centralized-rate-limiter 0.2.2

Utility for handling rate limiting in a centralized manner

CentralizedRateLimiter

A robust, centralized rate limiting utility for Python applications that need precise control over API request rates with built-in retry mechanisms and support for multiple rate limits.

🔧 Installation

pip install centralized-rate-limiter

🌟 Features

• Multiple Rate Limits: Support for multiple concurrent rate limits (e.g., per-second, per-minute, per-hour)
• Centralized Configuration: Set rate limiting parameters once and reuse across your application for all request methods
• Comprehensive Retry Logic: Built-in exponential backoff and configurable retry attempts
• Thread-Safe: Reliable rate limiting in multi-threaded environments
• Flexible HTTP Support: Works with all standard HTTP methods
• Session Management: Extends requests.Session for seamless integration
• Type Hints: Full type annotation support for better IDE integration

🎯 Quick Start

from centralized_rate_limiter import get_rate_limiter

# Create a rate limiter with single rate limit (backward compatible)
rate_limiter = get_rate_limiter(
    requests_per_second=10,
    total_retries=5,
    backoff_factor=0.25
)

# Create a rate limiter with multiple rate limits
rate_limiter = get_rate_limiter(
    requests_per_second=10,  # Base rate: 10 requests per second
    additional_limits=[
        (60, 60),     # 60 requests per minute
        (2000, 3600)  # 2000 requests per hour
    ],
    total_retries=5,
    backoff_factor=0.25
)

# Use it for your API calls
response = rate_limiter.get('https://api.example.com/data')

# Works with all HTTP methods
post_response = rate_limiter.post(
    'https://api.example.com/create',
    json={'key': 'value'}
)

🔧 Advanced Usage

Custom Configuration

from centralized_rate_limiter import RateLimitedSession

# Create a session with multiple rate limits
session = RateLimitedSession(
    requests_per_second=5,
    additional_limits=[
        (100, 60),    # 100 requests per minute
        (2000, 3600)  # 2000 requests per hour
    ],
    total_retries=3,
    backoff_factor=0.5
)

# Use session in your application
responses = []
for endpoint in endpoints:
    response = session.get(endpoint)
    responses.append(response)

Error Handling

from requests.exceptions import RetryError

try:
    response = rate_limiter.get('https://api.example.com/data')
    data = response.json()
except RetryError:
    print("Max retries exceeded")
except Exception as e:
    print(f"An error occurred: {e}")

📊 Configuration Parameters

Parameter	Type	Default	Description
requests_per_second	int	10	Maximum number of requests allowed per second
additional_limits	List[Tuple[int, float]]	None	Additional rate limits as (max_calls, period) tuples
total_retries	int	5	Maximum number of retry attempts for failed requests
backoff_factor	float	0.25	Multiplier for exponential backoff between retries

Additional Limits Format

The additional_limits parameter accepts a list of tuples, where each tuple contains:

• First element: Maximum number of calls allowed in the period
• Second element: Period in seconds

Example:

additional_limits=[
    (60, 60),     # 60 calls per 60 seconds (per minute)
    (2000, 3600)  # 2000 calls per 3600 seconds (per hour)
]

🤔 When to Use This vs. Other Solutions

Use CentralizedRateLimiter When You Need:

1. Multiple Rate Limits

   • You need to handle multiple concurrent rate limits (e.g., per-second, per-minute, per-hour)
   • You want to enforce different rate limits for different time windows
   • You need to comply with complex API rate limit requirements

2. Centralized Rate Limiting and Retry Logic

   • You want a single solution that handles both rate limiting and retries
   • You want a single rate limit for all your requests methods
   • You need fine-grained control over retry behavior

3. Thread-Safe Operation

   • Your application makes API calls from multiple threads
   • You need reliable rate limiting in concurrent scenarios

4. Session Management

   • You want to maintain session state across requests
   • You need to reuse connections for better performance

Consider Alternatives When:

1. Distributed Rate Limiting Required

   • Consider python-redis-rate-limit for distributed systems
   • Use Flask-Limiter for API endpoint protection

2. Simpler Requirements

   • Use requests-ratelimiter for basic rate limiting
   • Use backoff package for simple retry logic

3. Async Operation

   • Consider aiohttp with aiohttp-client-manager
   • Use asyncio based solutions for async workflows

🔍 How It Works

The library uses a combination of:

