Using Redis to implement distributed current limiting

Title: Using Redis to implement distributed current limiting

Text:

With the rapid development of the Internet, the number of concurrent visits to websites and services continues to increase In order to protect the stability of the back-end system, limiting concurrent access has become an important task. In a distributed system, in order to ensure shared status between multiple service instances, we can use Redis as a distributed current limiting tool.

Redis is a high-performance key-value storage system with fast read and write speeds and rich data structure support, and is widely used in distributed systems. Below we will introduce how to use Redis to implement distributed current limiting and provide specific code examples.

First, we need to determine the current limiting strategy. Common current limiting algorithms include leaky bucket algorithm and token bucket algorithm. In this article, we use the token bucket algorithm as an example.

The principle of the token bucket algorithm is to distribute tokens to each request. When the number of tokens in the token bucket is insufficient, new requests are rejected. We can use Redis counters and sorted sets to implement the token bucket algorithm.

The following is a sample code for using Redis to implement distributed rate limiting (written in Python language):

import redis
import time


class DistributedRateLimiter:
    def __init__(self, host, port, password, limit, interval):
        self.r = redis.Redis(host=host, port=port, password=password)
        self.limit = limit
        self.interval = interval

    def limit_request(self, key):
        current_time = int(time.time() * 1000)
        self.r.zremrangebyscore(key, 0, current_time - self.interval)
        requests_count = self.r.zcard(key)
        if requests_count < self.limit:
            self.r.zadd(key, {current_time: current_time})
            return True
        return False


if __name__ == '__main__':
    limiter = DistributedRateLimiter('localhost', 6379, 'password', 100, 1000)
    for _ in range(10):
        if limiter.limit_request('api:rate_limit'):
            print('Allow request')
        else:
            print('Limit exceeded')

In the above code, we created a named DistributedRateLimiter class, which contains the relevant logic of the current limiting algorithm. The construction method accepts Redis connection parameters, current limiting threshold and current limiting interval.

limit_requestThe method is used to determine current limit. It first cleans up expired tokens, and then gets the number of requests in the current token bucket. If the number of requests is less than the limit, the current time is Added to the sorted set and returns the flag that allows the request.

In the main function of the sample code, we create a DistributedRateLimiter object and loop to determine the request current limit. When the current limit passes, 'Allow request' is output, otherwise 'Limit exceeded' is output.

Through the above examples, we can use Redis to implement distributed current limiting to ensure the stability of the system during concurrent access. Of course, the specific current limiting strategies and parameters need to be adjusted and optimized according to the actual situation.

It should be noted that the above example is just a simple demonstration. Actual distributed current limiting may need to consider more factors, such as clock synchronization between multiple instances, Redis performance and availability, etc.

To sum up, Redis, as a high-performance key-value storage system, can help us achieve distributed current limiting. We can use Redis's data structures and commands to store and calculate the status of requests to limit concurrent access. Through reasonable current limiting strategies and parameter configurations, we can protect the back-end system from overload and improve system availability and stability.

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