GraphScope VS sonic

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

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 :)

Website: https://graphscope.io

Documentation: https://graphscope.io/docs

Any comments and contributions from the community are welcomed!

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 :)

Sonic - a project written in Rust, uses custom network communication protocol for fast communication between the client and the server.

This is uncanny, I just discovered ArchiveBox earlier today and set up a self-hosted instance on some home hardware for a collection of bookmarks of useful guides, tutorials, and references I've collected over the years.

Setting it up on K8s with sonic [1] as the search backend and importing a few hundred URLs only took ~an hour or so, and the cached pages look great for the most part.

[1] https://github.com/valeriansaliou/sonic

If you don't need advanced search features, you can use Sonic (https://github.com/valeriansaliou/sonic). It's blazing fast and you can save lot of money on servers.

What about Sonic? Maybe it requires a bit of integration, but it's simple and blazing fast.

git clone https://github.com/valeriansaliou/sonic.git

- Sonic (https://github.com/valeriansaliou/sonic)

There isn't enough out there comparing all these for the simple typical fuzzy search/search box usecase -- which I think is 80% of people doing search today.

Like other people are pointing out, most of these engines won't have all the features of ES (or more accurately Lucene) but I am pretty convinced that most of the time it doesn't actually matter and if someone is searching on your site excessively maybe there's a problem with your UX (unless you're a search engine or repository of information).

[0]: https://supabase.com/blog/postgres-full-text-search-vs-the-r...