MiDBench: Multimodel Industrial Big Data Benchmark

Abstract

Driven by the increasing industrial data over decades, big data systems have evolved rapidly. The diversity and complexity of industrial applications raise great challenge for companies to choose appropriate big data systems. Therefore, big data system benchmark becomes a research hotspot. Most of the state-of-the-art benchmarks focus on specific domains or data formats. This paper presents our efforts on multimodel industrial big data benchmark, called MiDBench. MiDBench focuses on big data systems in crane assembly, wind turbines monitoring and simulation results management scenarios, which correspond to bills of materials (a.b.a BoM), time series and unstructured data format respectively. Currently, we have chose and developed eleven typical workloads of these three types application domains in our benchmark suite and we generate synthetic data by scaling the sample data. For the sake of fairness, we chose widely acceptable throughput and response time as metrics. Through the above we have established a set of benchmark applicable to high-end manufacturing with high credibility. Overall, experiment results show that Neo4j (representing graph database) performs better than Oracle (representing relation database) for processing BoM data. IotDB is better than InfluxDB in time series data for query and stress test. MongoDB performs better than ElasticSearch in simulation results management domain.