Apache HugeGraph-Computer

Apache HugeGraph-Computer is a comprehensive graph computing solution providing two complementary systems for different deployment scenarios:

• Vermeer (Go): High-performance in-memory computing engine for single-machine deployments
• Computer (Java): Distributed BSP/Pregel framework for large-scale cluster computing

Quick Comparison

Feature	Vermeer (Go)	Computer (Java)
Best for	Quick start, flexible deployment	Large-scale distributed computing
Deployment	Single binary, multi-node capable	Kubernetes or YARN cluster
Memory model	In-memory first	Auto spill to disk
Setup time	Minutes	Hours (requires K8s/YARN)
Algorithms	20+ algorithms	45+ algorithms
Architecture	Master-Worker	BSP (Bulk Synchronous Parallel)
API	REST + gRPC	Java API
Web UI	Built-in dashboard	N/A
Data sources	HugeGraph, CSV, HDFS	HugeGraph, HDFS

Architecture Overview

graph TB
subgraph HugeGraph-Computer
subgraph Vermeer["Vermeer (Go) - In-Memory Engine"]
VM[Master :6688] --> VW1[Worker 1 :6789]
VM --> VW2[Worker 2 :6789]
VM --> VW3[Worker N :6789]
end
subgraph Computer["Computer (Java) - Distributed BSP"]
CM[Master Service] --> CW1[Worker Pod 1]
CM --> CW2[Worker Pod 2]
CM --> CW3[Worker Pod N]
end
end
HG[(HugeGraph Server)] <--> Vermeer
HG <--> Computer
style Vermeer fill:#e1f5fe
style Computer fill:#fff3e0

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Vermeer Architecture (In-Memory Engine)

Vermeer is designed with a Master-Worker architecture optimized for high-performance in-memory graph computing:

graph TB
subgraph Client["Client Layer"]
API[REST API Client]
UI[Web UI Dashboard]
end
subgraph Master["Master Node"]
HTTP[HTTP Server :6688]
GRPC_M[gRPC Server :6689]
GM[Graph Manager]
TM[Task Manager]
WM[Worker Manager]
SCH[Scheduler]
end
subgraph Workers["Worker Nodes"]
W1[Worker 1 :6789]
W2[Worker 2 :6789]
W3[Worker N :6789]
end
subgraph DataSources["Data Sources"]
HG[(HugeGraph)]
CSV[Local CSV]
HDFS[HDFS]
end
API --> HTTP
UI --> HTTP
GRPC_M <--> W1
GRPC_M <--> W2
GRPC_M <--> W3
W1 -.-> HG
W2 -.-> HG
W3 -.-> HG
W1 -.-> CSV
W1 -.-> HDFS
style Master fill:#e1f5fe
style Workers fill:#f3e5f5
style DataSources fill:#fff9c4

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Component Overview:

Component	Description
Master	Coordinates workers, manages graph metadata, schedules computation tasks via HTTP (:6688) and gRPC (:6689)
Workers	Execute graph algorithms, store graph partition data in memory, communicate via gRPC (:6789)
REST API	Graph loading, algorithm execution, result queries (port 6688)
Web UI	Built-in monitoring dashboard accessible at /ui/
Data Sources	Supports loading from HugeGraph (via gRPC), local CSV files, and HDFS

HugeGraph Ecosystem Integration

┌─────────────────────────────────────────────────────────────┐
│ HugeGraph Ecosystem │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐ │
│ │ Hubble │ │ Toolchain │ │ HugeGraph-AI │ │
│ │ (UI) │ │ (Tools) │ │ (LLM/RAG) │ │
│ └──────┬──────┘ └──────┬──────┘ └────────┬────────┘ │
│ │ │ │ │
│ └──────────────────┼────────────────────┘ │
│ │ │
│ ┌───────▼───────┐ │
│ │ HugeGraph │ │
│ │ Server │ │
│ └───────┬───────┘ │
│ │ │
│ ┌──────────────────┼──────────────────┐ │
│ │ │ │ │
│ ┌──────▼──────┐ ┌──────▼──────┐ ┌─────▼─────┐ │
│ │ Vermeer │ │ Computer │ │ Store │ │
│ │ (Memory) │ │ (BSP/K8s) │ │ (PD) │ │
│ └─────────────┘ └─────────────┘ └───────────┘ │
└─────────────────────────────────────────────────────────────┘

