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Query performance prediction, the task of predicting the latency of a query,is one of the most challenging problem in database management systems. Existingapproaches rely on features and performance models engineered by human experts,but often fail to capture the complex interactions between query operators andinput relations, and generally do not adapt naturally to workloadcharacteristics and patterns in query execution plans. In this paper, we arguethat deep learning can be applied to the query performance prediction problem,and we introduce a novel neural network architecture for the task: aplan-structured neural network. Our approach eliminates the need forhuman-crafted feature selection and automatically discovers complex performancemodels both at the operator and query plan level. Our novel neural networkarchitecture can match the structure of any optimizer-selected query executionplan and predict its latency with high accuracy. We also propose a number ofoptimizations that reduce training overhead without sacrificing effectiveness.We evaluated our techniques on various workloads and we demonstrate that ourplan-structured neural network can outperform the state-of-the-art in queryperformance prediction.

Plan-Structured Deep Neural Network Models for Query Performance Prediction