GraphScope
parallel-disk-usage
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GraphScope | parallel-disk-usage | |
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10 | 7 | |
3,090 | 336 | |
1.7% | - | |
9.7 | 7.4 | |
1 day ago | 13 days ago | |
C++ | Rust | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GraphScope
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Show HN: Graphlearn-for-PyTorch, distributed graph learning on PyTorch
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?
- GitHub โallowsโ unauthorized users โmergingโ PRs, bypass write permission check
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GraphScope VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
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GraphScope: A One-Stop Large-Scale Graph Computing System
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 :)
parallel-disk-usage
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Ask HN: What Underrated Open Source Project Deserves More Recognition?
pdu: https://github.com/KSXGitHub/parallel-disk-usage
Great compliment to ncdu for a single-view disk report and blazing fast.
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Ncdu โ NCurses Disk Usage
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.
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Your favorite Rust CLI utility? I have my top 10 below.
pdu is dust but much faster
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Parallel Disk Usage (pdu) โ A highly parallelized, blazing fast disk usage visualizer
GitHub repository
Thanks for telling me this. I create a new benchmark.
What are some alternatives?
indradb - A graph database written in rust
janusgraph - JanusGraph: an open-source, distributed graph database
kondo - Cleans dependencies and build artifacts from your projects.
grex - A command-line tool and Rust library with Python bindings for generating regular expressions from user-provided test cases
libvineyard - vineyard (v6d): an in-memory immutable data manager. [Moved to: https://github.com/alibaba/v6d]
walkdir - Rust library for walking directories recursively.
euler - A distributed graph deep learning framework.
MPAndroidChart - A powerful ๐ Android chart view / graph view library, supporting line- bar- pie- radar- bubble- and candlestick charts as well as scaling, panning and animations.
zenith - Zenith - sort of like top or htop but with zoom-able charts, CPU, GPU, network, and disk usage
sonic - ๐ฆ Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
libgrape-lite - ๐ A C++ library for parallel graph processing (GRAPE) ๐
exa - A modern replacement for โlsโ.