From 755a9ae05b99b468a7d95d1d768e533ac52e9ac1 Mon Sep 17 00:00:00 2001 From: imbajin Date: Mon, 9 Mar 2026 16:38:07 +0800 Subject: [PATCH 1/3] docs: describe HugeGraph as full-stack graph system Reword documentation to present HugeGraph as a full-stack graph system covering graph database (OLTP), graph computing (OLAP) and graph AI. Updated /docs/introduction/_index.md to expand the project summary and highlight GraphRAG/graph --- content/cn/blog/_index.md | 2 +- content/cn/docs/guides/architectural.md | 2 +- content/cn/docs/introduction/_index.md | 201 +++++++++++++++--------- content/en/blog/_index.md | 2 +- content/en/docs/guides/architectural.md | 2 +- content/en/docs/introduction/_index.md | 200 ++++++++++++++--------- 6 files changed, 251 insertions(+), 158 deletions(-) diff --git a/content/cn/blog/_index.md b/content/cn/blog/_index.md index 82d54cf56..28750062b 100644 --- a/content/cn/blog/_index.md +++ b/content/cn/blog/_index.md @@ -14,4 +14,4 @@ menu: - **HugeGraph-AI**: AI 与图计算结合的应用案例 - **HugeGraph Computing (OLAP)**: 大规模图计算和分析实践 -探索更多内容，提升您的图数据库使用体验！ +探索更多内容，深入了解 HugeGraph 图系统的全栈能力！ diff --git a/content/cn/docs/guides/architectural.md b/content/cn/docs/guides/architectural.md index c876ed584..509686dc4 100644 --- a/content/cn/docs/guides/architectural.md +++ b/content/cn/docs/guides/architectural.md @@ -6,7 +6,7 @@ weight: 1 ### 1 概述 -作为一款通用的图数据库产品，HugeGraph 需具备图数据库的基本功能。HugeGraph 支持 OLTP 和 OLAP 两种图计算类型，其中 OLTP 实现了 [Apache TinkerPop3](https://tinkerpop.apache.org) 框架，支持 [Gremlin](https://tinkerpop.apache.org/gremlin.html) 和 [Cypher](https://en.wikipedia.org/wiki/Cypher) 查询语言，拥有功能齐全的应用工具链，还提供了插件式后端存储驱动框架。 +作为一套覆盖**图数据库、图计算与图 AI** 的全栈图系统，HugeGraph 以高性能图引擎（HugeGraph Server）为核心，支持 OLTP 和 OLAP 两种图计算类型。其中 OLTP 层实现了 [Apache TinkerPop3](https://tinkerpop.apache.org) 框架，支持 [Gremlin](https://tinkerpop.apache.org/gremlin.html) 和 [Cypher](https://en.wikipedia.org/wiki/Cypher) 查询语言，拥有功能齐全的应用工具链，还提供了插件式后端存储驱动框架。 下面是 HugeGraph 的整体架构图： diff --git a/content/cn/docs/introduction/_index.md b/content/cn/docs/introduction/_index.md index 076c81c3d..2aab21b84 100644 --- a/content/cn/docs/introduction/_index.md +++ b/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**：默认支持 `RocksDB`（单机）/ `HStore + HBase`（分布式），旧版本（≤1.5.0）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) + +--- + +#### 🤖 HugeGraph-AI — 图 AI 生态 -HugeGraph 支持在线及离线环境下的图操作，支持批量导入数据，支持高效的复杂关联关系分析，并且能够与大数据平台无缝集成。 -HugeGraph 支持多用户并行操作，用户可输入 Gremlin/Cypher 查询语句，并及时得到图查询结果，也可在用户程序中调用 HugeGraph API 进行图分析或查询。 +HugeGraph 独立的 AI 组件，连接图与大语言模型（LLM）： -本系统具备如下特点： +- **GraphRAG**：基于图的检索增强生成，实现 LLM 智能问答 +- **知识图谱构建**：自动从非结构化文本中提取实体和关系，构建知识图谱 +- **图神经网络**：支持 GNN 模型的训练与推理 +- **20+ 图机器学习算法**：内置丰富的图分析算法，持续更新 +- **Python Client**：为 AI 应用提供便捷的 Python SDK -- 易用：HugeGraph 支持 Gremlin/Cypher 图查询语言与 RESTful API，同时提供图检索常用接口，具备功能齐全的周边工具，轻松实现基于图的各种查询分析运算。 -- 高效：HugeGraph 在图存储和图计算方面做了深度优化，提供多种批量导入工具，轻松完成百亿级数据快速导入，通过优化过的查询达到图检索的毫秒级响应。支持数千用户并发的在线实时操作。 -- 通用：HugeGraph 支持 Apache Gremlin 标准图查询语言和 Property Graph 标准图建模方法，支持基于图的 OLTP 和 OLAP 方案。集成 Apache Hadoop 及 Apache Spark 大数据平台。 -- 可扩展：支持分布式存储、数据多副本及横向扩容，内置多种后端存储引擎，也可插件式轻松扩展后端存储引擎。 -- 开放：HugeGraph 代码开源（Apache 2 License），客户可自主修改定制，选择性回馈开源社区。 +📖 [HugeGraph-AI 快速开始](/cn/docs/quickstart/hugegraph-ai/quick_start) + +--- + +#### 🛠️ HugeGraph Toolchain — 工具链 + +围绕图系统的完整工具生态（[toolchain 仓库](https://github.com/apache/hugegraph-toolchain)）： + +| 工具 | 说明 | +|------|------| +| [Hubble](/cn/docs/quickstart/toolchain/hugegraph-hubble) | Web 可视化平台：数据建模 → 批量导入 → 在线/离线分析 一站式操作 | +| [Loader](/cn/docs/quickstart/toolchain/hugegraph-loader) | 数据导入工具：支持本地文件、HDFS、MySQL 等多数据源，TXT/CSV/JSON 等格式 | +| [Client](/cn/docs/quickstart/client/hugegraph-client) | 多语言 SDK：Java / Python / Go | +| [Spark-connector](/cn/docs/quickstart/toolchain/hugegraph-spark-connector) | Spark 集成：支持通过 Spark 批量读写图数据，适合大数据离线处理场景 | +| [Tools](/cn/docs/quickstart/toolchain/hugegraph-tools) | 命令行运维工具：图管理、备份恢复、Gremlin 执行等 | + +--- ### 部署模式 -HugeGraph 支持多种部署模式，满足不同规模和场景的需求： +HugeGraph 支持两种主要部署模式： + +| 模式 | 核心组件 | 适用场景 | 数据规模 | 高可用 | +|------|---------|---------|---------|-------| +| **单机 (Standalone)** | Server + RocksDB | 开发、测试、单节点生产 | < 1TB | 基础 | +| **分布式 (Distributed)** | Server + PD（3-5节点）+ Store（3+节点） | 生产环境、水平扩展 | < 1000TB | ✅ | -**单机模式 (Standalone)** -- Server + RocksDB 后端存储 -- 适合开发测试和中小规模数据（< 4TB） -- Docker 快速启动: `docker run hugegraph/hugegraph` -- 详见 [Server 快速开始](/cn/docs/quickstart/hugegraph/hugegraph-server) +**Docker 快速体验：** -**分布式模式 (Distributed)** -- HugeGraph-PD: 元数据管理和集群调度 -- HugeGraph-Store (HStore): 分布式存储引擎 -- 支持水平扩展和高可用（< 1000TB 数据规模） -- 适合生产环境和大规模图数据应用 +```bash +docker run -itd --name=hugegraph -p 8080:8080 hugegraph/hugegraph +``` + +--- -### 快速入门指南 +### 快速入门导航 -| 使用场景 | 推荐路径 | +| 我想要... | 从这里开始 | |---------|---------| -| 快速体验 | [Docker 部署](/cn/docs/quickstart/hugegraph/hugegraph-server#docker) | -| 构建 OLTP 应用 | Server → REST API / Gremlin / Cypher | -| 图分析 (OLAP) | [Vermeer](/cn/docs/quickstart/computing/hugegraph-computer) (推荐) 或 Computer | -| 构建 AI 应用 | [HugeGraph-AI](/cn/docs/quickstart/hugegraph-ai) (GraphRAG/知识图谱) | -| 批量导入数据 | [Loader](/cn/docs/quickstart/toolchain/hugegraph-loader) + [Hubble](/cn/docs/quickstart/toolchain/hugegraph-hubble) | - -### 功能特性 - -- 支持从多数据源批量导入数据 (包括本地文件、HDFS 文件、MySQL 数据库等数据源)，支持多种文件格式导入 (包括 TXT、CSV、JSON 等格式) -- 具备可视化操作界面，可用于操作、分析及展示图，降低用户使用门槛 -- 优化的图接口：最短路径 (Shortest Path)、K 步连通子图 (K-neighbor)、K 步到达邻接点 (K-out)、个性化推荐算法 PersonalRank 等 -- 基于 Apache TinkerPop3 框架实现，支持 Gremlin 图查询语言 -- 支持属性图，顶点和边均可添加属性，支持丰富的属性类型 -- 具备独立的 Schema 元数据信息，拥有强大的图建模能力，方便第三方系统集成 -- 支持多顶点 ID 策略：支持主键 ID、支持自动生成 ID、支持用户自定义字符串 ID、支持用户自定义数字 ID -- 可以对边和顶点的属性建立索引，支持精确查询、范围查询、全文检索 -- 存储系统采用插件方式，支持 RocksDB(单机/集群)、Cassandra、ScyllaDB、HBase、MySQL、PostgreSQL、Palo 以及 Memory 等 -- 与 HDFS、Spark/Flink、GraphX 等大数据系统集成，支持 BulkLoad 操作导入海量数据 -- 支持高可用 HA、数据多副本、备份恢复、监控、分布式 Trace 等 - -### Modules - -- [HugeGraph-Server](/cn/docs/quickstart/hugegraph/hugegraph-server): HugeGraph-Server 是 HugeGraph 项目的核心部分，包含 Core、Backend、API 等子模块； - - Core：图引擎实现，向下连接 Backend 模块，向上支持 API 模块； - - Backend：实现将图数据存储到后端，支持的后端包括：Memory、Cassandra、ScyllaDB、RocksDB、HBase、MySQL 及 PostgreSQL，用户根据实际情况选择一种即可； - - API：内置 REST Server，向用户提供 RESTful API，同时完全兼容 Gremlin 查询。(支持分布式存储和计算下推) -- [HugeGraph-Toolchain](https://github.com/apache/hugegraph-toolchain): (工具链) - - [HugeGraph-Client](/cn/docs/quickstart/client/hugegraph-client)：HugeGraph-Client 提供了 RESTful API 的客户端，用于连接 HugeGraph-Server，支持 Java/Python/Go 多语言版本； - - [HugeGraph-Loader](/cn/docs/quickstart/toolchain/hugegraph-loader)：HugeGraph-Loader 是基于 HugeGraph-Client 的数据导入工具，将普通文本数据转化为图形的顶点和边并插入图形数据库中； - - [HugeGraph-Hubble](/cn/docs/quickstart/toolchain/hugegraph-hubble)：HugeGraph-Hubble 是 HugeGraph 的 Web -可视化管理平台，一站式可视化分析平台，平台涵盖了从数据建模，到数据快速导入，再到数据的在线、离线分析、以及图的统一管理的全过程； - - [HugeGraph-Tools](/cn/docs/quickstart/toolchain/hugegraph-tools)：HugeGraph-Tools 是 HugeGraph 的部署和管理工具，包括管理图、备份/恢复、Gremlin 执行等功能。 -- [HugeGraph-Computer](/cn/docs/quickstart/computing/hugegraph-computer)：HugeGraph-Computer 是分布式图处理系统 (OLAP)。 - 它是 [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf) 的一个实现。它可以运行在 Kubernetes/Yarn - 等集群上，支持超大规模图计算。同时提供 Vermeer 轻量级图计算引擎，适合快速开始和中小规模图分析。 -- [HugeGraph-AI](/cn/docs/quickstart/hugegraph-ai)：HugeGraph-AI 是 HugeGraph 独立的 AI - 组件，提供 LLM/GraphRAG 智能问答、自动化知识图谱构建、图神经网络训练/推理、Python-Client 等功能，内置 20+ 图机器学习算法，持续更新中。 - -### Contact Us - -- [GitHub Issues](https://github.com/apache/hugegraph/issues): 使用途中出现问题或提供功能性建议，可通过此反馈 (推荐) -- 邮件反馈：[dev@hugegraph.apache.org](mailto:dev@hugegraph.apache.org) ([邮箱订阅方式](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/)) -- SEC 反馈： [security@hugegraph.apache.org](mailto:security@hugegraph.apache.org) (报告安全相关问题) -- 微信公众号：Apache HugeGraph, 欢迎扫描下方二维码加入我们！ - - QR png +| 🚀 **快速体验** | [Docker 部署](/cn/docs/quickstart/hugegraph/hugegraph-server) | +| 🔍 **运行图查询** (OLTP) | [HugeGraph Server 快速开始](/cn/docs/quickstart/hugegraph/hugegraph-server) | +| 📈 **大规模图计算** (OLAP) | [Vermeer / Computer](/cn/docs/quickstart/computing/hugegraph-computer) | +| 🤖 **构建 AI/RAG 应用** | [HugeGraph-AI](/cn/docs/quickstart/hugegraph-ai/quick_start) | +| 📥 **批量导入数据** | [HugeGraph Loader](/cn/docs/quickstart/toolchain/hugegraph-loader) | +| 🖥️ **可视化管理** | [Hubble Web UI](/cn/docs/quickstart/toolchain/hugegraph-hubble) | + +--- + +### 系统特性 + +- **易用**：Gremlin/Cypher 双查询语言 + RESTful API，功能齐全的工具链，轻松上手 +- **高效**：图存储与查询深度优化，毫秒级响应，支持数千并发在线操作，百亿级数据快速导入 +- **通用**：支持 OLTP + OLAP 双模式，无缝对接 Apache Hadoop、Spark、Flink 大数据生态 +- **可扩展**：分布式存储、数据多副本、横向扩容，插件式后端可灵活扩展 +- **开放**：Apache 2.0 License，完全开源，欢迎社区贡献 + +--- + +### 联系我们 + +- [GitHub Issues](https://github.com/apache/hugegraph/issues)：问题反馈与功能建议（推荐） +- 邮件：[dev@hugegraph.apache.org](mailto:dev@hugegraph.apache.org)（[订阅方式](/cn/docs/contribution-guidelines/subscribe/)） +- 安全问题：[security@hugegraph.apache.org](mailto:security@hugegraph.apache.org) +- 微信公众号：Apache HugeGraph + +微信公众号二维码 diff --git a/content/en/blog/_index.md b/content/en/blog/_index.md index 2c389678c..298dcd5a0 100644 --- a/content/en/blog/_index.md +++ b/content/en/blog/_index.md @@ -14,4 +14,4 @@ Here you will find technical articles, best practices, and community updates fro - **HugeGraph-AI**: Application cases combining AI with graph computing - **HugeGraph Computing (OLAP)**: Large-scale graph computing and analytical practices -Explore more content to enhance your graph database experience! +Explore more content to deeply understand the full-stack capabilities of the HugeGraph graph system! diff --git a/content/en/docs/guides/architectural.md b/content/en/docs/guides/architectural.md index 2b03f920d..5dda31cff 100644 --- a/content/en/docs/guides/architectural.md +++ b/content/en/docs/guides/architectural.md @@ -6,7 +6,7 @@ weight: 1 ### 1 Overview -As a general-purpose graph database product, HugeGraph needs to possess basic graph database functionality. HugeGraph supports two types of graph computation: OLTP and OLAP. For OLTP, it implements the [Apache TinkerPop3](https://tinkerpop.apache.org) framework and supports the [Gremlin](https://tinkerpop.apache.org/gremlin.html) and [Cypher](https://en.wikipedia.org/wiki/Cypher) query