This algorithm divides time into fixed durations/windows. For example each window is 10 seconds long. When a new request comes in, the current time is used to determine the window and a counter is increased. If the counter is larger than the set limit, the request is rejected.
- Very cheap in terms of data size and computation
- Newer requests are not starved due to a high burst in the past
- Can cause high bursts at the window boundaries to leak through
- Causes request stampedes if many users are trying to access your server, whenever a new window begins
from upstash_ratelimit import Ratelimit, FixedWindow from upstash_redis import Redis ratelimit = Ratelimit( redis=Redis.from_env(), limiter=FixedWindow(max_requests=10, window=10), )
Builds on top of fixed window but instead of a fixed window, we use a rolling window. Take this example: We have a rate limit of 10 requests per 1 minute. We divide time into 1 minute slices, just like in the fixed window algorithm. Window 1 will be from 00:00:00 to 00:01:00 (HH:MM:SS). Let’s assume it is currently 00:01:15 and we have received 4 requests in the first window and 5 requests so far in the current window. The approximation to determine if the request should pass works like this:
limit = 10 # 4 request from the old window, weighted + requests in current window rate = 4 * ((60 - 15) / 60) + 5 = 8 return rate < limit # True means we should allow the request
- Solves the issue near boundary from fixed window.
- More expensive in terms of storage and computation
- It’s only an approximation because it assumes a uniform request flow in the previous window
from upstash_ratelimit import Ratelimit, SlidingWindow from upstash_redis import Redis ratelimit = Ratelimit( redis=Redis.from_env(), limiter=SlidingWindow(max_requests=10, window=10), )
Consider a bucket filled with maximum number of tokens that refills constantly at a rate per interval. Every request will remove one token from the bucket and if there is no token to take, the request is rejected.
- Bursts of requests are smoothed out and you can process them at a constant rate.
- Allows setting a higher initial burst limit by setting maximum number of tokens higher than the refill rate
- Expensive in terms of computation
from upstash_ratelimit import Ratelimit, TokenBucket from upstash_redis import Redis ratelimit = Ratelimit( redis=Redis.from_env(), limiter=TokenBucket(max_tokens=10, refill_rate=5, interval=10), )