hugegraph-toolchain

A comprehensive suite of client SDKs, data tools, and management utilities for Apache HugeGraph graph database. Build applications, load data, and manage graphs with production-ready tools.

Hubble's primary authentication and connection design targets HugeGraph 1.8/master: PD discovery supplies the server address, anonymous mode uses a real unauthenticated client, and account/GraphSpace permissions are reduced to four readable presets. A thin adapter keeps 1.7 usable and limits 1.5 to its standalone core graph workflow; version checks are centralized rather than spread across UI pages.

Hubble brings graph exploration, schema preparation, asynchronous analysis, and distributed cluster operations into one workspace.

The cluster overview keeps service topology, node health, source status, and capacity facts in one operational view.

Quick Navigation: Architecture | Quick Start | Modules | Build | Docker | Related Projects

HugeGraph Ecosystem:

1. hugegraph - Core graph database (pd / store / server / commons)
2. hugegraph-computer - Distributed graph computing system
3. hugegraph-ai - Graph AI/LLM/Knowledge Graph integration
4. hugegraph-website - Documentation and website

graph TB
    subgraph server ["HugeGraph Server"]
        SERVER[("Graph Database")]
    end

    subgraph distributed ["Distributed Mode (Optional)"]
        PD["hugegraph-pd<br/>(Placement Driver)"]
        STORE["hugegraph-store<br/>(Storage Nodes)"]
    end

    subgraph clients ["Client SDKs"]
        CLIENT["hugegraph-client<br/>(Java)"]
    end

    subgraph data ["Data Tools"]
        LOADER["hugegraph-loader<br/>(Batch Import)"]
        SPARK["hugegraph-spark-connector<br/>(Spark I/O)"]
    end

    subgraph mgmt ["Management Tools"]
        HUBBLE["hugegraph-hubble<br/>(Web UI)"]
        TOOLS["hugegraph-tools<br/>(CLI)"]
    end

    SERVER <-->|REST API| CLIENT
    PD -.->|coordinates| STORE
    SERVER -.->|distributed backend| PD

    CLIENT --> LOADER
    CLIENT --> HUBBLE
    CLIENT --> TOOLS
    CLIENT --> SPARK
    HUBBLE -.->|PD discovery UI| PD

    LOADER -.->|Sources| SRC["CSV | JSON | HDFS<br/>MySQL | Kafka"]
    SPARK -.->|I/O| SPK["Spark DataFrames"]

    style distributed stroke-dasharray: 5 5

                    ┌─────────────────────────┐
                    │   HugeGraph Server      │
                    │   (Graph Database)      │
                    └───────────┬─────────────┘
                                │ REST API
        ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┼ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐
          Distributed (Optional)│
        │   ┌───────────┐       │       ┌───────────┐   │
            │hugegraph- │◄──────┴──────►│hugegraph- │
        │   │    pd     │               │   store   │   │
            └───────────┘               └───────────┘
        └ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
                                │
          ┌─────────────────────┼─────────────────────┐
          │                     │                     │
          ▼                     ▼                     ▼
 ┌────────────────┐    ┌────────────────┐    ┌─────────���──────┐
 │ hugegraph-     │    │ Other Client   │    │  Other REST    │
 │ client (Java)  │    │ SDKs (Go/Py)   │    │  Clients       │
 └───────┬────────┘    └────────────────┘    └────────────────┘
         │ depends on
 ┌───────┼───────────┬───────────────────┐
 │       │           │                   │
 ▼       ▼           ▼                   ▼
┌────────┐ ┌────────┐ ┌──────────┐ ┌───────────────────┐
│ loader │ │ hubble │ │  tools   │ │ spark-connector   │
│ (ETL)  │ │ (Web)  │ │  (CLI)   │ │ (Spark I/O)       │
└────────┘ └────────┘ └──────────┘ └───────────────────┘

# Hubble Web UI (port 8088)
docker run -d -p 8088:8088 --name hubble hugegraph/hugegraph-hubble

# Loader (batch data import)
docker run --rm hugegraph/hugegraph-loader ./bin/hugegraph-loader.sh -f example.json

Before committing a new source or test file, run the same license-header check used by CI (license-eye from apache/skywalking-eyes is required):

./tools/check-license-header.sh

Do not use shortened Apache headers: the complete header configured in .licenserc.yaml is required.

Purpose: Official Java SDK for HugeGraph Server

Key Features:

• Schema management (PropertyKey, VertexLabel, EdgeLabel, IndexLabel)
• Graph operations (CRUD vertices/edges)
• Gremlin query execution
• Built-in traversers (shortest path, k-neighbor, k-out, paths, etc.)
• Multi-graph and authentication support

Entry Point: org.apache.hugegraph.driver.HugeClient

Quick Example:

HugeClient client = HugeClient.builder("http://localhost:8080", "hugegraph").build();

// Schema management
client.schema().propertyKey("name").asText().ifNotExist().create();
client.schema().vertexLabel("person")
    .properties("name")
    .ifNotExist()
    .create();

// Graph operations
Vertex vertex = client.graph().addVertex(T.label, "person", "name", "Alice");

📖 Documentation | 📁 Source

import "github.com/apache/hugegraph-toolchain/hugegraph-client-go"

client := hugegraph.NewClient("http://localhost:8080", "hugegraph")
// Schema and graph operations

Looking for other languages? See hugegraph-python-client in the hugegraph-ai repository.

