async-pipeline 0.1.2

Async pipeline with functional methods

async_pipeline

complex async data pipelines with functional methods

Features

async_pipeline aims to simplify the creation of asynchronous pipelines.

Main features are:

• Simplicity. Allows defining not only linear pipelines, but also complex pipelines with cross structures.
• Fast. Each pipeline process is implemented through a coroutine process, which could avoid blocking by async functions.
• Easy-to-read. Linear previous and after stages can be connected using pipeline operator, making it more readable.
• Easy-to-use. Provides functional like methods.

Quick Start

install

python setup.py install

Simple linear pipeline

In this example, the data is multiplied by 2, and then filtered out those greater than 5. The functions in each stage can be synchronous or asynchronous.

import asyncio
from async_pipeline.pipe import Pipe

async def double(x):
    await asyncio.sleep(0.01)
    return x * 2
    
pipe = Pipe()  # create a pipe
stage1 = pipe.data(range(5))  # define data stage
stage2 = pipe.map(func=double, src=stage1)  # define map stage, async func
stage3 = pipe.filter(func=lambda x: x > 5, src=stage2)  # define filter stage, sync func
stage4 = pipe.result(src=stage3)  # define result stage, store result in stage4.result
pipe.run()  # run pipe
print(stage4.result)  # [6, 8]

Use pipe operator to abbreviate:

pipe = Pipe()
stage = range(5) | pipe.map(func=double) | pipe.filter(func=lambda x: x > 5) | pipe.result()
pipe.run()

Complex non-liner pipeline

This pipeline can be described in the following diagram:

  graph LR;
      inp((data))--'>0'-->n1((21));
      inp((data))--'<0'-->n2((_2));
      inp((data))--'==0'-->n3((drop));
      n1((21))-->n4((31));
      n1((21))-->n5((32));
      n4((31))-->n6((4));
      n5((32))-->n6((4));
      n2((22))-->n7((5));
      n6((4))-->n7((5));
      n7((5))-->n8((end));

from async_pipeline.pipe import Pipe

pipe = Pipe()
stage1 = [-1, 0, 1] | pipe.partition({
    'p': {
        'match': "elem > 0",
        'handle': pipe.map(func=lambda x: str(x) + '_21') 
                  | pipe.multiply(
                      pipe.map(func=lambda x, y: str(x) + y, func_kw={'y': '_31'}),
                      pipe.map(func=lambda x: str(x) + '_32')
                  )
    },
    'n': {
        'match': "elem < 0",
        'handle': pipe.map(func=lambda x: str(x) + '_22')
    }
})
stage2 = pipe.concat(
                pipe.concat(stage1['p'][0], stage1['p'][1]) | pipe.map(func=lambda x: str(x) + '_4'),
                stage1['n']
          ) \
          | pipe.map(func=lambda x: str(x) + '_5') \
          | pipe.result()
pipe.run()
print(sorted(stage2.result))  # ['-1_22_5', '1_21_31_4_5', '1_21_32_4_5']

Supported Python Versions

• 3.7
• 3.8
• 3.9
• 3.10

Unittest

pip install -r requirements/test.txt
python -m unittest

API Reference

class Pipe

All methods are called through pipe instance.

pipe = Pipe()

Create a pipe.

Different pipes do not affect each other.

pipe.run()

Execute stages in pipe, let the data start flowing.

Before this, all Stage functions are just record as coroutines. Each pipe can be executed only once.

await pipe.async_run()

The asynchronous version of run.

pipe.cancel()

Cancel the coroutines in the pipeline that have not yet executed.

class Stage

Each section of the pipeline, i.e. each process, is called a stage.

stages are always associated with a pipeline, so the stage instance is created through the pipe interface

pipe.data(src)

Create a Data Stage. Place it at the beginning.

src is data source, needs to be iterable or async iterable.

This method is optional because the data source can be placed directly, except that the code check will say "Class 'xx' does not define '_or_'"

stage = pipe.data(range(10))

pipe.result(src=None, output=None)

Create a Result Stage. Place it at the end.

src is previous stage. When using the pipeline operator, the left side of | is the previous Stage, so src is not specified at this time.

output locate the pipeline output, can be a list or any object that accepts append method. Default is self.result.

# 1
previous = pipe.data(range(10))
end = pipe.result(src=previous)
# 2
end = previous | pipe.result()
# 3
result = []
end = previous | pipe.result(output=result)

pipe.map(func, src=None, func_kw=None)

Create a Map Stage, it executes result = func(elem, **func_kw) for each element passed in, and outputs the result.

func is function object, either sync or async.

func_kw is parameters of func other than element, like kwargs dict.

src is previous stage, None when using |.

