EMIT: Micro-Invasive Database Configuration Tuning

TL;DR

This paper introduces EMIT, a micro-invasive database tuning method that uses workload synthesis and knowledge transfer to efficiently optimize database configurations with minimal intrusion.

Contribution

The paper presents a novel, low-intrusion database tuning approach combining workload synthesis, configuration filtering, and knowledge transfer to improve tuning efficiency and practicality.

Findings

Reduces database performance impact during tuning

Effectively filters high-performing configurations

Accelerates convergence of tuning process

Abstract

The process of database knob tuning has always been a challenging task. Recently, database knob tuning methods has emerged as a promising solution to mitigate these issues. However, these methods still face certain limitations.On one hand, when applying knob tuning algorithms to optimize databases in practice, it either requires frequent updates to the database or necessitates acquiring database workload and optimizing through workload replay. The former approach involves constant exploration and updating of database configurations, inevitably leading to a decline in database performance during optimization. The latter, on the other hand, requires the acquisition of workload data, which could lead to data leakage issues. Moreover, the hyperparameter configuration space for database knobs is vast, making it challenging for optimizers to converge. These factors significantly hinder the practical implementation of database tuning. To address these concerns, we proposes an efficient and micro-invasive knob tuning method. This method relies on workload synthesis on cloned databases to simulate the workload that needs tuning, thus minimizing the intrusion on the database. And we utilizing a configuration replacement strategy to filter configuration candidates that perform well under the synthesized workload to find best configuration. And during the tuning process, we employ a knowledge transfer method to extract a common high-performance space, to boost the convergence of the optimizer.