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, streamparse@googlegroups.com.
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.