paco

Small and idiomatic utility library for coroutine-driven asynchronous generic programming in Python.

Built on top of asyncio, paco provides missing capabilities from Python stdlib in order to write asynchronous cooperative multitasking in a nice-ish way. Also, paco aims to port some of functools and itertools standard functions to the asynchronous world.

paco can be your utility belt to deal with asynchronous, I/O-bound, non-blocking concurrent code in a cleaner and idiomatic way.

Features

• Simple and idiomatic API, extending Python stdlib with async coroutines gotchas.
• Built-in configurable control-flow concurrency support (throttle).
• No fancy abstractions: it just works with the plain asynchronous coroutines.
• Useful iterables, decorators, functors and convenient helpers.
• Coroutine-based functional helpers: compose, throttle, partial, timeout, times, until, race...
• Asynchronous coroutines port of Python built-in functions: filter, map, dropwhile, filterfalse, reduce...
• Supports asynchronous iterables and generators (PEP0525)
• Concurrent iterables and higher-order functions.
• Better asyncio.gather() and asyncio.wait() with optional concurrency control and ordered results.
• Works with both async/await and yield from coroutines syntax.
• Reliable coroutine timeout limit handler via context manager.
• Designed for intensive I/O bound concurrent non-blocking tasks.
• Good interoperability with asyncio and Python stdlib functions.
• Composable pipelines of functors via | operator overloading.
• Small and dependency free.
• Compatible with Python +3.4.

Installation

Using pip package manager:

pip install --upgrade paco

Or install the latest sources from Github:

pip install -e git+git://github.com/h2non/paco.git#egg=paco

API

• paco.ConcurrentExecutor
• paco.apply
• paco.compose
• paco.concurrent
• paco.constant
• paco.curry
• paco.defer
• paco.dropwhile
• paco.each
• paco.every
• paco.filter
• paco.filterfalse
• paco.flat_map
• paco.gather
• paco.identity
• paco.interval
• paco.map
• paco.once
• paco.partial
• paco.race
• paco.reduce
• paco.repeat
• paco.run
• paco.series
• paco.some
• paco.throttle
• paco.thunk
• paco.timeout
• paco.TimeoutLimit
• paco.times
• paco.until
• paco.wait
• paco.whilst
• paco.wraps

Examples

Asynchronously and concurrently execute multiple HTTP requests.

import paco
import aiohttp

async def fetch(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as res:
            return res

async def fetch_urls():
    urls = [
        'https://www.google.com',
        'https://www.yahoo.com',
        'https://www.bing.com',
        'https://www.baidu.com',
        'https://duckduckgo.com',
    ]

    # Map concurrent executor with concurrent limit of 3
    responses = await paco.map(fetch, urls, limit=3)

    for res in responses:
        print('Status:', res.status)

# Run in event loop
paco.run(fetch_urls())

Concurrent pipeline-style composition of transform functors over an iterable object.

import paco

async def filterer(x):
    return x < 8

async def mapper(x):
    return x * 2

async def drop(x):
    return x < 10

async def reducer(acc, x):
    return acc + x

async def task(numbers):
    return await (numbers
                   | paco.filter(filterer)
                   | paco.map(mapper)
                   | paco.dropwhile(drop)
                   | paco.reduce(reducer, initializer=0))

# Run in event loop
number = paco.run(task((1, 2, 3, 4, 5, 6, 7, 8, 9, 10)))
print('Number:', number) # => 36

License

MIT - Tomas Aparicio