Workload-Aware Query Recommendation Using Deep Learning

Abstract

Users interact with databases by writing sequences of SQL queries that are are often stored in query workloads. Current SQL query recommendation approaches make little use of query workloads. Our work presents a novel workload-aware approach to query recommendation. We use deep learning prediction models trained on query pairs extracted from large-scale query workloads to build our approach. Our algorithms suggest contextual (query fragments) and structural (query templates) information to aid users in formulating their next query. We evaluate our algorithms on two real-world datasets: the Sloan Digital Sky Survey (SDSS) and SQLShare. We perform a thorough analysis of the workloads and then empirically show that our workload-aware, deep-learning approach vastly outperforms known collaborative filtering approaches.