libvineyard
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libvineyard | euler | |
---|---|---|
4 | 2 | |
403 | 2,873 | |
- | 0.2% | |
9.1 | 0.0 | |
almost 3 years ago | 7 months ago | |
C++ | C++ | |
Apache License 2.0 | Apache License 2.0 |
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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.
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!)
euler
What are some alternatives?
GraphScope - 🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
libgrape-lite - 🍇 A C++ library for parallel graph processing (GRAPE) 🍇
efficient-gnns - Code and resources on scalable and efficient Graph Neural Networks
GNNs-Recipe - 🟠 A study guide to learn about Graph Neural Networks (GNNs)
vg - tools for working with genome variation graphs
simulacrum - A framework for procedural content generation with C++20
feather - Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow
motion_planning - Robot path planning, mapping and exploration algorithms