Suppose a recommendation query for online users: Given a customer and a product category, find this customer’s friends within 3-hop friendship who have bought products in the given category, also return the feedback with the 5-rating reviews. 1, which includes the customers’ feedback from a table, a social network graph and the orders information in JSON files. To illustrate the challenge of multi-model data management, consider the toy example depicted in Fig. MMDB is beneficial for modern applications that require dealing with heterogeneous data sources while embracing the agile development, ranging from social commerce, integrated health-care services, online recommender system, to smart traffic management. The recent Gartner Magic quadrant for operational database management systems predicts that, in the near future, all leading operational DBMSs will offer multiple data models in a unified platform. Compared to the polyglot persistence technology that employs separate data stores to satisfy various use cases, MMDB is considered as the next generation of data management system incorporating flexibility, scalability, and consistency. Multi-model dataBase (MMDB) is an emerging trend for the database management system, which utilizes a single platform to manage data stored in different models, such as document, graph, relational, and key-value. We provide a comprehensive analysis with respect to internal data representations, multi-model query and transaction processing, and performance results for distributed execution.
Finally, the extensive experiments based on the proposed benchmark were performed on four representatives of MMDBs: ArangoDB, OrientDB, AgensGraph and Spark SQL. Furthermore, in order to generate a comprehensive and unbiased query set, we develop an efficient algorithm to solve a new problem called multi-model parameter curation to judiciously control the query selectivity on diverse models.
#FILTER BY DATE IN ARANGODB GENERATOR#
We propose a three-phase framework to simulate the real-life distributions and develop a multi-model data generator to produce the benchmarking data. UniBench consists of a set of mixed data models that mimics a social commerce application, which covers data models including JSON, XML, key-value, tabular, and graph. In this paper, we propose UniBench, a generic multi-model benchmark for a holistic evaluation of state-of-the-art MMDBs. As more and more platforms are developed to deal with multi-model data, it has become crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Examples of data models include document, graph, relational, and key-value. A multi-model database (MMDB) is designed to support multiple data models against a single, integrated back-end.
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