Rate limiting strategies

Fixed Window

This is the most memory efficient strategy to use as it maintains one counter per resource and rate limit. It does however have its drawbacks as it allows bursts within each window - thus allowing an ‘attacker’ to by-pass the limits. The effects of these bursts can be partially circumvented by enforcing multiple granularities of windows per resource.

For example, if you specify a 100/minute rate limit on a route, this strategy will allow 100 hits in the last second of one window and a 100 more in the first second of the next window. To ensure that such bursts are managed, you could add a second rate limit of 2/second on the same route.

Fixed Window with Elastic Expiry

This strategy works almost identically to the Fixed Window strategy with the exception that each hit results in the extension of the window. This strategy works well for creating large penalties for breaching a rate limit.

For example, if you specify a 100/minute rate limit on a route and it is being attacked at the rate of 5 hits per second for 2 minutes - the attacker will be locked out of the resource for an extra 60 seconds after the last hit. This strategy helps circumvent bursts.

Moving Window


The moving window strategy is only implemented for the redis and in-memory storage backends. The strategy requires using a list with fast random access which is not very convenient to implement with a memcached storage.

This strategy is the most effective for preventing bursts from by-passing the rate limit as the window for each limit is not fixed at the start and end of each time unit (i.e. N/second for a moving window means N in the last 1000 milliseconds). There is however a higher memory cost associated with this strategy as it requires N items to be maintained in memory per resource and rate limit.