A Statistical Approach Towards Robust Progress Estimation

TL;DR

This paper introduces a statistical estimator selection framework that improves SQL progress estimation robustness across diverse queries by modeling estimator performance conditions, validated on real-world and benchmark workloads.

Contribution

It presents a novel statistical framework for selecting SQL progress estimators, enhancing robustness and accuracy across varied query types.

Findings

Significant increase in estimation robustness.

Framework generalizes well to different query types.

Improved accuracy with novel special purpose estimators.

Abstract

The need for accurate SQL progress estimation in the context of decision support administration has led to a number of techniques proposed for this task. Unfortunately, no single one of these progress estimators behaves robustly across the variety of SQL queries encountered in practice, meaning that each technique performs poorly for a significant fraction of queries. This paper proposes a novel estimator selection framework that uses a statistical model to characterize the sets of conditions under which certain estimators outperform others, leading to a significant increase in estimation robustness. The generality of this framework also enables us to add a number of novel "special purpose" estimators which increase accuracy further. Most importantly, the resulting model generalizes well to queries very different from the ones used to train it. We validate our findings using a large number of industrial real-life and benchmark workloads.