Metadata-Version: 2.4 Name: pyrate-limiter Version: 4.1.0 Summary: Python Rate-Limiter using Leaky-Bucket Algorithm Project-URL: Homepage, https://github.com/vutran1710/PyrateLimiter Project-URL: Repository, https://github.com/vutran1710/PyrateLimiter Project-URL: Documentation, https://pyrate-limiter.readthedocs.io Author-email: vutr License-Expression: MIT License-File: LICENSE Keywords: aiohttp,httpx,leaky-bucket,limiter,rate,rate-limiter,rate_limiter,ratelimit,ratelimiter,ratelimiting,requests,throttle Classifier: Development Status :: 5 - Production/Stable Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Programming Language :: Python :: 3.13 Classifier: Programming Language :: Python :: 3.14 Classifier: Topic :: Software Development :: Documentation Classifier: Topic :: Software Development :: Libraries :: Python Modules Classifier: Typing :: Typed Requires-Python: >=3.10 Description-Content-Type: text/markdown # PyrateLimiter The request rate limiter using Leaky-bucket Algorithm. Full project documentation can be found at [pyratelimiter.readthedocs.io](https://pyratelimiter.readthedocs.io). [![PyPI version](https://badge.fury.io/py/pyrate-limiter.svg)](https://badge.fury.io/py/pyrate-limiter) [![PyPI - Python Versions](https://img.shields.io/pypi/pyversions/pyrate-limiter)](https://pypi.org/project/pyrate-limiter) [![codecov](https://codecov.io/gh/vutran1710/PyrateLimiter/branch/master/graph/badge.svg?token=E0Q0YBSINS)](https://codecov.io/gh/vutran1710/PyrateLimiter) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/vutran1710/PyrateLimiter/graphs/commit-activity) [![PyPI license](https://img.shields.io/pypi/l/ansicolortags.svg)](https://pypi.python.org/pypi/pyrate-limiter/) > **Upgrading from v3.x?** See the [Migration Guide](https://github.com/vutran1710/PyrateLimiter/blob/master/docs/migrating.md) for breaking changes.
## Contents - [Features](#features) - [Installation](#installation) - [Web Requests](#web-request-rate-limiting) - [AIOHTTP](#aiohttp) - [HTTPX](#httpx) - [Requests](#requests) - [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) - [`as_decorator()`: use limiter as decorator](#as_decorator-use-limiter-as-decorator) - [Limiter API](#limiter-api) - [Context Manager](#context-manager) - [Weight](#weight) - [Handling exceeded limits](#handling-exceeded-limits) - [Bucket analogy](#bucket-analogy) - [Blocking vs Non-blocking](#blocking-vs-non-blocking) - [Backends](#backends) - [InMemoryBucket](#inmemorybucket) - [MultiprocessBucket](#multiprocessbucket) - [SQLiteBucket](#sqlitebucket) - [RedisBucket](#redisbucket) - [PostgresBucket](#postgresbucket) - [BucketAsyncWrapper](#bucketasyncwrapper) - [Async or Sync or Multiprocessing](#async-or-sync-or-multiprocessing) - [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 with configurable blocking or non-blocking behavior. - 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: ```python from pyrate_limiter import Duration, Rate, Limiter limiter = Limiter(Rate(5, Duration.SECOND * 2)) # Blocking mode (default) - waits until permit available for i in range(6): limiter.try_acquire(str(i)) print(f"Acquired permit {i}") # Non-blocking mode - returns False if bucket full for i in range(6): success = limiter.try_acquire(str(i), blocking=False) if not success: print(f"Rate limited at {i}") ``` ## limiter_factory [limiter_factory.py](https://github.com/vutran1710/PyrateLimiter/blob/master/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](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/asyncio_ratelimit.py) - Rate limiting asyncio tasks w/ a decorator: [asyncio_decorator.py](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/asyncio_decorator.py) - HTTPX rate limiting - asyncio, single process and multiprocess examples [httpx_ratelimiter.py](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/httpx_ratelimiter.py) - Multiprocessing using an in-memory rate limiter - [in_memory_multiprocess.py](https://github.com/vutran1710/PyrateLimiter/blob/master/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](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/sql_filelock_multiprocess.py) ## Web Request Rate Limiting pyrate_limiter provides three extras for popular web request libraries: - [AIOHTTP](https://pypi.org/project/aiohttp/) - [HTTPX](https://pypi.org/project/httpx/) - [Requests](https://pypi.org/project/requests/) ### AIOHTTP ```py from pyrate_limiter import limiter_factory from pyrate_limiter.extras.aiohttp_limiter import RateLimitedSession limiter = limiter_factory.create_inmemory_limiter(rate_per_duration=2, duration=Duration.SECOND) session = RateLimitedSession(limiter) ``` Example: [aiohttp_ratelimiter.py](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/aiohttp_ratelimiter.py) ### HTTPX ```py from pyrate_limiter import limiter_factory from pyrate_limiter.extras.httpx_limiter import AsyncRateLimiterTransport, RateLimiterTransport import httpx limiter = limiter_factory.create_inmemory_limiter(rate_per_duration=1, duration=Duration.SECOND, max_delay=Duration.HOUR) url = "https://example.com" with httpx.Client(transport=RateLimiterTransport(limiter=limiter)) as client: client.get(url) # or async async with httpx.AsyncClient(transport=AsyncRateLimiterTransport(limiter=limiter)) as client: client.get(url) ... ``` Example: [httpx_ratelimiter.py](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/httpx_ratelimiter.py) ### Requests ```py from pyrate_limiter import limiter_factory from pyrate_limiter.extras.requests_limiter import RateLimitedRequestsSession limiter = limiter_factory.create_inmemory_limiter(rate_per_duration=2, duration=Duration.SECOND) session = RateLimitedRequestsSession(limiter) .... ``` Example: [requests_ratelimiter.py](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/requests_ratelimiter.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=MonotonicClock(), 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 MonotonicClock, SQLiteClock base_clock = MonotonicClock() ``` 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](https://github.com/vutran1710/PyrateLimiter/blob/master/examples/asyncio_ratelimit.py) #### `as_decorator()`: use limiter as decorator `Limiter` can be used as a decorator with `name` and `weight` parameters. The decorator works with both synchronous and asynchronous functions: ```python from pyrate_limiter import Rate, Duration, Limiter limiter = Limiter(Rate(5, Duration.SECOND)) @limiter.as_decorator(name="api_call", weight=1) def handle_something(*args, **kwargs): """function logic""" @limiter.as_decorator(name="background_job", weight=2) async def handle_something_async(*args, **kwargs): """async function logic""" ``` For full example see [asyncio_decorator.py](https://github.com/vutran1710/PyrateLimiter/blob/master/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. #### Context Manager Limiter supports the context manager protocol for automatic cleanup: ```python from pyrate_limiter import Limiter, RequestRate, Duration, InMemoryBucket # Define a simple rate and create a bucket rate = RequestRate(5, Duration.SECOND) bucket = InMemoryBucket(rate) # Use Limiter as a context manager with Limiter(bucket) as limiter: limiter.try_acquire("item") # Resources automatically released # Or manually close limiter = Limiter(bucket) try: limiter.try_acquire("item") finally: limiter.close() ``` ### 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](#advanced-usage) below for more details. ### Handling exceeded limits When a rate limit is exceeded, you can choose between blocking and non-blocking behavior. #### Bucket analogy 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) #### Blocking vs Non-blocking By default, `try_acquire` blocks until a permit becomes available: ```python from pyrate_limiter import Rate, Limiter, Duration rate = Rate(3, Duration.SECOND) limiter = Limiter(rate) # Blocking (default) - waits until permit is available for i in range(5): limiter.try_acquire("item") # blocks if bucket is full print(f"Acquired {i}") ``` For non-blocking behavior, set `blocking=False` to return immediately: ```python # Non-blocking - returns False immediately if bucket is full for i in range(5): success = limiter.try_acquire("item", blocking=False) if not success: print(f"Rate limited at request {i}") break ``` For async code, use `try_acquire_async` with optional timeout: ```python # Async with timeout (in seconds) success = await limiter.try_acquire_async("item", timeout=5) if not success: print("Timed out waiting for permit") ``` The `buffer_ms` parameter (default 50ms) adds a small delay buffer to account for timing variations: ```python from pyrate_limiter import Duration, InMemoryBucket, Limiter, RequestRate rate = RequestRate(5, Duration.SECOND) bucket = InMemoryBucket(rate) limiter = Limiter(bucket, buffer_ms=100) # 100ms buffer ``` ### 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](https://github.com/vutran1710/PyrateLimiter/blob/master/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()](https://github.com/vutran1710/PyrateLimiter/blob/master/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()](https://github.com/vutran1710/PyrateLimiter/blob/master/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 ![](https://raw.githubusercontent.com/vutran1710/PyrateLimiter/master/docs/_static/components.jpg) ### 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 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!