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Azure Metrics Collector

Author: Peter Karacsonyi

Collecting Azure tenant level metrics and exposing them with a prometheus client

Contribution

Open new issue and provide info -> create new branch linked to issue -> Pull -> add verbose messages to your commits -> Push -> MR

To add new metrics:

1. write the logic to query data from azure in metrics_collection/metrics_collector.py
2. define a gauge in metrics_collection/gauges.py
3. call the gauge create function in update_metrics(), set it to a value running your query function and or you can even define another update function.
   When using another function don't forget to decorate it with @scheduler.scheduled_job().
   If different update schedule is needed, define a variable and use it in the decorator.

Deploying the server as a linux service

Configuration

1. Choose a user to run the service with
2. Copy azure credentials to a file named ~/.azure_creds (under the chose user's home directory) where the first line is a CLIENT_ID and second is the secret (values only)

Before installing the application, check configuration items in the deploy.sh file and change defaults if needed!
Current configuration settings and their defaults:

• python path PYTHON='/usr/bin/python3'
• install dir INSTALL_DIR='/opt/azmetrics'
• run as RUNAS='<yourUserName>'
• prometheus server port PROMETHEUS_HTTP_PORT='8000'
• data refresh interval (cron format) REFRESH_CRON='0 * * * *'
• subscription filter SUB_FILTER_KEYWORD='landingzone'

Install

After setting the configuration items in deploy.sh, run deploy.sh
To learn more what the deployment is actually doing, look into deploy.sh

Uninstalling

run uninstall_service.sh

Running Standalone

1. Check default configuration values in the config = {} dict in metrics_server.py.
2. Run /usr/bin/python3 metrics_server.py

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