tanbih-pipeline 0.7.2

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

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 command line variable 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.

Credits

Yifan Zhang (yzhang at hbku.edu.qa)