3 stable releases
1.0.2 | Mar 30, 2024 |
---|---|
1.0.1 | Mar 23, 2024 |
1.0.0 | Mar 22, 2024 |
#1040 in Database interfaces
121 downloads per month
25KB
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Unofficial Upstash rate limit SDK for Rust
A rate-limiting SDK built for the Rust ecosystem that uses in-memory data storage.
Inspiration
This rate limit SDK is inspired by the official TypeScript rate limit SDK created by Upstash team.
Setup
- To setup the ratelimiter, first create a client instance of Redis that can store the request counts for a given set window:
let connection_str = std::env::var("UPSTASH_REDIS_URL").unwrap_or_else(|_| panic!("Expecting UPSTASH_REDIS_URL to be set"));
let Ok(redis) = redis::Client::open(connection_str) else {
panic!("Failed to connect")
};
-
Create a client instance of the
RatelimitConfiguration
using the Redis client: -
Use the client configuration to create a new instance of any one of the three rate-limiting algorithms:
For example: Using the fixed window algorithm to limit 10 requests in 30 seconds of the window.
let client = RatelimitConfiguration::new(redis, true, Some(String::from("my-custom-prefix")));
let ratelimit = FixedWindow::new(client, 10, "30s");
In the above client configuration, using the Ephemeral cache to avoid making Redis calls if the request is already blocked and adding a custom prefix string will override the default prefix string,
Use the ratelimit
instance to call the limit function in any request calls to rate limit your requests:
let limit_response = state.ratelimit.limit("some-unique-identifier-like-ip", None).await;
Custom rate
By default every algorithm consumes one token per request, but if you want rate-limit the requests based on the payload size or any other factor, you can do so by providing the rate value to the limit function call:
let limit_response = state.ratelimit.limit("some-unique-identifier-like-ip", Some(10)).await;
This will consume 10 tokens in one request.
Examples
Check the examples directory
Roadmap
-
Single Region (may have latency issues)
- Fixed window algorithm ✅
- Sliding window algorithm ✅
- Token bucket algorithm ✅
- Cached fixed window algorithm 🛠️
- Analytics 🛠️
- Forced timeout 🛠️
- Hard reset 🛠️
-
Multiple Region (no latency issues)
- Fixed window algorithm 🛠️
- Sliding window algorithm 🛠️
Dependencies
~10–21MB
~283K SLoC