Application-Oriented Workload Generation for Transactional Database Performance Evaluation
Luyi Qu, Yuming Li, Rong Zhang, Ting Chen, Ke Shu, Weining Qian, Aoying Zhou · 2022 IEEE 38th International Conference on Data Engineering (ICDE) · 2022
Generating synthetic workloads is essential and critical to performance evaluation of database systems. When evaluating database performance for a specific application, the similarity between synthetic workloads and real application workloads determines the credibility of the evaluation results. However, it meets a great challenge to catch workload characteristics with respect to a target application considering the complexity of transaction executions. To address this problem, we propose a workload duplicator (Lauca) that can generate synthetic workloads with highly similar performance metrics compared to the real workloads of a specific application. By carefully studying the application-oriented workload generation problem, we present Transaction Logic and Data Access Distribution to characterize workloads of online transaction processing (OLTP) applications, and propose novel generation algorithms to guarantee the high fidelity of synthetic workloads. To the best of our knowledge, Lauca is the first application-oriented transactional workload generator. We conduct extensive experiments based on TPCC, SmallBank and YCSB on both centralized and distributed databases. The experimental results show that Lauca consistently generates high quality synthetic workloads.