TuGraph Analytics (the project name is GeaFlow) is an open-source distributed real-time graph computing engine developed by Ant Group. It is widely used in scenarios such as financial risk control, social networks, knowledge graphs, and data applications. The core competence of GeaFlow is streaming graph computing, which provides a high-time efficiency and low-latency graph computing mode compared to offline graph computing. Compared with traditional streaming computing engines such as Flink and Storm, which are real-time processing systems for table data, GeaFlow mainly focuses on real-time processing of graph data, supporting more complex relationship analysis and calculations, such as real-time search for multi-degree relationships and loop detection. At the same time, it also supports real-time analysis and processing of graph-table integration and can handle both table data and graph data at the same time. For more information on GeaFlow use cases, please refer to the GeaFlow introduction document
You need to first fork a copy of GeaFlow code on Github and then try to compile the source code. Compiling GeaFlow requires mvn and JDK8 environment. You can then attempt to run a real-time graph computing job on your local machine to experience how the streaming graph computing job is run. Running a GeaFlow job locally requires a Docker environment. For more detailed information on how to get started quickly, please refer to the quickstart document.
GeaFlow supports two sets of programming interfaces: DSL and API. You can develop streaming graph computing jobs using GeaFlow's SQL extension language SQL+ISO/GQL or use GeaFlow's high-level API programming interface to develop applications in Java. For more information on DSL application development, please refer to the DSL development document, and for the high-level API application development, please refer to the API application development document.
Here is the document list for GeaFlow:
- GeaFlow Introduction
- Quick start
- Concepts
- GeaFlow Development
- Deployment
- Principle introduction
Thank you very much for contributing to GeaFlow, whether it's bug reporting, documentation improvement, or major feature development, we warmly welcome all contributions. For more information on how to contribute, please refer to our guidelines:Contributing to GeaFlow.
You can contact us through DingTalk or WeChat group.
Thanks to some outstanding open-source projects in the industry, such as Apache Flink, Apache Spark, and Apache Calcite, some modules of GeaFlow were developed with their references. We would like to express our special gratitude for their contributions.