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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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> **Upgrading from v3.x?** See the [Migration Guide](https://github.com/vutran1710/PyrateLimiter/blob/master/docs/migrating.md) for breaking changes.

<br>

## 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

<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)

#### 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!
