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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for streamparse-3.3.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 32fc7443aee34def5186a8c439818c5a9141a7d45ec029e8fcdd1789db34c1e3 |
|
MD5 | 48165a3a601ca74269fa6d4bbe4926a4 |
|
BLAKE2b-256 | 75ff2102868e80d589ac5a2a830ffc4da8af6ccc6042d4e84591a7c3b90ebf15 |