tanbih-pipeline 0.8.6

a pipeline framework for streaming processing

a flexible stream processing framework supporting RabbitMQ, Pulsar, Kafka and Redis.

Features

• at-least-once guaranteed with acknowledgement on every message

• horizontally scalable through consumer groups

• flow is controlled in deployment, develop it once, use it everywhere

• testability provided with FILE and MEMORY input/output

Installation

$ pip install tanbih-pipeline

You can install the required backend dependencies with:

$ pip install tanbih-pipeline[redis]
$ pip install tanbih-pipeline[kafka]
$ pip install tanbih-pipeline[pulsar]
$ pip install tanbih-pipeline[rabbitmq]
$ pip install tanbih-pipeline[azure]

If you want to support all backends, you can:

$ pip install tanbih-pipeline[full]

Generator

Generator is to be used when developing a data source in our pipeline. A source will produce output without input. A crawler can be seen as a generator.

>>> from pipeline import Generator, Message
>>>
>>> class MyGenerator(Generator):
...     def generate(self):
...         for i in range(10):
...             yield {'id': i}
>>>
>>> generator = MyGenerator('generator', '0.1.0', description='simple generator')
>>> generator.parse_args("--kind MEM --out-topic test".split())
>>> generator.start()
>>> [r.get('id') for r in generator.destination.results]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Processor

Processor is to be used to process input. Modification will be in-place. A processor can produce one output for each input, or no output.

>>> from pipeline import Processor, Message
>>>
>>> class MyProcessor(Processor):
...     def process(self, msg):
...         msg.update({'processed': True})
...         return None
>>>
>>> processor = MyProcessor('processor', '0.1.0', description='simple processor')
>>> config = {'data': [{'id': 1}]}
>>> processor.parse_args("--kind MEM --in-topic test --out-topic test".split(), config=config)
>>> processor.start()
>>> [r.get('id') for r in processor.destination.results]
[1]

Splitter

Splitter is to be used when writing to multiple outputs. It will take a function to generate output topic based on the processing message, and use it when writing output.

>>> from pipeline import Splitter, Message
>>>
>>> class MySplitter(Splitter):
...     def get_topic(self, msg):
...         return '{}-{}'.format(self.destination.topic, msg.get('id'))
...
...     def process(self, msg):
...         msg.update({
...             'processed': True,
...         })
...         return None
>>>
>>> splitter = MySplitter('splitter', '0.1.0', description='simple splitter')
>>> config = {'data': [{'id': 1}]}
>>> splitter.parse_args("--kind MEM --in-topic test --out-topic test".split(), config=config)
>>> splitter.start()
>>> [r.get('id') for r in splitter.destinations['test-1'].results]
[1]

Usage

Writing a Worker

Choose Generator, Processor or Splitter to subclass from.

Environment Variables

Application accepts following environment variables:

environment variable	command line argument	options
PIPELINE	–kind	KAFKA, PULSAR, FILE
PULSAR	–pulsar	pulsar url
TENANT	–tenant	pulsar tenant
NAMESPACE	–namespace	pulsar namespace
SUBSCRIPTION	–subscription	pulsar subscription
KAFKA	–kafka	kafka url
GROUPID	–group-id	kafka group id
INTOPIC	–in-topic	topic to read
OUTTOPIC	–out-topic	topic to write to

Custom Code

Define add_arguments to add new arguments to worker.

Define setup to run initialization code before worker starts processing messages. setup is called after command line arguments have been parsed. Logic based on options (parsed arguments) goes here.

Options

Errors

The value None above is error you should return if dct or dcts is empty. Error will be sent to topic errors with worker information.

Contribute

Use pre-commit to run black and flake8

Credits

Yifan Zhang (yzhang at hbku.edu.qa)