Python api rate limiting github

Python api rate limiting github

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Python API Rate Limiting on GitHub

As developers, we often interact with APIs to fetch data or perform specific tasks. GitHub, being a popular platform for version control and collaboration, offers several APIs that enable us to access its vast repository of data. However, due to security concerns and server load management, GitHub implements rate limiting on its APIs.

What is Rate Limiting?

Rate limiting is a mechanism that regulates the frequency at which requests can be made to an API within a specific time window. This helps prevent abuse, spamming, or overloading of servers. In other words, it prevents malicious actors from making excessive requests to exhaust server resources or disrupt normal usage.

How Does Rate Limiting Work on GitHub?

When you interact with GitHub APIs (e.g., using Python's requests library), your IP address is tracked, and the API checks the rate at which requests are being made. If the request frequency exceeds the allowed limit, you'll receive an error response or be temporarily blocked.

GitHub uses a sliding window approach to enforce rate limiting:

Python API Rate Limiting Library

To work around rate limiting when using Python APIs with GitHub, consider implementing the following strategies:

Some popular Python libraries that can help with API rate limiting include:

Conclusion

When working with GitHub APIs using Python, it is essential to be aware of the rate limiting mechanisms in place. By implementing caching, exponential backoff, or pagination strategies, you can effectively manage requests and avoid errors related to rate limiting. Additionally, consider using dedicated rate limiting libraries to simplify your workflow.

How was that?

Python rate_limit async

The elusive art of implementing a robust rate limiter in Python!

Asynchronous programming has made it easier to tackle this problem, and I'm excited to share my solution with you. Before diving into the code, let's first understand what rate limiting is all about.

What is Rate Limiting?

Rate limiting is a mechanism used to control the frequency at which a system (in this case, a Python application) can process requests or perform actions. This is often necessary in situations where an application is vulnerable to abuse, such as:

Why Async?

Asynchronous programming allows your code to run concurrently with other tasks or threads, improving performance and responsiveness. In this case, we'll use async to handle rate limiting efficiently without blocking our main application thread.

The Code: rate_limit.py

import asyncio
from functools import wraps
class RateLimiter:
def init(self, limit: int, time_window: int):
self.limit = limit
self.time_window = time_window
self.requests_made = 0
self.last_request_time = asyncio.get_running_loop().time()
async def is_allowed(self) -> bool:
now = asyncio.get_running_loop().time()
if (now - self.last_request_time) / 1000 > self.time_window:
self.requests_made = 0
if self.requests_made < self.limit:
return True
return False
def check_and_sleep(self):
loop = asyncio.get_running_loop()
async def sleep_and_check():
while not await self.is_allowed():
await asyncio.sleep(1)
loop.run_until_complete(sleep_and_check())
def rate_limit(func):
@wraps(func)
async def wrapper(*args, **kwargs):
limiter = RateLimiter(limit=10, time_window=60)  # Example values: 10 requests per minute
await limiter.check_and_sleep()
return await func(*args, **kwargs)
return wrapper
@rate_limit
async def example_request():
print("Request made!")
await asyncio.sleep(1)  # Simulate a long-running operation
Test the rate limiter
async def main():
for _ in range(50):  # Make 50 requests within a minute
await example_request()
asyncio.run(main())

How it Works

The RateLimiter class keeps track of the number of requests made (requests_made) and the time of the last request (last_request_time). The is_allowed method checks if the request is allowed based on the current time window.

The check_and_sleep method uses this information to block the application thread until the rate limit is met. If the request is not allowed, it waits for the specified time before checking again.

The rate_limit decorator wraps your function and applies the rate limiter. When you call a decorated function (like example_request), the rate limiter will ensure that the function is only executed within the allowed limits.

Conclusion

In this example, we've implemented a basic rate limiter using async programming in Python. This approach ensures that our application can efficiently handle a large number of requests while preventing abuse and protecting against DoS attacks. You can modify the rate limiting parameters (e.g., limit and time_window) to suit your specific needs.

Remember, this is just a basic example, and you may need to fine-tune or extend it for more complex scenarios. Happy coding!