From c47d27ea4358decb44bf834a00ba2673ba6ef7b8 Mon Sep 17 00:00:00 2001 From: coderzc Date: Sun, 27 Nov 2022 13:45:31 +0000 Subject: [PATCH] improve doc 26a2e8d48fdd6a0b40a6d6316aff981ab1c5c397 --- docs/_print/index.html | 2 +- docs/index.xml | 2 +- docs/introduction/readme/index.html | 6 +++--- en/sitemap.xml | 2 +- index.html | 2 +- sitemap.xml | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/_print/index.html b/docs/_print/index.html index 195b5f252..897311d0f 100644 --- a/docs/_print/index.html +++ b/docs/_print/index.html @@ -4,7 +4,7 @@ Click here to print.

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Documentation

Welcome to HugeGraph docs

1 - Introduction with HugeGraph

Summary

HugeGraph is an easy-to-use, efficient, general-purpose open source graph database system(Graph Database, GitHub project address), implemented the Apache TinkerPop3 framework and is fully compatible with the Gremlin query language, With complete toolchain components, it helps users to 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 support large-scale distributed graph computing (OLAP).

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.

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.

Features

HugeGraph supports graph operations in online and offline environments, supports batch import of data, supports efficient complex relationship analysis, and can be seamlessly integrated with big data platforms. +It supports large-scale distributed graph computing (OLAP).

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.

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.

Features

HugeGraph supports graph operations in online and offline environments, supports batch import of data, supports efficient complex relationship analysis, and can be seamlessly integrated with big data platforms. HugeGraph supports multi-user parallel operations. Users can enter Gremlin query statements and get graph query results in time. They can also call HugeGraph API in user programs for graph analysis or query.

This system has the following features:

The functions of this system include but are not limited to:

Modules

Contact Us

2 - Download HugeGraph

Latest version

The latest HugeGraph: 0.12.0, released on 2021-12-31.

componentsdescriptiondownload
HugeGraph-ServerThe main program of HugeGraph0.12.0
HugeGraph-HubbleWeb-based Visual Graphical Interface1.6.0
HugeGraph-LoaderData import tool0.12.0
HugeGraph-ToolsCommand line toolset1.6.0

Versions mapping

serverclientloaderhubblecommontools
0.12.02.0.10.12.01.6.02.0.11.6.0
0.11.21.9.10.11.11.5.01.8.11.5.0
0.10.41.8.00.10.10.10.01.6.161.4.0
0.9.21.7.00.9.00.9.01.6.01.3.0
0.8.01.6.40.8.00.8.01.5.31.2.0
0.7.41.5.80.7.00.7.01.4.91.1.0
0.6.11.5.60.6.10.6.11.4.31.0.0
0.5.61.5.00.5.60.5.01.4.0
0.4.51.4.70.2.20.4.11.3.12

Note: The latest graph analysis and display platform is Hubble, which supports server v0.10 +.

3 - Quick Start

3.1 - HugeGraph-Server Quick Start

1 HugeGraph-Server Overview

HugeGraph-Server is the core part of the HugeGraph Project, contains submodules such as Core、Backend、API.

The Core Module is an implementation of the Tinkerpop interface; The Backend module is used to save the graph data to the data store, currently supported backends include：Memory、Cassandra、ScyllaDB、RocksDB; The API Module provides HTTP Server, which converts Client’s HTTP request into a call to Core Module.

There will be two spellings HugeGraph-Server and HugeGraphServer in the document, and other modules are similar. There is no big difference in the meaning of these two ways of writing, which can be distinguished as follows: HugeGraph-Server represents the code of server-related components, HugeGraphServer represents the service process.

2 Dependency

2.1 Install JDK-1.8

HugeGraph-Server developed based on jdk-1.8, project’s code uses many classes and methods in jdk-1.8, please install and configure by yourself.

Be sure to execute the java -version command to check the jdk version before reading

java -version

2.2 Install GCC-4.3.0(GLIBCXX_3.4.10) or update version (optional)

If you are using the RocksDB backend, be sure to execute the gcc --version command to check the gcc version; if you are using other backends, this is not required.

gcc --version

3 Deploy

There are three ways to deploy HugeGraph-Server components:

3.1 One-click deployment

HugeGraph-Tools provides a command-line tool for one-click deployment, users can use this tool to quickly download、decompress、configure and start HugeGraphServer and HugeGraphStudio with one click. diff --git a/docs/index.xml b/docs/index.xml index f31feada2..8e2ab24e8 100644 --- a/docs/index.xml +++ b/docs/index.xml @@ -1604,7 +1604,7 @@ restserver.url=http://0.0.0.0:8080 <p>HugeGraph is an easy-to-use, efficient, general-purpose open source graph database system(Graph Database, <a href="https://github.com/hugegraph/hugegraph">GitHub project address</a>), implemented the <a href="https://tinkerpop.apache.org">Apache TinkerPop3</a> framework and is fully compatible with the <a href="https://tinkerpop.apache.org/gremlin.html">Gremlin</a> query language, With complete toolchain components, it helps users to 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 support large-scale distributed graph computing (OLAP).</p> +It supports large-scale distributed graph computing (OLAP).</p> <p>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.</p> <p>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.</p> <h3 id="features">Features</h3> diff --git a/docs/introduction/readme/index.html b/docs/introduction/readme/index.html index 5979bdf72..2fabeab9e 100644 --- a/docs/introduction/readme/index.html +++ b/docs/introduction/readme/index.html @@ -1,6 +1,6 @@ Introduction with HugeGraph | HugeGraph +implemented …">

Introduction with HugeGraph

Summary

HugeGraph is an easy-to-use, efficient, general-purpose open source graph database system(Graph Database, GitHub project address), implemented the Apache TinkerPop3 framework and is fully compatible with the Gremlin query language, With complete toolchain components, it helps users to 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 support large-scale distributed graph computing (OLAP).

