metrics from by applying the PodMonitoring file.
Before finishing up here, generate some load on the application with a really simple interaction with the app.
This will run for 2 minutes, and when done, you can create a visualization of what this looks like!
Observing the app via metrics
In this last section, quickly use
gcloud to deploy a custom monitoring dashboard that shows the metrics from this application in a line chart. Be sure to copy the entirety of this code block:
Once created, navigate to Monitoring > Dashboards to see the newly created
Prometheus Dashboard Example -- click through below to see how to get there.
You've seen the basics of deploying a GKE app with Prometheus Metrics and creating a Cloud Monitoring Dashboard from it.
Finish your quest
This self-paced lab is part of the Monitor Environments with Google Cloud managed Service for Prometheus skill badge quest. A quest is a series of related labs that form a learning path. Completing this quest will earn you a badge to recognize your achievement.
Next Steps / Learn More
If you'd like to learn more, check out the Prometheus documentation and look for the deploying self-managed collection.
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Manual Last Updated: August 16, 2022
Lab Last Tested: August 16, 2022
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