Rate limiting strategies¶
Flask-Limiter comes with three different rate limiting strategies built-in. Pick
the one that works for your use-case by specifying it in your flask config as
RATELIMIT_STRATEGY
(one of fixed-window
, fixed-window-elastic-expiry
,
or moving-window
), or as a constructor keyword argument. The default
configuration is fixed-window
.
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¶
Warning
The moving window strategy is not implemented for the memcached storage backend. 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.