parallel-disk-usage VS GraphScope

pdu: https://github.com/KSXGitHub/parallel-disk-usage

Great compliment to ncdu for a single-view disk report and blazing fast.

ncdu is one of the most useful CLI tool out there! Been using it for many years as well.

Another disk scanner worth plugging that I came across for some use cases where I needed to generate single-view reports is pdu - it has the same concurrency implementation that other ncdu alternatives use so the performance is much better too.

https://github.com/KSXGitHub/parallel-disk-usage

pdu is dust but much faster

Thanks for telling me this. I create a new benchmark.

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 :)