Python ratelimit sleep_and_retry

Python ratelimit sleep_and_retry

I'll respond in English only as you requested!

Python's ratelimit decorator is a convenient way to implement rate limiting on your API endpoints or other functions that may be called frequently. It helps prevent overload and ensures that the system remains responsive.

The sleep_and_retry pattern is another important concept in Python, often used with the ratelimit decorator. The idea is to pause execution for a specified amount of time before retrying an operation that failed due to rate limiting or other reasons.

Here's how you can implement this in Python:

import time
from functools import wraps
def sleep_and_retry(attempts=5, delay=1):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
for attempt in range(1, attempts + 1):
try:
return func(*args, **kwargs)
except Exception as e:
if attempt == attempts:
raise
time.sleep(delay)
return None
return wrapper
return decorator
def ratelimit(max_calls=5, timeout=None, queue_full_cb=None):
def decorator(func):
calls = []
@wraps(func)
def wrapper(*args, **kwargs):
if len(calls) < max_calls:
result = func(*args, **kwargs)
return result
else:
time.sleep(timeout or 1.0)
while len(calls) >= max_calls:
time.sleep(1)
return func(*args, **kwargs)
return wrapper
return decorator
@ratelimit(max_calls=3, timeout=2)
@sleep_and_retry(attempts=5, delay=1)
def my_api_call():
Your API call code here...
pass

In this example, my_api_call is decorated with both the rate limiter and the sleep retry. This means that if you try to call my_api_call more than 3 times within a period of 2 seconds, it will wait for 2 seconds before trying again. If the API call still fails after the second attempt, it will pause execution for another second before retrying once more.

Remember to adjust the parameters in the rate limiter and sleep retry decorators according to your specific use case.

Python ratelimit GitHub

Here's a response in English:

Python is an incredibly powerful and popular programming language that has gained widespread adoption across various industries, including software development, data analysis, artificial intelligence, and more. One of its key features is the ability to handle rate limiting for APIs, which helps prevent abuse or excessive requests to GitHub.

Rate limiting is a crucial mechanism in API design that prevents malicious users from flooding an API with repeated requests, overwhelming the system and potentially causing performance issues. By imposing limits on the number of requests within a given time frame, APIs can ensure fair usage and protect against denial-of-service (DoS) attacks.

To implement rate limiting for GitHub using Python, we'll rely on its built-in requests library, which provides an easy-to-use interface for sending HTTP requests and handling responses. Here's a basic example of how to achieve rate limiting:

   import requests
import time
from threading import Semaphore
Define constants for our API request rate limits
MAX_REQUESTS = 5      # maximum number of requests within a minute
MIN_DELAY_BETWEEN_REQUESTS = 60    # minimum seconds delay between consecutive requests

   def make_request(url, params):
while True:
if semaphore.acquire(timeout=MIN_DELAY_BETWEEN_REQUESTS * 1000):    # acquire lock after delay
try:
response = requests.get(url, params=params)    # send GET request to GitHub API
response.raise_for_status()    # check the status code of the response (400-500)
return response.json()    # return the JSON data
finally:
semaphore.release()    # release lock
Create a Semaphore object for our rate limiting
semaphore = Semaphore(value=MAX_REQUESTS)

   def query_github_api():
url = 'https://api.github.com/search/repositories'    # replace with your desired API URL
params = {'q': 'python', 'sort': 'stars'}    # example search parameters
while True:
data = make_request(url, params)    # send the request and get the response data
print(data)    # display the JSON data for debugging purposes
query_github_api()    # start querying GitHub API

This is just a basic example, and there are many ways to enhance the rate limiting mechanism for more complex scenarios.