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⏲️ Easy rate limiting for Python. Rate limiting async and thread-safe decorators and context managers that use a token bucket algorithm.

Project description

⏲️ Easy rate limiting for Python

limiter makes it easy to add rate limiting to Python projects, using a token bucket algorithm. limiter can provide Python projects and scripts with:

Here are some features and benefits of using limiter:

Example

Here's an example of using a limiter as a decorator and context manager:

from aiohttp import ClientSession
from limiter import Limiter


limit_downloads = Limiter(rate=2, capacity=5, consume=2)


@limit_downloads
async def download_image(url: str) -> bytes:
  async with ClientSession() as session, session.get(url) as response:
    return await response.read()


async def download_page(url: str) -> str:
  async with (
    ClientSession() as session,
    limit_downloads,
    session.get(url) as response
  ):
    return await response.text()

Usage

You can define limiters and use them dynamically across your project.

Note: If you're using Python version 3.9.x or below, check out the documentation for version 0.2.0 of limiter here.

Limiter instances

Limiter instances take rate, capacity and consume arguments.

  • rate is the token replenishment rate per second. Tokens are automatically added every second.
  • consume is the amount of tokens consumed from the token bucket upon successfully taking tokens from the bucket.
  • capacity is the total amount of tokens the token bucket can hold. Token replenishment stops when this capacity is reached.

Limiting blocks of code

limiter can rate limit all Python callables, and limiters can be used as context managers.

You can define a limiter with a set refresh rate and total token capacity. You can set the amount of tokens to consume dynamically with consume, and the bucket parameter sets the bucket to consume tokens from:

from limiter import Limiter


REFRESH_RATE: int = 2
BURST_RATE: int = 3
MSG_BUCKET: str = 'messages'

limiter: Limiter = Limiter(rate=REFRESH_RATE, capacity=BURST_RATE)
limit_msgs: Limiter = limiter(bucket=MSG_BUCKET)


@limiter
def download_page(url: str) -> bytes:
  ...


@limiter(consume=2)
async def download_page(url: str) -> bytes:
  ...


def send_page(page: bytes):
  with limiter(consume=1.5, bucket=MSG_BUCKET):
    ...


async def send_page(page: bytes):
  async with limit_msgs:
    ...


@limit_msgs(consume=3)
def send_email(to: str):
  ...


async def send_email(to: str):
  async with limiter(bucket=MSG_BUCKET):
    ...

In the example above, both limiter and limit_msgs share the same limiter. The only difference is that limit_msgs will take tokens from the MSG_BUCKET bucket by default.

assert limiter.limiter is limit_msgs.limiter
assert limiter.bucket != limit_msgs.bucket
assert limiter != limit_msgs

Creating new limiters

You can reuse existing limiters in your code, and you can create new limiters from the parameters of an existing limiter using the new() method.

Or, you can define a new limiter entirely:

# you can reuse existing limiters
limit_downloads: Limiter = limiter(consume=2)

# you can use the settings from an existing limiter in a new limiter
limit_downloads: Limiter = limiter.new(consume=2)

# or you can simply define a new limiter
limit_downloads: Limiter = Limiter(REFRESH_RATE, BURST_RATE, consume=2)


@limit_downloads
def download_page(url: str) -> bytes:
  ...


@limit_downloads
async def download_page(url: str) -> bytes:
  ...


def download_image(url: str) -> bytes:
  with limit_downloads:
    ...


async def download_image(url: str) -> bytes:
  async with limit_downloads:
    ...

Let's look at the difference between reusing an existing limiter, and creating new limiters with the new() method:

limiter_a: Limiter = limiter(consume=2)
limiter_b: Limiter = limiter.new(consume=2)
limiter_c: Limiter = Limiter(REFRESH_RATE, BURST_RATE, consume=2)

assert limiter_a != limiter
assert limiter_a != limiter_b != limiter_c

assert limiter_a != limiter_b
assert limiter_a.limiter is limiter.limiter
assert limiter_a.limiter is not limiter_b.limiter

assert limiter_a.attrs == limiter_b.attrs == limiter_c.attrs

The only things that are equivalent between the three new limiters above are the limiters' attributes, like the rate, capacity, and consume attributes.

Creating anonymous, or single-use, limiters

You don't have to assign Limiter objects to variables. Anonymous limiters don't share a token bucket like named limiters can. They work well when you don't have a reason to share a limiter between two or more blocks of code, and when a limiter has a single or independent purpose.

limiter, after version v0.3.0, ships with a limit type alias for Limiter:

from limiter import limit


@limit(capacity=2, consume=2)
async def send_message():
  ...


async def upload_image():
  async with limit(capacity=3) as limiter:
    ...

The above is equivalent to the below:

from limiter import Limiter


@Limiter(capacity=2, consume=2)
async def send_message():
  ...


async def upload_image():
  async with Limiter(capacity=3) as limiter:
    ...

Both limit and Limiter are the same object:

assert limit is Limiter

Jitter

A Limiter's jitter argument adds jitter to help with contention.

The value is in units, which is milliseconds by default, and can be any of these:

  • False, to add no jitter. This is the default.
  • True, to add a random amount of jitter.
  • A number, to add a fixed amount of jitter.
  • A range object, to add a random amount of jitter within the range.
  • A tuple of two numbers, start and stop, to add a random amount of jitter between the two numbers.
  • A tuple of three numbers: start, stop and step, to add jitter like you would with range.

For example, if you want to use a random amount of jitter between 0 and 100 milliseconds:

limiter = Limiter(rate=2, capacity=5, consume=2, jitter=(0, 100))
limiter = Limiter(rate=2, capacity=5, consume=2, jitter=(0, 100, 1))
limiter = Limiter(rate=2, capacity=5, consume=2, jitter=range(0, 100))
limiter = Limiter(rate=2, capacity=5, consume=2, jitter=range(0, 100, 1))

All of the above are equivalent to each other in function.

You can also supply values for jitter when using decorators or context-managers:

limiter = Limiter(rate=2, capacity=5, consume=2)


@limiter(jitter=range(0, 100))
def download_page(url: str) -> bytes:
  ...


async def download_page(url: str) -> bytes:
    async with limiter(jitter=(0, 100)):
        ...

You can use the above to override default values of jitter in a Limiter instance.

To add a small amount of random jitter, supply True as the value:

limiter = Limiter(rate=2, capacity=5, consume=2, jitter=True)

# or

@limiter(jitter=True)
def download_page(url: str) -> bytes:
  ...

To turn off jitter in a Limiter configured with jitter, you can supply False as the value:

limiter = Limiter(rate=2, capacity=5, consume=2, jitter=range(10))


@limiter(jitter=False)
def download_page(url: str) -> bytes:
  ...


async def download_page(url: str) -> bytes:
    async with limiter(jitter=False):
        ...

Or create a new limiter with jitter turned off:

limiter: Limiter = limiter.new(jitter=False)

Units

units is a number representing the amount of units in one second. The default value is 1000 for 1,000 milliseconds in one second.

Similar to jitter, units can be supplied at all the same call sites and constructors that jitter is accepted.

If you want to use a different unit than milliseconds, supply a different value for units.

Installation

Requirements

Install via PyPI

$ python3 -m pip install limiter

License

See LICENSE. If you'd like to use this project with a different license, please get in touch.

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