- 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.
Path Info
Table of Contents
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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_userpassword that is available on the lab startup page. -
Challenge
Review the promql.py program in GitHub.
To review the
promql.pyprogram, 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.
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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]:9090While 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=10You 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.pyprogram:100 - avg(irate(node_cpu_seconds_total{job="node",mode="idle"}[5m])) by (instance) * 100And:
(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.pyoutput with any of the following commands:tail promql.outOr:
more promql.outOr:
tail -f promql.out
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