• Thread-safe rate limiting for multiple concurrent limits
• Decorator-based rate limiting using the ratelimit package
• urllib3.util.Retry for configurable retry behavior
• Custom session management extending requests.Session

Example retry sequence:

Request 1 (fails) → Wait 0.25s → Retry 1 (fails) → Wait 0.5s → Retry 2 (succeeds)

Example with multiple rate limits:

# Configuration
limits = [
    (60, 60),     # 60 requests per minute
    (2000, 3600)  # 2000 requests per hour
]

# The rate limiter will ensure all limits are respected:
# - No more than 60 requests in any 60-second window
# - No more than 2000 requests in any 3600-second (1 hour) window

🛠️ Development

Prerequisites

• Python 3.7+
• Make (optional, but recommended)
• Git

Setting Up Development Environment

1. Clone the repository:

git clone https://github.com/waddafunk/CentralizedRateLimiter.git
cd CentralizedRateLimiter

2. Install development dependencies:

make dev-install

Or without make:

python -m pip install -e ".[dev]"

Development Tools

The project uses several development tools, all configured in pyproject.toml:

Code Formatting

• Black: Code formatter with a line length of 88 characters
• isort: Import statement organizer, configured to work with Black
• pyupgrade: Automatically upgrades Python syntax for newer versions

# Format all code
make format

or without make

# Individual tools
python -m black .
python -m isort .
python -m pyupgrade --py37-plus **/*.py

Code Quality

• pylint: Static code analysis
• flake8: Style guide enforcement

# Run all linters
make lint

or without make

python -m pylint centralized_rate_limiter tests
python -m flake8 centralized_rate_limiter tests

Testing

• pytest: Test framework
• pytest-cov: Coverage reporting
• responses: HTTP request mocking

# Run tests with coverage
make test

or without make

make coverage

Development Workflow

1. Create a new branch for your feature:

git checkout -b feature/your-feature-name

2. Make your changes and ensure quality:

make format  # Format code
make lint    # Check code quality
make test    # Run tests
# Or run all checks at once:
make all

3. Build and check the package:

make build

4. Submit a pull request

Available Make Commands

Command	Description
make install	Install package for production
make dev-install	Install package with development dependencies
make format	Format code using black and isort
make lint	Run code quality checks
make test	Run tests with pytest
make coverage	Generate test coverage report
make clean	Remove build artifacts and cache files
make build	Build distribution packages
make publish	Prepare package for PyPI publishing
make all	Run all quality checks and tests
make help	Show available commands

Project Structure

centralized_rate_limiter/
├── .git/
├── .gitignore
├── centralized_rate_limiter/
│   ├── __init__.py
│   └── rate_limiting.py
├── tests/
│   └── test_rate_limiting.py
├── Makefile
├── pyproject.toml
├── README.md
└── LICENSE

Configuration Files

pyproject.toml

The pyproject.toml file contains all project configurations:

• Build system configuration
• Package metadata
• Dependencies (both runtime and development)
• Tool configurations:
  • pytest settings
  • coverage settings
  • black configuration
  • isort configuration

Makefile

The Makefile provides convenient commands for common development tasks. See the commands table above for available operations.

Continuous Integration

The project uses GitHub Actions for CI/CD, running the following checks on each pull request:

• Code formatting (black, isort)
• Linting (pylint, flake8)
• Tests with coverage reporting
• Package building

🤝 Contributing

Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.

📜 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

📚 API Reference

RateLimitedSession

class RateLimitedSession(requests.Session):
    """A rate-limited HTTP session with built-in retry logic."""
    
    def __init__(
        self,
        requests_per_second: int = 10,
        total_retries: int = 5,
        backoff_factor: float = 0.25,
        additional_limits: Optional[List[Tuple[int, float]]] = None
    ) -> None:
        """Initialize a new rate-limited session."""

get_rate_limiter

def get_rate_limiter(
    requests_per_second: int = 10,
    total_retries: int = 5,
    backoff_factor: float = 0.25,
    additional_limits: Optional[List[Tuple[int, float]]] = None
) -> RateLimitedSession:
    """Create a new rate-limited session with specified parameters."""

🙏 Acknowledgments

• Requests library
• ratelimit package
• urllib3 library

📧 Contact

For questions and support, please open an issue in the GitHub repository.

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Made with ❤️ for the Python community