Load testing in python with locust io ep 3 data and failures

okay, let's dive deep into load testing with locust, specifically focusing on handling data variations and simulating failures in your tests. this will be a comprehensive tutorial.

**episode 3: data-driven load testing and failure injection with locust**

this tutorial will cover the following:

1. **setting up locust:** basic installation and structure of a locust test.
2. **data-driven testing:**
* loading data from external sources (csv, json).
* using the data in your requests for dynamic scenarios.
* managing data iteration and recycling.
3. **simulating failures:**
* introducing errors in your test scenarios (e.g., connection timeouts, http errors).
* using `events` to capture and react to failures.
* creating custom exceptions and handling them gracefully.
4. **advanced techniques:**
* using locust's built-in statistics and reporting.
* customizing the locust web ui (if desired).
* running locust in distributed mode.
5. **best practices:**
* writing robust and maintainable locust tests.
* interpreting locust's results effectively.
* planning your load tests for realistic scenarios.

**1. setting up locust**

if you don't already have locust installed, use pip:

* **`httpuser`:** represents a user that can make http requests.
* **`taskset`:** a group of tasks that a user can perform. think of it as a scenario.
* **`host`:** the base url of the target application.
* **`wait_time`:** a function or distribution that determines the time a user waits between executi ...

#LoadTesting #LocustIO #PythonTesting

Load testing
Python
Locust IO
performance testing
scalability testing
stress testing
concurrent users
test scenarios
response time
HTTP requests
failure handling
metrics collection
distributed load testing
automation
benchmarking