Copilot commented on code in PR #456: URL: https://github.com/apache/hugegraph-doc/pull/456#discussion_r2904100666
########## 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. Review Comment: The storage backend description appears inaccurate/inconsistent with the rest of the docs. HugeGraph-Server quickstart states that for 1.7.0+ the supported backends are RocksDB (standalone default), HStore (distributed), HBase, and Memory; HBase isn’t part of the “distributed default” backend, and “older versions (≤1.5.0)” should likely be phrased as “1.5.x or earlier” to match existing version guidance. ```suggestion - **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. ``` ########## 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 Review Comment: 这里对存储后端的表述与现有文档不一致：HugeGraph-Server 快速开始中提到 1.7.0+ 支持的后端包括 RocksDB（单机默认）、HStore（分布式）、HBase、Memory。HBase 并不是 “分布式默认后端” 的一部分；另外“旧版本（≤1.5.0）”建议改为“1.5.x 及以前”以匹配现有版本说明。 ```suggestion - **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 ``` ########## 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 | Review Comment: This table changes the standalone data scale threshold to “< 1TB”, but other docs (e.g., docs home and FAQ) still describe standalone as “< 4TB”. Please reconcile this value across the documentation (either update the other pages or keep the same threshold here) so readers don’t get conflicting guidance. ```suggestion | **Standalone** | Server + RocksDB | Development, testing, single-node production | < 4TB | Basic | ``` ########## 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 | Review Comment: 这里将单机模式的数据规模写为“< 1TB”，但站内其他页面（如文档首页、FAQ）仍使用“< 4TB”。建议统一口径，避免读者获得相互矛盾的部署建议。 ```suggestion | **单机 (Standalone)** | Server + RocksDB | 开发、测试、单节点生产 | < 4TB | 基础 | ``` ########## 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! Review Comment: The sentence reads redundant: “HugeGraph graph system”. Consider removing one “graph” (e.g., “HugeGraph system” or “HugeGraph full-stack graph system”) to improve readability. ```suggestion Explore more content to deeply understand the full-stack capabilities of the HugeGraph system! ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
