Apache HugeGraph vs GUN
Apache HugeGraph
hugegraph.apache.orgGUN
gun.ecoSide by side
| Figure | Apache HugeGraph | GUN |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Graph Databases | 8.7 (#5) | 7.4 (#25) |
| Integrations | The project documents integration with Spark and SeaTunnel and compatibility with Hadoop and Flink. | The docs list storage options and adapters including localStorage, IndexedDB, Node.js filesystem, AWS S3, IPFS, Level, MongoDB, MySQL, Cassandra, Elasticsearch, and LMDB. |
| Product | Apache HugeGraph is a graph system covering graph database, graph computing, and graph AI. | GUN describes itself as an open source, decentralized graph database and data protocol for developers. |
| Security | User authentication is disabled by default; the project advises enabling authentication and authorization, maintaining an IP allowlist, and avoiding direct public exposure of query endpoints in production. | SEA provides cryptographic features including key pairs, signing, encryption, verification, and certificates for authorizing writes. |
| Storage | The Server supports RocksDB as its default standalone backend, HStore for distributed storage, and HBase. | Browser peers use localStorage by default, with an IndexedDB adapter also available through RAD. |
| Support | The project directs users to its Apache community, GitHub issues and discussions, developer mailing list, and Slack channel. | The tutorial directs users to the GUN chatroom for help, Stack Overflow for API questions, and GitHub Issues for bug reports. |
Plans
In their own words
Apache HugeGraph
Apache HugeGraph is an open-source graph system combining a graph database, graph computing, and graph AI. It supports Gremlin and Cypher/OpenCypher queries, a REST API, ACID transactions, graph algorithms, and vector search. HugeGraph-AI adds Python clients, graph machine-learning tools, and LLM tools for knowledge-graph construction and GraphRAG applications. Storage options include RocksDB for standalone use, HStore for distributed storage, and HBase. The project documents integrations with Spark and SeaTunnel and compatibility with Hadoop and Flink. Its toolchain provides graph loading, visualization, backup, restore, and migration; Hubble is a web interface for visualization and management. Documentation covers standalone deployments up to 2 TB and distributed deployments up to 1 PB. The project describes high-speed imports and millisecond-level real-time queries for hundreds of billions of graph data. It runs on Linux and macOS and can be self-hosted. The open-source plan costs nothing. Java 11 is recommended and required from version 1.5.0.