Copilot commented on code in PR #456: URL: https://github.com/apache/hugegraph-doc/pull/456#discussion_r2904512777
########## content/en/docs/introduction/_index.md: ########## @@ -7,93 +7,139 @@ aliases: - /docs/introduction/README/ --- -### Summary +### What is Apache HugeGraph? -Apache HugeGraph is an easy-to-use, efficient, general-purpose open-source graph database system -(Graph Database, [GitHub project address](https://github.com/apache/hugegraph)), implementing the [Apache TinkerPop3](https://tinkerpop.apache.org) framework and fully compatible with the [Gremlin](https://tinkerpop.apache.org/gremlin.html) query language, -while also supporting the [Cypher](https://opencypher.org/) query language (OpenCypher standard). -With complete toolchain components, it helps users easily build applications and products based on graph databases. HugeGraph supports fast import of more than 10 billion vertices and edges, and provides millisecond-level relational query capability (OLTP). -It also supports large-scale distributed graph computing (OLAP). +[Apache HugeGraph](https://hugegraph.apache.org/) is an easy-to-use, efficient, and general-purpose open-source **full-stack graph system** ([GitHub](https://github.com/apache/hugegraph)), covering three major areas: **Graph Database** (OLTP real-time queries), **Graph Computing** (OLAP large-scale analysis), and **Graph AI** (GraphRAG / Graph Machine Learning). -Typical application scenarios of HugeGraph include deep relationship exploration, association analysis, path search, feature extraction, data clustering, community detection, knowledge graph, etc., and are applicable to business fields such as network security, telecommunication fraud, financial risk control, advertising recommendation, social network, and intelligence Robots, etc. +HugeGraph supports the rapid storage and querying of tens of billions of vertices and edges, possessing excellent OLTP performance. Its graph engine is fully compliant with the [Apache TinkerPop 3](https://tinkerpop.apache.org) framework and supports both [Gremlin](https://tinkerpop.apache.org/gremlin.html) and [Cypher](https://en.wikipedia.org/wiki/Cypher) (OpenCypher standard) query languages. -### Features +**Typical Application Scenarios:** Deep relationship exploration, association analysis, path search, feature extraction, community detection, knowledge graphs, etc. +**Applicable Fields:** Network security, telecom anti-fraud, financial risk control, personalized recommendations, social networks, intelligent Q&A, etc. -HugeGraph supports graph operations in online and offline environments, batch importing of data and efficient complex relationship analysis. It can seamlessly be integrated with big data platforms. -HugeGraph supports multi-user parallel operations. Users can enter Gremlin/Cypher query statements and get graph query results in time. They can also call the HugeGraph API in user programs for graph analysis or queries. +--- + +### Ecosystem Overview + +```text +┌──────────────────────────────────────────────────────────────┐ +│ Apache HugeGraph - Full-Stack Graph System │ +├──────────────────┬────────────────────┬──────────────────────┤ +│ Graph DB (OLTP) │ Graph Compute │ Graph AI │ +│ HugeGraph │ Vermeer (Memory) │ HugeGraph-AI │ +│ Server │ Computer (Dist.) │ GraphRAG/GNN/Py │ +├──────────────────┴────────────────────┴──────────────────────┤ +│ HugeGraph Toolchain │ +│ Hubble | Loader | Client(Java/Go/Py) | Spark | Tools │ +└──────────────────────────────────────────────────────────────┘ +``` + +--- + +### Core Components + +#### 🗄️ HugeGraph Server — Graph Engine (OLTP) + +The core module of the HugeGraph project, providing high-performance graph data storage and real-time query capabilities: + +- **Core Engine**: Supports Property Graph modeling, including complete Schema management for VertexLabel, EdgeLabel, PropertyKey, and IndexLabel. +- **Dual Query Languages**: Fully compatible with Gremlin (TinkerPop 3) and Cypher (OpenCypher). +- **REST API**: Built-in REST Server, providing RESTful graph operation interfaces. +- **Multi-type Indexes**: Exact query, range query, and complex condition combination queries. +- **Pluggable Storage Backends**: For 1.7.0 and later, supports `RocksDB` (standalone default), `HStore` (distributed), `HBase`, and `Memory`; for 1.5.x or earlier, supports `MySQL` / `PostgreSQL` / `Cassandra`, etc. + +**Submodules:** +- `Core`: Graph engine implementation, connecting downwards to Backend and upwards to API. +- `Backend`: Adapter