How to Design a Scalable Rate Limiting Algorithm (opens in new tab)

For a different view on the topic watch "Stop Rate Limiting! Capacity Management Done Right" https://www.youtube.com/watch?v=m64SWl9bfvk

The basic premise is not do do req/s limiting but rather concurrency limiting which results in req/s limiting by itself.

Concurrency limiting is rather simple and doesn't require a lot of code complexity.

> 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.

In a highly distributed system you’d probably want to avoid a centralised data store altogether for fast moving data like rate limits. CRDTs and bucket weighting might be a more effective strategy.

The article states that tracking per-node could cause a problem with race conditions but that assumes it’s the counter that’s the problem. If the node cluster is aware of the other nodes and the relative load of the cluster, you can use this value to weight the isolated rate limiter and the only data that needs to be shared can be broadcast between the nodes using a pub/sub mechanism.

If some variance is permitted (+/- a margin either side of the limit) then having the nodes synchronise their “token weight” based on the size of the cluster means that the nodes can then manage the rate limit in-memory without ever needing to track in a data store.

It does trade-off accuracy, but for accuracy you can then revert to the set-then-get centralised counter, the trade-off being performance because of increased round trip time to the day store.

In most rate limit scenarios, at least from what we’ve seen, extreme accuracy isn’t usually that important vs. being able to scale amd rate limit without having to also scale a data layer to handle the counters.

Disregarding the article completely, I'll share my opinion on Kong because we use quite a bit at my workplace. We use an old version (0.9.x as of right now). The things I shared below might not be true anymore in new versions but I can't tell and are regarding the plugin system.

IMO, the idea of taking things like authentication, rate limiting, etc in a proxy is a wonderful idea (https://2tjosk2rxzc21medji3nfn1g-wpengine.netdna-ssl.com/wp-...). So in theory, the approach Kong takes is wonderful, but I think the implementation is not so much.

Kong is layer over nginx and uses lua as a scripting language to add all sorts of stuff. Quickly, you reach the limit of the plugins capability so you think, well, I will write my own plugins. Well, to me it was a very unpleasing experience. I found that the thing was hard to test and hard to maintain. Maybe my criticism is more lua than Kong but since Kong relies heavily on lua, there is not much I can do.

There is also magic happening. You declare a schema.lua files to configure the store to hold your data. Then automatically you've got a DAO interface available with a bunch of method on it to work with the store. You don't know what methods are available or what arguments should be passed into these functions.

Anyway, this is my take after spending quite a few hours working on home made plugins in lua for Kong.

In the end, I'm glad Kong is open source and it's a great piece of software. It helped us reduce our applications complexity but make sure you don't start to shift to much logic into it because the plugin system can be hard to work with.

This may be how Kong does it but it's not really 'high performance'. The right way to do rate limiting is to limit by IP using a counting Bloom Filter or Cuckoo filter along with random samples. When you hit a false positive then you have a second normal rate limiter to 'mop up' IPs that are over the first limiter.

This doesn't give you a hard exact limit but gets the job done storing far less state. You also need to bucket by IP sub-ranges in IPV6 to stop people crap flooding you with tons of unique IP's

I have been using Kong since 2016 on various APIs and I have been impressed by the continuous increase of performance after every release. I wish upgrades were easier but I know they are working on it (the "migrations" between each major version), but overall it's a fast and pluggable layer for any API inside or outside the firewall, especially if you are running containers.

On this note for those of you who haven't noticed it yet, they have released an Alpine version of their Docker image, but it's still not the default one. I would actually recommend using it to further reduce the size of Kong containers: https://konghq.com/blog/kong-alpine-docker/

Nice article (although the illustrations aren't very... illustrative). Anybody knows how does Kong compare with WSO2 API Manager?

Excellent article! One question though:

> 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.

Can you elaborate on this? Why is it more performant? And what are the trade-offs vs. get-then-set?

The performance degradation of doing strict rate limit relying on Kong Cluster data store (Postgres or Cassandra) is amplified by the lake of database connection pool management. Each incoming http request induce the creation of a new database connection, this process is very expensive especially with Cassandra when authentication is enabled.

I use Kong EE for a client, I have read Kong EE documentation carefully and made a lot of tests. The current implementation (0.29) have this behavior (that not meet our needs).

1) All incoming requests are take into account, including those which have been rejected (with 429 error). If the consumer exceed his limit during many consecutive time windows, all the requests of the consecutive time windows will be rejected (with 429 error).

2) For a windows size of 1 second, the computed weight of the previous windows is always 100%. If the limit was reach during the previous second, all requests made in the current windows will be rejected (with 429 errors).

One thing that RateLim.it uses to its advantage is calculating the “nextPossiblePass” for each limit. This allows clients to cache forever that something is over the limit until time X and not have to make another request.

In the bursty case this let’s clients effectively short circuit and protect the system.

Disclaimer: I work on https://www.ratelim.it/documentation/basic_rate_limits