Opening The Black-Box: Explaining Learned Cost Models For Databases

TL;DR

This paper introduces a novel explainability approach for learned cost models in databases, enabling better understanding and debugging of complex neural models that predict query costs.

Contribution

It develops new explanation techniques tailored for LCMs and provides an interactive tool to improve their interpretability and troubleshooting.

Findings

First explainability techniques adapted for LCMs

Interactive tool demonstrates practical explainability

Enables systematic debugging of neural cost models

Abstract

Learned Cost Models (LCMs) have shown superior results over traditional database cost models as they can significantly improve the accuracy of cost predictions. However, LCMs still fail for some query plans, as prediction errors can be large in the tail. Unfortunately, recent LCMs are based on complex deep neural models, and thus, there is no easy way to understand where this accuracy drop is rooted, which critically prevents systematic troubleshooting. In this demo paper, we present the very first approach for opening the black box by bringing AI explainability approaches to LCMs. As a core contribution, we developed new explanation techniques that extend existing methods that are available for the general explainability of AI models and adapt them significantly to be usable for LCMs. In our demo, we provide an interactive tool to showcase how explainability for LCMs works. We believe this is a first step for making LCMs debuggable and thus paving the road for new approaches for systematically fixing problems in LCMs.