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pypeln module

Pypeline

Pypeline is a simple yet powerful python library for creating concurrent data pipelines.

  • Pypeline was designed to solve simple medium data tasks that require concurrency and parallelism but where using frameworks like Spark or Dask feel exaggerated or unnatural.
  • Pypeline exposes an easy to use, familiar, functional API.
  • Pypeline enables you to build pipelines using Processes, Threads and asyncio.Tasks via the exact same API.
  • Pypeline allows you to have control over the memory and cpu resources used at each stage of your pipeline.

Installation

Install Pypeline using pip:

pip install pypeln

Basic Usage

With Pypeline you can easily create multi-stage data pipelines using 3 type of workers:

Processes

You can create a pipeline based on multiprocessing.Process workers by using the process module:

from pypeln import process as pr
import time
from random import random

def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1

def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3

data = range(10) # [0, 1, 2, ..., 9]

stage = pr.map(slow_add1, data, workers = 3, maxsize = 4)
stage = pr.filter(slow_gt3, stage, workers = 2)

data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]

At each stage the you can specify the numbers of workers. The maxsize parameter limits the maximum amount of elements that the stage can hold simultaneously.

Threads

You can create a pipeline based on threading.Thread workers by using the thread module:

from pypeln import thread as th
import time
from random import random

def slow_add1(x):
    time.sleep(random()) # <= some slow computation
    return x + 1

def slow_gt3(x):
    time.sleep(random()) # <= some slow computation
    return x > 3

data = range(10) # [0, 1, 2, ..., 9]

stage = th.map(slow_add1, data, workers = 3, maxsize = 4)
stage = th.filter(slow_gt3, stage, workers = 2)

data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]

Here we have the exact same situation as in the previous case except that the worker are Threads.

Tasks

You can create a pipeline based on asyncio.Task workers by using the asyncio_task module:

from pypeln import asyncio_task as aio
import asyncio
from random import random

async def slow_add1(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x + 1

async def slow_gt3(x):
    await asyncio.sleep(random()) # <= some slow computation
    return x > 3

data = range(10) # [0, 1, 2, ..., 9]

stage = aio.map(slow_add1, data, workers = 3, maxsize = 4)
stage = aio.filter(slow_gt3, stage, workers = 2)

data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]

Conceptually similar but everything is running in a single thread and Task workers are created dynamically.

Mixed Pipelines

You can create pipelines using different worker types such that each type is the best for its given task so you can get the maximum performance out of your code:

data = get_iterable()
data = aio.map(f1, data, workers = 100)
data = th.flat_map(f2, data, workers = 10)
data = filter(f3, data)
data = pr.map(f4, data, workers = 5, maxsize = 200)

Notice that here we even used a regular python filter, since stages are iterables Pypeline integrates smoothly with any python code, just be aware of how each stage behaves.

Pipe Operator

In the spirit of being a true pipeline library, Pypeline also lets you create your pipelines using the pipe | operator:

data = (
    range(10)
    | pr.map(slow_add1, workers = 3, maxsize = 4)
    | pr.filter(slow_gt3, workers = 2)
    | list
)

Architecture

A Pypeline pipeline has the following structure:

diagram

  • Its composed of several concurrent stages
  • At each stage it contains on or more worker entities that perform a task.
  • Related stages are connected by a queue, workers from one stage put items into it, and workers from the other stage get items from it.
  • Source stages consume iterables.
  • Sink stages can be converted into iterables which consume them.

Resources

* Pypeline API Documentation

Benchmarks

Related Stuff

Contributors

"""
# Pypeline

_Pypeline is a simple yet powerful python library for creating concurrent data pipelines._

* Pypeline was designed to solve simple _medium_ data tasks that require concurrency and parallelism but where using frameworks like Spark or Dask feel exaggerated or unnatural.
* Pypeline exposes an easy to use, familiar, functional API.
* Pypeline enables you to build pipelines using Processes, Threads and asyncio.Tasks via the exact same API.
* Pypeline allows you to have control over the memory and cpu resources used at each stage of your pipeline.

## Installation

Install Pypeline using pip:

    pip install pypeln


## Basic Usage
With Pypeline you can easily create multi-stage data pipelines using 3 type of workers:

### Processes
You can create a pipeline based on [multiprocessing.Process](https://docs.python.org/3.4/library/multiprocessing.html#multiprocessing.Process) workers by using the `process` module:


    from pypeln import process as pr
    import time
    from random import random

    def slow_add1(x):
        time.sleep(random()) # <= some slow computation
        return x + 1

    def slow_gt3(x):
        time.sleep(random()) # <= some slow computation
        return x > 3

    data = range(10) # [0, 1, 2, ..., 9] 

    stage = pr.map(slow_add1, data, workers = 3, maxsize = 4)
    stage = pr.filter(slow_gt3, stage, workers = 2)

    data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]

At each stage the you can specify the numbers of `workers`. The `maxsize` parameter limits the maximum amount of elements that the stage can hold simultaneously.

