indradb
GraphScope
Our great sponsors
| indradb | GraphScope | |
|---|---|---|
| 5 | 10 | |
| 2,068 | 3,101 | |
| 1.7% | 0.8% | |
| 3.5 | 9.7 | |
| about 2 months ago | 7 days ago | |
| Rust | C++ | |
| Mozilla Public 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.
indradb
-
IndraDB Help / Tutorial
I've been racking my brain for a few days now trying to get an IndraDB prototype up and running but having no luck. My goal is to use the indra-lib with Sled as the datastore.
- IndraDB – A graph database written in Rust
-
Call for Help - Open Source Datom/EAV/Fact database in Rust.
[1] https://github.com/indradb/indradb
-
Show HN: Mongita is to MongoDB as SQLite is to SQL
For rust, I manage a graph database that can be embedded as a library: https://github.com/indradb/indradb/
-
IndraDB: A graph database written in rust
https://docs.rs/indradb-lib/2.0.0/indradb/ and https://github.com/indradb/indradb
GraphScope
-
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?
[1]: https://github.com/alibaba/GraphScope
- GitHub “allows” unauthorized users “merging” PRs, bypass write permission check
-
GraphScope VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
-
GraphScope on Colab: Large-Scale Graph Computing from Notebooks to Kubernetes
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/...
- GraphScope v0.6 Released: Code with Eager, Executive with Lazy
-
GraphScope: A One-Stop Large-Scale Graph Computing System
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 :)
What are some alternatives?
oxigraph - SPARQL graph database
janusgraph - JanusGraph: an open-source, distributed graph database
ArangoDB - 🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
libvineyard - vineyard (v6d): an in-memory immutable data manager. [Moved to: https://github.com/alibaba/v6d]
mongita - "Mongita is to MongoDB as SQLite is to SQL"
euler - A distributed graph deep learning framework.
mongodb-memory-server - Spinning up mongod in memory for fast tests. If you run tests in parallel this lib helps to spin up dedicated mongodb servers for every test file in MacOS, *nix, Windows or CI environments (in most cases with zero-config).
MPAndroidChart - A powerful 🚀 Android chart view / graph view library, supporting line- bar- pie- radar- bubble- and candlestick charts as well as scaling, panning and animations.
datalevin - A simple, fast and versatile Datalog database
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
lungo - A MongoDB compatible embeddable database and toolkit for Go.
parallel-disk-usage - Highly parallelized, blazing fast directory tree analyzer