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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.



  • HEAD <>_
  • Stable <>_

User Group

Follow the project's progress, get involved, submit ideas and ask for help via our Google Group, [email protected] <!forum/streamparse>__.


Alphabetical, by last name:

  • Dan Blanchard (@dsblanch <>__)
  • Keith Bourgoin (@kbourgoin <>__)
  • Arturo Filastò (@hellais <>__)
  • Jeffrey Godwyll (@rey12rey <>__)
  • Daniel Hodges (@hodgesds <>__)
  • Wieland Hoffmann (@mineo <>__)
  • Tim Hopper (@tdhopper <>__)
  • Omer Katz (@thedrow <>__)
  • Aiyesha Ma (@Aiyesha <>__)
  • Andrew Montalenti (@amontalenti <>__)
  • Rohit Sankaran (@roadhead <>__)
  • Viktor Shlapakov (@vshlapakov <>__)
  • Mike Sukmanowsky (@msukmanowsky <>__)
  • Cody Wilbourn (@codywilbourn <>__)
  • Curtis Vogt (@omus <>__)


See the releases <>__ page on GitHub.


See the Roadmap <>__.

.. |logo| image:: .. |Build Status| image:: :target: .. |Demo| image::

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