Spark Neo4j is the fastest way to launch or deploy a graph analytics engine for big data graph processing using the new Docker Compose framework.
This image combines Neo4j and Apache Spark GraphX containers onto a single Docker host. This approach makes it easy to take advantage of these two powerful tools without worrying about configuring and installing any other dependencies.
The fundamental goal of this Docker image is to get you up and running as fast as possible with a graph analytics engine. It should take no longer than 30 minutes for you to launch Spark Neo4j on Mac OSX or Linux.
Get Docker: https://docs.docker.com/installation/
To install Spark Neo4j on your machine, follow this install guide:
Graph Analytics Engine
This Docker image is an all-in-one graph processing solution combining graph storage and graph processing in a single platform.
A Neo4j graph database container provides an out of the box database management system with robust (fully ACID) graph data storage and query capabilities. This container configures Neo4j for high-performance OLTP use cases.
An Apache Spark GraphX container provides a single system that handles iterative graph computation and ETL from data sourced from Neo4j.
Closed-loop Data Processing
The results of an analysis by the Apache Spark container are applied back to Neo4j. These results can be explored using Neo4j's powerful query capabilities to lookup graph metrics calculated by Spark.
- Closeness Centrality
- Betweenness Centrality
- Triangle Counting
- Connected Components
- Strongly Connected Components
This library is licensed under the Apache License, Version 2.0.