A Scalable Query-Aware Enormous Database Generator for Database Evaluation

摘要

Query-aware synthetic data generation is an essential and highly challenging task, important for database management system (DBMS) testing, database application testing and application-driven benchmarking. Prior studies on query-aware data generation suffer common problems of limited parallelization, poor scalability, and excessive memory consumption, making these systems unsatisfactory to terabyte scale data generation. In order to fill the gap between the existing data generation techniques and the emerging demands of enormous query-aware test databases, we design and implement a new data generator, called Touchstone. Touchstone adopts the random sampling algorithm instantiating query parameters and the new data generation schema generating the test database, to achieve fully parallel data generation, linear scalability and austere memory consumption. It has full support of outer joins as well as non-equi-joins for application-oriented data generation. Our experimental results show that Touchstone consistently outperforms the state-of-the-art solution on TPC-H workload by a 1000× speedup without sacrificing simulation fidelity.