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This Is How Stripe Does Rate Limiting to Build Scalable APIs
#9: Read Now - Awesome Rate Limiter (4 minutes)
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A rate limiter is important to building a scalable API. Because it prevents bad users from abusing the API.
A rate limiter keeps a counter on the number of requests received. And reject a request if the threshold exceeds. Requests are rate-limited at the user or IP address level.
And it is a good choice if a change in the pace of the requests doesn't affect the user experience.
Other potential reasons to rate limit are:
Prevent low-priority traffic from affecting high-priority traffic
Prevent service degradation
Rejecting low-priority requests under heavy load is called load shedding.
This post outlines how Stripe scales their API with the rate limiter. Consider sharing this post with someone who wants to study scalability patterns.
Rate Limiter Workflow
The rules for rate limiting are predefined. And here is the rate limiter workflow:
Check rate limiter rule
Reject a request if the threshold exceeds
Otherwise let the request pass-through
Rate Limiter Implementation
Stripe uses the token bucket algorithm to do rate limiting. Here is a quick overview of this algorithm:
Imagine there is a bucket filled with tokens. Every request must pick a token from the bucket to pass through.
No requests get a token if the bucket is empty. So, further requests get rejected.
And tokens get refilled at a steady pace.
Other popular rate-limiting algorithms are sliding windows and leaky buckets.
They used Redis to build the rate limiter. Because it is in-memory and provides low latency.
Things they considered when implementing the rate limiter are:
Quality check rate limiter logic and allow bypass on failures
Show a clear response to the user: status code 429 - too many requests or 503 - service unavailable
Enable panic mode on the rate limiter. This allowed switching it off on failures
Set up alerts and monitoring
Tune rate limiter to match traffic patterns
It's difficult to rate-limit a distributed system. Because each request from a single user might not hit the same server. I don't know how Stripe solved this problem. But here is a potential solution.
Redirect the traffic from an IP address to the same data center using DNS. And create an isolated rate limiter in each data center.
Yet a new TCP connection might hit a different server within a data center.
So set up a caching proxy (twemproxy) in each data center. Because it allows to share state across many servers. Put another way, many servers share a single cache: rate limiter.
Rate Limiting Types
Stripe categorizes rate limiting into 4 types:
1. Request Rate Limiter
Each user gets n requests per second. This rate limiter type acts as the first line of defense for an API. And it is the most popular type.
2. Concurrent Requests Rate Limiter
The number of concurrent requests that are in progress is rate-limited. This protects resource-intensive API. And prevents resource contention.
3. Fleet Usage Load Shedder
The critical APIs reserve 20% of computing capacity. And requests to non-critical APIs get rejected if the critical API doesn’t get 20% of the resources.
4. Worker Utilization Load Shedder
The non-critical traffic gets shed on server overload. And it gets re-enabled after a delay. This rate limiter type is the last line of defense for an API.
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