Zhang, Chao, Jiaheng Lu. "Big Data System Benchmarking: State of the Art, Current Practices, and Open Challenges." In IEEE BIG DATA 2020 TUTORIAL, 2020.

Zhang, Chao, Jiaheng Lu. "Holistic Evaluation in Multi-Model Databases Benchmarking." In Distributed and Parallel Databases, 2019.

Zhang, Chao, et al. "UniBench: A benchmark for multi-model database management systems." TPCTC. Springer, Cham, 2018.

Essentially, any MMDB can be implemented in UniBench either with or without data transformation. Since there is no query language standard, one can find all the query definitions in ArangoDB AQL, OrientDB SQL and AgensGraph SQL/Cypher, Spark SQL (partially) as follows:

To run this benchmark, you need to have the systems under test installed, and JRE >=8 installed. The current implementations include four state-of-the-art MMDB. Namely, ArangoDB (Query Language: AQL), OrientDB (Query Language: Orient SQL), AgensGraph (Query Language: SQL/Cypher), and Spark (partial evaluation using Spark SQL), you may also employ Unibench to evaluate a new MMDB by the following steps: (1) write an importing script or program, (2) extends the MMDB abstract class. (3) implement the connection and query methods in the corresponding MMDB class.

Note that ArangoDB uses the default _System database and the password is empty. OrientDB uses a database named test that you may create beforehand. Download the latest release with SF1 dataset at https://github.com/HY-UDBMS/UniBench/releases/tag/0.2, and try out the first multi-model query as follows:

./DataImporting_ArangoDB.sh
java -jar Unibench.jar ArangoDB Q1

var IDs = new Array();

  for(var i=0;i<array1.length;i++){
      for(var j=0;j<array2.length;j++){
        if( (array1[i].field('asin')==array2[j].field('asin')) && (array1[i].field('cnt')>array2[j].field('cnt')) )
				IDs.push(array1[i]);
     }
  }

return IDs;

./Orientimport.sh