Purpose: Batch data import tool from multiple data sources

Key Features:

• Sources: CSV, JSON, HDFS, MySQL, Kafka, existing HugeGraph
• JSON-based mapping configuration
• Parallel loading with configurable threads
• Error handling and retry mechanisms
• Progress tracking and logging

Entry Point: bin/hugegraph-loader.sh

Quick Example:

# Load data from CSV
./bin/hugegraph-loader.sh -f mapping.json -g hugegraph

# Example mapping.json structure
{
  "vertices": [
    {
      "label": "person",
      "input": { "type": "file", "path": "persons.csv" },
      "mapping": { "name": "name", "age": "age" }
    }
  ]
}

📖 Documentation | 📁 Source

Purpose: Web-based graph management and visualization platform

Key Features:

• Multi-graph workspace & connection management
• Interactive schema management with graphical editor
• Comprehensive data loading dashboard
• Dynamic graph visualization with path and topology canvas
• Built-in Gremlin query console & algorithm explorer
• Fine-grained user authentication & multi-language localization (i18n)

Technology Stack: Spring Boot + React + TypeScript + MobX + Ant Design

Entry Point: bin/start-hubble.sh (default port: 8088)

Quick Start:

cd hugegraph-hubble/apache-hugegraph-hubble-*/bin
./start-hubble.sh      # Background mode
./start-hubble.sh -f   # Foreground mode
./stop-hubble.sh       # Stop server

📖 Documentation | 📁 Source

Purpose: Command-line utilities for graph operations

Key Features:

• Backup and restore graphs
• Graph migration
• Graph cloning
• Metadata management
• Batch operations

Entry Point: bin/hugegraph CLI commands

Quick Example:

# Backup graph
bin/hugegraph backup -t all -d ./backup

# Restore graph
bin/hugegraph restore -t all -d ./backup

📁 Source

Purpose: Spark integration for reading and writing HugeGraph data

Key Features:

• Read HugeGraph vertices/edges as Spark DataFrames
• Write DataFrames to HugeGraph
• Spark SQL support
• Distributed graph processing

Entry Point: Scala API with Spark DataSource v2

Quick Example:

// Read vertices as DataFrame
val vertices = spark.read
  .format("hugegraph")
  .option("host", "localhost:8080")
  .option("graph", "hugegraph")
  .option("type", "vertex")
  .load()

// Write DataFrame to HugeGraph
df.write
  .format("hugegraph")
  .option("host", "localhost:8080")
  .option("graph", "hugegraph")
  .save()

📁 Source

<!-- Note: Use the latest release version in Maven Central -->
<dependency>
    <groupId>org.apache.hugegraph</groupId>
    <artifactId>hugegraph-client</artifactId>
    <version>1.7.0</version>
</dependency>

<dependency>
    <groupId>org.apache.hugegraph</groupId>
    <artifactId>hugegraph-loader</artifactId>
    <version>1.7.0</version>
</dependency>

Check Maven Central for the latest versions.

mvn clean install -DskipTests -Dmaven.javadoc.skip=true -ntp

Checkstyle is enforced via tools/checkstyle.xml:

• Max line length: 120 characters
• 4-space indentation (no tabs)
• No star imports
• No System.out.println

Run checkstyle:

mvn checkstyle:check

Official Docker images are available on Docker Hub:

Examples:

# Hubble
docker run -d -p 8088:8088 --name hubble hugegraph/hugegraph-hubble

# Loader (mount config and data)
docker run --rm \
  -v /path/to/config:/config \
  -v /path/to/data:/data \
  hugegraph/hugegraph-loader \
  ./bin/hugegraph-loader.sh -f /config/mapping.json

Build images locally:

# Loader
docker build -f hugegraph-loader/Dockerfile \
  -t hugegraph/hugegraph-loader:latest .

# Hubble
docker build -f hugegraph-hubble/Dockerfile \
  -t hugegraph/hugegraph-hubble:latest .

Multi-platform builds use BuildKit's automatic platform arguments. The Maven and Node build stages run on $BUILDPLATFORM, while the final JRE stage uses $TARGETPLATFORM. Java bytecode and frontend assets are architecture-neutral, so they are built once without QEMU. Target-stage package installation still runs for each architecture.

This optimization applies only to architecture-independent build outputs. A component that compiles native code must use a target-platform build stage or separate platform stages. Loader and Hubble packaging is validated on arm64; native dependencies must still be audited. Loader includes arm64 variants for Snappy, LZ4, Commons Crypto, and gRPC tcnative. Some optional legacy HBase and Jansi natives remain x86-only, so their fallback paths require target-runtime validation when those optional features are used.

• HugeGraph Toolchain Overview
• hugegraph-client Guide
• hugegraph-loader Guide
• hugegraph-hubble Guide
• API Reference

Welcome to contribute to HugeGraph! Please see How to Contribute for more information.

Note: It's recommended to use GitHub Desktop to simplify the PR and commit process.

Thank you to all the people who already contributed to HugeGraph!

• GitHub Issues - Report bugs and request features
• Email: dev@hugegraph.apache.org (subscriber only)
• Slack: ASF Channel
• WeChat: Apache HugeGraph (scan QR code below)

hugegraph-toolchain is licensed under Apache 2.0 License.