pyPiper 0.4.0

A pipelining framework designed for data analysis but can be useful to other applications

A parallel pipelining framework for Python. Developers can create nodes and chain them together to create pipelines.

Classes that extend Node must implement run method that will be called whenever new data is available.

Installation

pip install pypiper

Example

from pyPiper import Node, Pipeline

class Generate(Node):
    def setup(self, size):
        self.size = size
        self.pos = 0

    def run(self, data):
        if self.pos < self.size:
            self.emit(self.pos)
            self.pos = self.pos + 1
        else:
            self.close()

class Square(Node):
    def run(self, data):
        self.emit(data**2)


pipeline = Pipeline(Generate("gen", size=10) | Square("square"))
print(pipeline)
pipeline.run()

Nodes can also specify a batch size that dictates how much data should be pushed to the node. For example, building on the previous example. In this case batch_size is specified in the nodes setup method. Alternatively, it can be set when creating the node (ex. Printer("print", batch_size=5))

class Printer(Node):
    def setup(self):
        self.batch_size = Node.BATCH_SIZE_ALL

    def run(self, data):
        print(data)

pipeline = Pipeline(Generate("gen", size=10) | Square("square") | Printer("print"))
print(pipeline)
pipeline.run()

Parallel Execution

To process pipelines in parallel, pass n_threads > 1 when creating the pipeline. Parallel execution is done using multiprocessing and is well suited to CPU intensive tasks such as audio processing and feature extraction. For example:

class Generate(Node):
    def setup(self, size):
        self.pos = 0
        self.stateless = False

    def run(self, data):
        if self.pos < self.size:
            self.emit(self.pos)
            self.pos = self.pos + 1
        else:
            self.close()

pipeline = Pipeline(Generate("gen", size=10) | Square("square") | Printer("print"), n_threads=2)
print(pipeline)
pipeline.run()

Note that since the generate Node needs to store state (pos``variable), it must set``self.stateless = False`. This keeps the nodes state synchronized between different processes.

Stream Names

You can also name input and output streams. For example:

gen = EvenOddGenerate("gen", size=20, out_streams=["even", "odd"])
double = Double("double", out_streams="num", in_streams="even")
square = Square("square", out_streams="num", in_streams="odd")

printer1 = Printer("p1", in_streams="num", batch_size=Node.BATCH_SIZE_ALL)
printer2 = Printer("p2", in_streams="num", batch_size=Node.BATCH_SIZE_ALL)

p = Pipeline(gen | [double | printer1, square | printer2], quiet=False)

p.run()

EvenOddGenerate generates a pair of numbers. using the out_streams parameter, we name the first number even and second number odd. When initializing the double and square nodes, we tell double to take the even number and square to take the odd number.