| commit | f8e9280dbfcfd639ad1424d11aac8d2dc012c99f | [log] [tgz] |
|---|---|---|
| author | xincai98 <xincai98@126.com> | Fri Nov 22 14:53:01 2024 +0800 |
| committer | GitHub <noreply@github.com> | Fri Nov 22 14:53:01 2024 +0800 |
| tree | 6a75cc4ff9356f558d7fa1edd91bb19eca883397 | |
| parent | 15192830ccb35528487247f97db1e6a229f0782a [diff] |
[ISSUE-385] Fix compile warning (#387)
🌐️ English | 中文
TuGraph Analytics (alias: GeaFlow) is a distributed graph compute engine 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
For GeaFlow design paper: GeaFlow: A Graph Extended and Accelerated Dataflow System
Step 1: Package the JAR and submit the Quick Start task
git clone https://github.com/TuGraph-family/tugraph-analytics./build.sh --module=gealfow --output=package./bin/gql_submit.sh --gql geaflow/geaflow-examples/gql/loop_detection_file_demo.sqlStep 2: Launch the console and experience submitting the Quick Start task through the console
./build.sh --module=gealfow-consoledocker run -d --name geaflow-console -p 8888:8888 geaflow-console:0.1For more details：Quick Start。
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.
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.
Why using graphs for relational operations is more appealing than table joins?
Association Analysis Demo Based on GQL:
--GQL Style Match (s:student)-[sc:selectCource]->(c:cource) Return c.name ;
Association Analysis Demo Based on SQL:
--SQL Style SELECT c.name FROM course c JOIN selectCourse sc ON c.id = sc.targetId JOIN student s ON sc.srcId = s.id ;
Thank you very much for contributing to GeaFlow, whether bug reporting, documentation improvement, or major feature development, we warmly welcome all contributions.
For more information: Contribution.
You can contact us through the following methods:
If you are interested in GeaFlow, please give our project a ⭐️ .
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.