Distributed Rate Limiter

Problem Overview

Design and implement a distributed rate limiter using a token bucket algorithm with lazy token refill. You are given two pre-implemented classes:

• DistributedCache: A distributed cache system that supports basic operations like get, set, and delete.
• Clock: A clock class that provides the current time in milliseconds.

Problem Statement

Implement a distributed rate limiter using the token bucket algorithm with lazy token refill. The rate limiter should handle requests from multiple nodes in a distributed system.

Constraints

1. The rate limiter should support a maximum of 1 million keys.
2. The rate limiter should handle requests with a rate limit of r requests per t milliseconds for each key.
3. The rate limiter should use lazy token refill, meaning tokens are only refilled when a request is made.

Examples

1. If r = 5 and t = 1000, then a key can handle up to 5 requests per second.
2. If a key has no tokens left, the request should be blocked until tokens are available.

Hints

1. Use the DistributedCache to store the token bucket state for each key.
2. Use the Clock class to get the current time in milliseconds.
3. Implement a method to refill tokens for a given key based on the current time.

Solution

Here's a high-level solution for the distributed rate limiter:

`python class DistributedRateLimiter: def init(self, distributed_cache, clock): self.distributed_cache = distributed_cache self.clock = clock

def allow_request(self, key, r, t):
    current_time = self.clock.get_current_time()
    token_bucket = self.distributed_cache.get(key)

    if token_bucket is None:
        token_bucket = {"tokens": r, "last_refill_time": current_time}
        self.distributed_cache.set(key, token_bucket)

    tokens = token_bucket["tokens"]
    last_refill_time = token_bucket["last_refill_time"]

    # Calculate the number of tokens to refill
    tokens_to_refill = (current_time - last_refill_time) // t
    tokens += tokens_to_refill
    token_bucket["tokens"] = tokens
    token_bucket["last_refill_time"] = current_time
    self.distributed_cache.set(key, token_bucket)

    # Check if the request can be handled
    if tokens >= 1:
        token_bucket["tokens"] -= 1
        self.distributed_cache.set(key, token_bucket)
        return True
    else:
        return False

`

This solution uses the DistributedCache to store the token bucket state for each key and the Clock class to get the current time in milliseconds. It refills tokens lazily when a request is made and checks if the request can be handled based on the available tokens.

Please note that this is a high-level solution and may require additional optimizations and error handling for production use.