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.

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.

Features

HugeGraph supports graph operations in online and offline environments, supports batch import of data, supports efficient complex relationship analysis, and can be seamlessly integrated with big data platforms. -HugeGraph supports multi-user parallel operations. Users can enter Gremlin query statements and get graph query results in time. They can also call HugeGraph API in user programs for graph analysis or query.

This system has the following features:

  • Ease of use: HugeGraph supports Gremlin graph query language and RESTful API, provides common interfaces for graph retrieval, and has 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 platform.
  • 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.

The functions of this system include but are not limited to:

  • 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 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 plug-in mode, supporting RocksDB, Cassandra, ScyllaDB, HBase, MySQL, PostgreSQL, Palo, and InMemory, etc.
  • Integrate with big data systems such as Hadoop and Spark GraphX, and support Bulk Load operations
  • Support high availability HA, multiple copies of data, backup recovery, monitoring, etc.

Modules

  • HugeGraph-Server: HugeGraph-Server is the core part of the HugeGraph project, including submodules such as Core, Backend, and API;
    • Core: Graph engine implementation, connecting the Backend module downward and supporting the API module upward;
    • Backend: Realize the storage of graph data to the backend. The supported backends include: 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 query.
  • HugeGraph-Client: HugeGraph-Client provides a RESTful API client for connecting to HugeGraph-Server. Currently, only Java version is implemented. Users of other languages can implement it by themselves;
  • HugeGraph-Loader: HugeGraph-Loader is a data import tool based on HugeGraph-Client, which converts ordinary text data into graph vertices and edges and inserts them into graph database;
  • HugeGraph-Computer: HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework;
  • HugeGraph-Hubble: HugeGraph-Hubble is HugeGraph’s web visualization management platform, a one-stop visual analysis platform. The platform covers the whole process from data modeling, to rapid data import, to online and offline analysis of data, and unified management of graphs;
  • HugeGraph-Tools: HugeGraph-Tools is HugeGraph’s deployment and management tools, including functions such as managing graphs, backup/restore, Gremlin execution, etc.

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Last modified November 27, 2022: Update README.md (b19c38f)
+It supports large-scale distributed graph computing (OLAP).

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.

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.

Features

HugeGraph supports graph operations in online and offline environments, supports batch import of data, supports efficient complex relationship analysis, and can be seamlessly integrated with big data platforms. +HugeGraph supports multi-user parallel operations. Users can enter Gremlin query statements and get graph query results in time. They can also call HugeGraph API in user programs for graph analysis or query.

This system has the following features:

The functions of this system include but are not limited to:

Modules

Contact Us


Last modified November 27, 2022: improve doc (26a2e8d)
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\ No newline at end of file diff --git a/index.html b/index.html index af72346a0..efad5420d 100644 --- a/index.html +++ b/index.html @@ -17,7 +17,7 @@

Apache HugeGraph

                    Incubating

Learn More -Download

HugeGraph is a convenient, efficient, and adaptable graph database

compatible with the Apache TinkerPop3 framework and the Gremlin query language.

HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges

Graph,millisecond-level OLTP query capability, and support large-scale distributed

graph processing for OLAP analysis. The main scenarios of HugeGraph include

correlation search, fraud detection, and knowledge graph.

Convenient

Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines.

Efficient

Has been deeply optimized in graph storage and graph computation. It provides multiple batch import tools that can easily complete the fast-import of tens of billions of data, achieves millisecond-level response for graph retrieval through ameliorated queries, and supports concurrent online and real-time operations for thousands of users.

Adaptable

Adapts to the Apache Gremlin standard graph query language and the Property Graph standard modeling method, and both support graph-based OLTP and OLAP schemes. Furthermore, HugeGraph can be integrated with Hadoop and Spark’s big data platforms, and easily extend the back-end storage engine through plug-ins.

The first graph database project in Apache

Get The Toolchain

It inlcudes graph loader & dashboard & backup tools

Efficient

We do a Pull Request contributions workflow on GitHub. New users are always welcome!

Read more …

Follow us on Wechat!

Follow the official account “HugeGraph” to get the latest news

PS: twitter account it’s on the way

Read more …

Welcome to the HugeGraph open source community!

+Download

HugeGraph is a convenient, efficient, and adaptable graph database

compatible with the Apache TinkerPop3 framework and the Gremlin query language.

HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges

Graph,millisecond-level OLTP query capability, and large-scale distributed

graph processing (OLAP). The main scenarios of HugeGraph include

correlation search, fraud detection, and knowledge graph.

Convenient

Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines.

Efficient

Has been deeply optimized in graph storage and graph computation. It provides multiple batch import tools that can easily complete the fast-import of tens of billions of data, achieves millisecond-level response for graph retrieval through ameliorated queries, and supports concurrent online and real-time operations for thousands of users.

Adaptable

Adapts to the Apache Gremlin standard graph query language and the Property Graph standard modeling method, and both support graph-based OLTP and OLAP schemes. Furthermore, HugeGraph can be integrated with Hadoop and Spark’s big data platforms, and easily extend the back-end storage engine through plug-ins.

The first graph database project in Apache

Get The Toolchain

It inlcudes graph loader & dashboard & backup tools

Efficient

We do a Pull Request contributions workflow on GitHub. New users are always welcome!

Read more …

Follow us on Wechat!

Follow the official account “HugeGraph” to get the latest news

PS: twitter account it’s on the way

Read more …

Welcome to the HugeGraph open source community!

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