Hello everybody !!!
I am super excited to share here my debut meetup and as it was an online event consequently was also my first international talk !!
Thanks everybody for the support and the feedbacks (glad that was useful for so many people). It was a great experience and I will definitely do this more often 🙂
Check the slides here
Thank you !!
Today I am going to post a comparison of these two different load tests framework. I know most people use Jmeter and it has been longer in the market, but I have recently used Locust and also Artillery (which will post a comparison later) and the results are great, the team was able to improve the creation of the tests and also the maintainability.
In the end of the day you need to use the right tool for your needs, Jmeter is a great and powerful tool, but depending on what you really need (something more lighter) then Jmeter might become an overcomplex, slow, hard to maintain tool.
Jmeter is most used when:
- You need to perform a complex load including different protocols
- If you need the script recording functionality
- If you need to simulate specific load with some custom ramp-up patterns
- If you just prefer UI desktop app for scripts creation, or you just do not know Python well enough
Locust solves some specific problems:
- You can write performance scripts pretty fast
- Push to your VCS and easily maintain the scripts
- Spend minimum time on maintenance without additional GUI applications
- Simulate thousands of test users on local machine without the need to have multiple slaves
Today I am going to share this really interesting webinar of my friend Julio de Lima, he is one of the top QA influencers in Brazil, and in this video he is talking about how to reduce the scope of load tests using Machine Learning.
Load testing execution produces a huge amount of data. Investigation and analysis are time-consuming, and numbers tend to hide important information about issues and trends. Using machine learning is a good way to solve data issues by giving meaningful insights about what happened during test execution.
Julio de Lima will show you how to use K-means clustering, a machine learning algorithm, to reduce almost 300,000 records to fewer than 1,000 and still get good insights into load testing results. He will explain K-means clustering, detail what use cases and applications this method can be used in, and give the steps to help you reproduce a K-means clustering experiment in your own projects. You’ll learn how to use this machine learning algorithm to reduce the scope of your load testing and getting meaningful analysis from your data faster.
Thank you Julio 🙂