GSP1027

Overview

The Google Cloud Managed Service for Prometheus charges for the number of samples ingested into Cloud Monitoring and for read requests to the Monitoring API. The number of samples ingested is the primary contributor to your cost.

In this lab, you will explore cost control mechanisms when utilizing the Managed Service for Prometheus on Google Cloud.

Objectives

In this lab, you will learn how to:

• Deploy Google Managed Prometheus (GMP) in a Google Kubernetes Engine (GKE) cluster as well as a python application
• Reduce the number of time series metrics you send to the managed service by filtering the metric data generated
• Reduce the number of samples collected by changing the scraping interval

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 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:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents 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: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that 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 dialog opens for you to select your payment method. On the left is the Lab Details pane 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 pane.

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 pane.

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 access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

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.

2. Click through the following windows:

   • Continue through the Cloud Shell information window.
   • Authorize Cloud Shell to use your credentials to make Google Cloud API calls.

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.

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

gcloud auth list

4. Click Authorize.

Output:

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

5. (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. Deploy GKE cluster

• Deploy a basic GKE cluster to setup lab:

gcloud beta container clusters create gmp-cluster --num-nodes=1 --zone {{{project_0.default_zone|ZONE}}} --enable-managed-prometheus gcloud container clusters get-credentials gmp-cluster --zone={{{project_0.default_zone|ZONE}}}

Task 2. Deploy managed collection

Configure a PodMonitoring resource

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: monitoring.googleapis.com/v1 kind: PodMonitoring metadata: namespace: gmp-system name: collector labels: app.kubernetes.io/name: collector app.kubernetes.io/part-of: google-cloud-managed-prometheus spec: selector: matchLabels: app.kubernetes.io/name: collector endpoints: - port: prom-metrics interval: 10s - port: cfg-rel-metrics interval: 10s

• To apply this resource, run the following command:

kubectl -n gmp-system apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/prometheus-engine/main/examples/self-pod-monitoring.yaml

Your managed collector is now scraping the matching pods.

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-system apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/prometheus-engine/main/examples/example-app.yaml Check if prometheus has been deployed

Task 3. Cloud Monitoring

To view your Managed Service for Prometheus data as Cloud Monitoring time series, use Metrics Explorer. To configure Metrics Explorer to display metrics, do the following:

1. From the Cloud console, go to Monitoring.
2. In the Monitoring navigation pane, click Metrics Explorer.
3. Specify the data to appear on the chart. You can use the MQL editor to do so.

   • To use the MQL tab, do the following:

     a. Click PromQL from the top right and select the MQL radio option in a new Query.

     b. Enter the following query:

     fetch prometheus_target::prometheus.googleapis.com/up/gauge

     c. Click Run Query.

Task 4. Populate a Graph

• Go to Monitoring > Metrics Explorer and create another Query.

• Select the PromQL radio option and run the query below by clicking Run Query to see metrics:

go_memstats_heap_alloc_bytes

This will populate a graph similar to the image below when selected.

Task 5. Filter exported metrics

If you collect a lot of data, you might want to prevent some time series from being sent to Managed Service for Prometheus to keep costs down.

To filter exported metrics, you can configure a set of PromQL series selectors in the OperatorConfig resource. A time series is exported to Managed Service for Prometheus if it satisfies at least one of the selectors.

1. Open the OperatorConfig resource for editing:

kubectl -n gmp-public edit operatorconfig config

2. After the apiVersion line, press "i" to go into insert mode. Go to the final line and press enter to go to a new line. Ensure there are no indents at the beginning of the line. Then paste the following:

collection: filter: matchOneOf: - '{job="prom-example"}' - '{__name__=~"job:.+"}'

The file should look like the following:

3. To save the file and exit press "Esc" then type ":wq" then enter.

This addition causes only metrics for the "prometheus" job as well as metrics produced by recording rules that aggregate to the job level—when following naming best practices—to be exported. Samples for all other time series are filtered out. By default, no selectors are specified and all time series are exported.

The filter.matchOneOf configuration section has the same semantics as the match[] parameters for Prometheus federation.

4. Create a op-config.yaml file:

vi op-config.yaml

5. Copy the following into the op-config.yaml file:

apiVersion: monitoring.googleapis.com/v1alpha1 collection: filter: matchOneOf: - '{job="prom-example"}' - '{__name__=~"job:.+"}' kind: OperatorConfig metadata: annotations: components.gke.io/layer: addon kubectl.kubernetes.io/last-applied-configuration: | {"apiVersion":"monitoring.googleapis.com/v1alpha1","kind":"OperatorConfig","metadata":{"annotations":{"components.gke.io/layer":"addon"},"labels":{"addonmanager.kubernetes.io/mode":"Reconcile"},"name":"config","namespace":"gmp-public"}} creationTimestamp: "2022-03-14T22:34:23Z" generation: 1 labels: addonmanager.kubernetes.io/mode: Reconcile name: config namespace: gmp-public resourceVersion: "2882" uid: 4ad23359-efeb-42bb-b689-045bd704f295

6. Upload the config file you created to verify:

export PROJECT=$(gcloud config get-value project) gsutil mb -p $PROJECT gs://$PROJECT gsutil cp op-config.yaml gs://$PROJECT gsutil -m acl set -R -a public-read gs://$PROJECT Check if metrics filter has been applied

Task 6. Run the query

1. Click + Add query to create a new Query and type up/gauge into the Select a metric input filter.

2. Select the resulting prometheus metric and select Apply.

Task 7. Monitor the app

1. Create a prom-example-config.yaml file you created to verify:

vi prom-example-config.yaml

2. Copy the following into the file:

apiVersion: monitoring.googleapis.com/v1alpha1 kind: PodMonitoring metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {"apiVersion":"monitoring.googleapis.com/v1alpha1","kind":"PodMonitoring","metadata":{"annotations":{},"labels":{"app.kubernetes.io/name":"prom-example"},"name":"prom-example","namespace":"gmp-test"},"spec":{"endpoints":[{"interval":"30s","port":"metrics"}],"selector":{"matchLabels":{"app":"prom-example"}}}} creationTimestamp: "2022-03-14T22:33:55Z" generation: 1 labels: app.kubernetes.io/name: prom-example name: prom-example namespace: gmp-test resourceVersion: "2648" uid: c10a8507-429e-4f69-8993-0c562f9c730f spec: endpoints: - interval: 60s port: metrics selector: matchLabels: app: prom-example status: conditions: - lastTransitionTime: "2022-03-14T22:33:55Z" lastUpdateTime: "2022-03-14T22:33:55Z" status: "True" type: ConfigurationCreateSuccess observedGeneration: 1

3. Run the below commands in the cloud shell.

export PROJECT=$(gcloud config get-value project) gsutil cp prom-example-config.yaml gs://$PROJECT gsutil -m acl set -R -a public-read gs://$PROJECT Check if scrape interval has been changed

This completes the lab.

Congratulations

You have learned how to reduce the costs associated with using the Managed Service for Prometheus. You deployed prometheus and an example application. Then, you applied a metrics filter and changed the scraping interval to reduce the costs associated with ingesting metrics using the Google Managed Prometheus service.

Next steps / Learn more

• You can read more about Google Cloud Managed Service for Prometheus.

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Manual Last Updated February 17, 2025

Lab Last Tested February 17, 2025

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