README.md - hugegraph-computer - Git at Google

 # Apache HugeGraph-Computer

 [![License](https://img.shields.io/badge/license-Apache%202-0E78BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)
 [![Build Status](https://github.com/apache/hugegraph-computer/actions/workflows/computer-ci.yml/badge.svg)](https://github.com/apache/hugegraph-computer/actions/workflows/computer-ci.yml)
 [![codecov](https://codecov.io/gh/apache/hugegraph-computer/branch/master/graph/badge.svg)](https://codecov.io/gh/apache/hugegraph-computer)
 [![Docker Pulls](https://img.shields.io/docker/pulls/hugegraph/hugegraph-computer)](https://hub.docker.com/repository/docker/hugegraph/hugegraph-computer)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/apache/hugegraph-computer)

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

 - **[Vermeer](./vermeer/README.md)** (Go): High-performance in-memory computing engine for single-machine deployments
 - **[Computer](./computer/README.md)** (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

 ```mermaid
 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
 ```

 ## Vermeer Architecture (In-Memory Engine)

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

 ```mermaid
 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
 ```

 **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

 ```bash
 # Pull the image
 docker pull hugegraph/vermeer:latest

 # Change config path in docker-compose.yml
 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

 ```bash
 # 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](./vermeer/README.md)** 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](./computer/README.md)** 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](./computer/README.md#available-algorithms).

 ## 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](https://hugegraph.apache.org/docs/quickstart/hugegraph-computer/)
 - [Vermeer Documentation](./vermeer/README.md)
 - [Computer Documentation](./computer/README.md)
 - [HugeGraph Documentation](https://hugegraph.apache.org/docs/)

 ## Related Projects

 1. [hugegraph](https://github.com/apache/hugegraph) - Graph database core (Server + PD + Store)
 2. [hugegraph-toolchain](https://github.com/apache/hugegraph-toolchain) - Graph tools (Loader/Hubble/Tools/Client)
 3. [hugegraph-ai](https://github.com/apache/hugegraph-ai) - Graph AI/LLM/Knowledge Graph system
 4. [hugegraph-website](https://github.com/apache/hugegraph-doc) - Documentation and website

 ## Contributing

 Welcome to contribute to HugeGraph-Computer! Please see:

 - [How to Contribute](https://hugegraph.apache.org/docs/contribution-guidelines/contribute/) for guidelines
 - [GitHub Issues](https://github.com/apache/hugegraph-computer/issues) for bug reports and feature requests

 We recommend using [GitHub Desktop](https://desktop.github.com/) to simplify the PR process.

 Thank you to all contributors!

 [![contributors graph](https://contrib.rocks/image?repo=apache/hugegraph-computer)](https://github.com/apache/hugegraph-computer/graphs/contributors)

 ## License

 HugeGraph-Computer is licensed under [Apache 2.0 License](https://github.com/apache/hugegraph-computer/blob/master/LICENSE).

 ## Contact Us

 - **GitHub Issues**: [Report bugs or request features](https://github.com/apache/hugegraph-computer/issues)
 - **Email**: [dev@hugegraph.apache.org](mailto:dev@hugegraph.apache.org) ([subscribe first](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/))
 - **Slack**: [Join ASF HugeGraph channel](https://the-asf.slack.com/archives/C059UU2FJ23)
 - **WeChat**: Scan QR code to follow Apache HugeGraph official account

 <img src="https://github.com/apache/hugegraph-doc/blob/master/assets/images/wechat.png?raw=true" alt="WeChat QR Code" width="350"/>
	# Apache HugeGraph-Computer

	[![License](https://img.shields.io/badge/license-Apache%202-0E78BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)
	[![Build Status](https://github.com/apache/hugegraph-computer/actions/workflows/computer-ci.yml/badge.svg)](https://github.com/apache/hugegraph-computer/actions/workflows/computer-ci.yml)
	[![codecov](https://codecov.io/gh/apache/hugegraph-computer/branch/master/graph/badge.svg)](https://codecov.io/gh/apache/hugegraph-computer)
	[![Docker Pulls](https://img.shields.io/docker/pulls/hugegraph/hugegraph-computer)](https://hub.docker.com/repository/docker/hugegraph/hugegraph-computer)
	[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/apache/hugegraph-computer)

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

	- [Vermeer](./vermeer/README.md) (Go): High-performance in-memory computing engine for single-machine deployments
	- [Computer](./computer/README.md) (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

	```mermaid
	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
	```

	## Vermeer Architecture (In-Memory Engine)

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

	```mermaid
	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
	```

	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

	```bash
	# Pull the image
	docker pull hugegraph/vermeer:latest

	# Change config path in docker-compose.yml
	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

	```bash
	# 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](./vermeer/README.md) 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](./computer/README.md) 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](./computer/README.md#available-algorithms).

	## 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](https://hugegraph.apache.org/docs/quickstart/hugegraph-computer/)
	- [Vermeer Documentation](./vermeer/README.md)
	- [Computer Documentation](./computer/README.md)
	- [HugeGraph Documentation](https://hugegraph.apache.org/docs/)

	## Related Projects

	1. [hugegraph](https://github.com/apache/hugegraph) - Graph database core (Server + PD + Store)
	2. [hugegraph-toolchain](https://github.com/apache/hugegraph-toolchain) - Graph tools (Loader/Hubble/Tools/Client)
	3. [hugegraph-ai](https://github.com/apache/hugegraph-ai) - Graph AI/LLM/Knowledge Graph system
	4. [hugegraph-website](https://github.com/apache/hugegraph-doc) - Documentation and website

	## Contributing

	Welcome to contribute to HugeGraph-Computer! Please see:

	- [How to Contribute](https://hugegraph.apache.org/docs/contribution-guidelines/contribute/) for guidelines
	- [GitHub Issues](https://github.com/apache/hugegraph-computer/issues) for bug reports and feature requests

	We recommend using [GitHub Desktop](https://desktop.github.com/) to simplify the PR process.

	Thank you to all contributors!

	[![contributors graph](https://contrib.rocks/image?repo=apache/hugegraph-computer)](https://github.com/apache/hugegraph-computer/graphs/contributors)

	## License

	HugeGraph-Computer is licensed under [Apache 2.0 License](https://github.com/apache/hugegraph-computer/blob/master/LICENSE).

	## Contact Us

	- GitHub Issues: [Report bugs or request features](https://github.com/apache/hugegraph-computer/issues)
	- Email: [dev@hugegraph.apache.org](mailto:dev@hugegraph.apache.org) ([subscribe first](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/))
	- Slack: [Join ASF HugeGraph channel](https://the-asf.slack.com/archives/C059UU2FJ23)
	- WeChat: Scan QR code to follow Apache HugeGraph official account

	<img src="https://github.com/apache/hugegraph-doc/blob/master/assets/images/wechat.png?raw=true" alt="WeChat QR Code" width="350"/>