Large scale, distributed graph processing made easy! Load your graph from multiple formats and compute measures (but not only)
@sparkling-graph / (5)
Library that provides ability to do manipulations and computations of graph using Spark and GraphX. Using features provided you can easily load your graph from CSV (more formats like GraphML are planned) and compute well known measures like Katz centrality, Eigenvector centrality, Local clustering and others. There is possibility to use fastutil library for storing data, so memory consumption can be reduced.
Include this package in your Spark Applications using:
spark-shell, pyspark, or spark-submit
> $SPARK_HOME/bin/spark-shell --packages ml.sparkling:sparkling-graph-examples_2.11:0.0.7
In your sbt build file, add:
libraryDependencies += "ml.sparkling" % "sparkling-graph-examples_2.11" % "0.0.7"
MavenIn your pom.xml, add:
<dependencies> <!-- list of dependencies --> <dependency> <groupId>ml.sparkling</groupId> <artifactId>sparkling-graph-examples_2.11</artifactId> <version>0.0.7</version> </dependency> </dependencies>