Verify prometheus has been deplyed
Verify node exporter is running
Collect Metrics from Exporters using the Managed Service for Prometheus
In this lab, you will explore using the Managed Service for Prometheus to collect metrics from other infrastructure sources via exporters.
In this lab, you will learn how to:
Deploy a GCE instance and configure the node-exporter tool
Build the GMP binary locally and deploy to the GCE instance
Apply a Prometheus configuration to begin collecting metrics
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.
- In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.
- Click Continue.
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, Cloud SDK documentation, see the gcloud command-line tool overview.
Deploy GKE cluster
Deploy a basic GKE cluster to set up the lab:
Set up a namespace
gmp-test Kubernetes namespace for resources you create as part of the example application:
Deploy the example application
The managed service provides a manifest for an example application that emits Prometheus metrics on its metrics port. The application uses three replicas.
To deploy the example application, run the following command:
Configure a PodMonitoring resource
To ingest the metric data emitted by the example application, you use target scraping. Target scraping and metrics ingestion are configured using Kubernetes custom resources. The managed service uses PodMonitoring custom resources (CRs).
A PodMonitoring CR scrapes targets only in the namespace the CR is deployed in. To scrape targets in multiple namespaces, deploy the same PodMonitoring CR in each namespace. You can verify the PodMonitoring resource is installed in the intended namespace by running
kubectl get podmonitoring -A.
For reference documentation about all the Managed Service for Prometheus CRs, see the prometheus-engine/doc/api reference.
The following manifest defines a PodMonitoring resource,
prom-example, in the
gmp-test namespace. The resource uses a Kubernetes label selector to find all pods in the namespace that have the label
app with the value
prom-example. The matching pods are scraped on a port named
metrics, every 30 seconds, on the
/metrics HTTP path.
To apply this resource, run the following command:
Your managed collector is now scraping the matching pods.
To configure horizontal collection that applies to a range of pods across all namespaces, use the ClusterPodMonitoring resource. The ClusterPodMonitoring resource provides the same interface as the PodMonitoring resource but does not limit discovered pods to a given namespace.
If you are running on GKE, then you can do the following:
To query the metrics ingested by the example application, see Query data from the Prometheus service.
To learn about filtering exported metrics and adapting your prom-operator resources, see Additional topics for managed collection.
Download the prometheus binary
Download the prometheus binary from the following bucket:
Run the prometheus binary
Save your project id to a variable:
Save your zone to a variable. These values will be used when running your promtheus binary.
Run the prometheus binary on cloud shell using command here:
After the prometheus binary begins you should be able to go to managed prometheus in the Console UI and run a PromQL query “up” to see the prometheus binary is available (will show localhost running one as the instance name).
Download and run the node exporter
Open a new tab in cloud shell to run the node exporter commands.
Download and run the exporter on the cloud shell box:
You should see output like this indicating that the Node Exporter is now running and exposing metrics on port 9100:
Create a config.yaml file
Stop the running prometheus binary and have a new config file which will take the metrics from node exporter:
Create a config.yaml file with the following spec:
Upload the config.yaml file you created to verify:
Paste the above configuration inside the editor then save and exit.
Re-run prometheus pointing to the new configuration file:
Use the following stat from the exporter to see its count in the PromQL query: Write any query in the PromQL query Editor prefixed with “node_” this should bring up an input list of metrics you can select to visualize in the graphical editor.
One that seems to give a good graph is “node_cpu_seconds_total”
In this lab you deployed a Compute Instance and configured node-exporter. You then configured the GMP binary to ingest metrics from node-exporter and viewed the metrics.
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
You can read more about Google cloud Managed Service for Prometheus.
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Manual Last Updated: June 21, 2022
Lab Last Tested: March 09, 2022
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