Getting Started with Vermeer (Recommended)

For quick start and single-machine deployments, we recommend Vermeer:

Docker Quick Start

# Pull the image
docker pull hugegraph/vermeer:latest
# Change config path in docker-compose.yaml
volumes:
- ~/:/go/bin/config # Change here to your actual config path, e.g., vermeer/config# Run with docker-compose
docker-compose up -d

Binary Quick Start

# Download and extract (example for Linux AMD64)
wget https://github.com/apache/hugegraph-computer/releases/download/vX.X.X/vermeer-linux-amd64.tar.gz
tar -xzf vermeer-linux-amd64.tar.gz
cd vermeer
# Run master and worker
./vermeer --env=master &
./vermeer --env=worker &

See the Vermeer README for detailed configuration and usage.

Getting Started with Computer (Distributed)

For large-scale distributed graph processing on Kubernetes or YARN clusters, see the Computer README for:

• Prerequisites and build instructions
• Kubernetes/YARN deployment guide
• 45+ algorithm implementations
• Custom algorithm development framework

Supported Algorithms

Vermeer Algorithms (20+)

Category	Algorithms
Centrality	PageRank, Personalized PageRank, Betweenness, Closeness, Degree
Community	Louvain, Weighted Louvain, LPA, SLPA, WCC, SCC
Path Finding	SSSP (Dijkstra), BFS Depth
Structure	Triangle Count, K-Core, K-Out, Clustering Coefficient, Cycle Detection
Similarity	Jaccard Similarity

Features:

• In-memory optimized implementations
• REST API for algorithm execution
• Real-time result queries

Computer (Java) Algorithms: For Computer's 45+ algorithm implementations including distributed Triangle Count, Rings detection, and custom algorithm development framework, see Computer Algorithm List.

When to Use Which

Choose Vermeer when:

• ✅ Quick prototyping and experimentation
• ✅ Interactive analytics with built-in Web UI
• ✅ Graphs up to hundreds of millions of edges
• ✅ REST API integration requirements
• ✅ Single machine or small cluster with high-memory nodes
• ✅ Sub-second query response requirements

Performance: Optimized for fast iteration on medium-sized graphs with in-memory processing. Horizontal scaling by adding worker nodes.

Choose Computer when:

• ✅ Billions of vertices/edges requiring distributed processing
• ✅ Existing Kubernetes or YARN infrastructure
• ✅ Custom algorithm development with Java
• ✅ Memory-constrained environments (auto disk spill)
• ✅ Integration with Hadoop ecosystem

Performance: Handles massive graphs via distributed BSP framework. Batch-oriented with superstep barriers. Elastic scaling on K8s.

Documentation

• Project Homepage
• Vermeer Documentation
• Computer Documentation
• HugeGraph Documentation

Related Projects

1. hugegraph - Graph database core (Server + PD + Store)
2. hugegraph-toolchain - Graph tools (Loader/Hubble/Tools/Client)
3. hugegraph-ai - Graph AI/LLM/Knowledge Graph system
4. hugegraph-website - Documentation and website

Contributing

Welcome to contribute to HugeGraph-Computer! Please see:

• How to Contribute for guidelines
• GitHub Issues for bug reports and feature requests

We recommend using GitHub Desktop to simplify the PR process.

Thank you to all contributors!

License

HugeGraph-Computer is licensed under Apache 2.0 License.

Contact Us

• GitHub Issues: Report bugs or request features
• Email: dev@hugegraph.apache.org (subscribe first)
• Slack: Join ASF HugeGraph channel
• WeChat: Scan QR code to follow Apache HugeGraph official account