distex 0.5.8

Async distributed process pool using asyncio

Introduction

The distex package provides a distributed process pool that uses asyncio to efficiently manage the local and remote worker processes.

Features:

• Scales from 1 to 1000’s of processors;

• Can handle in the order of 50.000 small tasks per second;

• Easy to use with ssh (secure shell);

• Full asynchronous support;

• Maps over unbounded iterables;

• Choice of pickle, dill or cloudpickle serialization for functions and data;

• Backward compatible with concurrent.futures.ProcessPool (PEP3148).

Installation

pip3 install -U distex

Dependencies:

• Python version 3.6 or higher;

• On Unix the uvloop package is recommended: pip3 install uvloop

• SSH client and server (optional).

Examples

A process pool can have local and remote workers in any combination. Here is a pool that uses 4 local workers:

from distex import Pool

def f(x):
    return x*x

pool = Pool(4)
for y in pool.map(f, range(100)):
    print(y)

To create a pool that also uses 8 workers on host maxi, using ssh:

pool = Pool(4, ['ssh://maxi/8'])

There is full support for every asynchronous construct imaginable:

import asyncio
from distex import Pool

def init():
    # pool initializer: set the start time for every worker
    import time
    __builtins__.t0 = time.time()

async def timer(i=0):
    # async code running in the pool
    import time
    await asyncio.sleep(1)
    return time.time() - t0

async def ait():
    # async iterator running on the user side
    for i in range(20):
        await asyncio.sleep(0.1)
        yield i

async def main():
    async with Pool(4, initializer=init, qsize=1) as pool:
        async for t in pool.map_async(timer, ait()):
            print(t)
        print(await pool.run_on_all_async(timer))


loop = asyncio.get_event_loop()
loop.run_until_complete(main())

High level architecture

Distex does not use remote ‘task servers’. Instead it is done the other way around: A local server is started first; Then the local and remote workers are started and each of them will connect on its own back to the server. When all workers have connected then the pool is ready for duty.

Each worker consists of a single-threaded process that is running an asyncio event loop. This loop is used both for communication and for running asynchronous tasks. Synchronous tasks are run in a blocking fashion.

When using ssh, a remote (or ‘reverse’) tunnel is created from a remote Unix socket to the local Unix socket that the local server is listening on. Multiple workers on a remote machine will use the same Unix socket and share the same ssh tunnel.

Documentation

Distex documentation

Changelog

Version 0.5.8

• PR #9 merged to fix server script

Version 0.5.7

• distex_proc entry point is now used to allow various Python setups

Version 0.5.6

• Fixed issue #5

Version 0.5.5

• Optimizations; some logging issues fixed.

Version 0.5.4

• Fixed issue #4

Version 0.5.3

• Small scheduling improvements

Version 0.5.2

• Optimizations for large data

• Better error handling when result can’t be pickled

Version 0.5.1

• Fixes for Windows

Version 0.5.0

• Initial release

author:

Ewald de Wit <[e-mail]>