Apache HugeGraph vs TuringDB
Apache HugeGraph
hugegraph.apache.orgTuringDB
turingdb.aiSide by side
| Figure | Apache HugeGraph | TuringDB |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · Graph Databases | 8.7 (#5) | 7.4 (#28) |
| Deployment | The Server can be deployed with Docker, a binary archive, a source build, or a legacy one-click deployment tool. | TuringDB can be self-hosted as a server, deployed on-premise or air-gapped, or embedded and driven from Python. |
| Integrations | The project documents integration with Spark and SeaTunnel and compatibility with Hadoop and Flink. | The documentation lists JSONL, CSV, Neo4j, GML, and Parquet as data import formats, and the product includes a Python SDK. |
| Product | Apache HugeGraph is a graph system covering graph database, graph computing, and graph AI. | TuringDB is an in-memory, columnar graph database with Git-style versioning built into its engine. |
| Scale | The project says it supports high-speed import and millisecond-level real-time queries for up to hundreds of billions of graph data. | The FAQ says TuringDB can hold hundreds of millions of nodes and billions of edges on a single machine. |
| Support | The project directs users to its Apache community, GitHub issues and discussions, developer mailing list, and Slack channel. | The Enterprise plan includes dedicated support and integrations. |
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.