GraphScope
libvineyard
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GraphScope | libvineyard | |
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10 | 4 | |
3,090 | 403 | |
1.7% | - | |
9.7 | 9.1 | |
2 days ago | almost 3 years ago | |
C++ | C++ | |
Apache License 2.0 | Apache License 2.0 |
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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 :)
libvineyard
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GraphScope: A One-Stop Large-Scale Graph Computing System
https://nbviewer.jupyter.org/github/alibaba/GraphScope/blob/...
The graphs on GraphScope is backed by vineyard (https://github.com/alibaba/libvineyard). And that enables GraphScope to have multiple specifically optimized runtimes (written in C++, rust and Python) for different tasks shares the distributed graph data in memory efficiently.
It makes sense to run such tasks in other machines/systems without adding too much burden to a graph db to avoid affect its quality of service.
2. Fully integration with Python makes it more flexible to do data analytics. For example, you can leverage the ability provided by numpy, pandas and mars (https://github.com/mars-project/mars) along GraphScope with zero-copy thanks to our storage engine vineyard (https://github.com/alibaba/libvineyard)
3. Besides distributed processing, extra performance can also come from the efficient graph layout in memory, and other optimizations on the compiler and runtime-level. GraphScope is ~100x faster on Gremlin, and even more on graph analytical algorithms like PageRank, compared with graph dbs like JanusGraph.
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Vineyard: An open-source in-memory data manager
6. Kubernetes-integration for large-scale big data applications
Github: https://github.com/alibaba/libvineyard (s are welcomed!)
What are some alternatives?
indradb - A graph database written in rust
janusgraph - JanusGraph: an open-source, distributed graph database
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.
parallel-disk-usage - Highly parallelized, blazing fast directory tree analyzer
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
Gephi - Gephi - The Open Graph Viz Platform
feather - Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow
v6d - vineyard (v6d): an in-memory immutable data manager. (Project under CNCF, TAG-Storage)