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Related work

Four academic benchmarks already address multi-model databases.

  • UniBench, from the University of Helsinki: relational, JSON, XML, key-value, and graph data in one social commerce scenario, with multi-model queries and transactions.
  • M2Bench, from Seoul National University: analytic tasks over relational, document, property graph, and array data, run on polyglot persistence (MySQL, MongoDB, Neo4j, and SciDB), ArangoDB, and AgensGraph.
  • MMSBench-Net, from the University of Basel: a scenario-based benchmark over relational, document, and graph data, used to compare Polypheny and SurrealDB.
  • GRBench, by Liu and colleagues: relational and graph data together, evaluated on relational extensions, multi-model databases, and dedicated graph-relational systems.

None of the four lists vectors or time series among its data models.

What MMDBBench does differently

  • It adds vectors and time series to the models a multi-model engine serves.
  • It runs a specialist database for each model as a baseline, so the question is what one engine gains or gives up against a specialist, not only how two multi-model systems compare.
  • Tasks are written in SQL, Cypher, and each engine's native language, and the answers are checked between engines.
  • It runs on one platform, x86 Linux, with pinned engine versions and published settings.
  • The raw result rows are open: see Result rows.

These descriptions come from each benchmark's own page or paper, linked above. They say what each one states about itself, not how it performs.