Collect Metrics from Exporters using the Managed Service for Prometheus

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Collect Metrics from Exporters using the Managed Service for Prometheus

1 hour 30 minutes 1 Credit


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In this lab, you will explore using the Managed Service for Prometheus to collect metrics from other infrastructure sources via exporters.

Lab Objectives

In this lab, you will learn how to:

  1. Deploy a GCE instance and configure the node-exporter tool

  2. Build the GMP binary locally and deploy to the GCE instance

  3. 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).
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 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 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.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. 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.
  5. 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.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

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. In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Cloud Shell icon

  1. 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:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

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

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

gcloud auth list


ACTIVE: * ACCOUNT: To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project


[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 For full documentation of 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:

gcloud beta container clusters create gmp-cluster --num-nodes=1 --zone us-central1-f --enable-managed-prometheus gcloud container clusters get-credentials gmp-cluster --zone=us-central1-f

Set up a namespace

Create the gmp-test Kubernetes namespace for resources you create as part of the example application:

kubectl create ns gmp-test Check if prometheus has been deployed

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:

kubectl -n gmp-test apply -f

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.

apiVersion: kind: PodMonitoring metadata: name: prom-example spec: selector: matchLabels: app: prom-example endpoints: - port: metrics interval: 30s

To apply this resource, run the following command:

kubectl -n gmp-test apply -f

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.

Note: An additional targetLabels field provides a simplified Prometheus-style relabel configuration. You can use relabeling to add pod labels as labels on the ingested time series. You can't overwrite the mandatory target labels; for a list of these labels, see the prometheus_target resource.

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:

git clone && cd prometheus git checkout v2.28.1-gmp.4 wget chmod a+x prometheus

Run the prometheus binary

Save your project id to a variable:

export PROJECT_ID=$(gcloud config get-value project)

Save your zone to a variable. These values will be used when running your promtheus binary.

export ZONE=us-central1-f

Run the prometheus binary on cloud shell using command here:

./prometheus \ --config.file=documentation/examples/prometheus.yml --export.label.project-id=$PROJECT_ID --export.label.location=$ZONE

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:

wget tar xvfz node_exporter-1.3.1.linux-amd64.tar.gz cd node_exporter-1.3.1.linux-amd64 ./node_exporter Note: The port that the node_exporter tool is running on you will use to modify the config of prometheus on the next few steps.

You should see output like this indicating that the Node Exporter is now running and exposing metrics on port 9100:

INFO[0000] Starting node_exporter (version=0.16.0, branch=HEAD, revision=d42bd70f4363dced6b77d8fc311ea57b63387e4f) source="node_exporter.go:82" INFO[0000] Build context (go=go1.9.6, user=root@a67a9bc13a69, date=20180515-15:53:28) source="node_exporter.go:83" INFO[0000] Enabled collectors: source="node_exporter.go:90" INFO[0000] - boottime source="node_exporter.go:97" ... INFO[0000] Listening on :9100 source="node_exporter.go:111"

Create a config.yaml file

Stop the running prometheus binary and have a new config file which will take the metrics from node exporter:

vi config.yaml

Create a config.yaml file with the following spec:

global: scrape_interval: 15s scrape_configs: - job_name: node static_configs: - targets: ['localhost:9100']

Upload the config.yaml file you created to verify:

export PROJECT=$(gcloud config get-value project) gsutil mb -p $PROJECT gs://$PROJECT gsutil cp config.yaml gs://$PROJECT gsutil -m acl set -R -a public-read gs://$PROJECT Check if config.yaml is configured correctly

Paste the above configuration inside the editor then save and exit.

Re-run prometheus pointing to the new configuration file:

./prometheus --config.file=config.yaml --export.label.project-id=$PROJECT --export.label.location=$ZONE

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.

You can make your badges public and link to them in your online résumé or social media account. Enroll in this quest and get immediate credit for completing this lab. See other available quests.

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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