Even better, it’s simple to specify when Redis should delete expired data. Designing a rate limiter has to be super-efficient because the rate limiter decision engine will be invoked on every single request and if the engine takes a long time to decide this, it will add some overhead in the overall response time of the request. Here are the existing rate limiter implementations I considered: Let’s look at how each of them work and compare them in terms of accuracy and memory usage. We will also explore the issues that come up when scaling across a cluster. Now that we have defined and designed the data stores and structures, it is time that we implement all the helper functions we saw in the pseudocode.

The other algorithms and approaches include Leaky Bucket, Token Bucket and Fixed Window.

For example, each node can create a data sync cycle that will synchronize with the centralized data store. In the example chart, you can see how rate limiting blocks requests over time. For example, if the current window is 25% through, then we weight the previous window’s count by 75%. A few weeks ago at Figma, we experienced our first-ever spam attack. In general terms, it allows us to control the rate at which user requests are processed by our server. By continuing to use our website, you agree to the use of cookies as described in our Cookie Policy. If you like what you read subscribe you can always subscribe to If the request would exceed the threshold rate, then it is held. engineering topics and share it through my weekly The entire approach could be visualized as follows. This would allow us to efficiently remove all of the set’s members with outdated timestamps and count the size of the set afterward. However, a single burst of traffic that occurs near the boundary of a window can result in twice the rate of requests being processed, because it will allow requests for both the current and next windows within a short time.

If the queue is full, then additional requests are discarded (or leaked). I write Finding a way to satisfy the last two requirements — accurately controlling web traffic and minimizing memory usage — was more of a challenge. Unfortunately, even checking a fast data store like Redis would result in milliseconds of additional latency for every request. If each stored timestamp value in Redis were even a 4-byte integer, this would take ~20 MB (4 bytes per timestamp * 10,000 users * 500 requests/user = 20,000,000 bytes). Redis is an in-memory data store that offers extremely quick reads and writes relative to PostgreSQL, an on-disk relational database.

Each request would increment a Redis key that included the request’s timestamp. If you’re a company building web applications at consumer scale, our rate limiter can prevent users from harming your website’s availability with a spate of requests. However, a burst of traffic can fill up the queue with old requests and starve more recent requests from being processed. Additionally, if you load balance servers for fault tolerance or increased throughput, you must use a policy to coordinate and enforce the limit between them. Let’s review each of them so you can pick the best one for your needs. Rate limiting is usually applied per access token or per user or per region/IP.

Part 2: Monitor your production systems and application analytics using Graphite. The inner dictionary is actually holding the number of requests served during the corresponding epoch second. Using fixed window counters with a 1:60 ratio between the counter’s time window and the rate limit’s enforcement time window, our rate limiter was accurate down to the second and significantly minimized memory usage. For a generic rate-limiting system that we intend to design here, this is abstracted by a configuration key key on which the capacity (limit) will be configured; the key could hold any of the aforementioned value or its combinations. Setting up Graphite using Nginx on an Ubuntu server. As a result, it does not scale well to handle large bursts of traffic or denial of service attacks. When rate limiting was enabled at 12:02, additional requests shown in red are denied.

The Enterprise edition of rate limiting adds support for the sliding window algorithm for better control and performance. At a regular interval, the first item on the queue is processed. Then ssh into a command line on your new instance for the next step. It also avoids the starvation problem of leaky bucket, and the bursting problems of fixed window implementations.

one hour — slightly reduced the precision of the rate limiter. When the available token count drops to zero, the rate limiter knows the user has exceeded the limit. All rate limits on the Instagram Platform are controlled separately for each access token, and on a sliding 1-hour window. Our token bucket implementation could achieve atomicity if each process were to fetch a Redis lock for the duration of its Redis operations. Additionally, if many consumers wait for a reset window, for example at the top of the hour, then they may stampede your API at the same time. The other algorithms and approaches include Leaky Bucket, Token Bucket and Fixed Window. One of the largest problems with a centralized data store is the potential for race conditions in high concurrency request patterns. There would be cases where the time_window_sec is large - an hour or even a day, suppose it is an hour, so if in the Request Store we hold the requests count against the epoch seconds there will be 3600 entries for that key and on every request, we will be iterating over at least 3600 keys and computing the sum.

You also may want to rate limit APIs that serve sensitive data, because this could limit the data exposed if an attacker gains access in some unforeseen event. My favorite way to get started is to use the AWS cloud formation template since I get a pre-configured dev machine in just a few clicks. While the spam attack is over for now, new types of incidents can and will happen in the future and we’ll continue to adjust our rate limiter as needed. newsletter , Multiple MySQL server running on same Ubuntu 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 overall high-level design of the entire system looks something like this. In practice, however, a large enforcement time window — e.g. Let’s compare the memory usage of this algorithm with our calculation from the sliding window log algorithm. This would quickly become a major performance bottleneck, and does not scale well, particularly when using remote servers like Redis as the backing datastore. When the web application processes a request, it would insert a new member into the sorted set with a sort value of the Unix microsecond timestamp. Logs with timestamps beyond a threshold are discarded.

As decided before we would be using a NoSQL key-value store to hold the configuration data. These logs are usually stored in a hash set or table that is sorted by time.

Every point on the axis represents an API call. The windows are typically defined by the floor of the current timestamp, so 12:00:03 with a 60 second window length, … The first step is to install the Enterprise edition of Kong. The rate windows are an intuitive way she to present rate limit data to API consumers. It accepts the configuration key `key` and checks the number of requests made against it

The granularity configuration could be persisted in the configuration as a new attribute which would help us take this call. We prevent this by regularly removing these counters when there are a considerable number of them. This way we keep on aggregating the requests per second and then sum them all during aggregation to compute the number of requests served in the required time window. Kong’s rate limiting plug-in is highly configurable. We count requests from each sender using multiple fixed time windows 1/60th the size of our rate limit’s time window. To facilitate sharding and making things seamless for the decision engine we will have a Request Store proxy which will act as the entry point to access Request Store data. It’s also easy to implement on a single server or load balancer, and is memory efficient for each user given the limited queue size. extremely efficient storage when they have fewer than 100 keys. The sorted set’s size would then be equal to the number of requests in the most recent sliding window of time. When the code runs in a multi-threaded environment, all the threads executing the function for the same key key, all will try to increment the same counter. Accelerate your journey into microservices. Since the information does not change often and making a disk read every time is expensive, we cache the results in memory for faster access. It’s a simple, memory-efficient algorithm that records the number of requests from a sender occurring in the rate limit’s time interval. It defaults to rate limiting by IP address using fixed windows, and synchronizes across all nodes in your cluster using your default datastore. There are actually many different ways to enable rate limiting, and we will explore the pros and cons of different rate limiting algorithms.

In a fixed window algorithm, a window size of n seconds (typically using human-friendly values, such as 60 or 3600 seconds) is used to track the rate. Like tackling tough engineering problems like this? Imagine if there was only one available token for a user and that user issued multiple requests. Traditionally, web applications respond to requests from users who surpass the rate limit with a HTTP response code of 429. In the event that a user makes requests every minute, the user’s hash can grow large from holding onto counters for bygone timestamps. But the rate of window [00:00:30, 00:01:30) is in fact 4 per minute. Although the fixed window approach offers a straightforward mental model, it can sometimes let through twice the number of allowed requests per minute. The Rate limiter has the following components. The advantage of this algorithm is that it ensures more recent requests gets processed without being starved by old requests. # The window returned, holds the number of requests served since the start_time. Kong is an open source API gateway that makes it very easy to build scalable services with rate limiting.