languages. It comes with a complete application toolchain and provides a plugin-based backend storage driver framework. +As a full-stack graph system covering **Graph Database, Graph Computing, and Graph AI**, HugeGraph is centered around a high-performance graph engine (HugeGraph Server) and supports both OLTP and OLAP graph computation types. For the OLTP layer, it implements the [Apache TinkerPop3](https://tinkerpop.apache.org) framework and supports the [Gremlin](https://tinkerpop.apache.org/gremlin.html) and [Cypher](https://en.wikipedia.org/wiki/Cypher) query languages. It comes with a complete application toolchain and provides a pluggable backend storage driver framework. Below is the overall architecture diagram of HugeGraph: diff --git a/content/en/docs/introduction/_index.md b/content/en/docs/introduction/_index.md index 51a172364..9cc80e00b 100644 --- a/content/en/docs/introduction/_index.md +++ b/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**: Defaults to `RocksDB` (standalone) / `HStore + HBase` (distributed); older versions (≤1.5.0) support 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) + +--- + +#### 🤖 HugeGraph-AI — Graph AI Ecosystem + +An independent AI component of HugeGraph, bridging graphs with Large Language Models (LLMs): -This system has the following features: +- **GraphRAG**: Graph-based Retrieval-Augmented Generation, enabling LLM intelligent Q&A. +- **Knowledge Graph Construction**: Automatically extracting entities and relationships from unstructured text to build knowledge graphs. +- **Graph Neural Networks**: Supports training and inference of GNN models. +- **20+ Graph Machine Learning Algorithms**: Built-in rich graph analysis algorithms, continuously updated. +- **Python Client**: Convenient Python SDK for AI applications. -- Ease of use: HugeGraph supports the Gremlin/Cypher graph query languages and a RESTful API, providing common interfaces for graph retrieval, and peripheral tools with complete functions to easily implement various graph-based query and analysis operations. -- Efficiency: HugeGraph has been deeply optimized in graph storage and graph computing, and provides a variety of batch import tools, which can easily complete the rapid import of tens of billions of data, and achieve millisecond-level response for graph retrieval through optimized queries. Supports simultaneous online real-time operations of thousands of users. -- Universal: HugeGraph supports the Apache Gremlin standard graph query language and the Property Graph standard graph modeling method, and supports graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big data platforms. -- Scalable: supports distributed storage, multiple copies of data, and horizontal expansion, built-in multiple back-end storage engines, and can easily expand the back-end storage engine through plug-ins. -- Open: HugeGraph code is open source (Apache 2 License), customers can modify and customize independently, and selectively give back to the open-source community. +📖 [HugeGraph-AI Quick Start](/docs/quickstart/hugegraph-ai/quick_start) + +--- + +#### 🛠️ HugeGraph Toolchain + +A complete tool ecosystem surrounding the graph system ([toolchain repository](https://github.com/apache/hugegraph-toolchain)): + +| Tool | Description | +|------|-------------| +| [Hubble](/docs/quickstart/toolchain/hugegraph-hubble) | Web