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VLDB2023顶会

M2Bench: A Database Benchmark for Multi-Model Analytic Workloads

Bogyeong Kim, Kyoseung Koo, Undraa Enkhbat, Sohyun Kim, Juhun Kim, Bongki Moon

2023年份
13被引次数
1顶会引用

摘要

As the world becomes increasingly data-centric, the tasks dealt with by a database management system (DBMS) become more complex and diverse. Compared with traditional workloads that typically require only a single data model, modern-day computational tasks often involve multiple data sources and rely on more than one data model. Unfortunately, however, there is currently no standard benchmark program that can evaluate a DBMS in the various aspects of multi-model databases, especially when the array data model is concerned. In this paper, we propose M2Bench, a new benchmark program capable of evaluating a multi-model DBMS that supports several important data models such as relational, document-oriented, property graph, and array models. M2Bench consists of multi-model workloads that are inspired by real-world problems. Each task of the workload mimics a real-life scenario where at least two different models of data are involved. To demonstrate the efficacy of M2Bench, we evaluated polyglot or multi-model database systems with the M2Bench workloads and unfolded the diverse characteristics of the database systems for each data model.

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