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# PyrateLimiter
The request rate limiter using Leaky-bucket Algorithm.
Full project documentation can be found at [pyratelimiter.readthedocs.io](https://pyratelimiter.readthedocs.io).
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<br>
## Contents
- [Features](#features)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [limiter_factory](#limiter_factory)
- [Examples](#examples)
- [Basic usage](#basic-usage)
- [Key concepts](#key-concepts)
- [Defining rate limits & buckets](#defining-rate-limits-and-buckets)
- [Defining clock & routing logic](#defining-clock--routing-logic-with-bucketfactory)
- [Wrapping all up with Limiter](#wrapping-all-up-with-limiter)
- [asyncio and event loops](#asyncio-and-event-loops)
- [Decorators](#as_decorator-use-limiter-as-decorator)
- [Limiter API](#limiter-api)
- [Weight](#weight)
- [Handling exceeded limits](#handling-exceeded-limits)
- [Bucket analogy](#bucket-analogy)
- [Rate limit exceptions](#rate-limit-exceptions)
- [Rate limit delays](#rate-limit-delays)
- [Backends](#backends)
- [InMemoryBucket](#inmemorybucket)
- [MultiprocessBucket](#multiprocessbucket)
- [SQLiteBucket](#sqlitebucket)
- [RedisBucket](#redisbucket)
- [PostgresBucket](#postgresbucket)
- [BucketAsyncWrapper](#bucketasyncwrapper)
- [Async or Sync?](#async-or-sync)
- [Advanced Usage](#advanced-usage)
- [Component-level Diagram](#component-level-diagram)
- [Time sources](#time-sources)
- [Leaking](#leaking)
- [Concurrency](#concurrency)
- [Custom backend](#custom-backend)
## Features
- Supports unlimited rate limits and custom intervals.
- Separately tracks limits for different services or resources.
- Manages limit breaches by raising exceptions or applying delays.
- Offers multiple usage modes: direct calls or decorators.
- Fully compatible with both synchronous and asynchronous workflows.
- Provides SQLite and Redis backends for persistent limit tracking across threads or restarts.
- Includes MultiprocessBucket and SQLite File Lock backends for multiprocessing environments.
## Installation
**PyrateLimiter** supports **python ^3.8**
Install using pip:
```
pip install pyrate-limiter
```
Or using conda:
```
conda install --channel conda-forge pyrate-limiter
```
## Quickstart
To limit 5 requests within 2 seconds and raise an exception when the limit is exceeded:
```python
from pyrate_limiter import Duration, Rate, Limiter, BucketFullException
limiter = Limiter(Rate(5, Duration.SECOND * 2))
for i in range(6):
try:
limiter.try_acquire(i)
except BucketFullException as err:
print(err, err.meta_info)
```
## limiter_factory
[limiter_factory.py](pyrate_limiter.limiter_factory.py) provides several functions to simplify common cases:
- create_sqlite_limiter(rate_per_duration: int, duration: Duration, ...)
- create_inmemory_limiter(rate_per_duration: int, duration: Duration, ...)
- + more to be added...
## Examples
- Rate limiting asyncio tasks: [asyncio_ratelimit.py](examples/asyncio_ratelimit.py)
- Rate limiting asyncio tasks w/ a decorator: [asyncio_decorator.py](examples/asyncio_decorator.py)
- HTTPX rate limiting - asyncio, single process and multiprocess examples [httpx_ratelimiter.py](examples/httpx_ratelimiter.py)
- Multiprocessing using an in-memory rate limiter - [in_memory_multiprocess.py](examples/in_memory_multiprocess.py)
- Multiprocessing using SQLite and a file lock - this can be used for distributed processes not created within a multiprocessing [sql_filelock_multiprocess.py](examples/sql_filelock_multiprocess.py)
## Basic Usage
### Key concepts
#### Clock
- Timestamps incoming items
#### Bucket
- Stores items with timestamps.
- Functions as a FIFO queue.
- Can `leak` to remove outdated items.
#### BucketFactory
- Manages buckets and clocks, routing items to their appropriate buckets.
- Schedules periodic `leak` operations to prevent overflow.
- Allows custom logic for routing, conditions, and timing.
#### Limiter
- Provides a simple, intuitive API by abstracting underlying logic.
- Seamlessly supports both sync and async contexts.
- Offers multiple interaction modes: direct calls, decorators, and (future) context managers.
- Ensures thread-safety via RLock, and if needed, asyncio concurrency via asyncio.Lock
### Defining rate limits and buckets
For example, an API (like LinkedIn or GitHub) might have these rate limits:
```
- 500 requests per hour
- 1000 requests per day
- 10000 requests per month
```
You can define these rates using the `Rate` class. `Rate` class has 2 properties only: **limit** and **interval**
```python
from pyrate_limiter import Duration, Rate
hourly_rate = Rate(500, Duration.HOUR) # 500 requests per hour
daily_rate = Rate(1000, Duration.DAY) # 1000 requests per day
monthly_rate = Rate(10000, Duration.WEEK * 4) # 10000 requests per month
rates = [hourly_rate, daily_rate, monthly_rate]
```
Rates must be properly ordered:
- Rates' intervals & limits must be ordered from least to greatest
- Rates' ratio of **limit/interval** must be ordered from greatest to least
Buckets validate rates during initialization. If using a custom implementation, use the built-in validator:
```python
from pyrate_limiter import validate_rate_list
assert validate_rate_list(my_rates)
```
Then, add the rates to the bucket of your choices
```python
from pyrate_limiter import InMemoryBucket, RedisBucket
basic_bucket = InMemoryBucket(rates)
# Or, using redis
from redis import Redis
redis_connection = Redis(host='localhost')
redis_bucket = RedisBucket.init(rates, redis_connection, "my-bucket-name")
# Async Redis would work too!
from redis.asyncio import Redis
redis_connection = Redis(host='localhost')
redis_bucket = await RedisBucket.init(rates, redis_connection, "my-bucket-name")
```
If you only need a single Bucket for everything, and python's built-in `time()` is enough for you, then pass the bucket to Limiter then ready to roll!
```python
from pyrate_limiter import Limiter
# Limiter constructor accepts single bucket as the only parameter,
# the rest are 3 optional parameters with default values as following
# Limiter(bucket, clock=TimeClock(), raise_when_fail=True, max_delay=None)
limiter = Limiter(bucket)
# Limiter is now ready to work!
limiter.try_acquire("hello world")
```
If you want to have finer grain control with routing & clocks etc, then you should use `BucketFactory`.
### Defining Clock & routing logic with BucketFactory
When multiple bucket types are needed and items must be routed based on certain conditions, use `BucketFactory`.
