Skip to main content

parallel implementations of collections with support for map/reduce style operations

Project description

New in 0.2: a convenient functional interface to the module!

This package provides a convenient interface to perform map/filter/reduce style operation on standard Python data structures and generators in multiple processes. The parallelism is achieved using the Python 2.7 backport of the concurrent.futures package. If you can define your problem in terms of map/reduce/filter operations, it will run on several parallel Python processes on your machine, taking advantage of multiple cores. Otherwise these datastructures are equivalent to their non-parallel peers found in the standard library.

Examples at https://github.com/gterzian/Python-Parallel-Collections Feedback and contributions highly sought after!

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

python-parallel-collections-0.2.1.tar.gz (6.5 kB view details)

Uploaded Source

File details

Details for the file python-parallel-collections-0.2.1.tar.gz.

File metadata

File hashes

Hashes for python-parallel-collections-0.2.1.tar.gz
Algorithm Hash digest
SHA256 d10f19fd05a8a85afecc6ff9f7f7a43edd7c92fc7d7389c5aa406e1631486eba
MD5 33a49281ebc51defe0a2be06b6287577
BLAKE2b-256 e0f8507b7efcb7b588777c0967dbeb2aed04b5787fd4527562b3158502d5de2e

See more details on using hashes here.

Supported by

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