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Python rate limiting library using Redis for shared state.

Project description

Red Bucket

Python rate limiting library using Redis for shared state.


To install the latest released version:

pip install redbucket

To install the current development version from the master branch on GitHub:

pip install -U git+


The following snippet configures a rate limiter with two rate limiting zones:

from redbucket import RedisRateLimiter, RateLimit, Zone
from redis import Redis

# Accept up to 5 requests per user per second.
# Allow bursts of up to 10 requests.
user_zone = Zone('user', rate=5)
user_limit = RateLimit(user_zone, burst=10)

# Accept up to 20 requests per IP per second.
# Allow up to 10 excess requests with a delay.
ip_zone = Zone('ip', rate=20)
ip_limit = RateLimit(ip_zone, delay=10)

redis = Redis()
rate_limiter = RedisRateLimiter(redis)
rate_limiter.configure(user=user_limit, ip=ip_limit)

We can now use the rate limiter as follows:

def example_operation(user, ip_address):
    response = rate_limiter.request(user=user, ip=ip_address)
    if not response.accepted:
        raise Exception("Rate limit exceeded")  # Reject request
    if response.delay > 0:
        sleep(response.delay)  # Wait for delay seconds
    ...  # Perform operation

Note that we don't have to specify a key for every zone. For example, we could exclude certain IP addresses from IP rate limiting while still applying the user rate limit, as follows:

def example_operation(user, ip_address):
    if is_whitelisted(ip_address):
        response = rate_limiter.acquire(user=user)
        response = rate_limiter.acquire(user=user, ip=ip_address)


The default RedisRateLimiter uses Redis's Lua script engine to atomically update rate limiter state. This implementation requires Redis 3.2 or greater. For older Redis versions, you can use the RedisTransactionalRateLimiter.

Where supported, the script-based implementation is recommended as it handles each rate limiting request in a single round-trip to the Redis server, whereas the transactional implementation performs several consecutive Redis commands per request.

State encoding

By default, rate limiter state is stored in Redis using a packed binary representation. You can switch to JSON for a less efficient but more human-readable encoding.

rate_limiter = RedisRateLimiter(redis, codec='json')

Rate limiting model

Red Bucket uses a rate limiting model inspired by Nginx. Rate limiting state is stored in one or more zones. Each zone has a name and a base rate. A zone represents a namespace of rate limiting keys. A rate limit references a zone along with two parameters, burst and delay. A rate limiter references one or more rate limits.

To apply rate limiting to an operation, the application makes a request to the rate limiter by specifying a key for each desired rate limit. The rate limiter evaluates each rate limit to determine whether to reject, delay, or immediately accept the request. If using multiple rate limits, the most restrictive outcome will be applied.

For a rate limit with base rate r, burst value b, and delay value d, the rate limiter maintains a virtual counter for each key that continuously increases at a rate of r requests per second up to a maximum value of b + 1. When a request arrives, the rate limiter checks the current value of the counter v. If v − 1 ≥ 0, the rate limiter decrements the counter by 1 and accepts the request immediately. If v − 1 ≥ −d, the rate limiter decrements the counter by 1 and accepts the request after a delay of (v − 1) / r seconds. Otherwise, i.e. if v − 1 < −d, the rate limiter rejects the request.

In practice, this means that from an initial idle state for a given key, the rate limiter will allow a burst of b + 1 requests immediately, throttle the next d requests to the desired rate, and reject any further requests past the limit.

Comparison with Nginx

As noted above, Red Bucket's rate limiting model is inspired by Nginx and uses a similar algorithm. However, the way burst and delay are specified is a little different. The following configuration examples illustrate the difference.

  • Basic rate limiting

    # nginx
    limit_req zone=mylimit;
    # redbucket
  • Burst with delay

    # nginx
    limit_req zone=mylimit burst=20;
    # redbucket
    RateLimit(zone=mylimit, delay=20)
  • Burst with no delay

    # nginx
    limit_req zone=mylimit burst=20 nodelay;
    # redbucket
    RateLimit(zone=mylimit, burst=20)
  • Two-stage rate limiting

    # nginx
    limit_req zone=ip burst=12 delay=8;
    # redbucket
    RateLimit(zone=mylimit, burst=8, delay=4)


This project uses Tox to manage virtual environments for unit tests and other checks. Unit tests are written using the Pytest framework.

The unit tests require a Redis instance. By default, the tests attempt to connect to port 6379 of localhost. This can be overridden by setting the REDIS_URL environment variable.

export REDIS_URL=redis://localhost:6379

To start a Redis Docker container for running the tests, you can use the script. For example, to run the tests against a Docker redis instance, you can run:

./ tox

You can also run the script without a command and execute output to set environment variables in your current shell.

eval "$(./"

By default, the script uses the redis:alpine image. You can supply a different tag for the redis image with the -t flag, or a different image name with -i.

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