### Threads
You can create a pipeline based on [threading.Thread](https://docs.python.org/3/library/threading.html#threading.Thread) workers by using the `thread` module:

    from pypeln import thread as th
    import time
    from random import random

    def slow_add1(x):
        time.sleep(random()) # <= some slow computation
        return x + 1

    def slow_gt3(x):
        time.sleep(random()) # <= some slow computation
        return x > 3

    data = range(10) # [0, 1, 2, ..., 9] 

    stage = th.map(slow_add1, data, workers = 3, maxsize = 4)
    stage = th.filter(slow_gt3, stage, workers = 2)

    data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]

Here we have the exact same situation as in the previous case except that the worker are Threads.

### Tasks
You can create a pipeline based on [asyncio.Task](https://docs.python.org/3.4/library/asyncio-task.html#asyncio.Task) workers by using the `asyncio_task` module:

    from pypeln import asyncio_task as aio
    import asyncio
    from random import random

    async def slow_add1(x):
        await asyncio.sleep(random()) # <= some slow computation
        return x + 1

    async def slow_gt3(x):
        await asyncio.sleep(random()) # <= some slow computation
        return x > 3

    data = range(10) # [0, 1, 2, ..., 9] 

    stage = aio.map(slow_add1, data, workers = 3, maxsize = 4)
    stage = aio.filter(slow_gt3, stage, workers = 2)

    data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]

Conceptually similar but everything is running in a single thread and Task workers are created dynamically.

## Mixed Pipelines
You can create pipelines using different worker types such that each type is the best for its given task so you can get the maximum performance out of your code:

    data = get_iterable()
    data = aio.map(f1, data, workers = 100)
    data = th.flat_map(f2, data, workers = 10)
    data = filter(f3, data)
    data = pr.map(f4, data, workers = 5, maxsize = 200)

Notice that here we even used a regular python `filter`, since stages are iterables Pypeline integrates smoothly with any python code, just be aware of how each stage behaves.


## Pipe Operator
In the spirit of being a true pipeline library, Pypeline also lets you create your pipelines using the pipe `|` operator:


    data = (
        range(10)
        | pr.map(slow_add1, workers = 3, maxsize = 4)
        | pr.filter(slow_gt3, workers = 2)
        | list
    )

## Architecture

A Pypeline pipeline has the following structure:

![diagram](https://github.com/cgarciae/pypeln/raw/master/docs/guide/diagram_small.png =250x250)

* Its composed of several concurrent **stages**
* At each stage it contains on or more **worker** entities that perform a task.
* Related stages are connected by a **queue**, workers from one stage *put* items into it, and workers from the other stage *get* items from it.
* Source stages consume iterables.
* Sink stages can be converted into iterables which 
consume them.

## Resources
<!-- * [Pypeline Guide](https://cgarciae.gitbook.io/pypeln) -->
* [Pypeline API Documentation](https://cgarciae.github.io/pypeln/)

## Benchmarks
* [Making an Unlimited Number of Requests with Python aiohttp + pypeln](https://medium.com/@cgarciae/making-an-infinite-number-of-requests-with-python-aiohttp-pypeln-3a552b97dc95)
  * [Code](https://github.com/cgarciae/pypeln/tree/master/benchmarks/100_million_downloads)


## Related Stuff
* [mpipe](https://vmlaker.github.io/mpipe/)
* [Process Pools](https://docs.python.org/3.4/library/multiprocessing.html?highlight=process#module-multiprocessing.pool)
* [Making 100 million requests with Python aiohttp](https://www.artificialworlds.net/blog/2017/06/12/making-100-million-requests-with-python-aiohttp/)
* [Python multiprocessing Queue memory management](https://stackoverflow.com/questions/52286527/python-multiprocessing-queue-memory-management/52286686#52286686)

## Contributors
* [cgarciae](https://github.com/cgarciae)
* [davidnet](https://github.com/davidnet)
"""

from __future__ import absolute_import, print_function



import sys
from . import process
from . import thread

if sys.version_info >= (3, 5):
    from . import asyncio_task
    from .asyncio_task import TaskPool

__all__ = ["process", "thread", "asyncio_task"]

Sub-modules

pypeln.process

The process module lets you create pipelines using objects from python's multiprocessing module according to Pypeline's general architecture. Use this module when you are in need of true...

pypeln.thread

The process module lets you create pipelines using objects from python's multiprocessing module according to Pypeline's general architecture. Use this module when you are in need of true...