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Apache HugeGraph vs Eclipse RDF4J

What both publish

FactApache HugeGraphEclipse RDF4J
IntegrationsThe project documents integration with Spark and SeaTunnel and compatibility with Hadoop and Flink.Documented compatible systems include GraphDB, Halyard, Stardog, Amazon Neptune, Blazegraph, Oracle RDF, MarkLogic, Strabon and Virtuoso.
LicenseThe HugeGraph-AI documentation identifies Apache License 2.0.The project states that Eclipse RDF4J is licensed under the Eclipse Distribution License version 1.0.
Runtime requirementThe download page recommends Java 11 and says Java 11 is required starting with version 1.5.0.The latest stable release, RDF4J 6.1.0, requires Java 25 or newer.
SecurityUser 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.The Eclipse project page links to the Eclipse Foundation vulnerability reporting channel.
StorageThe Server supports RocksDB as its default standalone backend, HStore for distributed storage, and HBase.RDF4J includes a transactional in-memory store with optional disk persistence and a transactional Native Store that targets medium-sized datasets around 100 million triples.

Apache HugeGraph plans

  • Apache HugeGraph (open source)Free

Eclipse RDF4J plans

  • Eclipse RDF4JFree

Pick Apache HugeGraph if…

Apache HugeGraph may suit teams building graph database, graph computing, or graph AI systems that can manage self-hosted infrastructure. It is also relevant to developers using its documented query languages, APIs, and ecosystem integrations.

Pick Eclipse RDF4J if…

It suits Java developers building applications with RDF, Linked Data, SPARQL endpoints, or Semantic Web data. Consider its stated dataset focus and Java runtime requirement when choosing a store.

Charts they share