rapidtest 0.3.1

A comprehensive testing framework for REST APIs with functional and performance testing capabilities

RapidTest 🚀

A super lightweight and blazingly fast library to simplify REST API testing. Designed to be intuitive, fast to implement, and with clear colorized reports - no heavy dependencies!

✨ Features

• Simplicity: Perform HTTP requests (GET, POST, PUT, PATCH, DELETE) in a single line.
• Automatic Validation: Automatically compare status codes and response bodies.
• Fast & Lightweight: Minimal dependencies, maximum speed!
• Colorized Reports: Clean console output with ANSI colors to identify failures quickly.
• Data Generator: Integrated random data generator (using Faker) for dynamic testing.
• Performance Testing: Built-in load testing with threading - no external tools needed!

🛠️ Installation

Install the required dependencies:

pip install rapidtest

Or install dependencies manually:

pip install requests faker

🚀 Quick Start

1. Initialize RapidTest

from rapidtest import Test

# Configure your API's base URL
tester = Test(url="http://localhost:8000")

2. Run Tests

GET

tester.get(endpoint="/users", expected_status=200)

POST with Body Validation

user_data = {"username": "hector", "password": "123"}
tester.post(endpoint="/user", json=user_data, expected_status=201, expected_body=user_data)

PUT / PATCH / DELETE

# Update data
tester.put(endpoint="/user/hector", json={"password": "new_password"}, expected_status=200)

# Delete resource  
tester.delete(endpoint="/user/hector", expected_status=204)

3. Generate Random Data

Use the Data class to create test data on the fly:

from rapidtest import Data

user = Data.generate_auth_user()
email = Data.generate_email()
name = Data.generate_name()
phone = Data.generate_phone()

print(user) # {'username': '...', 'password': '...'}

Available Data Generation Methods

The Data class includes the following static methods:

User Data

• generate_auth_user() - Generates a dictionary with username and password
• generate_name() - Generates a random full name
• generate_email() - Generates a random email address
• generate_password() - Generates a secure random password
• generate_phone() - Generates a random phone number

Location Data

• generate_address() - Generates a random postal address
• generate_city() - Generates a random city name
• generate_state() - Generates a random state/province name
• generate_zipcode() - Generates a random postal code
• generate_country() - Generates a random country name

Other Data

• generate_id() - Generates a unique UUID
• generate_job() - Generates a random job title
• generate_text() - Generates random text (short paragraph)
• generate_paragraph() - Generates a long random paragraph
• generate_date() - Generates a random date (ISO format)
• generate_datetime() - Generates random date and time (ISO format)
• generate_time() - Generates a random time

Additional Parameters

All HTTP methods support additional parameters:

# Query parameters
tester.get(endpoint="/users", params={"page": 1, "limit": 10})

# Custom headers
tester.post(endpoint="/auth", json=user_data, headers={"Content-Type": "application/json"})

# Form data
tester.post(endpoint="/upload", data={"file": "content"})

# Additional requests arguments
tester.get(endpoint="/secure", timeout=30, verify=False)

🚀 Performance Testing

Use the Performance class to run simple load tests on your APIs:

from rapidtest import Performance

# Initialize performance test
perf = Performance(
    base_url="http://localhost:8000",
    users=10,       # Number of concurrent users
    duration=30,    # Test duration in seconds  
    timeout=10      # Request timeout
)

# Add endpoint to test
perf.add_get_task(endpoint="/api/users")

# Run the test (results shown in terminal)
results = perf.run()

# Check results
print(f"Success rate: {results['success_rate']}%")
print(f"Average response time: {results['avg_response_time']}ms")
print(f"Requests per second: {results['requests_per_second']}")

Simple Performance Testing Features

• No external dependencies: Uses only requests and threading
• Real-time terminal output: See results as they happen
• Basic load simulation: Multiple concurrent users
• Essential metrics: Response times, success rates, RPS
• Python 3.12 compatible: No Locust/gevent issues

Performance Test Output

See real results in action:

🚀 Starting simple performance test
📍 URL: http://localhost:8000/api/users
👥 Users: 10
⏱️  Duration: 30s
--------------------------------------------------

============================================================
📊 PERFORMANCE TEST RESULTS  
============================================================
🎯 Total Requests:      1245
✅ Successful:          1245
❌ Failed:              0
📈 Success Rate:        100.0%
⚡ Requests/sec:        41.5
⏱️  Avg Response Time:   12.3ms
🐌 Min Response Time:   8.1ms
🐇 Max Response Time:   89.2ms
👥 Concurrent Users:    10
⏰ Test Duration:       30s
============================================================
🟢 Excellent performance!

📊 Reports

See how your tests look with real colorized output:

✅ Successful Test

❌ Failed Test

Colors:

• ✅ Green for PASSED tests
• ❌ Red for FAILED tests
• 🔵 Blue for labels and info
• 🟡 Yellow for warnings

📁 Project Structure

• rapidtest/
  • RapidTest.py: Core library logic
  • RapidData.py: Random data generator
  • Performance.py: Simple performance testing (no external deps)
  • Utils.py: Formatting and reporting utilities
  • __init__.py: Module configuration

🔧 Dependencies

• requests>=2.25.1: For making HTTP requests
• faker>=13.0.0: For generating fake data

That's it! 🎉

✨ Ultra lightweight - removed Rich dependency for maximum speed
🚀 Fastest startup - minimal imports, instant execution
🧵 Built-in performance testing - uses only standard library threading

📋 Requirements

• Python >=3.7

📖 Project Information

• Version: 0.3.0
• Author: Hector Rosales
• License: MIT
• Homepage: https://github.com/hector-dev/rapidtest

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⚡ Built for speed and simplicity - because testing should be fast and fun! 🛠️✨