HugeGraph is a high performance scalable graph database.
HugeGraph is a large-scale and easy-to-use graph database. In the cases of 100+ billion data (vertices and edges), HugeGraph has complete HTAP capabilities built in its internal system. The design goal of HugeGraph is to achieve a balance between availability, performance and cost.
We believe that the HugeGraph project will benefit the open source community if HugeGraph is introduced into the Apache Software Foundation.
HugeGraph graph database was initially developed at Baidu, the leading search engine company in China. HugeGraph was designed to solve the large-scale graph analysis requirements for the task of anti-fraud. Relational databases are usually at a disadvantage in dealing with relational analysis, due to slow join performance, especially in the case of a large-scale graph with multiple dimensions and deep association relationships. Graph databases are in general considered a good alternative in dealing with large scale relational analysis.
Generally, most graph databases can only process up to 1-billion-scale graphs, but it lacks a system to process 100-billion-scale graphs. Some graph databases may only support online query (OLTP), and some graph platforms only support graph computing (OLAP). HugeGraph is designed to support both online query and graph computing in the scenarios of 100+ billion data.
Although most of the main development of HugeGraph has been completed, there are several areas that still need future development. Some areas we want to focus on in the Apache incubation stage include:
HugeGraph was incubated at Baidu in 2016 and open sourced on GitHub in 2018. The project(include sub-projects) now has 30+ contributors from many companies. Some of these contributors become committers, and the project has hundreds of known users around the world. We will follow Apache's Meritocracy way to re-organize the community roles. We have set up the PPMC Team and Committer Team. Of course, contributions are welcomed and highly valued. New contributors are guided and reviewed by existing PMC members. When an active contributor has submitted enough good patches, PMC will start a vote to promote him/her to become a member of Committer Team or PMC Team.
Baidu has been building a community around HugeGraph users and developers for the last 3 years, and now we make use of GitHub as code hosting and community communication. The most of core developers are from Baidu. Besides, there are 10+ contributors from other companies such as NetEase, 360, Iflytek and Huya. We hope to grow the base of contributors by inviting all those who offer contributions through The Apache Way.
The core developers are all experienced open source developers. They have operated the HugeGraph Community for 3 years, and they are contributors of Linux kernel, OpenStack, Ceph, RocksDB, Apache TinkerPop, Apache Hadoop and Apache Groovy.
HugeGraph implements the API of Apache TinkerPop, which defines the Gremlin. Gremlin is a powerful graph traversal language, and various complicated graph queries can be accomplished through Gremlin. The TinkerPop ecosystem based on Java is relatively mature in terms of graph databases. We use Java language, which is developed efficient and stable, to develop upper-level components like graph engine, graph computing, graph API and graph tools; and we manage storage through JNI which is able to freely manage memory and execute efficiently.
We have checked and believe the name is suitable and the project has legal permission to continue using its current name. There is no other projects found using this name through Google search.
In the early stage of the project, we referred to the storage structure of Titan/Janus Graph, some folks thought that HugeGraph was forked from Titan/Janus. In fact, HugeGraph is not based on these projects. HugeGraph is developed completely from scratch and in the process it addressed many new challenges. Certainly, the project was inspired by Titan/Janus and we are really gratitious for such inspirations.
Due to a small and limited number of committers, the project has a relatively small risk of becoming an orphan project. However, the committers have been operating the HugeGraph Community for 3 years in the spirit of open source, and continue to develop new contributors to participate.
HugeGraph has been open sourced on GitHub for 3 years, during which committers submitted code and documents in full compliance with open source specifications and requirements.
Expect to enter incubation in 4 months and graduate in about 2 years.
The developers on the current list come from several different companies plus many independent volunteers, but the most of committers are from Baidu. The developers are geographically concentrated in China now. They are experienced with working in a distributed environment in other open source projects, e.g. OpenStack.
Most of the developers are paid by their employer to contribute to this project. Given some volunteer developers and the committers' sense of ownership for the code, the project could continue even if no salaried developers contributed to the project.
HugeGraph follows the Apache TinkerPop specification, uses Apache Commons, Apache HttpClient, and Apache HttpCore to implement the basic functions. Users can choose Apache Cassandra or Apache HBase as one of the storage backends of HugeGraph.
Although we expect that the Apache brand may help attract more contributors, our interest in starting this project is based on the factors mentioned in the fundamentals section. We are interested in joining ASF to increase our connections in the open source world. Based on extensive collaboration, it is possible to build a community of developers and committers that live longer than the founder.
HugeGraph documentation is provided on https://hugegraph.github.io/hugegraph-doc/ in Simplified Chinese, the complete English version of the documentation is being prepared.
This project consists of 2 core sub-projects and 2 other sub-projects, all of which are hosted by GitHub hugegraph organization since 2018. The codes are already under Apache License Version 2.0. The git address of sub-project repositories are as follows:
hugegraph
, core sub-project, including graph server, graph engine and graph storage: https://github.com/hugegraph/hugegraphhugegraph-computer
, core sub-project, including graph computing and graph algorithms: https://github.com/hugegraph/hugegraph-computerhugegraph-commons
: https://github.com/hugegraph/hugegraph-commonshugegraph-toolchain
, including hugegraph-client
, hugegraph-loader
, hugegraph-tools
, hugegraph-hubble
, hugegraph-test
, hugegraph-doc
: https://github.com/hugegraph/hugegraph-toolchainThe codes are currently under Apache License Version 2.0, and have been verified there is no intellectual property or license issues when being released to open source by Baidu in 2018. Baidu will provide SGA and all committers will sign ICLA after HugeGraph is accepted into the Incubator.
As all dependencies are managed by Apache Maven, none of the external libraries need to be packaged in a source distribution. All dependencies have Apache compatible licenses except for 4 dependencies: MySQL Connector(GPL), word(GPL), JBoss Logging 3(LGPL) and jnr-posix(LGPL+GPL), we will remove these dependencies in future.
HugeGraph has the following external dependencies:
Apache License
Apache License 2.0
Apache License 2.0 + GPLv2 License
BSD License
CDDL + GPL License
CDDL + GPLv2 License
Eclipse Distribution License
ISC/BSD License
LGPL License
LGPL + GPL License
LGPL 2.1 + MPL 1.1 + Apache License 2.0
MIT License
GPL + MIT License
GPL License
None
None
hugegraph
: https://github.com/hugegraph/hugegraph.githugegraph-computer
: https://github.com/hugegraph/hugegraph-computer.githugegraph-commons
: https://github.com/hugegraph/hugegraph-commons.githugegraph-toolchain
: https://github.com/hugegraph/hugegraph-toolchain.gitThe community would like to continue using GitHub Issues (but will moved to github.com/apache/).
We are expecting the Apache Incubator could sponsor this project.