First, define your clock (time source). Most use cases work with the built-in clocks:
```python
from pyrate_limiter.clock import TimeClock, MonotonicClock, SQLiteClock
base_clock = TimeClock()
```
PyrateLimiter does not assume routing logic, so you implement a custom BucketFactory. At a minimum, these two methods must be defined:
```python
from pyrate_limiter import BucketFactory
from pyrate_limiter import AbstractBucket
class MyBucketFactory(BucketFactory):
# You can use constructor here,
# nor it requires to make bucket-factory work!
def wrap_item(self, name: str, weight: int = 1) -> RateItem:
"""Time-stamping item, return a RateItem"""
now = clock.now()
return RateItem(name, now, weight=weight)
def get(self, _item: RateItem) -> AbstractBucket:
"""For simplicity's sake, all items route to the same, single bucket"""
return bucket
```
### Creating buckets dynamically
If more than one bucket is needed, the bucket-routing logic should go to BucketFactory `get(..)` method.
When creating buckets dynamically, it is needed to schedule leak for each newly created buckets.
To support this, BucketFactory comes with a predefined method call `self.create(..)`. It is meant to create the bucket and schedule that bucket for leaking using the Factory's clock
```python
def create(
self,
clock: AbstractClock,
bucket_class: Type[AbstractBucket],
*args,
**kwargs,
) -> AbstractBucket:
"""Creating a bucket dynamically"""
bucket = bucket_class(*args, **kwargs)
self.schedule_leak(bucket, clock)
return bucket
```
By utilizing this, we can modify the code as following:
```python
class MultiBucketFactory(BucketFactory):
def __init__(self, clock):
self.clock = clock
self.buckets = {}
def wrap_item(self, name: str, weight: int = 1) -> RateItem:
"""Time-stamping item, return a RateItem"""
now = clock.now()
return RateItem(name, now, weight=weight)
def get(self, item: RateItem) -> AbstractBucket:
if item.name not in self.buckets:
# Use `self.create(..)` method to both initialize new bucket and calling `schedule_leak` on that bucket
# We can create different buckets with different types/classes here as well
new_bucket = self.create(YourBucketClass, *your-arguments, **your-keyword-arguments)
self.buckets.update({item.name: new_bucket})
return self.buckets[item.name]
```
### Wrapping all up with Limiter
Pass your bucket-factory to Limiter, and ready to roll!
```python
from pyrate_limiter import Limiter
limiter = Limiter(
bucket_factory,
raise_when_fail=False, # Default = True
max_delay=1000, # Default = None
)
item = "the-earth"
limiter.try_acquire(item)
heavy_item = "the-sun"
limiter.try_acquire(heavy_item, weight=10000)
```
### asyncio and event loops
To ensure the event loop isn't blocked, use `try_acquire_async` with an **async bucket**, which leverages `asyncio.Lock` for concurrency control.
If your bucket isn't async, wrap it with `BucketAsyncWrapper`. This ensures `asyncio.sleep` is used instead of `time.sleep`, preventing event loop blocking:
```python
await limiter.try_acquire_async(item)
```
Example: [asyncio_ratelimit.py](examples/asyncio_ratelimit.py)
#### `as_decorator()`: use limiter as decorator
`Limiter` can be used as a decorator, but you must provide a `mapping` function that maps the wrapped function's arguments to `limiter.try_acquire` arguments (either a `str` or a `(str, int)` tuple).
The decorator works with both synchronous and asynchronous functions:
```python
decorator = limiter.as_decorator()
def mapping(*args, **kwargs):
return "demo", 1
@decorator(mapping)
def handle_something(*args, **kwargs):
"""function logic"""
@decorator(mapping)
async def handle_something_async(*args, **kwargs):
"""function logic"""
```
Async Example:
```python
my_beautiful_decorator = limiter.as_decorator()
def mapping(some_number: int):
return str(some_number)
@my_beautiful_decorator(mapping)
def request_function(some_number: int):
requests.get('https://example.com')
# Async would work too!
@my_beautiful_decorator(mapping)
async def async_request_function(some_number: int):
requests.get('https://example.com')
```
For full example see [asyncio_decorator.py](examples/asyncio_decorator.py)
### Limiter API
#### `bucket()`: get list of all active buckets
Return list of all active buckets with `limiter.buckets()`
#### `dispose(bucket: int | BucketObject)`: dispose/remove/delete the given bucket
Method signature:
```python
def dispose(self, bucket: Union[int, AbstractBucket]) -> bool:
"""Dispose/Remove a specific bucket,
using bucket-id or bucket object as param
"""
```
Example of usage:
```python
active_buckets = limiter.buckets()
assert len(active_buckets) > 0
bucket_to_remove = active_buckets[0]
assert limiter.dispose(bucket_to_remove)
```
If a bucket is found and get deleted, calling this method will return **True**, otherwise **False**.
If there is no more buckets in the limiter's bucket-factory, all the leaking tasks will be stopped.
### Weight
Item can have weight. By default item's weight = 1, but you can modify the weight before passing to `limiter.try_acquire`.
Item with weight W > 1 when consumed will be multiplied to (W) items with the same timestamp and weight = 1. Example with a big item with weight W=5, when put to bucket, it will be divided to 5 items with weight=1 + following names
```
BigItem(weight=5, name="item", timestamp=100) => [
item(weight=1, name="item", timestamp=100),
item(weight=1, name="item", timestamp=100),
item(weight=1, name="item", timestamp=100),
item(weight=1, name="item", timestamp=100),
item(weight=1, name="item", timestamp=100),
]
```
Yet, putting this big, heavy item into bucket is expected to be transactional & atomic - meaning either all 5 items will be consumed or none of them will. This is made possible as bucket `put(item)` always check for available space before ingesting. All of the Bucket's implementations provided by **PyrateLimiter** follows this rule.
Any additional, custom implementation of Bucket are expected to behave alike - as we have unit tests to cover the case.
See [Advanced usage options](#advanced-usage) below for more details.
### Handling exceeded limits
When a rate limit is exceeded, you have two options: raise an exception, or add delays.
#### Bucket analogy
<img height="300" align="right" src="https://upload.wikimedia.org/wikipedia/commons/c/c4/Leaky_bucket_analogy.JPG">
At this point it's useful to introduce the analogy of "buckets" used for rate-limiting. Here is a
quick summary:
- This library implements the [Leaky Bucket algorithm](https://en.wikipedia.org/wiki/Leaky_bucket).
- It is named after the idea of representing some kind of fixed capacity -- like a network or service -- as a bucket.
- The bucket "leaks" at a constant rate. For web services, this represents the **ideal or permitted request rate**.
