Lab
A Cloud Guru

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.

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Path Info

Level

Beginner

Duration

1h 0m

Published

Apr 05, 2019

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
```

Author

A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

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