janusgraph VS GraphScope

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.

JanusGraph

There were a couple years where it was mostly abandoned. Good to see this solid graph database being well maintained. the milestones[1] mostly show a lot of upgrading libraries, some enhancements/features sprinkled in, but for a while Janus was nearly abandoned.

Maintenance re-started in 2017, with IBM & Google stepping up to back it[2].

[1] https://github.com/JanusGraph/janusgraph/milestones?state=cl...

[2] https://architecht.io/google-ibm-back-new-open-source-graph-...

QOMPLX partnered with the graph database experts at Expero to implement their system with JanusGraph, which uses Scylla as an underlying fast and scalable storage layer. We had the privilege to learn from their use case at Scylla Summit this January, which we share with you today.

At the time of writing this article, JanusGraph's latest release page on GitHub says that JanusGraph is compatible with Cassandra 3.11.0. So I will get this version's download link from the Apache Cassandra archive, and I will use the wget shell command to download it to my /opt directory. Then I will use the tar command to extract the contents of the downloaded archive.

Optimizing distributed sampling and feature lookup looks really attractive. It's really challenging to deploy GNN training at an industrial-scale for a large graph.

Will GLT be part of graphscope[1] and replacing the current graphscope-for-learning implementation?

[1]: https://github.com/alibaba/GraphScope

We are glad to announce the landing of GraphScope on Colab: https://colab.research.google.com/github/alibaba/GraphScope.

GraphScope is a one-stop graph computing systems from Alibaba aimed to address challenges in large-scale graph computation in real production environments. GraphScope releases v0.9, enabling data scientists to develop graph computing workflows for analytical, interactive query and GNN workloads on small graphs in jupyter notebooks in a interactive manner. Once finishing the development and debugging, users can easily deployed their workflows to Kubernetes with one-line change!

To try GraphScope, you could find it on Colab[1], Jupyter Hub[2], or install GraphScope to your environment using pip by:

pip3 install graphscope

For more details of our v0.9 release, please refer to https://github.com/alibaba/GraphScope/releases/tag/v0.9.0

[1]: https://colab.research.google.com/github/alibaba/GraphScope/...

Thanks for you interests on GraphScope!

We do have a concrete plan for k8s-less deployment and we already have an issue [1] to track that. That will be available before the end of March 2021.

To simplify the environment setup process we will release a docker image for end-users, but without docker will be ok as well (requires building from sources).

GraphScope use vineyard [2] as the storage layer for im-memory graph data structures. And current the graph type (aka. ArrowPropertyFragment in GraphScope) uses a set of arrow tables and arrays under the hood.

GraphScope supports a `to_vineyard_dataframe` method on the computation context [3]. We also has a plan for integration between vineyard and dask (may could be delivered in March as well). At that time the interop between dask would be straightforward.

[1]: https://github.com/alibaba/GraphScope/discussions/113

[2]: https://github.com/alibaba/libvineyard

[3]: https://graphscope.io/docs/reference/context.html#graphscope...

GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the vineyard store that offers efficient in-memory data transfers.

We just released the version 0.2.0. And along with the release, we launched a public JupyterLab service where you can have a try in your browser: https://try.graphscope.app

Github: https://github.com/alibaba/graphscope. (stars are welcome :)