A Docker-based framework for comprehensive performance testing, combining backend load testing with Apache JMeter and frontend testing with sitespeed.io and WebPageTest.
Performance Testing Framework is a Docker-based, integrated platform for conducting comprehensive performance tests on both backend and frontend applications. It combines Apache JMeter for backend load testing with sitespeed.io and a private WebPageTest instance for frontend performance analysis, providing real-time monitoring, error diagnostics, and test comparison capabilities through Grafana dashboards.
Performance engineers, QA teams, and developers who need a reproducible, all-in-one environment for load testing web applications and analyzing performance bottlenecks.
Developers choose this framework because it bundles and pre-configures a suite of industry-standard open-source tools into a single, self-hosted stack, eliminating setup complexity and providing a unified dashboard for real-time insights and historical test comparisons.
Framework allows to perform load testing with Apache Jmeter, view application/server metrics in real-time with Grafana, analyze errors cause with detailed traces for failed requests, compare different test runs in scripted dashboard and perform frontend performance testing with sitespeed.io+webpagetest
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Pre-configured with Jenkins for CI/CD, Grafana for dashboards, and multiple databases like InfluxDB, reducing setup complexity and ensuring a cohesive workflow.
Custom Grafana dashboards provide live metrics during test execution, as demonstrated in the README with animated GIFs showing dynamic updates.
JSR223 listeners in JMeter generate comprehensive traces for failed requests, including response data and assertion results, aiding in root cause analysis.
Supports both backend load testing with Apache JMeter and frontend performance analysis with sitespeed.io and WebPageTest, enabling end-to-end evaluation.
The README details non-trivial Windows setup steps, such as sharing drives and setting environment variables, which can be error-prone and time-consuming.
Heavily reliant on Docker; running multiple containers may consume significant system resources and isn't feasible in environments without Docker support.
Last major updates appear to be from 2017-2018, risking compatibility issues with newer tool versions and missing modern performance testing features.