as per the configuration. If you’re interested in learning more about the Enterprise edition, just contact Kong’s sales team to request a demo.

Rapidly design, publish and consume APIs and services. A designer’s obsession always circles back to one simple question: How can we improve the user’s experience? Despite the token bucket algorithm’s elegance and tiny memory footprint, its Redis operations aren’t atomic. For example, if our rate limit were 5 requests per minute and a user made 5 requests at 11:00:59, they could make 5 more requests at 11:01:00 because a new counter begins at the start of each minute. A better approach is to use a “set-then-get” mindset, relying on atomic operators that implement locks in a very performant fashion, allowing you to quickly increment and check counter values without letting the atomic operations get in the way. Our token bucket implementation could achieve atomicity if each process were to fetch a Redis lock for the duration of its Redis operations. In this article, we dive deep into an intuitive and heuristic approach for rate-limiting that uses a sliding window.

The two main problems with this approach are increased latency making requests to the data store, and race conditions, which we will discuss next.

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: Namely, the rate limiter continues to count requests even after the user exceeds the rate limit.

Picking this stack will keep our server cost down and would also help us make optimum utilization of the resources. One of the largest problems with a centralized data store is the potential for race conditions in high concurrency request patterns. 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. For each unique user, we would record their last request’s Unix timestamp and available token count within a hash in Redis.

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. Unlike the token bucket algorithm, this approach’s Redis operations are atomic. As decided before we would be using a NoSQL key-value store to hold the configuration data. Each request would increment a Redis key that included the request’s timestamp. The most frequent operations on this store will be. There are actually many different ways to enable rate limiting, and we will explore the pros and cons of different rate limiting algorithms. This is a bit more scalable. 5 essential ways to use design constraints. While processing the request and the response, Kong will execute any plugin that you have decided to add to the API. Logs with timestamps beyond a threshold are discarded. Want to learn more about Kong Enterprise? The primary role of the Configuration Store would be to. one hour — slightly reduced the precision of the rate limiter. Thus there will be a classical problem where multiple writers read the same old value and updates. It also avoids the starvation problem of leaky bucket, and the bursting problems of fixed window implementations. Request Store will hold the count of requests served against each key per unit time. Since decision engine is a typical service engine we would put it behind a load balancer that takes care of distributing requests to decision engine instances in a round-robin fashion ensuring it scales horizontally. In fact, the greater the number of nodes, the more likely the user will be able to exceed the global limit. You can quickly install Kong on one of your dev machines to test it out.

Build, secure and observe your modern Service Mesh. 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. Apart from the improvements mentioned above there some approaches that would further improve the performance. Let’s go ahead and protect it from an excessive number of requests by adding the rate-limiting functionality using the community edition Rate-Limiting plugin, with a limit of 5 requests per minute from every consumer: If we now make more than 5 requests, Kong will respond with the following error message: Looking good! 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. Rate limiting is usually applied per access token or per user or per region/IP. Finding a way to satisfy the last two requirements — accurately controlling web traffic and minimizing memory usage — was more of a challenge. 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. It’s a simple, memory-efficient algorithm that records the number of requests from a sender occurring in the rate limit’s time interval. This will store the counts for each window and consumer. 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. In general terms, it allows us to control the rate at which user requests are processed by our server. 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. 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. 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.

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