processor 0.1.0

A microframework to build source -> filter -> action workflows.

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Simple rules

Python processor is a tool for creating chained pipelines for dataprocessing. It have very few key concepts:

Data object

Any python dict with two required fields: source and type.

Source

Any function which returns iterable sequence of data objects. See full list of sources in the docs.

Output

A function which accepts a data object as input and could output another. See full list of outputs in the docs. (or same) data object as result.

Predicate

Pipeline consists from sources outputs, but predicate decides which data object should be processed by which output.

Quick example

Here is example of pipeline which reads IMAP folder and sends all emails to Slack chat:

run_pipeline(
    sources=[sources.imap('imap.gmail.com'
                          'username',
                          'password'
                          'INBOX')],
    rules=[(for_any_message, [email_to_slack, outputs.slack(SLACK_URL)])])

Here you construct a pipeline, which uses sources.imap for reading imap folder “INBOX” of [e-mail]. Function for_any_message is a predicate saying something like that: lambda data_object: True. In more complex case predicates could be used for routing dataobjects to different processors.

Functions email_to_slack and outputs.slack(SLACK_URL) are processors. First one is a simple function which accepts data object, returned by imap source and transforming it to the data object which could be used by slack.output. We need that because slack requires a different set of fields. Call to outputs.slack(SLACK_URL) returns a function which gets an object and send it to the specified Slack’s endpoint.

It is just example, for working snippets, continue reading this documention ;-)

Note

By the way, did you know there is a Lisp dialect which runs on Python virtual machine? It’s name is HyLang, and python processor is written in this language.

Installation

Create viritualenv with python3::

virtualenv --python=python3 env
source env/bin/activate

If you are on OSX, then install lxml on OSX separately::

STATIC_DEPS=true pip install lxml

Then install the processor::

pip install processor

Usage

Now create an executable python script, where you’ll place your pipline’s configuration. For example, this simple code creates a process line which searches new results in Twitter and outputs them to console. Of cause, you can output them not only to console, but also post by email, to Slack chat or everywhere else if there is an output for it:

#!env/bin/python3
import os
from processor import run_pipeline, sources, outputs
from twiggy_goodies.setup import setup_logging


for_any_message = lambda msg: True

def prepare(tweet):
    return {'text': tweet['text'],
            'from': tweet['user']['screen_name']}

setup_logging('twitter.log')

run_pipeline(
    sources=[sources.twitter.search(
        'My Company',
        consumer_key='***', consumer_secret='***',
        access_token='***', access_secret='***',
        )],
    rules=[(for_any_message, [prepare, outputs.debug()])])

Running this code, will fetch new results for search by query My Company and output them on the screen. Of course, you could use any other output, supported by the processor. Browse online documentation to find out which sources and outputs are supported and for to configure them.

Documentation

https://python-processor.readthedocs.org/

Development

To run the all tests run:

tox

Authors

• Alexander Artemenko - http://dev.svetlyak.ru

Changelog

0.1.0 (2015-03-18)

• First release on PyPI.