libgrape-lite
libvineyard
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libgrape-lite | libvineyard | |
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3 | 4 | |
363 | 403 | |
1.7% | - | |
6.3 | 9.1 | |
about 12 hours ago | almost 3 years ago | |
C++ | C++ | |
Apache License 2.0 | Apache License 2.0 |
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libgrape-lite
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libgrape-lite VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
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GraphScope: A One-Stop Large-Scale Graph Computing System
We don't have a benchmark between the analytical engine in GraphScope (aka. GAE) with GraphX/Giraph. But we do have evaluated the performance of the underlying engine of GAE (libgrape-lite) with LDBC Graph Analytics Benchmark and it achieves higher performance comparably to the state-of-the-art systems [2].
[1]: https://github.com/alibaba/libgrape-lite
[2]: https://github.com/alibaba/libgrape-lite/blob/master/Perform...
Yes, we intend to cover the functionality provided by GraphLab, but with better performance (see https://github.com/alibaba/libgrape-lite/blob/master/Perform..., We are actually 10x~50x faster...).
Also we also provide the ability to do Gremlin queries on graphs as well as GNN with TensorFlow, neither is provided with GraphLab
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?
QuickQanava - :link: C++17 network / graph visualization library - Qt6 / QML node editor.
GraphScope - 🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
CXXGraph - Header-Only C++ Library for Graph Representation and Algorithms
euler - A distributed graph deep learning framework.
HPCInfo - Information about many aspects of high-performance computing. Wiki content moved to ~/docs.
mpl - A C++17 message passing library based on MPI
LightGraphs.jl - An optimized graphs package for the Julia programming language
dmtcp - DMTCP: Distributed MultiThreaded CheckPointing
feather - Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow
gtl - A collection of useful well-commented, self-contained, simple, and/or interesting C++ classes