- The bucket is "filled" at an intermittent, unpredicatble rate, representing the **actual rate of requests**.
- When the bucket is "full", it will overflow, representing **canceled or delayed requests**.
- Item can have weight. Consuming a single item with weight W > 1 is the same as consuming W smaller, unit items - each with weight=1, with the same timestamp and maybe same name (depending on however user choose to implement it)
#### Rate limit exceptions
By default, a `BucketFullException` will be raised when a rate limit is exceeded.
The error contains a `meta_info` attribute with the following information:
- `name`: The name of item it received
- `weight`: The weight of item it received
- `rate`: The specific rate that has been exceeded
Here's an example that will raise an exception on the 4th request:
```python
rate = Rate(3, Duration.SECOND)
bucket = InMemoryBucket([rate])
clock = TimeClock()
class MyBucketFactory(BucketFactory):
def wrap_item(self, name: str, weight: int = 1) -> RateItem:
"""Time-stamping item, return a RateItem"""
now = clock.now()
return RateItem(name, now, weight=weight)
def get(self, _item: RateItem) -> AbstractBucket:
"""For simplicity's sake, all items route to the same, single bucket"""
return bucket
limiter = Limiter(MyBucketFactory())
for _ in range(4):
try:
limiter.try_acquire('item', weight=2)
except BucketFullException as err:
print(err)
# Output: Bucket with Rate 3/1.0s is already full
print(err.meta_info)
# Output: {'name': 'item', 'weight': 2, 'rate': '3/1.0s', 'error': 'Bucket with Rate 3/1.0s is already full'}
```
The rate part of the output is constructed as: `limit / interval`. On the above example, the limit
is 3 and the interval is 1, hence the `Rate 3/1`.
#### Rate limit delays
You may want to simply slow down your requests to stay within the rate limits instead of canceling
them. In that case you pass the `max_delay` argument the maximum value of delay (typically in _ms_ when use human-clock).
```python
limiter = Limiter(factory, max_delay=500) # Allow to delay up to 500ms
```
Limiter has a default buffer_ms of 50ms. This means that when waiting, an additional 50ms will be added per step.
As `max_delay` has been passed as a numeric value, when ingesting item, limiter will:
- First, try to ingest such item using the routed bucket
- If it fails to put item into the bucket, it will call `wait(item)` on the bucket to see how much time remains until the bucket can consume the item again?
- Comparing the `wait` value to the `max_delay`.
- if `max_delay` >= `wait`: delay (wait + buffer_ms as latency-tolerance) using either `asyncio.sleep` or `time.sleep` until the bucket can consume again
- if `max_delay` < `wait`: it raises `LimiterDelayException` if Limiter's `raise_when_fail=True`, otherwise silently fail and return False
Example:
```python
from pyrate_limiter import LimiterDelayException
for _ in range(4):
try:
limiter.try_acquire('item', weight=2, max_delay=200)
except LimiterDelayException as err:
print(err)
# Output:
# Actual delay exceeded allowance: actual=500, allowed=200
# Bucket for 'item' with Rate 3/1.0s is already full
print(err.meta_info)
# Output: {'name': 'item', 'weight': 2, 'rate': '3/1.0s', 'max_delay': 200, 'actual_delay': 500}
```
### Backends
A few different bucket backends are available:
- **InMemoryBucket**: using python built-in list as bucket
- **MultiprocessBucket**: uses a multiprocessing lock for distributed concurrency with a ListProxy as the bucket
- **RedisBucket**, using err... redis, with both async/sync support
- **PostgresBucket**, using `psycopg2`
- **SQLiteBucket**, using sqlite3
- **BucketAsyncWrapper**: wraps an existing bucket with async interfaces, to avoid blocking the event loop
#### InMemoryBucket
The default bucket is stored in memory, using python `list`
```python
from pyrate_limiter import InMemoryBucket, Rate, Duration
rates = [Rate(5, Duration.MINUTE * 2)]
bucket = InMemoryBucket(rates)
```
This bucket only availabe in `sync` mode. The only constructor argument is `List[Rate]`.
#### MultiprocessBucket
MultiprocessBucket uses a ListProxy to store items within a python multiprocessing pool or ProcessPoolExecutor. Concurrency is enforced via a multiprocessing Lock.
The bucket is shared across instances.
An example is provided in [in_memory_multiprocess](examples/in_memory_multiprocess.py)
Whenever multiprocessing, bucket.waiting calculations will be often wrong because of the concurrency involved. Set Limiter.retry_until_max_delay=True so that the
item keeps retrying rather than returning False when contention causes an extra delay.
#### RedisBucket
RedisBucket uses `Sorted-Set` to store items with key being item's name and score item's timestamp
Because it is intended to work with both async & sync, we provide a classmethod `init` for it
```python
from pyrate_limiter import RedisBucket, Rate, Duration
# Using synchronous redis
from redis import ConnectionPool
from redis import Redis
rates = [Rate(5, Duration.MINUTE * 2)]
pool = ConnectionPool.from_url("redis://localhost:6379")
redis_db = Redis(connection_pool=pool)
bucket_key = "bucket-key"
bucket = RedisBucket.init(rates, redis_db, bucket_key)
# Using asynchronous redis
from redis.asyncio import ConnectionPool as AsyncConnectionPool
from redis.asyncio import Redis as AsyncRedis
pool = AsyncConnectionPool.from_url("redis://localhost:6379")
redis_db = AsyncRedis(connection_pool=pool)
bucket_key = "bucket-key"
bucket = await RedisBucket.init(rates, redis_db, bucket_key)
```
The API are the same, regardless of sync/async. If AsyncRedis is being used, calling `await bucket.method_name(args)` would just work!
#### SQLiteBucket
If you need to persist the bucket state, a SQLite backend is available. The SQLite bucket works in sync manner.
Manully create a connection to Sqlite and pass it along with the table name to the bucket class:
```python
from pyrate_limiter import SQLiteBucket, Rate, Duration
import sqlite3
rates = [Rate(5, Duration.MINUTE * 2)]
bucket = SQLiteBucket.init_from_file(rates)
```
```py
from pyrate_limiter import Rate, Limiter, Duration, SQLiteBucket
requests_per_minute = 5
rate = Rate(requests_per_minute, Duration.MINUTE)
bucket = SQLiteBucket.init_from_file([rate], use_file_lock=False) # set use_file_lock to True if using across multiple processes
limiter = Limiter(bucket, raise_when_fail=False, max_delay=max_delay)
```
You can also pass custom arguments to the `init_from_file` following its signature:
```python
class SQLiteBucket(AbstractBucket):
@classmethod
def init_from_file(
cls,
rates: List[Rate],
table: str = "rate_bucket",
db_path: Optional[str] = None,
create_new_table = True,
use_file_lock: bool = False
) -> "SQLiteBucket":
...
