limitor 0.3.1

Rate limiting package

Rate Limiting Algorithms

[!IMPORTANT] This is just for my own knowledge. Please do not use this if you stumble upon it.

Algorithms

Algorithms	Sync	Async
Leaky Bucket	Yes	Yes
Token Bucket	Yes	TBD
Generic Cell Rate Algorithm	Yes	TBD
LLM-Token	TBD	TBD

[!NOTE]
Implementations will be single-threaded, blocking requests (or the equivalent) with burst capabilities. With asyncio, we use non-blocking cooperative multitasking, not preemptive multi-threading

[!NOTE] All algorithms default to traffic shaping patterns as opposed to traffic policing. This means that transmitted pieces of data are not dropped and we wait until the request can be completed barring a timeout.

Development

Setup uv-based virtual environment

# Install uv
# for a mac or linux
brew install uv
# OPTIONAL: or
curl -LsSf https://astral.sh/uv/install.sh | sh

# python version are automatically downloaded as needed or: uv python install 3.12
uv venv financials --python 3.12


# to activate the virtual environment
source .venv/bin/activate

# to deactivate the virtual environment
deactivate

Create lock file + requirements.txt

# after pyproject.toml is created
uv lock

uv export -o requirements.txt --quiet

Upgrade dependencies

# can use sync or lock
uv sync --upgrade

or 

# to upgrade a specific package
uv lock --upgrade-package requests

Generating Docs

Enable GitHub Pages

1. On GitHub go to “Settings” -> “Pages”.

2. In the “Source” section, choose “Deploy from a branch” in the dropdown menu.

3. In the “Branch” section choose “gh-pages” and “/root” in the dropdown menus and click save.

You should now be able to verify the pages deployment in the Actions list.

Usage

LLM Token-Based Rate Limiting

import random
import time
from typing import Callable

from limitor.base import SyncRateLimit
from limitor.configs import BucketConfig
from limitor.leaky_bucket.core import SyncLeakyBucket


def rate_limit(capacity: int = 10, seconds: float = 1, bucket_cls: type[SyncRateLimit] = SyncLeakyBucket) -> Callable:
    bucket = bucket_cls(BucketConfig(capacity=capacity, seconds=seconds))

    def decorator(func):
        def wrapper(*args, **kwargs):
            amount = kwargs.get("amount", 1)
            bucket.acquire(amount=amount)
            return func(*args, **kwargs)
        return wrapper

    return decorator

# limit of 100,000 tokens per second

@rate_limit(capacity=100_000, seconds=1)
def process_request(amount=1):
    print(f"This is a rate-limited function: {time.strftime('%X')} - {amount} tokens")

for _ in range(100):
    # generate random prompt tokens between 5,000 and 30,000 for 100 sample requests
    llm_prompt_tokens = random.randint(5_000, 30_000)
    try:
        process_request(amount=llm_prompt_tokens)
    except Exception as error:
        print(f"Rate limit exceeded: {error}")

With User-Specific Rate Limits + Cache

import time
from typing import Optional

from cachetools import LRUCache, TTLCache

from limitor.base import SyncRateLimit
from limitor.configs import BucketConfig
from limitor.leaky_bucket.core import (
    AsyncLeakyBucket,
    SyncLeakyBucket,
)


def _get_user_cache(max_users, ttl):
    if ttl is not None:
        return TTLCache(maxsize=max_users, ttl=ttl)
    return LRUCache(maxsize=max_users)

def rate_limit_per_user(capacity=10, seconds=1, max_users=1000, ttl=None, bucket_cls: type[SyncRateLimit] = SyncLeakyBucket):
    buckets = _get_user_cache(max_users, ttl)
    global_bucket = bucket_cls(BucketConfig(capacity=capacity, seconds=seconds))

    def decorator(func):
        # optional use_id. if not set, it will default to a regular global rate limiter
        # if user_id is not set, this means the max_users / ttl parameters will be ignored
        def wrapper(*args, user_id=None, **kwargs):
            if user_id is None:
                bucket = global_bucket
            else:
                if user_id not in buckets:
                    buckets[user_id] = bucket_cls(BucketConfig(capacity=capacity, seconds=seconds))
                bucket = buckets[user_id]
            with bucket:
                return func(user_id, *args, **kwargs)

        return wrapper

    return decorator

@rate_limit_per_user(capacity=2, seconds=1, max_users=3, ttl=600)  # TTLCache: 10 min/user
def something_user(user_id):
    print(f"User {user_id} called at {time.strftime('%X')}")

for _ in range(20):
    try:
        x = 1 if _ % 2 == 0 else 0
        something_user(user_id=x)
    except Exception as error:
        print(f"Rate limit exceeded: {error}")

TODO: cleanup

[!NOTE] All of the below algorithms should produce identical results with identical parameters

Leaky Bucket

Synchronous

# no context manager, use directly

import time

from limitor.configs import BucketConfig
from limitor.leaky_bucket.core import SyncLeakyBucket

# 4 requests per 2 seconds and a 4 second burst capacity
config = BucketConfig(capacity=4, seconds=2)
sync_bucket = SyncLeakyBucket(config)
for i in range(7):
    sync_bucket.acquire(1)
    print(f"Current level: {sync_bucket._bucket_level}")
    time.sleep(0.3)  # Simulate some work being done

print("Waiting for bucket to leak...")
time.sleep(1)  # check how much leaks out of the bucket in 1 second
sync_bucket._leak()  # update the bucket level after waiting
print(f"Current level after leaking: {sync_bucket._bucket_level}")

# context manager

import time

from limitor.configs import BucketConfig
from limitor.leaky_bucket.core import SyncLeakyBucket

