https://github.com/graphframes/graphframes

https://github.com/graphframes/graphframes

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/graphframes/graphframes
• Owner: graphframes
• License: apache-2.0
• Created: 2016-01-20T23:17:56.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2024-03-16T10:09:47.000Z (8 months ago)
• Last Synced: 2024-05-15T14:13:21.957Z (6 months ago)
• Language: Scala
• Homepage: http://graphframes.github.io/graphframes
• Size: 2.32 MB
• Stars: 971
• Watchers: 58
• Forks: 232
• Open Issues: 175
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-spark - GraphFrames - commit/graphframes/graphframes.svg"> - Data frame based graph API. (Packages / Graph Processing)

README

        
# graphframes

[![Build Status](https://travis-ci.org/graphframes/graphframes.svg?branch=master)](https://travis-ci.org/graphframes/graphframes)

[![codecov.io](http://codecov.io/github/graphframes/graphframes/coverage.svg?branch=master)](http://codecov.io/github/graphframes/graphframes?branch=master)

# GraphFrames: DataFrame-based Graphs

This is a package for DataFrame-based graphs on top of Apache Spark.

Users can write highly expressive queries by leveraging the DataFrame API, combined with a new

API for motif finding.  The user also benefits from DataFrame performance optimizations

within the Spark SQL engine.

You can find user guide and API docs at https://graphframes.github.io/graphframes.

## Building and running unit tests

To compile this project, run `build/sbt assembly` from the project home directory.

This will also run the Scala unit tests.

To run the Python unit tests, run the `run-tests.sh` script from the `python/` directory.

You will need to set `SPARK_HOME` to your local Spark installation directory.

## Release new version

Please see guide `dev/release_guide.md`.

## Spark version compatibility

This project is compatible with Spark 2.4+.  However, significant speed improvements have been

made to DataFrames in more recent versions of Spark, so you may see speedups from using the latest

Spark version.

## Contributing

GraphFrames is collaborative effort among UC Berkeley, MIT, and Databricks.

We welcome open source contributions as well!

## Releases:

See [release notes](https://github.com/graphframes/graphframes/releases).