```
Options:
- `db_path`: If not provided, uses `tempdir / "pyrate-limiter.sqlite"`
- `use_file_lock`: Should be False for single process workloads. For multi process, uses a [filelock](https://pypi.org/project/filelock/) to ensure single access to the SQLite bucket across multiple processes, allowing multi process rate limiting on a single host.
Example: [limiter_factory.py::create_sqlite_limiter()](pyrate_limiter/limiter_factory.py)
#### PostgresBucket
Postgres is supported, but you have to install `psycopg[pool]` either as an extra or as a separate package. The PostgresBucket currently does not support async.
You can use Postgres's built-in **CURRENT_TIMESTAMP** as the time source with `PostgresClock`, or use an external custom time source.
```python
from pyrate_limiter import PostgresBucket, Rate, PostgresClock
from psycopg_pool import ConnectionPool
connection_pool = ConnectionPool('postgresql://postgres:postgres@localhost:5432')
clock = PostgresClock(connection_pool)
rates = [Rate(3, 1000), Rate(4, 1500)]
bucket = PostgresBucket(connection_pool, "my-bucket-table", rates)
```
#### BucketAsyncWrapper
The BucketAsyncWrapper wraps a sync bucket to ensure all its methods return awaitables. This allows the Limiter to detect
asynchronous behavior and use asyncio.sleep() instead of time.sleep() during delay handling,
preventing blocking of the asyncio event loop.
Example: [limiter_factory.py::create_inmemory_limiter()](pyrate_limiter/limiter_factory.py)
### Async or Sync or Multiprocessing
The Limiter is basically made of a Clock backend and a Bucket backend. The backends may be async or sync, which determines the Limiters internal behavior, regardless of whether the caller enters via a sync or async function.
try_acquire_async: When calling from an async context, use try_acquire_async. This uses a thread-local asyncio lock to ensure only one asyncio task is acquiring, followed by a global RLock so that only one thread is acquiring.
try_acquire: When called directly, the global RLock enforces only one thread at a time.
Multiprocessing: If using MultiprocessBucket, two locks are used in Limiter: a top level multiprocessing lock, then a thread level RLock
## Advanced Usage
### Component level diagram
### Time sources
Time source can be anything from anywhere: be it python's built-in time, or monotonic clock, sqliteclock, or crawling from world time server(well we don't have that, but you can!).
```python
from pyrate_limiter import TimeClock # use python' time.time()
from pyrate_limiter import MonotonicClock # use python time.monotonic()
```
Clock's abstract interface only requires implementing a method `now() -> int`. And it can be both sync or async.
### Leaking
Typically bucket should not hold items forever. Bucket's abstract interface requires its implementation must be provided with `leak(current_timestamp: Optional[int] = None)`.
The `leak` method when called is expected to remove any items considered outdated at that moment. During Limiter lifetime, all the buckets' `leak` should be called periodically.
**BucketFactory** provide a method called `schedule_leak` to help deal with this matter. Basically, it will run as a background task for all the buckets currently in use, with interval between `leak` call by **default is 10 seconds**.
```python
# Runnning a background task (whether it is sync/async - doesnt matter)
# calling the bucket's leak
factory.schedule_leak(bucket, clock)
```
You can change this calling interval by overriding BucketFactory's `leak_interval` property. This interval is in **miliseconds**.
```python
class MyBucketFactory(BucketFactory):
def __init__(self, *args):
self.leak_interval = 300
```
When dealing with leak using BucketFactory, the author's suggestion is, we can be pythonic about this by implementing a constructor
```python
class MyBucketFactory(BucketFactory):
def constructor(self, clock, buckets):
self.clock = clock
self.buckets = buckets
for bucket in buckets:
self.schedule_leak(bucket, clock)
```
### Concurrency
Generally, Lock is provided at Limiter's level, except SQLiteBucket case.
### Custom backends
If these don't suit your needs, you can also create your own bucket backend by implementing `pyrate_limiter.AbstractBucket` class.
One of **PyrateLimiter** design goals is powerful extensibility and maximum ease of development.
It must be not only be a ready-to-use tool, but also a guide-line, or a framework that help implementing new features/bucket free of the most hassles.
Due to the composition nature of the library, it is possbile to write minimum code and validate the result:
- Fork the repo
- Implement your bucket with `pyrate_limiter.AbstractBucket`
- Add your own `create_bucket` method in `tests/conftest.py` and pass it to the `create_bucket` fixture
- Run the test suite to validate the result
If the tests pass through, then you are just good to go with your new, fancy bucket!
