GUIDES on Apache HugeGraph

HugeGraph Architecture Overview

Mon, 01 Jan 0001 00:00:00 +0000

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 framework and supports the Gremlin and Cypher query languages. It comes with a complete application toolchain and provides a plugin-based backend storage driver framework.

Below is the overall architecture diagram of HugeGraph:

HugeGraph consists of three layers of functionality: the application layer, the graph engine layer, and the storage layer.

HugeGraph Design Concepts

Mon, 01 Jan 0001 00:00:00 +0000

1. Property Graph

There are two common graph data representation models, namely the RDF (Resource Description Framework) model and the Property Graph (Property Graph) model. Both RDF and Property Graph are the most basic and well-known graph representation modes, and both can represent entity-relationship modeling of various graphs. RDF is a W3C standard, while Property Graph is an industry standard and is widely supported by graph database vendors. HugeGraph currently uses Property Graph.

HugeGraph Plugin mechanism and plug-in extension process

Mon, 01 Jan 0001 00:00:00 +0000

Background

HugeGraph is not only open source and open, but also simple and easy to use. General users can easily add plug-in extension functions without changing the source code.
HugeGraph supports a variety of built-in storage backends, and also allows users to extend custom backends without changing the existing source code.
HugeGraph supports full-text search. The full-text search function involves word segmentation in various languages. Currently, there are 8 built-in Chinese word breakers, and it also allows users to expand custom word breakers without changing the existing source code.

Scalable dimension

Currently, the plug-in method provides extensions in the following dimensions:

HugeGraph Toolchain Local Testing Guide

Mon, 01 Jan 0001 00:00:00 +0000

This guide helps developers run HugeGraph toolchain tests locally.

1. Core Concepts

1.1 Core Dependency: HugeGraph Server

Integration and functional tests of the toolchain depend on HugeGraph Server, including Client, Loader, Hubble, Spark Connector, Tools, and other components.

1.2 Test Types

Unit Tests: Test individual functions/methods, no external dependencies required
API Tests (ApiTestSuite): Test API interfaces, requires running HugeGraph Server
Functional Tests (FuncTestSuite): End-to-end tests, require complete system environment

2. Environment Setup

2.1 System Requirements

Operating System: Linux / macOS (Windows use WSL2)
JDK: >= 11, configure JAVA_HOME
Maven: >= 3.5
Python: >= 3.11 (only required for Hubble tests)

2.2 Clone Code

git clone https://github.com/${GITHUB_USER_NAME}/hugegraph-toolchain.git
cd hugegraph-toolchain

3. Deploy Test Environment

Deployment Options

Script Deployment (Recommended): Precisely control Server version by specifying Commit ID, avoid interface incompatibility
Docker Deployment: Quick start, but may have version lag causing test failures

For detailed installation instructions, refer to Community Documentation

Backup and Restore

Mon, 01 Jan 0001 00:00:00 +0000

Description

Backup and Restore are functions of backup map and restore map. The data backed up and restored includes metadata (schema) and graph data (vertex and edge).

Backup

Export the metadata and graph data of a graph in the HugeGraph system in JSON format.

Restore

Re-import the data in JSON format exported by Backup to a graph in the HugeGraph system.

FAQ

Mon, 01 Jan 0001 00:00:00 +0000

How to choose the back-end storage? Choose RocksDB, Cassandra, ScyllaDB, Hbase or Mysql?

The choice of backend storage depends on specific needs. For installations on a single machine (node) with data volumes under 10 billion records, RocksDB is generally recommended. However, if a distributed backend is needed for scaling across multiple nodes, other options should be considered. ScyllaDB, designed as a drop-in replacement for Cassandra, offers protocol compatibility and better hardware utilization, often requiring less infrastructure. HBase, on the other hand, requires a Hadoop ecosystem to function effectively. Finally, while MySQL supports horizontal scaling, managing it in a distributed setup can be challenging.

Security Report

Mon, 01 Jan 0001 00:00:00 +0000

Reporting New Security Problems with Apache HugeGraph

Adhering to the specifications of ASF, the HugeGraph community maintains a highly proactive and open attitude towards addressing security issues in the remediation projects.

We strongly recommend that users first report such issues to our dedicated security email list, with detailed procedures specified in the ASF SEC code of conduct.

Please note that the security email group is reserved for reporting undisclosed security vulnerabilities and following up on the vulnerability resolution process. Regular software Bug/Error reports should be directed to Github Issue/Discussion or the HugeGraph-Dev email group. Emails sent to the security list that are unrelated to security issues will be ignored.