1. Background and Current Overview of Graph Systems A graph database is a specialized database that stores and processes graph-structured data, which has extensive applications in fields such as social networks, knowledge graphs, and recommendation systems.

2. Open Source History of HugeGraph HugeGraph supports multiple backend storage, graph computing frameworks, graph analysis algorithms, and rich tools and interfaces. HugeGraph was open-sourced in 2018 and officially joined Apache Incubating in 2022.

3. The Pros and Cons of Joining the Apache Foundation Joining the Apache Foundation is significant for HugeGraph, as it not only enhances the project’s visibility and influence but also enables the project to follow Apache’s open, inclusive, and collaborative philosophy and standards. However, joining Apache also presents challenges, such as adapting to Apache’s processes and culture, attracting and retaining more contributors and users, and balancing commercial and community interests.

4. How to Improve Community Operations with Limited Resources To improve community operations with limited resources, we have taken measures such as establishing project websites, documentation, forums, blogs, organizing online and offline events and training, developing contributor guidelines and code standards, and actively seeking opportunities for external contributors and collaboration with universities to encourage more people to participate in the project.

5. External Contributors and Collaboration with Universities We actively seek opportunities for external contributors and collaboration with universities to encourage more people to participate in the project.

6. Thoughts on “Open Source Cost Control” and “Cost Reduction and Efficiency Enhancement” Finally, we are also considering how to do better in terms of “open source cost control” and “cost reduction and efficiency enhancement,” such as using cloud computing and other technologies to reduce the deployment and operation costs of graph databases/graph computing systems, improving the development and testing efficiency of graph databases through standardization, modularization, and componentization, and enhancing the performance and functionality of graph databases through innovation, optimization, and collaboration.

Speakers:

Jin Ziwei is a senior development engineer at Baidu and a member of the Apache HugeGraph PPMC. He is currently responsible for the release management of the open source community. He specializes in distributed storage (file systems/KV databases) and graph storage/graph computing, and leads the performance optimization and technical evolution of the community. He is passionate about open source and infrastructure-related technologies and welcomes everyone to communicate with him (GitHub: imbajin).