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"description": "<img align=\"left\" width=\"95\" height=\"120\" src=\"https://raw.githubusercontent.com/vutran1710/PyrateLimiter/master/docs/_static/logo.png\">\n\n# PyrateLimiter\n\nThe request rate limiter using Leaky-bucket Algorithm.\n\nFull project documentation can be found at [pyratelimiter.readthedocs.io](https://pyratelimiter.readthedocs.io).\n\n[](https://badge.fury.io/py/pyrate-limiter)\n[](https://pypi.org/project/pyrate-limiter)\n[](https://codecov.io/gh/vutran1710/PyrateLimiter)\n[](https://github.com/vutran1710/PyrateLimiter/graphs/commit-activity)\n[](https://pypi.python.org/pypi/pyrate-limiter/)\n\n<br>\n\n## Contents\n\n - [Features](#features)\n - [Installation](#installation)\n - [Quickstart](#quickstart)\n - [limiter_factory](#limiter_factory)\n - [Examples](#examples)\n - [Basic usage](#basic-usage)\n - [Key concepts](#key-concepts)\n - [Defining rate limits & buckets](#defining-rate-limits-and-buckets)\n - [Defining clock & routing logic](#defining-clock--routing-logic-with-bucketfactory)\n - [Wrapping all up with Limiter](#wrapping-all-up-with-limiter)\n - [asyncio and event loops](#asyncio-and-event-loops)\n - [Decorators](#as_decorator-use-limiter-as-decorator)\n - [Limiter API](#limiter-api)\n - [Weight](#weight)\n - [Handling exceeded limits](#handling-exceeded-limits)\n - [Bucket analogy](#bucket-analogy)\n - [Rate limit exceptions](#rate-limit-exceptions)\n - [Rate limit delays](#rate-limit-delays)\n - [Backends](#backends)\n - [InMemoryBucket](#inmemorybucket)\n - [MultiprocessBucket](#multiprocessbucket)\n - [SQLiteBucket](#sqlitebucket)\n - [RedisBucket](#redisbucket)\n - [PostgresBucket](#postgresbucket)\n - [BucketAsyncWrapper](#bucketasyncwrapper)\n - [Async or Sync?](#async-or-sync)\n - [Advanced Usage](#advanced-usage)\n - [Component-level Diagram](#component-level-diagram)\n - [Time sources](#time-sources)\n - [Leaking](#leaking)\n - [Concurrency](#concurrency)\n - [Custom backend](#custom-backend)\n\n## Features\n\n- Supports unlimited rate limits and custom intervals.\n- Separately tracks limits for different services or resources.\n- Manages limit breaches by raising exceptions or applying delays.\n- Offers multiple usage modes: direct calls or decorators.\n- Fully compatible with both synchronous and asynchronous workflows.\n- Provides SQLite and Redis backends for persistent limit tracking across threads or restarts.\n- Includes MultiprocessBucket and SQLite File Lock backends for multiprocessing environments.\n\n## Installation\n\n**PyrateLimiter** supports **python ^3.8**\n\nInstall using pip:\n\n```\npip install pyrate-limiter\n```\n\nOr using conda:\n\n```\nconda install --channel conda-forge pyrate-limiter\n```\n\n## Quickstart\n\nTo limit 5 requests within 2 seconds and raise an exception when the limit is exceeded:\n\n```python\nfrom pyrate_limiter import Duration, Rate, Limiter, BucketFullException\n\nlimiter = Limiter(Rate(5, Duration.SECOND * 2))\n\nfor i in range(6):\n try:\n limiter.try_acquire(i)\n except BucketFullException as err:\n print(err, err.meta_info)\n```\n\n\n\n## limiter_factory\n[limiter_factory.py](pyrate_limiter.limiter_factory.py) provides several functions to simplify common cases:\n- create_sqlite_limiter(rate_per_duration: int, duration: Duration, ...)\n- create_inmemory_limiter(rate_per_duration: int, duration: Duration, ...)\n- + more to be added...\n\n## Examples\n- Rate limiting asyncio tasks: [asyncio_ratelimit.py](examples/asyncio_ratelimit.py)\n- Rate limiting asyncio tasks w/ a decorator: [asyncio_decorator.py](examples/asyncio_decorator.py)\n- HTTPX rate limiting - asyncio, single process and multiprocess examples [httpx_ratelimiter.py](examples/httpx_ratelimiter.py)\n- Multiprocessing using an in-memory rate limiter - [in_memory_multiprocess.py](examples/in_memory_multiprocess.py)\n- Multiprocessing using SQLite and a file lock - this can be used for distributed processes not created within a multiprocessing [sql_filelock_multiprocess.py](examples/sql_filelock_multiprocess.py)\n\n## Basic Usage\n\n### Key concepts\n\n#### Clock\n\n- Timestamps incoming items\n\n#### Bucket\n\n- Stores items with timestamps.\n- Functions as a FIFO queue.\n- Can `leak` to remove outdated items.\n\n#### BucketFactory\n\n- Manages buckets and clocks, routing items to their appropriate buckets.\n- Schedules periodic `leak` operations to prevent overflow.\n- Allows custom logic for routing, conditions, and timing.\n\n#### Limiter\n\n- Provides a simple, intuitive API by abstracting underlying logic.\n- Seamlessly supports both sync and async contexts.\n- Offers multiple interaction modes: direct calls, decorators, and (future) context managers.\n- Ensures thread-safety via RLock, and if needed, asyncio concurrency via asyncio.Lock\n\n### Defining rate limits and buckets\n\nFor example, an API (like LinkedIn or GitHub) might have these rate limits:\n\n```\n- 500 requests per hour\n- 1000 requests per day\n- 10000 requests per month\n```\n\nYou can define these rates using the `Rate` class. `Rate` class has 2 properties only: **limit** and **interval**\n\n```python\nfrom pyrate_limiter import Duration, Rate\n\nhourly_rate = Rate(500, Duration.HOUR) # 500 requests per hour\ndaily_rate = Rate(1000, Duration.DAY) # 1000 requests per day\nmonthly_rate = Rate(10000, Duration.WEEK * 4) # 10000 requests per month\n\nrates = [hourly_rate, daily_rate, monthly_rate]\n```\n\nRates must be properly ordered:\n\n- Rates' intervals & limits must be ordered from least to greatest\n- Rates' ratio of **limit/interval** must be ordered from greatest to least\n\nBuckets validate rates during initialization. If using a custom implementation, use the built-in validator:\n\n```python\nfrom pyrate_limiter import validate_rate_list\n\nassert validate_rate_list(my_rates)\n```\n\nThen, add the rates to the bucket of your choices\n\n```python\nfrom pyrate_limiter import InMemoryBucket, RedisBucket\n\nbasic_bucket = InMemoryBucket(rates)\n\n# Or, using redis\nfrom redis import Redis\n\nredis_connection = Redis(host='localhost')\nredis_bucket = RedisBucket.init(rates, redis_connection, \"my-bucket-name\")\n\n# Async Redis would work too!\nfrom redis.asyncio import Redis\n\nredis_connection = Redis(host='localhost')\nredis_bucket = await RedisBucket.init(rates, redis_connection, \"my-bucket-name\")\n```\n\nIf you only need a single Bucket for everything, and python's built-in `time()` is enough for you, then pass the bucket to Limiter then ready to roll!