Using Python to Extract Prometheus Metrics

Libraries: If you want this lab, consider one of these libraries.
Cloud

Using Python to Extract Prometheus Metrics

This lab guides the student through the use of a Python program to interface with the Prometheus API endpoint. The program will use PromQL examples to pull CPU and memory metrics and output them in a Comma Separated Value (CSV) file that may then be used for a Machine Learning program. This lab only covers the extract step and the Machine Learning part is covered in a subsequent lab.

Get started Contact sales

Lab Info

Level

Beginner

Last updated

Sep 17, 2025

Duration

1h 0m

Challenge

Gain access to the master node with a terminal emulator.
To use SSH to access the master node, enter:
```
ssh cloud_user@[Master's Public IP Address]
```
You will be prompted for the cloud_user password that is available on the lab startup page.
Challenge

Review the promql.py program in GitHub.
To review the promql.py program, navigate to the following GitHub address:
```
https://github.com/linuxacademy/content-aiops-essentials/blob/master/promql.py
```
Challenge

Start the promql.py program on the master node.
From your terminal session on the master node, enter the following command to start the Python program:
```
python3 promql.py > promql.out 2> promql.err &
```
Note: Be sure you use the ampersand after the command so it will run in background on your server.
Challenge

As the promql.py program is running, stress the cluster.
To stress the cluster, you may deploy the stress-test deployment with the following command:
```
kubectl create -f stress-test.yaml
```
Challenge

Run the Prometheus dashboard as you vary cluster load.
Navigate in your browser to the Prometheus dashboard:
```
http://[Master Node IP]:9090
```
While the Python program is gathering metrics, use the following command to vary the load by changing the number of replicas:
```
kubectl scale deployment.v1.apps/stress-test --replicas=10
```
You may then increase the number of replicas to 20, 30, 40, and so on.
```
kubectl scale deployment.v1.apps/stress-test --replicas=[number here]
```
If you want to use other PromQL in the Prometheus dashboard, here are the two examples we use in the Python promql.py program:
```
100 - avg(irate(node_cpu_seconds_total{job="node",mode="idle"}[5m])) by (instance) * 100
```
And:
```
(node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Cached_bytes + node_memory_Buffers_bytes)) / node_memory_MemTotal_bytes * 100
```
Challenge

Examine the Python program output.
You may look at the promql.py output with any of the following commands:
```
tail promql.out
```
Or:
```
more promql.out
```
Or:
```
tail -f promql.out
```

About the author

Pluralsight Skills

Pluralsight Skills gives leaders confidence they have the skills needed to execute technology strategy. Technology teams can benchmark expertise across roles, speed up release cycles and build reliable, secure products. By leveraging our expert content, skill assessments and one-of-a-kind analytics, keep up with the pace of change, put the right people on the right projects and boost productivity. It's the most effective path to developing tech skills at scale.

Real skill practice before real-world application

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

Learn by doing

Engage hands-on with the tools and technologies you’re learning. You pick the skill, we provide the credentials and environment.

Follow your guide

All labs have detailed instructions and objectives, guiding you through the learning process and ensuring you understand every step.

Turn time into mastery

On average, you retain 75% more of your learning if you take time to practice. Hands-on labs set you up for success to make those skills stick.