Skip to main content

CSP-style concurrency for Python

Project description

aiochan

Build Status Documentation Status codecov PyPI version PyPI version PyPI status GitHub license

logo

Aiochan is a library written to bring the wonderful idiom of CSP-style concurrency to python. The implementation is based on the battle-tested Clojure library core.async, while the API is carefully crafted to feel as pythonic as possible.

Why?

  • Doing concurrency in Python was painful
  • asyncio sometimes feels too low-level
  • I am constantly missing capabilities from golang and core.async
  • It is much easier to port core.async to Python than to port all those wonderful python packages to some other language.

What am I getting?

  • Pythonic API that includes everything you'd need for CSP-style concurrency programming
  • Works seamlessly with existing asyncio-based libraries
  • Fully tested
  • Fully documented
  • Guaranteed to work with Python 3.5.2 or above and PyPy 3.5 or above
  • Depends only on python's core libraries, zero external dependencies
  • Proven, efficient implementation based on Clojure's battle-tested core.async
  • Familiar semantics for users of golang's channels and Clojure's core.async channels
  • Flexible implementation that does not depend on the inner workings of asyncio at all
  • Permissively licensed
  • A beginner-friendly tutorial to get newcomers onboard as quickly as possible

How to install?

pip3 install aiochan

How to use?

Read the beginner-friendly tutorial that starts from the basics. Or if you are already experienced with golang or Clojure's core.async, start with the quick introduction and then dive into the API documentation.

I want to try it first

The quick introduction and the beginner-friendly tutorial can both be run in jupyter notebooks, online in binders if you want (just look for the binder link at the top of each tutorial).

Examples

In addition to the introduction and the tutorial, we have the complete set of examples from Rob Pike's Go concurrency patterns translated into aiochan. Also, here is a solution to the classical dining philosophers problem.

I still don't know how to use it

We are just starting out, but we will try to answer aiochan-related questions on stackoverflow as quickly as possible.

I found a bug

File an issue, or if you think you can solve it, a pull request is even better.

Do you use it in production? For what use cases?

aiochan is definitely not a toy and we do use it in production, mainly in the two following scenarios:

  • Complex data-flow in routing. We integrate aiochan with an asyncio-based web server. This should be easy to understand.
  • Data-preparation piplelines. We prepare and pre-process data to feed into our machine learning algorithms as fast as possible so that our algorithms spend no time waiting for data to come in, but no faster than necessary so that we don't have a memory explosion due to data coming in faster than they can be consumed. For this we make heavy use of parallel_pipe and parallel_pipe_unordered. Currently we are not aware of any other library that can completely satisfy this need of ours.

What's up with the logo?

It is our 'hello world' example:

import aiochan as ac

async def blue_python(c):
    while True:
        # do some hard work
        product = "a product made by the blue python"
        await c.put(product)

async def yellow_python(c):
    while True:
        result = await c.get()
        # use result to do amazing things
        print("A yellow python has received", result)

async def main():
    c = ac.Chan()

    for _ in range(3):
        ac.go(blue_python(c))

    for _ in range(3):
        ac.go(yellow_python(c))

in other words, it is a 3-fan-in on top of a 3-fan-out. If you run it, you will have an endless stream of A yellow python has received a product made by the blue python.

If you have no idea what this is, read the tutorial.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aiochan-0.2.7.tar.gz (27.6 kB view details)

Uploaded Source

Built Distribution

aiochan-0.2.7-py3-none-any.whl (26.7 kB view details)

Uploaded Python 3

File details

Details for the file aiochan-0.2.7.tar.gz.

File metadata

  • Download URL: aiochan-0.2.7.tar.gz
  • Upload date:
  • Size: 27.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for aiochan-0.2.7.tar.gz
Algorithm Hash digest
SHA256 a06e3e2f51d0ed33a686fa1fb9c322abec085077e173d2fb54dcab4ea04b46e6
MD5 b99d5ae2ccc37c71f4b760970a296fbe
BLAKE2b-256 5abe521edd69bb63c2a15141a128e3c7303973e09e8d356e1ef8da061fce0610

See more details on using hashes here.

File details

Details for the file aiochan-0.2.7-py3-none-any.whl.

File metadata

  • Download URL: aiochan-0.2.7-py3-none-any.whl
  • Upload date:
  • Size: 26.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for aiochan-0.2.7-py3-none-any.whl
Algorithm Hash digest
SHA256 85ffb776f83963a41682cf73f4329b717615d3d6191a0d9694d9fbb64b938101
MD5 e0e3e0e878337a12c10c270ef40b3ffa
BLAKE2b-256 f2eef237fe68cb27780789ab528d7f0b0e502ad4eeb196baf22bfb89b10c5418

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page