Run Locust-based Tests in Azure Load Testing

We are excited to announce support for Locust, a Python-based open-source performance testing framework, in Azure Load Testing. As a cloud-based and fully managed service for performance testing, Azure Load Testing helps you easily achieve high scale loads and quickly identify performance bottlenecks. We now support two load testing frameworks – Apache JMeter and Locust. You can use your existing Locust scripts and seamlessly leverage all the capabilities of Azure Load Testing.

Locust is a developer friendly framework that lets you write code to create load test scripts as opposed to using GUI based test creation. You can check-in the scripts into your repos, seek peer feedback, and better maintain the scripts as they evolve – just like you would do for your product code. As for extensibility, whether it is sending metrics to a database, simulating realistic user behavior, or using custom load patterns, you can just write Python code and achieve your objective. You can also use Azure Load Testing to integrate locust based load tests in to your CI/CD workflows.

Very soon, you will be able to get started from Visual Studio Code  (VS Code) and leverage the power of AI to get a Locust script generated. You can then run it at scale using Azure Load Testing and get the benefits of a managed service, all from within the VS Code experience.

User feedback in action

During the preview phase, many of you tried out Locust in Azure Load Testing and provided us invaluable feedback. We have put that into action and improved the offering to further enhance your experience. We have ensured that your experience of getting a Locust script working with Azure Load Testing is frictionless with zero to minimal modifications needed in your test scripts. This ensures that you can seamlessly run the same scripts in your local environment with lower load and on Azure Load Testing with high-scale load.  

• You can now install the dependencies required for your test script by specifying them in a ‘requirements.txt’ file and uploading it along with your test script.
• If your test requires any supporting Python modules in addition to your test script, you can now upload multiple python files and specify the main test script from which the execution should begin.
• If you use a Locust configuration file to define load or any other configuration for your load test, you can just upload your .conf file along with your test script. The precedence order followed by Locust to override the values is honored.
• Locust plugins are already available on the Azure Load Testing test engines. You can use them without having to separately upload or configure the plugins.
• You have multiple options to integrate Locust load tests into your automation flows. You can use CI/CD integration, Azure CLI, or REST APIs. Very soon, you’d also be able to use Azure SDKs.

Using Locust scripts with Azure Load Testing

All the capabilities of Azure Load Testing that help you configure your tests, generate high scale load, troubleshoot your tests, and analyze test results are supported for Locust-based tests. Let’s see this in action using a simple example of a user browsing multiple pages in a web application.

You can create a Locust script for this scenario by writing a few lines of Python code.

Figure 1: A sample Locust script

Once you run the script in your local environment and ensure that it is working as expected, you can run the same on Azure Load Testing.

To create a Locust-based test in Azure Load Testing,

• On the ‘Test plan’ tab, select ‘Locust’ as the load testing framework and upload your test script. You can also upload any supporting artifacts here.

Figure 2: Test framework selection

• On the ‘Load’ tab, configure the load that you want to generate. You can specify the overall number of users required and the spawn rate in load configuration. Azure Load Testing automatically populates the number of test engines required. You can update the count, if required.

You can also define the overall load required and the load pattern in your Locust script, or in a Locust configuration file. In that case, you can select the engine instances required to generate the target load.

Figure 3: Load configuration

• You also have the options to parameterize your test script, monitor app components and define test criteria.

Once you create the test and run it, you can see a rich test results dashboard that shows the performance metrics for the overall user journey as well as for specific pages. You can slice and dice the metrics to better understand performance and identify any anomalies. You can also correlate the client-side metrics with the server-side metrics from your app components to easily identify performance bottlenecks.

Figure 4: Results dashboard showing summary statistics

Figure 5: Results dashboard showing client-side metrics

Get started

Get started with Locust on Azure Load Testing today and let us know how it enhanced your performance testing journey. Stay tuned for more exciting updates! You can learn more about using Locust with Azure Load Testing here.

Have questions or feedback? Drop a comment below or share your feedback with us in the Azure Load Testing community!