\n\n```python\nfrom pyrate_limiter import Limiter\n\n# Limiter constructor accepts single bucket as the only parameter,\n# the rest are 3 optional parameters with default values as following\n# Limiter(bucket, clock=TimeClock(), raise_when_fail=True, max_delay=None)\nlimiter = Limiter(bucket)\n\n# Limiter is now ready to work!\nlimiter.try_acquire(\"hello world\")\n```\n\nIf you want to have finer grain control with routing & clocks etc, then you should use `BucketFactory`.\n\n### Defining Clock & routing logic with BucketFactory\n\nWhen multiple bucket types are needed and items must be routed based on certain conditions, use `BucketFactory`.\n\nFirst, define your clock (time source). Most use cases work with the built-in clocks:\n\n```python\nfrom pyrate_limiter.clock import TimeClock, MonotonicClock, SQLiteClock\n\nbase_clock = TimeClock()\n```\n\nPyrateLimiter does not assume routing logic, so you implement a custom BucketFactory. At a minimum, these two methods must be defined:\n\n```python\nfrom pyrate_limiter import BucketFactory\nfrom pyrate_limiter import AbstractBucket\n\n\nclass MyBucketFactory(BucketFactory):\n # You can use constructor here,\n # nor it requires to make bucket-factory work!\n\n def wrap_item(self, name: str, weight: int = 1) -> RateItem:\n \"\"\"Time-stamping item, return a RateItem\"\"\"\n now = clock.now()\n return RateItem(name, now, weight=weight)\n\n def get(self, _item: RateItem) -> AbstractBucket:\n \"\"\"For simplicity's sake, all items route to the same, single bucket\"\"\"\n return bucket\n```\n\n### Creating buckets dynamically\n\nIf more than one bucket is needed, the bucket-routing logic should go to BucketFactory `get(..)` method.\n\nWhen creating buckets dynamically, it is needed to schedule leak for each newly created buckets.\n\nTo support this, BucketFactory comes with a predefined method call `self.create(..)`. It is meant to create the bucket and schedule that bucket for leaking using the Factory's clock\n\n```python\ndef create(\n self,\n clock: AbstractClock,\n bucket_class: Type[AbstractBucket],\n *args,\n **kwargs,\n ) -> AbstractBucket:\n \"\"\"Creating a bucket dynamically\"\"\"\n bucket = bucket_class(*args, **kwargs)\n self.schedule_leak(bucket, clock)\n return bucket\n```\n\nBy utilizing this, we can modify the code as following:\n\n```python\nclass MultiBucketFactory(BucketFactory):\n def __init__(self, clock):\n self.clock = clock\n self.buckets = {}\n\n def wrap_item(self, name: str, weight: int = 1) -> RateItem:\n \"\"\"Time-stamping item, return a RateItem\"\"\"\n now = clock.now()\n return RateItem(name, now, weight=weight)\n\n def get(self, item: RateItem) -> AbstractBucket:\n if item.name not in self.buckets:\n # Use `self.create(..)` method to both initialize new bucket and calling `schedule_leak` on that bucket\n # We can create different buckets with different types/classes here as well\n new_bucket = self.create(YourBucketClass, *your-arguments, **your-keyword-arguments)\n self.buckets.update({item.name: new_bucket})\n\n return self.buckets[item.name]\n```\n\n### Wrapping all up with Limiter\n\nPass your bucket-factory to Limiter, and ready to roll!\n\n```python\nfrom pyrate_limiter import Limiter\n\nlimiter = Limiter(\n bucket_factory,\n raise_when_fail=False, # Default = True\n max_delay=1000, # Default = None\n)\n\nitem = \"the-earth\"\nlimiter.try_acquire(item)\n\nheavy_item = \"the-sun\"\nlimiter.try_acquire(heavy_item, weight=10000)\n```\n\n### asyncio and event loops\n\nTo ensure the event loop isn't blocked, use `try_acquire_async` with an **async bucket**, which leverages `asyncio.Lock` for concurrency control.\n\nIf your bucket isn't async, wrap it with `BucketAsyncWrapper`. This ensures `asyncio.sleep` is used instead of `time.sleep`, preventing event loop blocking:\n\n\n```python\nawait limiter.try_acquire_async(item)\n```\n\nExample: [asyncio_ratelimit.py](examples/asyncio_ratelimit.py)\n\n\n#### `as_decorator()`: use limiter as decorator\n\n`Limiter` can be used as a decorator, but you must provide a `mapping` function that maps the wrapped function's arguments to `limiter.try_acquire` arguments (either a `str` or a `(str, int)` tuple).\n\nThe decorator works with both synchronous and asynchronous functions:\n\n```python\ndecorator = limiter.as_decorator()\n\ndef mapping(*args, **kwargs):\n return \"demo\", 1\n\n@decorator(mapping)\ndef handle_something(*args, **kwargs):\n \"\"\"function logic\"\"\"\n\n@decorator(mapping)\nasync def handle_something_async(*args, **kwargs):\n \"\"\"function logic\"\"\"\n```\n\n\nAsync Example:\n```python\nmy_beautiful_decorator = limiter.as_decorator()\n\ndef mapping(some_number: int):\n return str(some_number)\n\n@my_beautiful_decorator(mapping)\ndef request_function(some_number: int):\n requests.get('https://example.com')\n\n# Async would work too!\n@my_beautiful_decorator(mapping)\nasync def async_request_function(some_number: int):\n requests.get('https://example.com')\n```\n\nFor full example see [asyncio_decorator.py](examples/asyncio_decorator.py)\n\n\n### Limiter API\n\n#### `bucket()`: get list of all active buckets\nReturn list of all active buckets with `limiter.buckets()`\n\n\n#### `dispose(bucket: int | BucketObject)`: dispose/remove/delete the given bucket\n\nMethod signature:\n```python\ndef dispose(self, bucket: Union[int, AbstractBucket]) -> bool:\n \"\"\"Dispose/Remove a specific bucket,\n using bucket-id or bucket object as param\n \"\"\"\n```\n\nExample of usage:\n```python\nactive_buckets = limiter.buckets()\nassert len(active_buckets) > 0\n\nbucket_to_remove = active_buckets[0]\nassert limiter.dispose(bucket_to_remove)\n```\n\nIf a bucket is found and get deleted, calling this method will return **True**, otherwise **False**.\nIf there is no more buckets in the limiter's bucket-factory, all the leaking tasks will be stopped.\n\n\n### Weight\n\nItem can have weight. By default item's weight = 1, but you can modify the weight before passing to `limiter.try_acquire`.\n\nItem with weight W > 1 when consumed will be multiplied to (W) items with the same timestamp and weight = 1. Example with a big item with weight W=5, when put to bucket, it will be divided to 5 items with weight=1 + following names\n\n```\nBigItem(weight=5, name=\"item\", timestamp=100) => [\n item(weight=1, name=\"item\", timestamp=100),\n item(weight=1, name=\"item\", timestamp=100),\n item(weight=1, name=\"item\", timestamp=100),\n item(weight=1, name=\"item\", timestamp=100),\n item(weight=1, name=\"item\", timestamp=100),\n]\n```\n\nYet, putting this big, heavy item into bucket is expected to be transactional & atomic - meaning either all 5 items will be consumed or none of them will. This is made possible as bucket `put(item)` always check for available space before ingesting. All of the Bucket's implementations provided by **PyrateLimiter** follows this rule.