culsans

Mixed sync-async queue, supposed to be used for communicating between classic synchronous (threaded) code and asynchronous one, between two asynchronous codes in different threads, and for any other combination that you want. Based on the queue module. Built on the aiologic package. Inspired by the janus library.

Like Culsans god, the queue object from the library has two faces: synchronous and asynchronous interface. Unlike Janus library, synchronous interface supports eventlet, gevent, and threading, while asynchronous interface supports asyncio, trio, and anyio.

Synchronous is fully compatible with standard queue, asynchronous one follows asyncio queue design.

Usage

Three queues are available:

Queue
LifoQueue
PriorityQueue

Each has two properties: sync_q and async_q.

Use the first to get synchronous interface and the second to get asynchronous one.

Example

import anyio
import culsans


def sync_run(sync_q: culsans.SyncQueue[int]) -> None:
    for i in range(100):
        sync_q.put(i)
    else:
        sync_q.join()


async def async_run(async_q: culsans.AsyncQueue[int]) -> None:
    for i in range(100):
        value = await async_q.get()

        assert value == i

        async_q.task_done()


async def main() -> None:
    queue: culsans.Queue[int] = culsans.Queue()

    async with anyio.create_task_group() as tasks:
        tasks.start_soon(anyio.to_thread.run_sync, sync_run, queue.sync_q)
        tasks.start_soon(async_run, queue.async_q)

    queue.shutdown()


anyio.run(main)

Extras

Both interfaces support some additional features that are not found in the original queues.

growing & shrinking

You can dynamically change the upperbound limit on the number of items that can be placed in the queue with queue.maxsize = N. If it increases (growing), the required number of waiting putters will be woken up. If it decreases (shrinking), items exceeding the new limit will remain in the queue, but all putters will be blocked until enough items are retrieved from the queue. And if maxsize is less than or equal to zero, all putters will be woken up.

async with anyio.create_task_group() as tasks:
    async_q = culsans.Queue(1).async_q

    for i in range(4):
        tasks.start_soon(async_q.put, i)

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 1

    async_q.maxsize = 2  # growing

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 2

    async_q.maxsize = 1  # shrinking

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 2

    async_q.get_nowait()

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 1

    async_q.maxsize = 0  # now the queue size is infinite

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 3

peek() & peek_nowait()

If you want to check the first item of the queue, but do not want to remove that item from the queue, you can use the peek() and peek_nowait() methods instead of the get() and get_nowait() methods.

sync_q = culsans.Queue().sync_q

sync_q.put("spam")

assert sync_q.peekable()
assert sync_q.peek() == "spam"
assert sync_q.peek_nowait() == "spam"
assert sync_q.qsize() == 1

These methods can be considered an implementation of partial compatibility with gevent queues.

clear()

In some scenarios it may be necessary to clear the queue. But it is inefficient to do this through a loop, and it causes additional difficulties when it is also necessary to ensure that no new items can be added during the clearing process. For this purpose, there is an atomic method clear() that clears the queue most efficiently.

async with anyio.create_task_group() as tasks:
    async_q = culsans.Queue(3).async_q

    for i in range(5):
        tasks.start_soon(async_q.put, i)

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 3

    async_q.clear()  # clearing

    await anyio.sleep(1e-3)
    assert async_q.qsize() == 2
    assert async_q.get_nowait() == 3
    assert async_q.get_nowait() == 4

Roughly equivalent to:

def clear(queue):
    while True:
        try:
            queue.get_nowait()
        except Empty:
            break
        else:
            queue.task_done()

Subclasses

You can create your own queues by inheriting from existing queue classes as if you were using the queue module. For example, this is how you can create an unordered queue that contains only unique items:

from culsans import Queue


class UniqueQueue(Queue):
    def _init(self, maxsize):
        self.data = set()

    def _qsize(self):
        return len(self.data)

    def _put(self, item):
        self.data.add(item)

    _peek = None

    def _peekable(self):
        return False

    def _get(self):
        return self.data.pop()

    def _clear(self):
        self.data.clear()

sync_q = UniqueQueue().sync_q

sync_q.put_nowait(23)
sync_q.put_nowait(42)
sync_q.put_nowait(23)

assert sync_q.qsize() == 2
assert sorted(sync_q.get_nowait() for _ in range(2)) == [23, 42]

All seven of these methods are called in exclusive access mode, so you can freely create your subclasses without thinking about whether your methods are thread-safe or not.

Greenlets

Libraries such as eventlet and gevent use greenlets instead of tasks. Since they do not use async-await syntax, their code is similar to synchronous code. There are three ways that you can tell culsans that you want to use greenlets instead of threads:

Set aiologic.lowlevel.current_green_library_tlocal.name (for the current thread).
Patch the threading module (for the main thread).
Specify AIOLOGIC_GREEN_LIBRARY environment variable (for all threads).

The value is the name of the library that you want to use.

Checkpoints

Sometimes it is useful when each asynchronous call switches execution to the next task and checks for cancellation and timeouts. For example, if you want to distribute CPU usage across all tasks. There are two ways to do this:

Set aiologic.lowlevel.<library>_checkpoints_cvar (for the current context).
Specify AIOLOGIC_<LIBRARY>_CHECKPOINTS environment variable (for all contexts).

The value is True or False for the first way, and a non-empty or empty string for the second.

Checkpoints are enabled by default for the trio library.

Compatibility

The interfaces are compliant with the Python API version 3.13, and the culsans library itself is almost fully compatible with the janus library version 1.1.0. If you are using janus in your application and want to switch to culsans, all you have to do is replace this:

import janus

with this:

import culsans as janus

and everything will work, except for the queue behavior after the close() call: new put() calls will still raise a RuntimeError, but all currently waiting ones will be woken up, and the join() and task_done() calls will succeed. This behavior corresponds to the queue behavior after the shutdown() call and solves aio-libs/janus#237.

Performance

Being built on the aiologic package, the culsans library has speed advantages. In sync -> async benchmarks, culsans.Queue is typically 5/6/3 times faster than janus.Queue on CPython 3.9-3.11/3.12/3.13, and 15 times faster on PyPy 3.10. However, if your application is performance sensitive and you do not need API compatibility, try aiologic queues. They are 6/7/4 times faster and 30 times faster in the same benchmarks.

Communication channels

GitHub Discussions: https://github.com/x42005e1f/culsans/discussions

Feel free to post your questions and ideas here.

License

The culsans library is offered under Zero-Clause BSD license.