Prediction of Horizontal Data Partitioning Through Query Execution Cost Estimation

TL;DR

This paper introduces a novel, simulation-based method using genetic algorithms to predict optimal horizontal data partitioning schemas that minimize query execution costs without data loading.

Contribution

The paper presents a formal model and a genetic algorithm-based approach for predicting optimal horizontal data partitions to improve system performance.

Findings

Effective in reducing workload execution cost

Validated with PostgreSQL's query optimizer

Demonstrates efficiency and correctness

Abstract

The excessively increased volume of data in modern data management systems demands an improved system performance, frequently provided by data distribution, system scalability and performance optimization techniques. Optimized horizontal data partitioning has a significant influence of distributed data management systems. An optimally partitioned schema found in the early phase of logical database design without loading of real data in the system and its adaptation to changes of business environment are very important for a successful implementation, system scalability and performance improvement. In this paper we present a novel approach for finding an optimal horizontally partitioned schema that manifests a minimal total execution cost of a given database workload. Our approach is based on a formal model that enables abstraction of the predicates in the workload queries, and are subsequently used to define all relational fragments. This approach has predictive features acquired by simulation of horizontal partitioning, without loading any data into the partitions, but instead, altering the statistics in the database catalogs. We define an optimization problem and employ a genetic algorithm (GA) to find an approximately optimal horizontally partitioned schema. The solutions to the optimization problem are evaluated using PostgreSQL's query optimizer. The initial experimental evaluation of our approach confirms its efficiency and correctness, and the numbers imply that the approach is effective in reducing the workload execution cost.