This would quickly become a major performance bottleneck, and does not scale well, particularly when using remote servers like Redis as the backing datastore. We would store these two values in a hash rather than as two separate Redis keys for memory efficiency. This will store the counts for each window and consumer.
When each request increments a counter in the hash, it also sets the hash to expire an hour later. # Since the request goes through, register it. A few weeks ago at Figma, we experienced our first-ever spam attack. It’s used by over 300,000 active instances globally. Suppose we have a limiter that permits 100 events per minute, and now the time comes at the "75s" point, then the internal windows will be as below:
Then the rate of both window [00:00, 00:01) and window [00:01, 00:02) is 2 per minute. If you’re interested in learning more about the Enterprise edition, just contact Kong’s sales team to request a demo. Next, we account for a weighted value of the previous window’s request rate based on the current timestamp to smooth out bursts of traffic. The overall high-level design of the entire system looks something like this. It combines a few standard techniques to control the rate at which people issue requests to your server, and it’s relatively accurate, simple, and space-efficient. This, however, would come at the expense of slowing down concurrent requests from the same user and introducing another layer of complexity. The above configuration defines that the user with id 241531 would be allowed to make 5 requests in 1 second. Since the operations are both read and write-heavy and will be made very frequently (on every request call), we chose an in-memory store for persisting it. We will also get practical by implementing a selected approa… In general terms, it allows us to control the rate at which user requests are processed by our server. Request Store is a nested dictionary where the outer dictionary maps the configuration key key to an inner dictionary, and the inner dictionary maps the epoch second to the request counter. The next step is adding an API on Kong using Kong’s admin API. A designer’s obsession always circles back to one simple question: How can we improve the user’s experience? Like the fixed window algorithm, we track a counter for each fixed window.
When rate limiting was enabled at 12:02, additional requests shown in red are denied. Since there would be billions of entries in this Configuration Store, using a SQL DB to hold these entries will lead to a performance bottleneck and hence we go with a simple key-value NoSQL database like MongoDB or DynamoDB for this use case. Namely, the rate limiter continues to count requests even after the user exceeds the rate limit. Setting up Graphite and Grafana on an Ubuntu server. For low latency, it uses an in-memory table of the counters and can synchronize across the cluster using asynchronous or synchronous updates. At a regular interval, the first item on the queue is processed. It’s really easy to use, and you can get this powerful control with a simple API call: The enterprise edition also adds support for Redis Sentinel, which makes Redis highly available and more fault tolerant.
The entire approach could be visualized as follows. We would need to horizontally scale this system and for that, we shard the store using configuration key key and use consistent hashing to find the machine that holds the data for the key. One way to avoid this problem is to put a “lock” around the key in question, preventing any other processes from accessing or writing to the counter. You can read more in the Enterprise rate limiting plugin documentation. For this, we iterate through the data from the inner dictionary second by second and keep on summing the requests count for the epoch seconds greater than the start_time. The sliding window prevents your API from being overloaded near window boundaries, as explained in the sections above. Our token bucket implementation could achieve atomicity if each process were to fetch a Redis lock for the duration of its Redis operations. The rate limit will be enforced precisely. If you like what you read subscribe you can always subscribe to In order to reduce the memory footprint, we could delete the items from the inner dictionary against the time older than the start_time because we are sure that the requests for a timestamp older than start_time would never come in the future.
In our situation, however, we preferred for our rate limiter to sometimes be a tad harsher instead of slightly lenient, so I calculated the sum of all counters in the last hour and one minute whenever the current timestamp wasn’t divisible by 60. Despite the token bucket algorithm’s elegance and tiny memory footprint, its Redis operations aren’t atomic. The limit is defined as the number of requests number_of_requests allowed within a time window time_window_sec (defined in seconds).
Every time we get a request, we make a decision to either serve it or not; hence we check the number_of_requests made in last time_window_sec seconds. Getting the rate limit configuration is a simple get on the Configuration Store by key. Arguably, it would also impose too severe of a restriction on how often the user could make requests.
It will abstract out all the complexities of distributed data, replication, and failures.
Every point on the axis represents an API call. This is also known as a first in first out (FIFO) queue. store data externally so that the multiple machines running our web application could share it, accurately limit excessive use of our web application. Had we not discovered the attack, we could have faced a huge surge in our delivery costs and a decline in our email sender reputation. It is not recommended to put this or similar code in production as it has a lot of limitations (discussed later), but the idea here is to design the rate limiter ground up including low-level data models, schema, data structures, and a rough algorithm.
The sorted set’s size would then be equal to the number of requests in the most recent sliding window of time. Like tackling tough engineering problems like this? Finding a way to satisfy the last two requirements — accurately controlling web traffic and minimizing memory usage — was more of a challenge. This is best illustrated through an example: For an hourly rate limit, when the rate limiter checks usage at 11:00:35, it ignores the requests that occurred between 10:00:00 and 10:00:59. The kind of datastore we choose determines the core performance of a system like this. A better solution that allows more flexible load-balancing rules is to use a centralized data store such as Redis or Cassandra. A better design will not only help us keep the response time to a bare minimum, but it also ensures that the system is extensible with respect to future requirement changes.
Other features include an admin GUI, more security features like role based access control, analytics, and professional support. Ultimately, the last two rate limiter approaches — fixed window counters and sliding window log — inspired the algorithm that stopped the spammers. A rate limiter restricts the intended or unintended excessive usage of a system by regulating the number of requests made to/from it by discarding the surplus ones.
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