Raphtory is an open-source platform for distributed real-time temporal graph analytics, allowing you to load and process large dynamic datsets across time. If you would like a brief summary of what its used for before fully diving into the getting started guide please check out this article from the Alan Turing Institute first!

If you like the sounds of what we are working on, come join the Slack!

0.5.0 - Alpha

We are just putting the final touches to a brand new version of Raphtory which has been completely rebuilt to run on top of Apache Pulsar. This has fixed a number of issues faced in prior versions, notably around message back pressure, and introduces many exciting features including integration with Jupyter. If you would like to try this version prior to the full open source release the jar for this is available here and new documentation is available on ReadTheDocs.

Table of Contents

• Getting started
• Community and Changelog
• Contributors
• License

Getting Started

The best way to get started with Raphtory is to vist our website where we have tutorials on how to use Raphtory for graph building, analysis, and more. Good entry points for this are:

• Installation
• Building a graph from your data
• Writing algorithms in Raphtory
• Six Degrees of Gandalf
• How to Deploy Raphtory - as a Graph and as a Service

We also have a page for algorithms implemented in Raphtory (both temporal and static). These can be used to analyse your own datasets once ingested or as a basis to implement your own custom algorithms.

Too Long Didn't Read - Let me run it now!

If you want to see how Raphtory runs without reading a mountain of documentation you can quickly get set up with an example Raphtory project via these steps:

1. Clone the example repo and pick one of the examples inside that takes your fancy.
2. Download the latest stable release of the Raphtory JAR, renaming it toraphtory.jar. Create a lib directory at the root of your chosen example project and move the jar to lib.
   • Here be dragons: You can also pick the nightly build, but there may be some quirks yet to be ironed out. If you find any please report the issue on Jira or on Slack.
3. Install SBT by following their guide. The example project uses SBT to compile the source code.
4. Initiate SBT by changing into the example project directory in the terminal and running the command sbt. You will know when the SBT interactive shell has started once it shows >.
5. Execute compile to build the project.
6. Execute run to start the project. You will then see Raphtory build the graph and execute an algorithm relevant to your chosen dataset.
7. The rest is then up to you - feel free to explore the data, submit different algorithms and ask any questsions you have on the Raphtory Slack!
8. Run analysis over your algorithms running different queries - Point Query, Range Query and Live Query

Note: Raphtory is built with Scala. We recommend using IntelliJ IDE for your code. They have a community version which is free. Follow their guide for installation.

Community and Changelog

• Follow the latest development on the official blog
• Follow the Raphtory Twitter
• Join the Slack group (we are always happy to answer any questions and chat about the project!) Feel free to join the #raphtory-development and #askaway channel to discuss current issues or ask any questions.
• Raise issues and bugs on the Raphtory Jira board

Contributors

Want to get involved? Please join the Raphtory Slack group and speak with us on how you could pitch in!

License

Raphtory is licensed under the terms of the Apache License (check out our LICENSE file).