I recently stumbled upon an open source project called Streamlit. Streamlit is a framework where you can build custom Machine Learning tools by focussing on data science rather than the user interface. You can spin up your app with the intuitive user interface and leverage Python modules at ease. In this blog post, I am going to present everything in detail about how I leveraged Streamlit for Apache JMeter and what are all the benefits, future plan, and more.
What is Streamlit?
Streamlit is an open-source Python library that makes it easy to build beautiful apps for machine learning.
What is Apache JMeter?
JMeter is an open source load testing tool and widely popular among developers and performance testers.
Apache JMeter with Streamlit for Machine Learning
By integrating Streamlit with Apache JMeter, you can build machine learning models to train to detect anomalies from your JMeter test results.
Please note that this project is just a beginning where you can execute and visualize the test results in interactive charts.
I am currently working on designing the machine learning model for performance testing metrics.
Prerequisites
- Apache JMeter
- Your favorite IDE (I ❤ VS Code), you could try PyCharm as well
- Anaconda
- Python 2.7.0 or later / Python 3.6.x or later
- PIP
- Streamlit
Set the environment variable JMETER_HOME to apache-jmeter folder.
Clone the repository
QAInsights
/
Streamlit-JMeter
Powering up Apache JMeter with Streamlit and opening the door for machine learning.
Powering up Apache JMeter with Streamlit
Overview
Apache JMeter is an open source load testing tool written in 100% pure Java. JMeter supports umpteen protocols including HTTP(S), FTP, SMTP, Web Services, JMS and much more. In JMeter, you can generate HTML results after you done with the execution or you can use Backend Listeners to monitor the runtime results.
Streamlit is an open source framework for Machine Learning and Data Sciences team. You can build tools to visualize the data and interactive prototypes.
Apache JMeter + Streamlit
By integrating Streamlit with Apache JMeter, you can build machine learning models to train to detect anomalies from your JMeter test results. This project is just a beginning where you can execute and visualize the test results in interactive charts.
Prerequisites
- Apache JMeter
- Your favorite IDE (I
❤ VS Code) - Python 2.7.0 or later / Python 3.6.x or later
- PIP
- Streamlit
Use…
Run the app.py by streamlit run .\app.py
This will open a new tab or launch the default browser with the URL http://localhost:8501
After successful launch, you could see the options to Execute and Analyze the JMeter test plan.
Please read my detailed blog post to explore further.
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