Using Python to Extract Prometheus Metrics

1 hour
  • 6 Learning Objectives

About this Hands-on Lab

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.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

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.

Review the program in GitHub.

To review the program, navigate to the following GitHub address:
Start the program on the master node.

From your terminal session on the master node, enter the following command to start the Python program:

python3 > promql.out 2> promql.err &

Note: Be sure you use the ampersand after the command so it will run in background on your server.

As the 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
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 program:

100 - avg(irate(node_cpu_seconds_total{job="node",mode="idle"}[5m])) by (instance) * 100


(node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Cached_bytes + node_memory_Buffers_bytes)) / node_memory_MemTotal_bytes * 100
Examine the Python program output.

You may look at the output with any of the following commands:

tail promql.out


more promql.out


tail -f promql.out

Additional Resources

This lab is very similar to the previous lab. No preparations are necessary.

What are Hands-on Labs

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.

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