upstash-ratelimit 1.1.0

Serverless ratelimiting package from Upstash

Upstash Ratelimit Python SDK

upstash-ratelimit is a connectionless rate limiting library for Python, designed to be used in serverless environments such as:

• AWS Lambda
• Vercel Serverless
• Google Cloud Functions
• and other environments where HTTP is preferred over TCP.

The SDK is currently compatible with Python 3.8 and above.

• Upstash Ratelimit Python SDK
• Quick Start
  • Install
  • Create database
  • Usage
  • Block until ready
  • Using multiple limits
• Ratelimiting algorithms
  • Fixed Window
    • Pros
    • Cons
    • Usage
  • Sliding Window
    • Pros
    • Cons
    • Usage
  • Token Bucket
    • Pros
    • Cons
    • Usage
• Contributing
  • Preparing the environment
  • Running tests

Quick Start

Install

pip install upstash-ratelimit

Create database

To be able to use upstash-ratelimit, you need to create a database on Upstash.

Usage

For possible Redis client configurations, have a look at the Redis SDK repository.

This library supports asyncio as well. To use it, import the asyncio-based variant from the upstash_ratelimit.asyncio module.

from upstash_ratelimit import Ratelimit, FixedWindow
from upstash_redis import Redis

# Create a new ratelimiter, that allows 10 requests per 10 seconds
ratelimit = Ratelimit(
    redis=Redis.from_env(),
    limiter=FixedWindow(max_requests=10, window=10),
    # Optional prefix for the keys used in Redis. This is useful
    # if you want to share a Redis instance with other applications
    # and want to avoid key collisions. The default prefix is
    # "@upstash/ratelimit"
    prefix="@upstash/ratelimit",
)

# Use a constant string to limit all requests with a single ratelimit
# Or use a user ID, API key or IP address for individual limits.
identifier = "api"
response = ratelimit.limit(identifier)

if not response.allowed:
    print("Unable to process at this time")
else:
    do_expensive_calculation()
    print("Here you go!")

The limit method also returns the following metadata:

@dataclasses.dataclass
class Response:
    allowed: bool
    """
    Whether the request may pass(`True`) or exceeded the limit(`False`)
    """

    limit: int
    """
    Maximum number of requests allowed within a window.
    """

    remaining: int
    """
    How many requests the user has left within the current window.
    """

    reset: float
    """
    Unix timestamp in seconds when the limits are reset
    """

Block until ready

You also have the option to try and wait for a request to pass in the given timeout.

It is very similar to the limit method and takes an identifier and returns the same response. However if the current limit has already been exceeded, it will automatically wait until the next window starts and will try again. Setting the timeout parameter (in seconds) will cause the method to block a finite amount of time.

from upstash_ratelimit import Ratelimit, SlidingWindow
from upstash_redis import Redis

# Create a new ratelimiter, that allows 10 requests per 10 seconds
ratelimit = Ratelimit(
    redis=Redis.from_env(),
    limiter=SlidingWindow(max_requests=10, window=10),
)

response = ratelimit.block_until_ready("id", timeout=30)

if not response.allowed:
    print("Unable to process, even after 30 seconds")
else:
    do_expensive_calculation()
    print("Here you go!")

Using multiple limits

Sometimes you might want to apply different limits to different users. For example you might want to allow 10 requests per 10 seconds for free users, but 60 requests per 10 seconds for paid users.

Here's how you could do that:

from upstash_ratelimit import Ratelimit, SlidingWindow
from upstash_redis import Redis

class MultiRL:
    def __init__(self) -> None:
        redis = Redis.from_env()
        self.free = Ratelimit(
            redis=redis,
            limiter=SlidingWindow(max_requests=10, window=10),
            prefix="ratelimit:free",
        )

        self.paid = Ratelimit(
            redis=redis,
            limiter=SlidingWindow(max_requests=60, window=10),
            prefix="ratelimit:paid",
        )

# Create a new ratelimiter, that allows 10 requests per 10 seconds
ratelimit = MultiRL()

ratelimit.free.limit("userIP")
ratelimit.paid.limit("userIP")

Ratelimiting algorithms

Fixed Window

This algorithm divides time into fixed durations/windows. For example each window is 10 seconds long. When a new request comes in, the current time is used to determine the window and a counter is increased. If the counter is larger than the set limit, the request is rejected.

Pros

• Very cheap in terms of data size and computation
• Newer requests are not starved due to a high burst in the past

Cons

• Can cause high bursts at the window boundaries to leak through
• Causes request stampedes if many users are trying to access your server, whenever a new window begins

Usage

from upstash_ratelimit import Ratelimit, FixedWindow
from upstash_redis import Redis

ratelimit = Ratelimit(
    redis=Redis.from_env(),
    limiter=FixedWindow(max_requests=10, window=10),
)

Sliding Window

Builds on top of fixed window but instead of a fixed window, we use a rolling window. Take this example: We have a rate limit of 10 requests per 1 minute. We divide time into 1 minute slices, just like in the fixed window algorithm. Window 1 will be from 00:00:00 to 00:01:00 (HH:MM:SS). Let's assume it is currently 00:01:15 and we have received 4 requests in the first window and 5 requests so far in the current window. The approximation to determine if the request should pass works like this:

limit = 10

# 4 request from the old window, weighted + requests in current window
rate = 4 * ((60 - 15) / 60) + 5 = 8

return rate < limit # True means we should allow the request

Pros

• Solves the issue near boundary from fixed window.

Cons

• More expensive in terms of storage and computation
• It's only an approximation because it assumes a uniform request flow in the previous window

Usage

from upstash_ratelimit import Ratelimit, SlidingWindow
from upstash_redis import Redis

ratelimit = Ratelimit(
    redis=Redis.from_env(),
    limiter=SlidingWindow(max_requests=10, window=10),
)

Token Bucket

Consider a bucket filled with maximum number of tokens that refills constantly at a rate per interval. Every request will remove one token from the bucket and if there is no token to take, the request is rejected.

Pros

• Bursts of requests are smoothed out and you can process them at a constant rate.
• Allows setting a higher initial burst limit by setting maximum number of tokens higher than the refill rate

Cons

• Expensive in terms of computation

Usage

from upstash_ratelimit import Ratelimit, TokenBucket
from upstash_redis import Redis

ratelimit = Ratelimit(
    redis=Redis.from_env(),
    limiter=TokenBucket(max_tokens=10, refill_rate=5, interval=10),
)

Custom Rates

When rate limiting, you may want different requests to consume different amounts of tokens. This could be useful when processing batches of requests where you want to rate limit based on items in the batch or when you want to rate limit based on the number of tokens.

To achieve this, you can simply pass rate parameter when calling the limit method:

from upstash_ratelimit import Ratelimit, FixedWindow
from upstash_redis import Redis

ratelimit = Ratelimit(
    redis=Redis.from_env(),
    limiter=FixedWindow(max_requests=10, window=10),
)

# pass rate as 5 to subtract 5 from the number of
# allowed requests in the window:
identifier = "api"
response = ratelimit.limit(identifier, rate=5)

Contributing

Preparing the environment

This project uses Poetry for packaging and dependency management. Make sure you are able to create the poetry shell with relevant dependencies.

You will also need a database on Upstash.

Running tests

To run all the tests, make sure the poetry virtual environment activated with all the necessary dependencies. Set the UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN environment variables and run:

poetry run pytest