visualization platform: one-stop operation for data modeling → batch importing → online/offline analysis. | +| [Loader](/docs/quickstart/toolchain/hugegraph-loader) | Data import tool: supports multiple data sources like local files, HDFS, MySQL, and formats like TXT/CSV/JSON. | +| [Client](/docs/quickstart/client/hugegraph-client) | Multi-language SDKs: Java / Python / Go. | +| [Spark-connector](/docs/quickstart/toolchain/hugegraph-spark-connector) | Spark integration: supports batch graph data read/write via Spark, suitable for big data offline processing. | +| [Tools](/docs/quickstart/toolchain/hugegraph-tools) | Command-line operational tools: graph management, backup/restore, Gremlin execution, etc. | + +--- ### Deployment Modes -HugeGraph supports multiple deployment modes to meet different scales and scenarios: - -**Standalone Mode** -- Server + RocksDB backend storage -- Suitable for development, testing, and small-to-medium scale data (< 4TB) -- Docker quick start: `docker run hugegraph/hugegraph` -- See [Server Quickstart](/docs/quickstart/hugegraph/hugegraph-server) - -**Distributed Mode** -- HugeGraph-PD: Metadata management and cluster scheduling -- HugeGraph-Store (HStore): Distributed storage engine -- Supports horizontal scaling and high availability (< 1000TB data scale) -- Suitable for production environments and large-scale graph data applications - -### Quick Start Guide - -| Use Case | Recommended Path | -|---------|---------| -| Quick experience | [Docker deployment](/docs/quickstart/hugegraph/hugegraph-server#docker) | -| Build OLTP applications | Server → REST API / Gremlin / Cypher | -| Graph analysis (OLAP) | [Vermeer](/docs/quickstart/computing/hugegraph-computer) (recommended) or Computer | -| Build AI applications | [HugeGraph-AI](/docs/quickstart/hugegraph-ai) (GraphRAG/Knowledge Graph) | -| Batch data import | [Loader](/docs/quickstart/toolchain/hugegraph-loader) + [Hubble](/docs/quickstart/toolchain/hugegraph-hubble) | - -### System Functions - -- Supports batch import of data from multiple data sources (including local files, HDFS files, MySQL databases, and other data sources), and supports import of multiple file formats (including TXT, CSV, JSON, and other formats) -- With a visual operation interface, it can be used for operation, analysis, and display diagrams, reducing the threshold for users to use -- Optimized graph interface: shortest path (Shortest Path), K-step connected subgraph (K-neighbor), K-step to reach the adjacent point (K-out), personalized recommendation algorithm PersonalRank, etc. -- Implemented based on the Apache TinkerPop3 framework, supports Gremlin graph query language -- Support attribute graph, attributes can be added to vertices and edges, and support rich attribute types -- Has independent schema metadata information, has powerful graph modeling capabilities, and facilitates third-party system integration -- Support multi-vertex ID strategy: support primary key ID, support automatic ID generation, support user-defined string ID, support user-defined digital ID -- The attributes of edges and vertices can be indexed to support precise query, range query, and full-text search -- The storage system adopts a plug-in method, supporting RocksDB (standalone/cluster), Cassandra, ScyllaDB, HBase, MySQL, PostgreSQL, Palo