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It is my absolute pleasure to present you with this guest post by Brandur Leach, redis-cell‘s author and winner of the 1st place in the Redis Modules Hackathon. The module is an efficient implementation of a rate limiter that can be used, for example, to protect from activity spikes. In this post, Brandur explains why the module came into being, how it works and the reasons behind his choice of programming language (spoiler: Rust).
When I first noticed the Redis module hackathon and started to brainstorm project ideas, I settled on one quite quickly. I’ve been in industry long enough to have seen Redis put to a huge variety of different uses–indeed it’s the Swiss Army knife of the modern production stack–but there’s one place in particular that I see it being brought to bear over and over again.
Web services that are exposed to a network tend to need various layers of protection. The most common form of protection is of course authentication, which ensures that the users accessing your resources are the ones that you expect to be accessing them. Another very common one is controlling the rate at which users allow to access those resources. This is obviously quite useful for public services that need protection against (both intentionally and unintentionally) malicious actors, but also for internal services to protect against certain types of accidental use — the thundering herd problem for example, where many consumers wake up simultaneously and contend for access to the same resource (like an API) simultaneously. Even companies like Google, who are widely known for the excellence in technical competency, have admitted to occasionally making this sort of mistake.
If you look at almost any commonly used APIs that you can find online, you’ll notice that the vast majority of them are controlling access using rate limiters. GitHub, Spotify, Heroku, and Uber are all good examples.
A naive rate limiter implementation might simply track the number of operations taken in the expected period of time and expire buckets as that period comes to an end. Most real world rate limiters use a slightly more sophisticated algorithm called “drip bucket”. It’s an easy metaphor that models rate limit capacity as a bucket that has a fixed-size hole in its bottom. As a user consumes operations, water is added to the bucket and its water level rises. If the bucket becomes full, no more operations are allowed, but luckily, the hole in the bucket is allowing water to escape at a constant rate. As long as the rate of water in and the rate of water out stay roughly equal, the system stays at equilibrium and operations are never limited.
Drip bucket is useful because its implementation is both computationally and storage efficient, but also because it offers a good user experience by providing a “rolling” time period. Even if a user accidentally burns through their entire limit in a single burst, they’ll have more limit available almost immediately instead of having to wait for the next window to start.
redis-cell implements a variation of drip bucket called “generic cell rate algorithm” (GCRA). It’s funtionally identical, but uses some clever logic so that each users being tracked needs only a single backend key to track their entire state.
The module exposes a single command: CL.THROTTLE. It’s invoked with parameters that describe the rate limit that should be enforced. For example:
CL.THROTTLE user123 15 30 60 1
▲ ▲ ▲ ▲ ▲
| | | | └───── apply 1 operation (default if omitted)
| | └──┴─────── 30 operations / 60 seconds
| └───────────── 15 max_burst
└─────────────────── key “user123”
It responds with an array where the foremost element indicates whether the request should be limited. The other elements contain quota and timing metadata that’s often returned from HTTP services as informative headers along with the response. For example:
127.0.0.1:6379> CL.THROTTLE user123 15 30 60
1) (integer) 0 # 0 means allowed; 1 means denied
2) (integer) 16 # total quota (`X-RateLimit-Limit` header)
3) (integer) 15 # remaining quota (`X-RateLimit-Remaining`)
4) (integer) -1 # if denied, time until user should retry (`Retry-After`)
5) (integer) 2 # time until limit resets to maximum capacity (`X-RateLimit-Reset`)
Rolling your own rate limiting module is quite possible of course, but redis-cell aims to provide a general and widely-useful implementation that can be integrated into a project built with any programming language or framework, just as long as its stack includes Redis.
redis-cell’s other notable feature is the language that it’s written in. Although the Redis module API was originally intended to be consumed by another C program (it’s exposed as a C header file in redismodule.h), the project is implemented in pure Rust. This is achieved through the use of the Rust FFI (foreign function interface) module which allows the program to break out of its normal safety rails and call directly into a systems level API. It’s also made possible because Rust programs are bootstrapped using only a tiny runtime, and much like C programs, have no garbage collector.
So why bother? Well, although I could probably be considered to be nominally literate in C, I don’t have anywhere near the expertise to be confident that I wouldn’t write a program that contained a memory overrun or some other unsafe operation that would manifest as a program-killing segmentation fault. As evidenced by widespread issues like Heartbleed, even highly competent C developers are not beyond this class of mistake.
The rust compilers guarantees that all my memory accesses are safe, and its strong type system goes a long way towards ensuring that I’m not accidentally misusing code in a way that could cause a runtime problem. This is good for me, but even better for would-be contributors the project; even someone who’s never written Rust before has only a miniscule chance of introducing a low-level problem as long as they can get the program to compile.
Let’s look at a simple example of this safety in action. The Redis module API provides certain functions to allocate memory inside of Redis, and in the default mode of operation, modules are tasked with freeing any memory that they allocate in this way. So if RedisModule_CreateString is invoked to create a new string, a call to RedisModule_FreeString is expected to eventually free it.
In Rust, I wrap theses string with a higher level type so that I don’t have to work with them directly:
pub struct RedisString {
ctx: *mut raw::RedisModuleCtx,
str_inner: *mut raw::RedisModuleString,
}
Now the trouble with manual memory management is that it can be dangerous. Say I have a function that allocates a string, performs an operation with it, and then frees the string before returning:
fn run_operation( )-> i64 {
let s = RedisString::create(…);
…
s.free();
return 0;
}
Even if it works perfectly fine at first, it’s easy for a bug to be introduced somewhere down the line by someone not intimately familiar with the original code. Say for example that a new return directive is introduced midway through the function:
fn run_operation() -> i64 {
let s = RedisString::create(…);
…
if error_occurred {
// s is leaked!
return 1;
}
…
s.free();
return 0;
}
The new conditional branch doesn’t free the string before leaving the function, so the program now has a memory leak. This is a very easy mistake to make in C.
With Rust, we can do things a little differently. By implementing the language’s built-in Drop trait (think of a trait like an interface in most languages), we can guarantee the memory safety of RedisString:
impl Drop for RedisString {
fn drop(&mut self) {
self.free();
}
}
Drop is like a destructor in C++; it’s called when an instance of the type goes out of scope. So in this case we ensure that our low-level free function always gets called. There are no gotchas or failure conditions to worry about.
We don’t even have to manually call free anymore. No matter how many new conditional branches are introduced, memory safety is always guaranteed:
fn run_operation() -> i64 {
let s = RedisString::create(…);
…
if error_occurred {
// s is freed here automatically
return 1;
}
…
// and here too!
return 0;
}
Foremost I’d like to thank Redis for hosting the Redis module hackathon and giving me the opportunity to work on this. I also appreciated the help of Daniel Farina in detangling some of the Redis internals and figuring out how its module system was laying out memory, and Itamar Haber for giving me some instruction on the correct use of some of the Redis module APIs.