# 1
stage = pipe.map(func=lambda x: x + 1, src=previous)
# 2
stage = previous | pipe.map(func=some_func)
# 3
stage = previous | pipe.map(func=lambda x, y: x + y, func_kw={'y': 1})

pipe.filter(func, src=None, func_kw=None)

Create a Filter Stage, it computes flag = func(elem, **func_kw) for each element passed in, and outputs the element with flag True.

func is function object, either a sync or async.

func_kw is parameters of func other than element, like kwargs dictionary.

src is previous stage, None when using |.

# 1
stage = pipe.filter(func=lambda x: x > 0, src=previous)
# 2
stage = previous | pipe.filter(func=some_bool_func)
# 3
stage = previous | pipe.filter(func=lambda x, y: x > y, func_kw={'y': 0})

pipe.flatten(src=None)

Create a Flatten Stage, it flattens each element passed in and output.

src is previous stage which outputs a two-level list, or iterable or async iterative two-level list. None when using |.

# 1
stage = pipe.flatten(src=[[1], [2, 3]])
# 2
stage = previous | pipe.flatten()

pipe.concat(*src)

Create a Concat Stage, it merges the list of stages.

*src is multi previous stages, or iterable or async iterative lists.

# 1
stage = pipe.concat([1, 2], [3, 4])
# 2
stage = pipe.concat(
    data | pipe.map(func=lambda x: x), 
    data | pipe.filter(func=lambda x: x > 0), 
)

pipe.partition(cases, src=None)

Create a Partition Stage, it outputs elements to branch by condition.

cases can be a key-value dictionary. The key is the allies, and the value is the matching logic with single param elem.

Elements are calculated by dict order, and output to the first match. Those do not match any branch will drop.

Next stage's src is partition[allies]

stage1 = range(-5, 6) | pipe.partition(cases={'p': 'elem > 0', 'n': 'elem < 0'})
stage21 = pipe.map(src=stage1['p'], func=lambda x: x + 1)
stage22 = pipe.map(src=stage1['n'], func=lambda x: x - 2)
end1 = pipe.result(src=stage21)  # end1.result [2, 3, 4, 5, 6]
end2 = pipe.result(src=stage22)  # end2.result [-7, -6, -5, -4, -3]

cases can also be a complex dictionary define both matching logic and next processing.

handle is the subsequent processing, it's src uses the result by match.

stage = range(-5, 6) | pipe.partition({
    'p': {
        'match': 'elem > 0',
        'handle': pipe.map(func=lambda x: x + 1)
    },
    'n': {
        'match': 'elem < 0',
        'handle': pipe.map(func=lambda x: x - 2)
    }
})
end1 = pipe.result(src=stage['p'])  # end1.result [2, 3, 4, 5, 6]
end2 = pipe.result(src=stage['n'])  # end2.result [-7, -6, -5, -4, -3]

src is previous stage, None when using |.

pipe.multiply(*targets, src=None)

Create a Multiply Stage, it multiply copies of the elements and output to targets branches.

targets are the subsequent pipelines.

src is previous stage, None when using |.

# 1
previous = pipe.data(range(10))
stage = pipe.multiply(
    pipe.map(func=lambda x: x),
    pipe.map(func=lambda x: 2 * x),
    src=previous
)
# 2
stage = pipe.data(range(10))
    pipe.map(func=lambda x: x),
    pipe.map(func=lambda x: 2 * x),
)

pipe.each(func, src=None, func_kw=None)

Create a Each Stage.

Similar to map, but elements are dropped after computation.

pipe.peek(func, src=None, func_kw=None)

Create a Peek Stage.

Similar to map, but still outputs the elements after computation.

Elements may changed if mutable.

pipe.filter_not(func, src=None, func_kw=None)

Create a FilterNot Stage.

Similar to filter, but the logic is reversed.

pipe.distinct(func=hash, src=None, func_kw=None)

Create a Distinct Stage.

Similar to filter, outputs the elements after de-duplication.

func calculate whether an element is duplicated or not, default is hash function.

Notice: python hash(-1) == hash(-2)

pipe.duplicate(func=hash, src=None, func_kw=None)

Create a Duplicate Stage.

Similar to filter, outputs the duplicate elements.

func calculate whether an element is duplicated or not, default is hash function.

Notice: python hash(-1) == hash(-2)

pipe.limit(num, src=None)

Create a Limit Stage.

Similar to filter, but only outputs the first num of elements.

# 1
previous = pipe.data(range(10))
stage = pipe.limit(num=5, src=previous)
# 2
stage = pipe.data(range(10)) | pipe.limit(num=5)