\n\nAny additional, custom implementation of Bucket are expected to behave alike - as we have unit tests to cover the case.\n\nSee [Advanced usage options](#advanced-usage) below for more details.\n\n### Handling exceeded limits\n\nWhen a rate limit is exceeded, you have two options: raise an exception, or add delays.\n\n#### Bucket analogy\n\n<img height=\"300\" align=\"right\" src=\"https://upload.wikimedia.org/wikipedia/commons/c/c4/Leaky_bucket_analogy.JPG\">\n\nAt this point it's useful to introduce the analogy of \"buckets\" used for rate-limiting. Here is a\nquick summary:\n\n- This library implements the [Leaky Bucket algorithm](https://en.wikipedia.org/wiki/Leaky_bucket).\n- It is named after the idea of representing some kind of fixed capacity -- like a network or service -- as a bucket.\n- The bucket \"leaks\" at a constant rate. For web services, this represents the **ideal or permitted request rate**.\n- The bucket is \"filled\" at an intermittent, unpredicatble rate, representing the **actual rate of requests**.\n- When the bucket is \"full\", it will overflow, representing **canceled or delayed requests**.\n- Item can have weight. Consuming a single item with weight W > 1 is the same as consuming W smaller, unit items - each with weight=1, with the same timestamp and maybe same name (depending on however user choose to implement it)\n\n#### Rate limit exceptions\n\nBy default, a `BucketFullException` will be raised when a rate limit is exceeded.\nThe error contains a `meta_info` attribute with the following information:\n\n- `name`: The name of item it received\n- `weight`: The weight of item it received\n- `rate`: The specific rate that has been exceeded\n\nHere's an example that will raise an exception on the 4th request:\n\n```python\nrate = Rate(3, Duration.SECOND)\nbucket = InMemoryBucket([rate])\nclock = TimeClock()\n\n\nclass MyBucketFactory(BucketFactory):\n\n def wrap_item(self, name: str, weight: int = 1) -> RateItem:\n \"\"\"Time-stamping item, return a RateItem\"\"\"\n now = clock.now()\n return RateItem(name, now, weight=weight)\n\n def get(self, _item: RateItem) -> AbstractBucket:\n \"\"\"For simplicity's sake, all items route to the same, single bucket\"\"\"\n return bucket\n\n\nlimiter = Limiter(MyBucketFactory())\n\nfor _ in range(4):\n try:\n limiter.try_acquire('item', weight=2)\n except BucketFullException as err:\n print(err)\n # Output: Bucket with Rate 3/1.0s is already full\n print(err.meta_info)\n # Output: {'name': 'item', 'weight': 2, 'rate': '3/1.0s', 'error': 'Bucket with Rate 3/1.0s is already full'}\n```\n\nThe rate part of the output is constructed as: `limit / interval`. On the above example, the limit\nis 3 and the interval is 1, hence the `Rate 3/1`.\n\n#### Rate limit delays\n\nYou may want to simply slow down your requests to stay within the rate limits instead of canceling\nthem. In that case you pass the `max_delay` argument the maximum value of delay (typically in _ms_ when use human-clock).\n\n```python\nlimiter = Limiter(factory, max_delay=500) # Allow to delay up to 500ms\n```\n\nLimiter has a default buffer_ms of 50ms. This means that when waiting, an additional 50ms will be added per step.\n\nAs `max_delay` has been passed as a numeric value, when ingesting item, limiter will:\n\n- First, try to ingest such item using the routed bucket\n- If it fails to put item into the bucket, it will call `wait(item)` on the bucket to see how much time remains until the bucket can consume the item again?\n- Comparing the `wait` value to the `max_delay`.\n- if `max_delay` >= `wait`: delay (wait + buffer_ms as latency-tolerance) using either `asyncio.sleep` or `time.sleep` until the bucket can consume again\n- if `max_delay` < `wait`: it raises `LimiterDelayException` if Limiter's `raise_when_fail=True`, otherwise silently fail and return False\n\nExample:\n\n```python\nfrom pyrate_limiter import LimiterDelayException\n\nfor _ in range(4):\n try:\n limiter.try_acquire('item', weight=2, max_delay=200)\n except LimiterDelayException as err:\n print(err)\n # Output:\n # Actual delay exceeded allowance: actual=500, allowed=200\n # Bucket for 'item' with Rate 3/1.0s is already full\n print(err.meta_info)\n # Output: {'name': 'item', 'weight': 2, 'rate': '3/1.0s', 'max_delay': 200, 'actual_delay': 500}\n```\n\n### Backends\n\nA few different bucket backends are available:\n\n- **InMemoryBucket**: using python built-in list as bucket\n- **MultiprocessBucket**: uses a multiprocessing lock for distributed concurrency with a ListProxy as the bucket\n- **RedisBucket**, using err... redis, with both async/sync support\n- **PostgresBucket**, using `psycopg2`\n- **SQLiteBucket**, using sqlite3\n- **BucketAsyncWrapper**: wraps an existing bucket with async interfaces, to avoid blocking the event loop\n\n\n#### InMemoryBucket\n\nThe default bucket is stored in memory, using python `list`\n\n```python\nfrom pyrate_limiter import InMemoryBucket, Rate, Duration\n\nrates = [Rate(5, Duration.MINUTE * 2)]\nbucket = InMemoryBucket(rates)\n```\n\nThis bucket only availabe in `sync` mode. The only constructor argument is `List[Rate]`.\n\n#### MultiprocessBucket\n\nMultiprocessBucket uses a ListProxy to store items within a python multiprocessing pool or ProcessPoolExecutor. Concurrency is enforced via a multiprocessing Lock.\n\nThe bucket is shared across instances.\n\nAn example is provided in [in_memory_multiprocess](examples/in_memory_multiprocess.py)\n\nWhenever multiprocessing, bucket.waiting calculations will be often wrong because of the concurrency involved. Set Limiter.retry_until_max_delay=True so that the\nitem keeps retrying rather than returning False when contention causes an extra delay.\n\n#### RedisBucket\n\nRedisBucket uses `Sorted-Set` to store items with key being item's name and score item's timestamp\nBecause it is intended to work with both async & sync, we provide a classmethod `init` for it\n\n```python\nfrom pyrate_limiter import RedisBucket, Rate, Duration\n\n# Using synchronous redis\nfrom redis import ConnectionPool\nfrom redis import Redis\n\nrates = [Rate(5, Duration.MINUTE * 2)]\npool = ConnectionPool.from_url(\"redis://localhost:6379\")\nredis_db = Redis(connection_pool=pool)\nbucket_key = \"bucket-key\"\nbucket = RedisBucket.init(rates, redis_db, bucket_key)\n\n# Using asynchronous redis\nfrom redis.asyncio import ConnectionPool as AsyncConnectionPool\nfrom redis.asyncio import Redis as AsyncRedis\n\npool = AsyncConnectionPool.from_url(\"redis://localhost:6379\")\nredis_db = AsyncRedis(connection_pool=pool)\nbucket_key = \"bucket-key\"\nbucket = await RedisBucket.init(rates, redis_db, bucket_key)\n```\n\nThe API are the same, regardless of sync/async. If AsyncRedis is being used, calling `await bucket.method_name(args)` would just work!