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sliding window rate limiting algorithm

In the event that a user makes requests every minute, the user’s hash can grow large from holding onto counters for bygone timestamps. A few weeks ago at Figma, we experienced our first-ever spam attack. To remedy this we could either. As a result, it does not scale well to handle large bursts of traffic or denial of service attacks. While processing the request and the response, Kong will execute any plugin that you have decided to add to the API. 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. For example, each node can create a data sync cycle that will synchronize with the centralized data store. Kong is an open source API gateway that makes it very easy to build scalable services with rate limiting. 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. When the available token count drops to zero, the rate limiter knows the user has exceeded the limit. While the precision of the sliding window log approach may be useful for a developer API, it leaves a considerably large memory footprint because it stores a value for every request. It would fetch the hash from Redis and refill the available tokens based on a chosen refill rate and the time of the user’s last request. During the attack, the spammers sent unsolicited invitations for documents to scores of email addresses. The request has been received by Kong and proxied to httpbin, which has mirrored back the headers for my request and my origin IP address. Just choose one of the HVM options, and set your instance sizes to use t2.micro as these are affordable for testing. 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. # The window returned, holds the number of requests served since the start_time. We count requests from each sender using multiple fixed time windows 1/60th the size of our rate limit’s time window. # Since the request goes through, register it. # the configuration holds the number of requests allowed in a time window. The other algorithms and approaches include Leaky Bucket, Token Bucket and Fixed Window. As a second approach, I considered fixed window counters. Thankfully, we got wind of the attack early on and avoided this outcome because our rate limiter detected the spammers’ flurry of requests. The sliding window prevents your API from being overloaded near window boundaries, as explained in the sections above. The kind of datastore we choose determines the core performance of a system like this. Part 1: Monitor your production systems and application analytics using Graphite. Similar to Request Store proxy we will have a proxy for Configuration Store that would be an abstraction over the distributed Configuration Stores. The rate limit will be enforced precisely. The advantage of this algorithm is that it does not suffer from the boundary conditions of fixed windows. Building a sliding window rate limiter with Redis, Everything You Need To Know About API Rate Limiting, Solutions to Competitive Programming Questions, Configuration Store - to keep all the rate limit configurations, Request Store - to keep all the requests made against one configuration key, Decision Engine - it uses data from the Configuration Store and Request Store and makes the decision, efficiently store configuration for a key, efficiently retrieve the configuration for a key, registering (storing and updating) requests count served against each key -, summing all the requests served in a given time window -, cleaning up the obsolete requests count -, pessimistic locks (always taking lock before incrementing), utilize atomic hardware instructions (fetch-and-add instruction). It also provides no guarantee that requests get processed in a fixed amount of time. 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. We could avoid this issue by adding another rate limit with a smaller threshold and shorter enforcement window — e.g. Design. This will store the counts for each window and consumer. However, it can be very expensive to store an unlimited number of logs for every request. This proved the value of Figma’s rate limiter and finally put a stop to the longstanding joke that I had built it in vain. The advantage of this algorithm is that it smooths out bursts of requests and processes them at an approximately average rate. Traditionally, web applications respond to requests from users who surpass the rate limit with a HTTP response code of 429. In this store, the key would be the configuration key (discussed above) which would identify the user/IP/token or any combination of it; while the value will be a tuple/JSON document that holds time_window_sec and capacity (limit). To reduce our memory footprint, we store our counters in a Redis hash — they offer extremely efficient storage when they have fewer than 100 keys. We prevent this by regularly removing these counters when there are a considerable number of them. The number of configurations would be high but it would be relatively simple to scale since we are using a NoSQL solution, sharding on configuration key key would help us achieve horizontal scalability.

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