C3-Predictor: An Efficient Framework for Performance Prediction of Concurrent Queries

Abstract

Query performance prediction is a challenging task in AI-enabled database (DB) research because prediction capability is crucial to optimizing DB operations (e.g., parameter optimization) and resource allocation in modern DB systems. However, existing methods often struggle to meet the flexible requirements for both prediction accuracy and query latency in real-world applications, and focus on predicting the performance of individual queries instead of concurrent queries, which is very universal in large and complex DB tasks. To address these limitations, we propose an efficient prediction model called <inline-formula><tex-math notation="LaTeX">$C^{3}$</tex-math><alternatives><mml:math><mml:msup><mml:mi>C</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="gao-ieq1-3729814.gif"/></alternatives></inline-formula>-Predictor (<italic>C</italic>oncurrency, <italic>C</italic>ost factor, and <italic>C</italic>onfidence), which integrates a cost factor model into a graph neural network (GNN) model to reduce the latency of prediction with guaranty of accuracy. First, the cost factor is used to achieve the performance prediction of concurrent queries through a concurrent adjustment mechanism. Second, a confidence estimation algorithm is applied to evaluate the cost factor model, while the confidence mechanism evaluates the reliability of initial predictions to determine whether to activate the GNN model. Third, the GNN model is used to calibrate prediction deviations from the cost factor model, which retains only those features that impact the prediction of concurrent queries to construct graphs, and applies the GNN model to predict query performance in an accurate fashion. Extensive experiments are conducted on varying types of workload. The results demonstrate that <inline-formula><tex-math notation="LaTeX">$C^{3}$</tex-math><alternatives><mml:math><mml:msup><mml:mi>C</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="gao-ieq2-3729814.gif"/></alternatives></inline-formula>-Predictor outperforms the state-of-the-art (SOTA) models, that is, for different levels of concurrent queries, <inline-formula><tex-math notation="LaTeX">$C^{3}$</tex-math><alternatives><mml:math><mml:msup><mml:mi>C</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="gao-ieq3-3729814.gif"/></alternatives></inline-formula>-Predictor achieves the lowest prediction error and the lowest prediction latency.