Package: api-throttler
API ThrottlerA Python toolkit to enforce API rate limit on the backend. The toolkit enable the service backend to limit the number of
API calls in a specified period, e.g., 15 API calls per 900 seconds. There are four throttler classes in the toolkit:FixedWindowThrottlerSlidingWindowThrottlerFixedWindowThrottlerRedisSlidingWindowThrottlerRedisThe first two throttler classes use local storage to save throttler data (e.g., API calls that have been served and
their timestamps), while the last two throttler classes use a Redis server to store throttler information. The
difference between the fixed window and sliding window throttlers is the fixed window throttler uses the timestamp when
the first feasible request is served as the starting timestamp to determine the number of allowed API calls in the
following period, while the sliding window throttler uses the current timestamp minus the specified period as the
starting timestamp to calculate the number of allowed API calls. The advantage of fixed window throttler is its
simplicity, but there could be many API calls allowed if they are at the end of last period and the beginning of the
current period. On the other hand, the sliding window throttler could resolve this issue, but it takes more memory.UsageTo use this API throttler toolkit, first install it using pip:pipinstallapi-throttlerThen, import the package in your python script and use appropriate throttler classes:importtimefromapi_throttlerimportThrottler,FixedWindowThrottler,SlidingWindowThrottler# Limit 3 calls per 10 secondsfixed_window_throttler=FixedWindowThrottler(calls=3,period=10)sliding_window_throttler=SlidingWindowThrottler(calls=3,period=10)defcall_api(throttler:Throttler,key:str='some_string_key'):ifnotthrottler.is_throttled(key):print('API call is NOT throttled')else:print('API call is throttled')print('Using fixed window API throttler')foriinrange(20):print(f'This is the{i}-th second')# Call API in the following i-th secondsifiin{0,8,9,10,11,12}:call_api(fixed_window_throttler)time.sleep(1)print('-'*40)print('Using sliding window API throttler')foriinrange(20):print(f'This is the{i}-th second')ifiin{0,8,9,10,11,12}:call_api(sliding_window_throttler)time.sleep(1)In the above example, the data of the throttler is saved in local memory. If you would like to save it in a redis
server, you can use theFixedWindowThrottlerRedisandSlidingWindowThrottlerRedisclasses. The following scripts
shows how to use the two classes usingfake redis:importtimeimportfakeredisfromapi_throttlerimportThrottler,FixedWindowThrottlerRedis,SlidingWindowThrottlerRediscache=fakeredis.FakeStrictRedis()# Limit 3 calls per 10 secondsfixed_window_throttler=FixedWindowThrottlerRedis(calls=3,period=10,cache=cache)sliding_window_throttler=SlidingWindowThrottlerRedis(calls=3,period=10,cache=cache)defcall_api(throttler:Throttler,key:str='some_string_key'):ifnotthrottler.is_throttled(key):print('API call is NOT throttled')else:print('API call is throttled')print('Using fixed window API throttler')foriinrange(20):print(f'This is the{i}-th second')# Call API in the following i-th secondsifiin{0,8,9,10,11,12}:call_api(fixed_window_throttler)time.sleep(1)print('-'*40)print('Using sliding window API throttler')foriinrange(20):print(f'This is the{i}-th second')ifiin{0,8,9,10,11,12}:call_api(sliding_window_throttler)time.sleep(1)If you would like to try a real example using API served by a Flask app and a redis server, please try to run theapp.pyusing Docker by typing the following command in your terminal:docker-composeup--build