\n\n#### SQLiteBucket\n\nIf you need to persist the bucket state, a SQLite backend is available. The SQLite bucket works in sync manner.\n\nManully create a connection to Sqlite and pass it along with the table name to the bucket class:\n\n```python\nfrom pyrate_limiter import SQLiteBucket, Rate, Duration\nimport sqlite3\n\nrates = [Rate(5, Duration.MINUTE * 2)]\nbucket = SQLiteBucket.init_from_file(rates)\n```\n\n```py\nfrom pyrate_limiter import Rate, Limiter, Duration, SQLiteBucket\n\nrequests_per_minute = 5\nrate = Rate(requests_per_minute, Duration.MINUTE)\nbucket = SQLiteBucket.init_from_file([rate], use_file_lock=False) # set use_file_lock to True if using across multiple processes\nlimiter = Limiter(bucket, raise_when_fail=False, max_delay=max_delay)\n```\n\nYou can also pass custom arguments to the `init_from_file` following its signature:\n\n```python\nclass SQLiteBucket(AbstractBucket):\n @classmethod\n def init_from_file(\n cls,\n rates: List[Rate],\n table: str = \"rate_bucket\",\n db_path: Optional[str] = None,\n create_new_table = True,\n use_file_lock: bool = False\n ) -> \"SQLiteBucket\":\n ...\n```\n\nOptions:\n- `db_path`: If not provided, uses `tempdir / \"pyrate-limiter.sqlite\"`\n- `use_file_lock`: Should be False for single process workloads. For multi process, uses a [filelock](https://pypi.org/project/filelock/) to ensure single access to the SQLite bucket across multiple processes, allowing multi process rate limiting on a single host.\n\nExample: [limiter_factory.py::create_sqlite_limiter()](pyrate_limiter/limiter_factory.py)\n\n#### PostgresBucket\n\nPostgres is supported, but you have to install `psycopg[pool]` either as an extra or as a separate package. The PostgresBucket currently does not support async.\n\nYou can use Postgres's built-in **CURRENT_TIMESTAMP** as the time source with `PostgresClock`, or use an external custom time source.\n\n```python\nfrom pyrate_limiter import PostgresBucket, Rate, PostgresClock\nfrom psycopg_pool import ConnectionPool\n\nconnection_pool = ConnectionPool('postgresql://postgres:postgres@localhost:5432')\n\nclock = PostgresClock(connection_pool)\nrates = [Rate(3, 1000), Rate(4, 1500)]\nbucket = PostgresBucket(connection_pool, \"my-bucket-table\", rates)\n```\n\n#### BucketAsyncWrapper\nThe BucketAsyncWrapper wraps a sync bucket to ensure all its methods return awaitables. This allows the Limiter to detect\nasynchronous behavior and use asyncio.sleep() instead of time.sleep() during delay handling,\npreventing blocking of the asyncio event loop.\n\nExample: [limiter_factory.py::create_inmemory_limiter()](pyrate_limiter/limiter_factory.py)\n\n### Async or Sync or Multiprocessing\n\nThe Limiter is basically made of a Clock backend and a Bucket backend. The backends may be async or sync, which determines the Limiters internal behavior, regardless of whether the caller enters via a sync or async function.\n\ntry_acquire_async: When calling from an async context, use try_acquire_async. This uses a thread-local asyncio lock to ensure only one asyncio task is acquiring, followed by a global RLock so that only one thread is acquiring.\n\ntry_acquire: When called directly, the global RLock enforces only one thread at a time.\n\nMultiprocessing: If using MultiprocessBucket, two locks are used in Limiter: a top level multiprocessing lock, then a thread level RLock\n\n\n## Advanced Usage\n\n### Component level diagram\n\n\n\n### Time sources\n\nTime source can be anything from anywhere: be it python's built-in time, or monotonic clock, sqliteclock, or crawling from world time server(well we don't have that, but you can!).\n\n```python\nfrom pyrate_limiter import TimeClock # use python' time.time()\nfrom pyrate_limiter import MonotonicClock # use python time.monotonic()\n```\n\nClock's abstract interface only requires implementing a method `now() -> int`. And it can be both sync or async.\n\n### Leaking\n\nTypically bucket should not hold items forever. Bucket's abstract interface requires its implementation must be provided with `leak(current_timestamp: Optional[int] = None)`.\n\nThe `leak` method when called is expected to remove any items considered outdated at that moment. During Limiter lifetime, all the buckets' `leak` should be called periodically.\n\n**BucketFactory** provide a method called `schedule_leak` to help deal with this matter. Basically, it will run as a background task for all the buckets currently in use, with interval between `leak` call by **default is 10 seconds**.\n\n```python\n# Runnning a background task (whether it is sync/async - doesnt matter)\n# calling the bucket's leak\nfactory.schedule_leak(bucket, clock)\n```\n\nYou can change this calling interval by overriding BucketFactory's `leak_interval` property. This interval is in **miliseconds**.\n\n```python\nclass MyBucketFactory(BucketFactory):\n def __init__(self, *args):\n self.leak_interval = 300\n```\n\nWhen dealing with leak using BucketFactory, the author's suggestion is, we can be pythonic about this by implementing a constructor\n\n```python\nclass MyBucketFactory(BucketFactory):\n\n def constructor(self, clock, buckets):\n self.clock = clock\n self.buckets = buckets\n\n for bucket in buckets:\n self.schedule_leak(bucket, clock)\n\n```\n\n### Concurrency\n\nGenerally, Lock is provided at Limiter's level, except SQLiteBucket case.\n\n### Custom backends\n\nIf these don't suit your needs, you can also create your own bucket backend by implementing `pyrate_limiter.AbstractBucket` class.\n\nOne of **PyrateLimiter** design goals is powerful extensibility and maximum ease of development.\n\nIt must be not only be a ready-to-use tool, but also a guide-line, or a framework that help implementing new features/bucket free of the most hassles.\n\nDue to the composition nature of the library, it is possbile to write minimum code and validate the result:\n\n- Fork the repo\n- Implement your bucket with `pyrate_limiter.AbstractBucket`\n- Add your own `create_bucket` method in `tests/conftest.py` and pass it to the `create_bucket` fixture\n- Run the test suite to validate the result\n\nIf the tests pass through, then you are just good to go with your new, fancy bucket!\n",
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"summary": "Python Rate-Limiter using Leaky-Bucket Algorithm",
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