layer for multiple backend storages. +- `API`: RESTful access layer, compatible with Gremlin/Cypher queries. + +📖 [Server Quick Start](/docs/quickstart/hugegraph/hugegraph-server) + +--- + +#### 📊 Graph Computing Engine (OLAP) + +Provides two complementary graph analysis engines: + +- **Vermeer** (Recommended): High-performance pure in-memory graph computing engine, simple to deploy, fast response, suitable for small to medium-scale graph analysis and quick onboarding. +- **HugeGraph-Computer**: Distributed OLAP engine based on the [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf) model, can run on Kubernetes / Yarn clusters, suitable for mega-scale graph algorithm tasks. + +📖 [Computing Quick Start](/docs/quickstart/computing/hugegraph-computer) Review Comment: The page recommends **Vermeer** as the preferred OLAP engine, but the “Computing Quick Start” link points to `/docs/quickstart/computing/hugegraph-computer` (Computer-only quick start). Consider linking to the Vermeer quick start (`/docs/quickstart/computing/hugegraph-vermeer`) or the OLAP section index, and update the “Vermeer / Computer” entry in the Quick Start Navigation table accordingly so the recommendation matches the target. ```suggestion 📖 [Computing Quick Start](/docs/quickstart/computing/hugegraph-vermeer) ``` ########## content/cn/docs/introduction/_index.md: ########## @@ -7,95 +7,142 @@ aliases: - /cn/docs/introduction/README/ --- -### Summary +### 什么是 Apache HugeGraph？ -Apache HugeGraph 是一款易用、高效、通用的开源图数据库系统（Graph Database，[GitHub 项目地址](https://github.com/apache/hugegraph)）， -实现了[Apache TinkerPop3](https://tinkerpop.apache.org)框架及完全兼容[Gremlin](https://tinkerpop.apache.org/gremlin.html)查询语言， -同时支持 [Cypher](https://opencypher.org/) 查询语言（OpenCypher 标准）， -具备完善的工具链组件，助力用户轻松构建基于图数据库之上的应用和产品。HugeGraph 支持百亿以上的顶点和边快速导入，并提供毫秒级的关联关系查询能力（OLTP）， -并支持大规模分布式图分析（OLAP）。 +[Apache HugeGraph](https://hugegraph.apache.org/) 是一套易用、高效、通用的开源**全栈图系统**（[GitHub](https://github.com/apache/hugegraph)）， +覆盖**图数据库**（OLTP 实时查询）、**图计算**（OLAP 大规模分析）与**图 AI**（GraphRAG / 图机器学习）三大领域。 -HugeGraph 典型应用场景包括深度关系探索、关联分析、路径搜索、特征抽取、数据聚类、社区检测、知识图谱等， -适用业务领域有如网络安全、电信诈骗、金融风控、广告推荐、社交网络和智能机器人等。 +HugeGraph 支持百亿以上的顶点和边的快速存储与查询，具备出色的 OLTP 性能。 +其图引擎完全兼容 [Apache TinkerPop 3](https://tinkerpop.apache.org) 框架，同时支持 +[Gremlin](https://tinkerpop.apache.org/gremlin.html) 和 [Cypher](https://en.wikipedia.org/wiki/Cypher)（OpenCypher 标准）双查询语言。 -本系统的主要应用场景是解决反欺诈、威胁情报、黑产打击等业务的图数据存储和建模分析需求，在此基础上逐步扩展及支持了更多的通用图应用。 +**典型应用场景：** 深度关系探索、关联分析、路径搜索、特征抽取、社区检测、知识图谱等， +**适用领域：** 网络安全、电信反欺诈、金融风控、广告推荐、社交网络、智能问答等。 -### Features +--- + +### 生态系统全景 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Apache HugeGraph - Full-Stack Graph System │ +├──────────────────┬────────────────────┬──────────────────────┤ +│ Graph DB (OLTP) │ Graph Compute │ Graph AI │ +│ HugeGraph │ Vermeer (Memory) │ HugeGraph-AI │ +│ Server │ Computer (Dist.) │ GraphRAG/GNN/Py │ +├──────────────────┴────────────────────┴──────────────────────┤ +│ HugeGraph Toolchain │ +│ Hubble | Loader | Client(Java/Go/Py) | Spark | Tools │ +└──────────────────────────────────────────────────────────────┘ +``` + +--- + +### 核心组件 + +#### 🗄️ HugeGraph Server — 图引擎（OLTP） + +HugeGraph 项目的核心模块，提供高性能的图数据存储与实时查询能力： + +- **图引擎核心**：支持属性图（Property Graph）建模，包含 VertexLabel、EdgeLabel、PropertyKey、IndexLabel 完整 Schema 管理 +- **双查询语言**：全面兼容 Gremlin（TinkerPop 3）和 Cypher（OpenCypher） +- **REST API**：内置 REST Server，提供 RESTful 图操作接口 +- **多类型索引**：精确查询、范围查询、复合条件组合查询 +- **插件式存储后端**：1.7.0+ 默认支持 `RocksDB`（单机默认）、`HStore`（分布式）、`HBase`、`Memory`，1.5.x 及以前还支持 MySQL / PostgreSQL / Cassandra 等 + +**子模块：** +- `Core` — 图引擎实现，向下连接 Backend，向上支持 API +- `Backend` — 多后端存储适配层 +- `API` — RESTful 接入层，兼容 Gremlin/Cypher 查询 + +📖 [Server 快速开始](/cn/docs/quickstart/hugegraph/hugegraph-server) + +--- + +#### 📊 图计算引擎（OLAP） + +提供两种互补的图分析引擎： + +- **Vermeer**（推荐）：高性能纯内存图计算引擎，部署简单、响应快，适合中小规模图分析和快速上手 +- **HugeGraph-Computer**：基于 [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf) 的分布式 OLAP 引擎，可运行在 Kubernetes / Yarn 上，适合超大规模图算法任务 + +📖 [图计算快速开始](/cn/docs/quickstart/computing/hugegraph-computer) Review Comment: 这里推荐 **Vermeer** 作为优先的 OLAP 引擎，但“图计算快速开始”链接目前指向 `/cn/docs/quickstart/computing/hugegraph-computer`（仅 HugeGraph-Computer 的快速开始）。建议改为指向 Vermeer 快速开始（`/cn/docs/quickstart/computing/hugegraph-vermeer`）或图计算章节首页，并同步更新下方“Vermeer / Computer”导航表中的链接，避免推荐项与落地页不一致。 ```suggestion 📖 [图计算快速开始](/cn/docs/quickstart/computing/hugegraph-vermeer) ``` -- This is an automated message from the Apache Git Service. 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