streamparse lets you run Python code against real-time streams of data. Integrates with Apache Storm.
Project description
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.
Documentation
User Group
Follow the project’s progress, get involved, submit ideas and ask for help via our Google Group, streamparse@googlegroups.com.
Contributors
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)
Changelog
See the releases page on GitHub.
Roadmap
See the Roadmap.
Release history Release notifications | RSS feed
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Source Distribution
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