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• Learned Approximate Query Processing

Learned Approximate Query Processing

Make it Light, Accurate and Fast

• Paper: ICDR 2021

Abstract

The advent of learning algorithms has revealed many opportunities for improving Data Systems’ functionality and performance. Approximate Query Processing (AQP) is one such area where machine learning (ML) models have been used to improve query execution efficiency and accuracy, outperforming the traditional samplingbased approaches. Based on our group’s experience in the MLfor-DBs area, [3–7, 29, 37–39], we contribute a novel AQP engine, coined DBEst++, which extends our previous effort (DBEst, [29]) and sets the state of the art in terms of accuracy and query execution efficiency. The DBEst++ salient design objective is to derive lightweight ML models for the task, allowing a plethora of ML models to coexist, covering a very large fraction of the expected analytical query workload without requiring very large memory footprints. The DBEst++ salient architectural feature rests on a novel blending of word embedding models with neural networks tasked with regression-based predictions for density estimation and aggregation-attribute values. We present design features and motivations/rationale behind DBEst++ and discuss how all the ML models are brought together. We also present how DBEst++ can deal with challenging scenarios, including how to deal with highcardinality categorical attributes and how to ensure high accuracy under data updates. We provide a detailed experimental evaluation using the TPC-DS and Flights datasets against state of the art learned and sampling-based AQP engines, showcasing DBEst++’s gains in terms of accuracy, response-times, and memory space overheads.