and Memory, etc. -- Integrated with big data systems such as HDFS, Spark/Flink, GraphX, etc., supports BulkLoad operation to import massive data. -- Supports HA(high availability), multiple data replicas, backup and recovery, monitoring, distributed Trace, etc. - -### Modules - -- [HugeGraph-Store]: HugeGraph-Store is a distributed storage engine to manage large-scale graph data by integrating storage and computation within a unified system. -- [HugeGraph-PD]: HugeGraph-PD (Placement Driver) manages metadata and coordinates storage nodes. -- [HugeGraph-Server](/docs/quickstart/hugegraph/hugegraph-server): HugeGraph-Server is the core part of the HugeGraph project, containing Core, Backend, API and other submodules; - - Core: Implements the graph engine, connects to the Backend module downwards, and supports the API module upwards; - - Backend: Implements the storage of graph data to the backend, supports backends including Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and PostgreSQL, users can choose one according to the actual situation; - - API: Built-in REST Server provides RESTful API to users and is fully compatible with Gremlin queries. (Supports distributed storage and computation pushdown) -- [HugeGraph-Toolchain](https://github.com/apache/hugegraph-toolchain): (Toolchain) - - [HugeGraph-Client](/docs/quickstart/client/hugegraph-client): HugeGraph-Client provides a RESTful API client for connecting to HugeGraph-Server, supporting Java/Python/Go multi-language versions; - - [HugeGraph-Loader](/docs/quickstart/toolchain/hugegraph-loader): HugeGraph-Loader is a data import tool based on HugeGraph-Client, which transforms ordinary text data into vertices and edges of the graph and inserts them into the graph database; - - [HugeGraph-Hubble](/docs/quickstart/toolchain/hugegraph-hubble): HugeGraph-Hubble is HugeGraph's Web -visualization management platform, a one-stop visualization analysis platform, the platform covers the whole process from data modeling, to fast data import, to online and offline analysis of data, and unified management of the graph; - - [HugeGraph-Tools](/docs/quickstart/toolchain/hugegraph-tools): HugeGraph-Tools is HugeGraph's deployment and management tool, including graph management, backup/recovery, Gremlin execution and other functions. -- [HugeGraph-Computer](/docs/quickstart/computing/hugegraph-computer): HugeGraph-Computer is a distributed graph processing system (OLAP). - It is an implementation of [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It can run on clusters such as Kubernetes/Yarn, and supports large-scale graph computing. Also provides Vermeer lightweight graph computing engine, suitable for quick start and small-to-medium scale graph analysis. -- [HugeGraph-AI](/docs/quickstart/hugegraph-ai): HugeGraph-AI is HugeGraph's independent AI - component, providing LLM/GraphRAG intelligent Q&A, automated knowledge graph construction, graph neural network training/inference, Python-Client and other features, with 20+ built-in graph machine learning algorithms, continuously updating. +HugeGraph supports two primary deployment modes: + +| Mode | Core Components | Suitable Scenarios | Data Scale | High Availability (HA) | +|------|-----------------|--------------------|------------|------------------------| +| **Standalone** | Server + RocksDB | Development, testing, single-node production | < 1TB | Basic | +| **Distributed** | Server + PD (3-5 nodes) + Store (3+ nodes) | Production environments, horizontal scaling | < 1000TB | ✅ | + +**Docker Quick Experience:** + +```bash +docker run -itd --name=hugegraph -p 8080:8080 hugegraph/hugegraph +``` + +--- + +### Quick Start Navigation + +| I want to... | Start Here | +|--------------|------------| +| 🚀 **Quick Experience** | [Docker Deployment](/docs/quickstart/hugegraph/hugegraph-server) | +| 🔍 **Run Graph Queries** (OLTP) | [Server Quick Start](/docs/quickstart/hugegraph/hugegraph-server) | +| 📈 **Large-scale Graph Computing** (OLAP) | [Vermeer / Computer](/docs/quickstart/computing/hugegraph-computer) | +| 🤖 **Build AI/RAG Applications** | [HugeGraph-AI](/docs/quickstart/hugegraph-ai/quick_start) | +| 📥 **Batch Import Data** | [HugeGraph-Loader](/docs/quickstart/toolchain/hugegraph-loader) | +| 🖥️ **Visual Management** | [Hubble Web UI](/docs/quickstart/toolchain/hugegraph-hubble) | + +--- + +### System Features + +- **Easy to Use**: Dual Gremlin/Cypher query languages + RESTful API, comprehensive toolchain, extremely easy to get started. +- **Efficient**: Deeply optimized graph storage and queries, millisecond-level response, supports thousands of concurrent online operations, fast import of billions of data records. +- **Universal**: Supports both OLTP and OLAP modes, seamlessly integrates with Apache Hadoop, Spark, and Flink big data ecosystems. +- **Scalable**: Distributed storage, multi-replica data, horizontal scaling, flexible expansion through pluggable backends. +- **Open**: Apache 2.0 License, fully open-source, warmly welcoming community contributions. + +--- ### Contact Us -- [GitHub Issues](https://github.com/apache/hugegraph/issues): Feedback on usage issues and functional requirements (quick response) -- Feedback Email: [dev@hugegraph.apache.org](mailto:dev@hugegraph.apache.org) ([subscriber](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/) only) -- Security Email: [security@hugegraph.apache.org](mailto:security@hugegraph.apache.org) (Report SEC problems) -- WeChat public account: Apache HugeGraph, welcome to scan this QR code to follow us. +- [GitHub Issues](https://github.com/apache/hugegraph/issues): Feedback on usage issues and functional requirements (Recommended) +- Email: [dev@hugegraph.apache.org](mailto:dev@hugegraph.apache.org) ([How to subscribe](/docs/contribution-guidelines/subscribe/)) +- Security: [security@hugegraph.apache.org](mailto:security@hugegraph.apache.org) (Report security issues) +- WeChat Public Account: Apache HugeGraph - QR png +WeChat QR Code From 42a3340a7a6a1dbd2ca4f244a00a88fa9b7386c5 Mon Sep 17 00:00:00 2001 From: imbajin Date: Mon, 9 Mar 2026 18:14:34 +0800 Subject: [PATCH 2/3] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- content/cn/docs/introduction/_index.md | 4 ++-- content/en/blog/_index.md | 2 +- content/en/docs/introduction/_index.md | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/content/cn/docs/introduction/_index.md b/content/cn/docs/introduction/_index.md index 2aab21b84..f8c6bb762 100644 --- a/content/cn/docs/introduction/_index.md +++ b/content/cn/docs/introduction/_index.md @@ -48,7 +48,7 @@ HugeGraph 项目的核心模块，提供高性能的图数据存储与实时查 - **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**：默认支持 `RocksDB`（单机）/ `HStore + HBase`（分布式），旧版本（≤1.5.0）还支持 MySQL / PostgreSQL / Cassandra 等 +- **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 @@ -104,7 +104,7 @@ HugeGraph 支持两种主要部署模式： | 模式 | 核心组件 | 适用场景 | 数据规模 | 高可用 | |------|---------|---------|---------|-------| -| **单机 (Standalone)** | Server + RocksDB | 开发、测试、单节点生产 | < 1TB | 基础 | +| **单机 (Standalone)** | Server + RocksDB | 开发、测试、单节点生产 | < 4TB | 基础 | | **分布式 (Distributed)** | Server + PD（3-5节点）+ Store（3+节点） | 生产环境、水平扩展 | < 1000TB | ✅ | **Docker 快速体验：** diff --git a/content/en/blog/_index.md b/content/en/blog/_index.md index 298dcd5a0..f6b7ad3d2 100644 --- a/content/en/blog/_index.md +++ b/content/en/blog/_index.md @@ -14,4 +14,4 @@ Here you will find technical articles, best practices, and community updates fro - **HugeGraph-AI**: Application cases combining AI with graph computing - **HugeGraph Computing (OLAP)**: Large-scale graph computing and analytical practices -Explore more content to deeply understand the full-stack capabilities of the HugeGraph graph system! +Explore more content to deeply understand the full-stack capabilities of the HugeGraph system! diff --git a/content/en/docs/introduction/_index.md b/content/en/docs/introduction/_index.md index 9cc80e00b..e023145cf 100644 --- a/content/en/docs/introduction/_index.md +++ b/content/en/docs/introduction/_index.md @@ -45,7 +45,7 @@ The core module of the HugeGraph project, providing high-performance graph data - **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**: Defaults to `RocksDB` (standalone) / `HStore + HBase` (distributed); older versions (≤1.5.0) support MySQL / PostgreSQL / Cassandra, etc. +- **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. @@ -101,7 +101,7 @@ HugeGraph supports two primary deployment modes: | Mode | Core Components | Suitable Scenarios | Data Scale | High Availability (HA) | |------|-----------------|--------------------|------------|------------------------| -| **Standalone** | Server + RocksDB | Development, testing, single-node production | < 1TB | Basic | +| **Standalone** | Server + RocksDB | Development, testing, single-node production | < 4TB | Basic | | **Distributed** | Server + PD (3-5 nodes) + Store (3+ nodes) | Production environments, horizontal scaling | < 1000TB | ✅ | **Docker Quick Experience:** From 504c3a7f33f1347fa4566262dc0522e84a024fac Mon Sep 17 00:00:00 2001 From: imbajin Date: Mon, 9 Mar 2026 18:22:33 +0800 Subject: [PATCH 3/3] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- content/cn/docs/introduction/_index.md | 2 +- content/en/docs/introduction/_index.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/cn/docs/introduction/_index.md b/content/cn/docs/introduction/_index.md index f8c6bb762..a586f1081 100644 --- a/content/cn/docs/introduction/_index.md +++ b/content/cn/docs/introduction/_index.md @@ -66,7 +66,7 @@ HugeGraph 项目的核心模块，提供高性能的图数据存储与实时查 - **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) +📖 [图计算快速开始](/cn/docs/quickstart/computing/hugegraph-vermeer) --- diff --git a/content/en/docs/introduction/_index.md b/content/en/docs/introduction/_index.md index e023145cf..00955a612 100644 --- a/content/en/docs/introduction/_index.md +++ b/content/en/docs/introduction/_index.md @@ -63,7 +63,7 @@ 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) +📖 [Computing Quick Start](/docs/quickstart/computing/hugegraph-vermeer) ---