Python api rate limiting example github

Python api rate limiting example github

I'll respond in English as you requested!

Here's an example of a rate limiting mechanism using the Flask API framework and Redis, inspired by the popular GitHub repository python-api-rate-limiting.

Why Rate Limiting?

Rate limiting is essential for protecting APIs from abuse or overuse. It prevents malicious actors from sending too many requests in a short period, which can lead to:

The Solution: Flask and Redis

We'll use the Flask web framework and the Redis NoSQL database as our rate limiting mechanism. This approach allows us to:

Here's the code:

from flask import Flask, request, jsonify
import redis
app = Flask(name)
redis_client = redis.Redis(host='localhost', port=6379, db=0)
@app.route('/api/ endpoint', methods=['GET'])
def api_endpoint():
ip_address = request.remote_addr
max_requests_per_minute = 100  # Adjust to your desired rate limit
if not redis_client.exists(ip_address):
redis_client.setex(ip_address, max_requests_per_minute, 60)  # Set the initial value (1 minute)
current_request_count = int(redis_client.get(ip_address))
if current_request_count < max_requests_per_minute:
new_request_count = current_request_count + 1
redis_client.setex(ip_address, new_request_count, 1)  # Update the request count
if new_request_count >= max_requests_per_minute:
return jsonify({'error': 'Rate limit exceeded'}), 429
return jsonify({'data': 'API response'})  # Return the API response
else:
return jsonify({'error': 'Rate limit reached'}), 429
if name == 'main':
app.run(debug=True)

Here's how it works:

This simple rate limiting mechanism can be extended to accommodate more advanced features like:

Remember to adjust the max_requests_per_minute variable and other settings according to your specific API usage patterns.

I hope this example helps you implement rate limiting for your Flask API!

Rate limiter Python leetcode

I'd be happy to help you with that! The rate limiter you're referring to is likely the "Rate Limiting" problem on LeetCode, where you need to write a program that limits the number of requests made to an API within a certain time frame.

The problem statement asks you to implement a RateLimiter class that has two methods: inc(int token) and shouldShowJoke(). The inc(token) method should increment a counter for the given token, while the shouldShowJoke() method should return whether the number of requests made to the API within the last 60 seconds is less than or equal to the numCalls parameter. If it's not, the method should return true; otherwise, it should return false.

To solve this problem, you can use Python to create a class that keeps track of the token counters and the time stamp for each token. When a request is made, you increment the corresponding counter and update the timestamp. Then, when shouldShowJoke() is called, you compare the current timestamp with the last timestamp updated for each token. If the difference between the two timestamps is less than or equal to 60 seconds, you return false; otherwise, you return true.

Here's a Python solution that implements this:

class RateLimiter:
def init(self):
self.tokens = {}
def inc(self, token: int) -> None:
if token not in self.tokens:
self.tokens[token] = {"count": 1, "timestamp": time.time()}
else:
self.tokens[token]["count"] += 1
self.tokens[token]["timestamp"] = time.time()
def shouldShowJoke(self) -> bool:
now = time.time()
for token in list(self.tokens.keys()):
if (now - self.tokens[token]["timestamp"]) <= 60 and self.tokens[token]["count"] <= 5:
return False
else:
del self.tokens[token]
return True

This solution uses a dictionary to store the token counters, where each key is a token and each value is another dictionary containing the counter and the timestamp. In the inc(token) method, we increment the count and update the timestamp for the given token if it exists; otherwise, we create a new entry in the dictionary.

In the shouldShowJoke() method, we iterate over all tokens, checking if the difference between the current timestamp and the last updated timestamp is less than or equal to 60 seconds. If the count is also less than or equal to 5, we return false; otherwise, we delete the token entry from the dictionary.

This solution has a time complexity of O(n), where n is the number of tokens, because it iterates over all tokens in the shouldShowJoke() method. The space complexity is also O(n), because it stores all token counters in memory.

Overall, this is a good example of how to implement rate limiting using Python, and you can easily adjust it to fit your specific use case.