janus

Thread-safe asyncio-aware queue for Python

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Thread-safe asyncio-aware queue for Python

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Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

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HN mentions by year3 dated mentions

Observed mentions: 2019-02-10 10:12:55 UTC to 2025-12-20 20:25:38 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	1	0	1
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	0	0	0
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	0	0	0
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	0	0	0
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	0	0	0
2025	2025-01-01 00:00:00 UTC	2026-01-01 00:00:00 UTC	2	1	1

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x42005e1f·Dec 20, 2025Submission

Show HN: Сulsans – Thread-safe async-aware queue for Python

In my previous post [0], I described how I came to create aiologic. Here, I want to do the same for a derivative library - Сulsans. In October 2024, I started thinking about how to present aiologic queues. Andrew Svetlov's Janus library [1] had been around for quite some time and was much more popular, so I knew that comparisons with it would be inevitable. However, Janus seemed to be in a suspended state: there had been no major changes for three years, and almost all commits during that period were made by Dependabot. So I asked a relevant question [2]. During the discussion, I pointed out Janus' performance issues and stated that they could be solved by implementing queues on top of my primitives. But since Janus is a mature library, such a radical change could not be accepted. Therefore, as proof of concept, I implemented a new librar

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y - Culsans. That is how its story began. Over time, both libraries underwent changes. Janus received significant performance improvements in 1.2.0, not least due to my PRs [3]. In 2.0.0, contrary to the above, backward compatibility was broken as a result of the implementation of shutdown methods. And Culsans became an independent library with its own features (which neither aiologic nor Janus have). So, what is Culsans? It is a library that provides a way to communicate within a single process between different threads, different tasks (including from different event loops; asyncio, Curio, Trio, AnyIO - whatever you want), and even different greenlets (eventlet/gevent), all in a single instance. Its queues are fully compatible with the standard queues via Janus-like interfaces (as well as with Janus itself) and provide additional features such as dynamic maxsize. In short, I invite you to try out my library and see for yourself. [0] https://news.ycombinator.com/item?id=46308839 [1] https://github.com/aio-libs/janus [2] https://github.com/aio-libs/janus/issues/679 [3] https://github.com/aio-libs/janus/pull/704

2 points · Open discussion

xg15·Sep 2, 2025

Everything that directly interacts with an event loop object and calls methods such as loop.call_soon() [1]. This is used by most of asyncio's synchronization primitives, e.g. async.Queue. A consequence is that you cannot use asyncio Queues to pass messages or work items between async functions and worker threads. (And of course you can't use regular blocking queues either, because they would block). The only solution is to build your own ad-hoc system using loop.call_soon_threadsafe() or use third-party libs like Janus[2]. [1] https://github.com/python/cpython/blob/e4e2390a64593b33d6556... [2] https://github.com/aio-libs/janus

In Python has had async for 10 years – why isn't it more popular?

Yxogenium·Feb 10, 2019

On a related note, I found that asyncio (cooperative multitasking in Python) has made huge strides in usability in Python 3.7. The code feels sequential, and it’s quite easy to understand the flow of events ; it’s very explicit yet you never have to run the callbacks yourself, the exceptions do show up, and you don’t even need to manipulate a `loop` object anymore! If you're trying to learn how to use it, my advice would be: * "Stick to the official documentation[1]!". Many resources found online are outdated, and give convoluted or plain wrong examples, and fail to mention the recent additions. * Use `asyncio.run`. The alternative way (using get_event_loop() and loop.run_until_complete() is cumbersome and hard to get right. Even the documentation wasn't correct[2].). The part on Coroutines and Tasks is well-written, and the best part is

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the examples. Simply reading all the examples on after another gives a a good insight on how asyncio should be used. * If you want to work with sockets, use the high-level "Streams"[3] if you want to stay in the standard library. `asyncio.start_server` is a powerful abstraction. If you’re willing to use a third-party library, I found that `pynng`[4] was a breeze to work with. It is compatible with asyncio and other async frameworks, and it found it more straightforward than pyzmq, which is also compatible with asyncio[5]. * If you want to run async functions in the main event loop and blocking functions in threads (with `loop.run_in_executor), Janus[7] seems to be a great way to share data. I have not used it yet though. My use case was that I wanted to read sensor data on one computer (server) and broadcast it to other computers (clients) which would in turn graph it live, or write it to disk. The server used `pyserial_asyncio`[6] to asynchronously read serial data and published it via TCP using a the pub/sub scheme from pynng (pynng.Pub0). The clients could then either synchronously or asynchronously receive the data by subscribing to the server (pynng.Sub0), and make plots in realtime. [1]: https://docs.python.org/3/library/asyncio.html [2]: https://github.com/python/asyncio/pull/465#issue-93620963 [3]: https://docs.python.org/3/library/asyncio-stream.html#asynci... [4]: https://pypi.org/project/pynng/ [5]: https://pyzmq.readthedocs.io/en/latest/api/zmq.asyncio.html [6]: https://github.com/pyserial/pyserial-asyncio [7]: https://github.com/aio-libs/janus

In Asynchronous programming and cooperative multitasking