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Taurus Yaml File Example Python

6 min read Oct 13, 2024
Taurus Yaml File Example Python

Working with Taurus YAML Files: A Python Guide

Taurus, a powerful open-source load testing tool, leverages YAML files to define and configure your test scenarios. This guide will equip you with the knowledge to create and understand Taurus YAML files for effective load testing using Python.

What is a Taurus YAML File?

Taurus YAML files are the heart of your load testing configuration. They allow you to specify:

  • Test Scenarios: Define the types of tests (e.g., functional, performance) you want to run.
  • Load Generators: Choose the software (e.g., JMeter, Gatling) that will simulate user traffic.
  • Execution Parameters: Configure the test duration, number of virtual users, and other critical settings.
  • Reporting and Monitoring: Specify the desired output formats (e.g., HTML, CSV) and integrate with monitoring tools.

Creating Your First Taurus YAML File

Let's start with a basic example:

---
scenarios:
  - name: basic_test
    executor: local
    execution:
      - duration: 30s
      - concurrency: 10
    scenario:
      - steps:
        - url: http://www.example.com/
          method: GET

Explanation:

  • ---: Marks the beginning of a YAML document.
  • scenarios: Defines a list of test scenarios.
  • name: Assigns a name to your scenario (e.g., "basic_test").
  • executor: Specifies the load generator (here, "local" implies using Taurus's built-in executor).
  • execution: Configures the test execution.
    • duration: Sets the test duration to 30 seconds.
    • concurrency: Specifies the number of virtual users (10 in this case).
  • scenario: Defines the test steps.
    • steps: Lists the actions to be performed.
      • url: The URL to be accessed.
      • method: The HTTP method (e.g., GET, POST).

Python and Taurus: A Powerful Combination

Python's versatility and extensive libraries make it an excellent choice for automating tasks related to Taurus YAML files. Here are some common use cases:

1. Dynamic YAML Generation

You can use Python to dynamically generate your YAML files based on variables or input data. This is especially useful for parameterizing tests and creating reusable templates.

import yaml

def create_yaml_file(url, concurrency):
  data = {
    "scenarios": [
      {
        "name": "dynamic_test",
        "executor": "local",
        "execution": {
          "duration": "30s",
          "concurrency": concurrency
        },
        "scenario": {
          "steps": [
            {
              "url": url,
              "method": "GET"
            }
          ]
        }
      }
    ]
  }

  with open("dynamic_test.yaml", "w") as f:
    yaml.dump(data, f)

create_yaml_file("https://www.example.com/", 20)

2. Programmatic Execution of Tests

Python can launch Taurus tests using the bzt command-line tool.

import subprocess

subprocess.run(["bzt", "dynamic_test.yaml"])

3. Parsing and Modifying Existing YAML Files

Python's yaml library allows you to read, parse, and modify existing YAML files programmatically. This is useful for creating reusable test scripts, managing test data, and performing complex modifications.

import yaml

with open("dynamic_test.yaml", "r") as f:
  data = yaml.safe_load(f)

data["scenarios"][0]["execution"]["concurrency"] = 30

with open("dynamic_test.yaml", "w") as f:
  yaml.dump(data, f)

Tips and Best Practices

  • Use Clear and Descriptive Naming: Choose descriptive names for your scenarios and parameters.
  • Comment Your Code: Add comments to explain your YAML file's structure and logic.
  • Leverage YAML Anchors and Aliases: These features allow you to define reusable blocks of configuration.
  • Test Thoroughly: Validate your YAML files against the Taurus documentation to ensure correctness.
  • Use Version Control: Store your YAML files in a version control system (e.g., Git) to track changes and collaborate effectively.

Conclusion

Taurus YAML files are essential for defining and configuring your load tests. Python, with its flexibility and powerful libraries, provides an efficient way to work with these files, from dynamic generation to programmatic execution. By mastering these techniques, you can enhance your load testing workflow and achieve more robust performance evaluations.

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