# 4 requests per 2 seconds and a 4 second burst capacity
config = BucketConfig(capacity=4, seconds=2)
context_sync = SyncLeakyBucket(config)  # use the same config as above
for _ in range(10):
    with context_sync as thing:
        print(f"Acquired 1 unit using context manager: {thing._bucket_level}")
        print(f"Current level {_} sent at {time.strftime('%X')}")
        time.sleep(0.3)  # simulate some work being done
print("Exited context manager.", context_sync._bucket_level)
# wait 1 second to let the bucket leak: should lower level from 4 --> 2
# our leak rate is 4 per 2 seconds aka 2 per second; hence, after 1 second, we should have 2 left in the bucket
time.sleep(1)
context_sync._leak()  # update the bucket level after waiting -- just to illustrate the leak
print(f"Current level after waiting 1 second: {context_sync._bucket_level}")

Token Bucket

Synchronous - similar to the above examples

# context manager

import time

from limitor.configs import BucketConfig
from limitor.token_bucket.core import SyncTokenBucket

# 4 requests per 2 seconds and a 4 second burst capacity
config = BucketConfig(capacity=4, seconds=2)
context_sync = SyncTokenBucket(config)  # use the same config as above
for _ in range(10):
    with context_sync as thing:
        print(f"Acquired 1 unit using context manager: {thing._bucket_level}")
        print(f"Current level {_} sent at {time.strftime('%X')}")
        # time.sleep(0.3)  # simulate some work being done
print("Exited context manager.", context_sync._bucket_level)
# wait 1 second to let the bucket leak: should lower level from 4 --> 2
# our leak rate is 4 per 2 seconds aka 2 per second; hence, after 1 second, we should have 2 left in the bucket
time.sleep(1)
context_sync._fill()  # update the bucket level after waiting -- just to illustrate the leak
print(f"Current level after waiting 1 second: {context_sync._bucket_level}")

time.sleep(1)
context_sync._fill()
print(f"Current level after waiting 1 second: {context_sync._bucket_level}")

Generic Cell Rate Algorithm

[!NOTE] Can be either the virtual scheduling algorithm or the continuous leaky bucket algorithm

# context manager

from datetime import datetime

from limitor.configs import BucketConfig
from limitor.generic_cell_rate.core import (
    SyncLeakyBucketGCRA,
    SyncVirtualSchedulingGCRA,
)

# 3 requests per 1.5 seconds and a 3 second burst capacity
config = BucketConfig(capacity=3, seconds=1.5)
context_sync = SyncLeakyBucketGCRA(config)  # can swap with VirtualSchedulingGCRA
for _ in range(12):
    with context_sync as thing:
        print(f"Current level {_} sent at {datetime.now().strftime('%X.%f')}")

# no context manager, use directly

from datetime import datetime

from limitor.configs import BucketConfig
from limitor.generic_cell_rate.core import (
    SyncLeakyBucketGCRA,
    SyncVirtualSchedulingGCRA,
)

# 10 requests per 5 seconds and a 10 second burst capacity
config = BucketConfig(capacity=10, seconds=5)
sync_bucket = SyncLeakyBucketGCRA(config)  # can swap with SyncVirtualSchedulingGCRA
for i in range(12):
    if i % 2 == 0:
        sync_bucket.acquire(1)
    else:
        sync_bucket.acquire(2)
    print(f"Current level {i + 1} sent at {datetime.now().strftime('%X.%f')}")

Async Rate Limiting

Leaky Bucket

import asyncio
import time

from limitor.configs import BucketConfig
from limitor.leaky_bucket.core import AsyncLeakyBucket


async def main():
    bucket = AsyncLeakyBucket(BucketConfig(capacity=2, seconds=2))
    for i in range(10):
        await bucket.acquire()
        print(f"Request {i + 1} allowed at {time.strftime('%X')}")


asyncio.run(main())

uneven requests

import asyncio
import time

from limitor.configs import BucketConfig
from limitor.leaky_bucket.core import AsyncLeakyBucket


async def request(bucket, amount, idx):
    await bucket.acquire(amount)
    print(f"Request {idx} (amount={amount}) allowed at {time.strftime('%X')}")


async def main():
    bucket = AsyncLeakyBucket(BucketConfig(capacity=3, seconds=3), max_concurrent=5)
    amounts = [1, 3, 2, 1, 2, 3, 1]
    tasks = [
        asyncio.create_task(request(bucket, amt, i))
        for i, amt in enumerate(amounts, 1)
    ]
    await asyncio.gather(*tasks)


asyncio.run(main())

Async HTTP Requests

import asyncio
import random
import time

import httpx

from limitor.configs import BucketConfig
from limitor.extra.leaky_bucket.core import AsyncLeakyBucket


async def fetch_url(bucket, client, url, idx, timeout):
    try:
        await bucket.acquire(timeout=timeout)
        response = await client.get(url, timeout=timeout)
        text = response.text
        print(f"Request {idx} succeeded: {len(text)} bytes at {time.strftime('%X')}")
    except asyncio.TimeoutError:
        print(f"Request {idx} timed out by rate limiter at {time.strftime('%X')}")
    except Exception as e:
        print(f"Request {idx} failed: {e}")


async def main():
    bucket = AsyncLeakyBucket(BucketConfig(capacity=2, seconds=2))
    urls = [
        "https://example.com",
        "https://httpbin.org/get",
        "https://httpbin.org/delay/1",
        "https://httpbin.org/delay/2",
        "https://example.com",
        "https://httpbin.org/get"
    ]
    async with httpx.AsyncClient() as client:
        tasks = [
            fetch_url(bucket, client, url, idx, random.uniform(0.5, 2.5))
            for idx, url in enumerate(urls, 1)
        ]
        await asyncio.gather(*tasks)
    await bucket.shutdownß()

    
asyncio.run(main())