Redis rate limiters

A library which regulates traffic, with respect to concurrency or time. It implements sync and async context managers for a semaphore- and a token bucket-implementation.

The rate limiters are distributed, using Redis, and leverages Lua scripts to improve performance and simplify the code. Lua scripts run on Redis, and make each implementation fully atomic, while also reducing the number of round-trips required.

Use is supported for standalone redis instances, and clusters. We currently only support Python 3.11, but can add support for older versions if needed.

Installation

pip install redis-rate-limiters

Usage

Semaphore

The semaphore classes are useful when you have concurrency restrictions; e.g., say you're allowed 5 active requests at the time for a given API token.

Beware that the client will block until the Semaphore is acquired, or the max_sleep limit is exceeded. If the max_sleep limit is exceeded, a MaxSleepExceededError is raised.

Here's how you might use the async version:

import asyncio

from httpx import AsyncClient
from redis.asyncio import Redis

from limiters import AsyncSemaphore


limiter = AsyncSemaphore(
    name="foo",    # name of the resource you are limiting traffic for
    capacity=5,    # allow 5 concurrent requests
    max_sleep=30,  # raise an error if it takes longer than 30 seconds to acquire the semaphore
    expiry=30,      # set expiry on the semaphore keys in Redis to prevent deadlocks
    connection=Redis.from_url("redis://localhost:6379"),
)

async def get_foo():
    async with AsyncClient() as client:
        async with limiter:
            client.get(...)


async def main():
    await asyncio.gather(
        get_foo() for i in range(100)
    )

and here is how you might use the sync version:

import requests
from redis import Redis

from limiters import SyncSemaphore


limiter = SyncSemaphore(
    name="foo",
    capacity=5,
    max_sleep=30,
    expiry=30,
    connection=Redis.from_url("redis://localhost:6379"),
)

def main():
    with limiter:
        requests.get(...)

Token bucket

The TocketBucket classes are useful if you're working with time-based rate limits. Say, you are allowed 100 requests per minute, for a given API token.

If the max_sleep limit is exceeded, a MaxSleepExceededError is raised.

Here's how you might use the async version:

import asyncio

from httpx import AsyncClient
from redis.asyncio import Redis

from limiters import AsyncTokenBucket


limiter = AsyncTokenBucket(
    name="foo",          # name of the resource you are limiting traffic for
    capacity=5,          # hold up to 5 tokens
    refill_frequency=1,  # add tokens every second
    refill_amount=1,     # add 1 token when refilling
    max_sleep=30,        # raise an error there are no free tokens for 30 seconds
    connection=Redis.from_url("redis://localhost:6379"),
)

async def get_foo():
    async with AsyncClient() as client:
        async with limiter:
            client.get(...)

async def main():
    await asyncio.gather(
        get_foo() for i in range(100)
    )

and here is how you might use the sync version:

import requests
from redis import Redis

from limiters import SyncTokenBucket


limiter = SyncTokenBucket(
    name="foo",
    capacity=5,
    refill_frequency=1,
    refill_amount=1,
    max_sleep=30,
    connection=Redis.from_url("redis://localhost:6379"),
)

def main():
    with limiter:
        requests.get(...)

Using them as a decorator

We don't ship decorators in the package, but if you would like to limit the rate at which a whole function is run, you can create your own, like this:

from limiters import AsyncSemaphore


# Define a decorator function
def limit(name, capacity):
  def middle(f):
    async def inner(*args, **kwargs):
      async with AsyncSemaphore(name=name, capacity=capacity):
        return await f(*args, **kwargs)
    return inner
  return middle


# Then pass the relevant limiter arguments like this
@limit(name="foo", capacity=5)
def fetch_foo(id: UUID) -> Foo:

Contributing

Contributions are very welcome. Here's how to get started:

• Set up a Python 3.11+ venv, and pip install poetry
• Install dependencies with poetry install
• Run pre-commit install to set up pre-commit
• Install just and run just setup If you prefer not to install just, just take a look at the justfile and run the commands yourself.
• Make your code changes, with tests
• Commit your changes and open a PR

Publishing a new version

To publish a new version:

• Update the package version in the pyproject.toml
• Open Github releases
• Press "Draft a new release"
• Set a tag matching the new version (for example, v0.4.2)
• Set the title matching the tag
• Add some release notes, explaining what has changed
• Publish

Once the release is published, our publish workflow should be triggered to push the new version to PyPI.