They run the pipeline by defining which JMeter test definition file and the number of JMeter workers required for the test. Then the developer can run the JMeter pipeline from the command line. First the solution creates the pipelines. The files contain setting like branch, path, variable, and so on. Generates resulting artifacts like dashboards and logs.ĭocker pipeline and JMeter pipeline definition files are in YAML (.yml) format. During setup, the solution provisions JMeter agents as ACI instances using the Remote Testing approach.Ĭonfigures all workers using its own protocol. Publishes the test results and artifacts to Azure Pipelines.įirst the solution creates and runs the Docker pipeline, and then it creates the JMeter pipeline.Īn Azure Pipelines triggers and controls the flow. Validates the JMeter test definition (.jmx file).ĭynamically provisions the load testing infrastructure. This structure provides flexibility for adding any JMeter plugin. One pipeline builds a custom JMeter Docker container and pushes the image to Azure Container Registry (ACR). The Microsoft CSE team structured the load testing implementation into two Azure Pipelines: Architectureĭownload a Visio file of this architecture. This article provides an overview of an implementation for a scalable cloud load testing pipeline. If you'd like us to expand the content with more information, such as potential use cases, alternative services, implementation considerations, or pricing guidance, let us know by providing GitHub feedback.
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