| # Guide |
| Here is the documentation map to help users quickly learn and use geaFlow. |
| |
| ## Introduction |
| **GeaFlow** is the [**fastest**](https://ldbcouncil.org/benchmarks/snb-bi/) open-source OLAP graph database developed by Ant Group. It supports core capabilities such as trillion-level graph storage, hybrid graph and table processing, real-time graph computation, and interactive graph analysis. Currently, it is widely used in scenarios such as data warehousing acceleration, financial risk control, knowledge graph, and social networks. |
| |
| For more information about GeaFlow: [GeaFlow Introduction](2.introduction.md) |
| |
| For GeaFlow design paper: [GeaFlow: A Graph Extended and Accelerated Dataflow System](https://dl.acm.org/doi/abs/10.1145/3589771) |
| |
| ## Quick Start |
| |
| Step 1: Package the JAR and submit the Quick Start task |
| |
| 1. Prepare Git、JDK8、Maven、Docker environment。 |
| 2. Download Code:`git clone https://github.com/apache/geaflow.git` |
| 3. Build Project:`./build.sh --module=geaflow --output=package` |
| 4. Test Job:`./bin/gql_submit.sh --gql geaflow/geaflow-examples/gql/loop_detection_file_demo.sql` |
| |
| Step 2: Launch the console and experience submitting the Quick Start task through the console |
| 5. Build console JAR and image (requires starting Docker):`./build.sh --module=geaflow-console` |
| 6. Start Console:`docker run -d --name geaflow-console -p 8888:8888 geaflow-console:0.1` |
| |
| For more details:[Quick Start](3.quick_start/1.quick_start.md)。 |
| |
| ## Development Manual |
| |
| 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. |
| * DSL application development: [DSL Application Development](5.application-development/2.dsl/1.overview.md) |
| * API application development: [API Application Development](5.application-development/1.api/1.overview.md) |
| |
| ## Real-time Capabilities |
| |
| Compared with traditional stream processing engines such as Flink and Storm, which use tables as their data model for real-time processing, GeaFlow's graph-based data model has significant performance advantages when handling join relationship operations, especially complex multi-hops relationship operations like those involving 3 or more hops of join and complex loop searches. |
| |
| [](reference/vs_join.md) |
| |
| |
| ## Partners |
| | | | | |
| |------------------|------------------|------------------| |
| | [](https://github.com/CGCL-codes/YiTu) | [](http://kw.fudan.edu.cn/) |  | |
| | [](http://www.whaleops.com/) | [](https://github.com/oceanbase/oceanbase) | [](https://github.com/secretflow/secretflow) | |
| |