# Deductive Optimization of Relational Data Storage

## TL;DR

This paper introduces a language for expressing diverse physical database layouts and uses deductive synthesis to optimize query execution, achieving competitive performance with state-of-the-art in-memory systems.

## Contribution

It presents a novel language and compiler framework for optimizing relational data storage layouts beyond traditional methods.

## Key findings

- Optimized queries perform competitively with leading in-memory database systems.
- The language supports a wide range of physical data layouts.
- Experimental results validate the effectiveness of the approach.

## Abstract

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build a compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of these specialized queries is competitive with a state-of-the-art in memory compiled database system.