Multi-agent Databases via Independent Learning

TL;DR

This paper introduces MADB, a multi-agent reinforcement learning system where learned database components collaborate to optimize query latency, demonstrating improved performance over non-cooperative approaches.

Contribution

The paper presents MADB, a novel multi-agent reinforcement learning framework enabling cooperative learned database components for end-to-end query optimization.

Findings

MADB outperforms non-cooperative learned components in reducing query latency.

Cooperative decision exchange among agents improves overall system performance.

Preliminary results validate the effectiveness of multi-agent collaboration in database tasks.

Abstract

Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing a single database component responsible for one task by its learning-based counterpart. However, query performance is not simply determined by the performance of a single component, but by the cooperation of multiple ones. As such, learning based database components need to collaborate during both training and execution in order to develop policies that meet end performance goals. Thus, the paper attempts to address the question "Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency?". To answer this question, we introduce MADB (Multi-Agent DB), a proof-of-concept system that incorporates a learned query scheduler and a learned query optimizer. MADB leverages a cooperative multi-agent reinforcement learning approach that allows the two components to exchange the context of their decisions with each other and collaboratively work towards reducing the query latency. Preliminary results demonstrate that MADB can outperform the non-cooperative integration of learned components.