Skip to main content

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

Project description

logo

Build Status

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.

Demo

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:

Roadmap

See the Roadmap.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

streamparse-2.1.1.tar.gz (144.0 kB view details)

Uploaded Source

File details

Details for the file streamparse-2.1.1.tar.gz.

File metadata

  • Download URL: streamparse-2.1.1.tar.gz
  • Upload date:
  • Size: 144.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for streamparse-2.1.1.tar.gz
Algorithm Hash digest
SHA256 a8f6758dd0d0d5952daf9dcfff85ac0fda4df6f53a1f5f637f15320b35f3a06c
MD5 72ea8cc9d4471940b81897b33c435db4
BLAKE2b-256 505cb600f22e1be4ae132985cc36d1e3f26799d3c21a517139ab0f92914d1c6b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page