Octavious is a very lightweight concurrency framework helps you parallelize your tasks through a plugin pipeline system.
Currently it supports concurrency technologies such as:
- Multiprocessing (python builtin)
- Threading (python builtin)
- Celery (message Queues)
- Gevent (green threads)
First of all, you have to define your processor that's going to be working as parallelized. Let's have look at the example below.
# processes/chucknjoke.py import urllib from octavious.processor import Processor class ChuckNJokeProcessor(Processor): def process(self, input): url = 'http://api.icndb.com/jokes/%s' % input return urllib.urlopen(url % input).read() processor = ChuckNJokeProcessor
In this example defined, ChuckNJokeProcessor joins to given url and brings the Chuck Norris Jokes as parallelized that are going to be processed via the plugins.
Plugins already have methods such as pre-process and post-process. If you want to make changes on your input data coming from your processor you have to implement the defined pre-process, and if you want to make changes on output data you have to implement the defined post process.
As you may see in the example below, this is our first plugin. We are going to take the RAW JSON data and decode.
# plugins/jsondeserializer.py import json from octavious.pipeline import Plugin class JsonDeserializerPlugin(Plugin): def post_process(self, input, output): return json.loads(output) plugin = JsonDeserializerPlugin
And our second plugin
# plugins/dictdigger.py from octavious.pipeline import Plugin class DictDiggerPlugin(Plugin): def __init__(self, path): self.path = path def post_process(self, input, output): entry = output for component in self.path.split('.'): entry = entry.get(component) return entry plugin = DictDiggerPlugin
This is for getting what data we want from tree structured dictionary.
Pipelines are for running the plugins in the sequence that we desire. You can create a pipeline with a bunch of plugins just like below.
from octavious.utils import pipeline, plugin chuck_norris_pipeline = pipeline( plugin('chuck_norris.plugins.dictdigger', 'value.joke'), plugin('chuck_norris.plugins.jsondeserializer'), )
As above we have 4 parallelizer options implemented for Octavious. You can set the parallelizer settings like below
chuck_norris_parallellizer = parallelizer( 'octavious.parallelizer.multiprocessing')
You can also use desired parallelizer backend with choice of
There are some auxiliary Processor implementations help you define parallelizing workflows.
- OneToManyProcessor wraps your processors to work with just one input.
- ManyToOneProcessor wraps your processor to works with multiple inputs.
We are going to work with ManyToOneProcessor in ChuckNorris example. This is our last setting below as a summary, that we will make our code work.
from octavious.utils import pipeline, processor, plugin, parallelizer from octavious.processor import ManyToOneProcessor, PipelineProcessor chn_processor = processor('chuck_norris.processes.chucknjoke') chn_parallellizer = parallelizer('octavious.parallelizer.gevent') chn_pipeline = pipeline( plugin('chuck_norris.plugins.dictdigger', 'value.joke'), plugin('chuck_norris.plugins.jsondeserializer'), ) manytoone = ManyToOneProcessor( PipelineProcessor(cnj_processor, chn_pipeline), chn_parallelizer) for result in manytoone(range(1, 4)): print '*', result
Run the example app
$ python -m examples.simple * Chuck Norris uses ribbed condoms inside out, so he gets the pleasure. * Chuck Norris doesn't read books. He stares them down until he gets the information he wants. * MacGyver can build an airplane out of gum and paper clips. Chuck Norris can kill him and take it.
Octavious has a bunch of unit tests. To run them, simply type
$ python -m unittest -v octavious.tests test_pipeline (octavious.tests.TestPipeline) ... ok test_call_symbol (octavious.tests.TestUtils) ... ok test_load_symbol (octavious.tests.TestUtils) ... ok ---------------------------------------------------------------------- Ran 3 tests in 0.001s OK
Currently in very early stages, please stay tuned!
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