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How does Locust typically scale beyond what a single machine can generate for very large load tests?
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How does Locust typically scale beyond what a single machine can generate for very large load tests?
Why might a team choose Locust over a tool with a proprietary GUI-based scripting language (like some traditional commercial load-testing tools)?
Why is monitoring response time percentiles (like the 95th or 99th percentile), not just the average, important when interpreting Locust test results?
Why does Locust's code-first approach (versus a purely visual/no-code load-testing tool) tend to appeal particularly to development teams already writing Python for their application?
What is a "Locust User" (an instance of a User class) meant to represent during a test run?
Why might a team run a Locust test in "headless" mode (via command-line flags) rather than through the web UI, particularly in a CI pipeline?
How does a Locust test scenario typically define simulated user behavior?
What does the `@task` decorator's optional weight argument (e.g. `@task(3)`) control in a Locust User class?
What is a reasonable use of Locust's "spawn rate" setting when starting a test?
What is a practical reason a developer might extend a Locust task with custom Python logic for handling authentication (like logging in once per user) before running the main test tasks?
What metrics does Locust typically report during and after a test run?
What is a general strategy for interpreting a Locust test that shows response times degrading as the number of simulated users increases?