Streamparse lets you run Python code against real-time streams of data via Apache Storm. With streamparse you can create Storm bolts and spouts in Python without having to write a single line of Java. It also provides handy CLI utilities for managing Storm clusters and projects.
The Storm/streamparse combo can be viewed as a more robust alternative to Python worker-and-queue systems, as might be built atop frameworks like Celery and RQ. It offers a way to do "real-time map/reduce style computation" against live streams of data. It can also be a powerful way to scale long-running, highly parallel Python processes in production.
Follow the project's progress, get involved, submit ideas and ask for help via
our Google Group,
[email protected] <https://groups.google.com/forum/#!forum/streamparse>__.
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releases <https://github.com/Parsely/streamparse/releases>__ page on
.. |logo| image:: https://raw.githubusercontent.com/Parsely/streamparse/master/doc/source/images/streamparse-logo.png .. |Build Status| image:: https://travis-ci.org/Parsely/streamparse.svg?branch=master :target: https://travis-ci.org/Parsely/streamparse .. |Demo| image:: https://raw.githubusercontent.com/Parsely/streamparse/master/doc/source/images/quickstart.gif