Introduction

This project has been created as many of our Azure customers required monitoring of private http endpoints. As Azure Application Insights only offers to run availability tests against public endpoints and Azure Connection Monitor didn't provide advanced enough http test configurations we decided to create an alternative solution.

Because we :heart: working with Postman we based the configuration of the availability tests on a Postman collection file. The Azure URL Monitor allows to run these tests from anywhere in your network and store the results in an Azure Application Insights instance.

Next to running Postman collection tests the Azure URL Monitor can run pro-active lifetime checks on SSL certificates.

NOTE: This project is still work in progress but feedback is always welcome.

Getting Started

To run the monitor successfully it needs at a minimum the Instrumentation Key of the Application Insights instance and an url to the Postman collection file.

The Instrumentation Key can be found on the Overview page of your Application Insights instance.

The Postman collection URL can either point to a publicly shared Postman collection using the JSON link option or to an exported collection file on storage accessible via http.

NOTE: If you don't have anything set up yet but want to to try out the monitor in your own Azure environment we have a Bicep deployment available that deploys everything you need.

Run Locally

The monitor relies on Newman, the command-line Collection Runner for Postman.

Installing Newman using NPM

• Newman can be installed locally to this project together with an updated PATH export

 npm install newman
 export PATH=$PWD/node_modules/.bin:$PATH

• Now run the Python specific initialization

Using your running Python environment:

• Optionally refresh the requirements file: pipenv requirements > requirements.txt

• Install dependencies using: pip install -r requirements.txt

Using pipenv to create new virtual Python environment:

• Initialize project specific Python env: pipenv install

• Later on a project specific Python env can be updated with: pipenv update

• Drop into virtual Python env: pipenv shell

Start using the main script:

• Run: ./monitor.py --help
• Or: python monitor.py --help

Example

./monitor.py --ai-instrumentation-key  <your key> \
               --pm-collection-url <your collection url>

NOTE: The python code doesn't take care of scheduling. See docker/entrypoint.sh how this is handled inside a container.

Run as a Container

Build it Yourself

Build the container:

docker build -t urlmonitor:latest --pull -f docker/Dockerfile .

Run the container:

docker run  -e AI_INSTRUMENTATION_KEY=<your key> \
              -e PM_COLLECTION_URL=<your collection url>\
              -e TEST_FREQUENCY_MINUTES=1 \
              urlmonitor

Use a Pre-Built Container

docker run  -e AI_INSTRUMENTATION_KEY=<your key> \
              -e PM_COLLECTION_URL=<your collection url>\
              -e TEST_FREQUENCY_MINUTES=1 \
              ghcr.io/eurofiber-cloudinfra/azure-url-monitor:latest

Run as an Azure Container Instance

Check the Bicep code of the demo deployment.

Run on an Azure Virtual Machine Scale Set

Check the Bicep code of the demo deployment.

Configuration Options