streamparse 3.2.0

streamparse lets you run Python code against real-time streams of data. Integrates with Apache Storm.

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

• HEAD

• Stable

User Group

Follow the project’s progress, get involved, submit ideas and ask for help via our Google Group, [e-mail].

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.