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This article presents a comprehensive overview of TuGraph-DB, covering the historical development of graph query languages, the capabilities and future plans of the TuGraph 4.0 query engine, and the proposed architectural evolution to support multiple languages and storage back‑ends.
Ant TuGraph’s open‑source graph computing engine, led by Fang Zhihong, will be introduced covering its development history, architectural design, technical principles, integrated stream‑batch‑graph processing capabilities, real‑world large‑scale graph use cases, and future roadmap, offering insights into design, implementation, and value.
This article provides a comprehensive overview of TuGraph-DB's graph query language development, the capabilities of its 4.0 query engine, and the current and future architectural plans, including support for GQL, integration with GeaX, and optimization strategies for complex graph workloads.
This guide walks through using the TuGraph graph database to design and deploy graph applications, covering real‑world scenarios, database selection, built‑in datasets, Docker/CentOS/Ubuntu deployment, model design, data import, debugging, operational monitoring, and integration with services or direct RESTful APIs.
This article introduces graph databases and explains why they are suited for relationship‑centric data, then details TuGraph’s key characteristics, architecture layers, supported query languages, analytics capabilities, market trends, roadmap plans, target users, and community resources, including a free cloud trial.
The September 1, 2022 Graph Intelligence Forum at the World AI Conference in Shanghai gathered leading researchers and industry experts to discuss advances in graph computing, learning, and databases, announced the open‑source TuGraph as the world’s fastest graph database, and emphasized standards, temporal graphs, and industry‑academia collaboration.
Ant Group’s large‑scale graph computing system TuGraph recently passed the China Academy of Information and Communications Technology’s Trusted Big Data assessment, becoming one of the first graph platforms certified for basic capabilities, compatibility, management, high availability and scalability, and highlighting its critical role in financial risk control.
The article explains how Ant Group leverages large‑scale graph computing—through its GeaBase and TuGraph platforms and a dedicated research team—to enhance real‑time fraud detection, drive industry standards, and explore future applications across finance, energy, and public services.