W4ngCoder

$camphor300:'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

W4ngCoder

Keybase proof

I hereby claim:

• I am W4ngCoder on github.
• I am wangcoder (https://keybase.io/wangcoder) on keybase.
• I have a public key whose fingerprint is 95F2 42D9 F7BF CD8C A727 C6DB 5C65 4491 BFD1 C30B

To claim this, I am signing this object:

W4ngCoder

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.