Generating application-specific data layouts for in-memory databases

Abstract

Database applications are often developed with object-oriented languages while using relational databases as the backend. To accelerate these applications, developers would manually design customized data structures to store data in memory, and ways to utilize such data structures to answer queries. Doing so is brittle and requires a lot of effort. Alternatively, developers might automate the process by using relational physical design tools to create materialized views and indexes instead. However, the characteristics of object-oriented database applications are often distinct enough from traditional database applications such that classical relational query optimization techniques often cannot speed up queries that arise from such applications, as our experiments show. To address this, we build Chestnut, a data layout generator for in-memory object-oriented database applications. Given a memory budget, Chestnut generates customized in-memory data layouts and query plans to answer queries written using a subset of the Rails API, a common framework for building object-oriented database applications. Chestnut differs from traditional query optimizers and physical designers in two ways. First, Chestnut automatically generates data layouts that are customized for the application after analyzing their queries, hence Chestnut-generated data layouts are designed to be efficient to answer queries from such applications. second, Chestnut uses a novel enumeration and verification-based algorithm to generate query plans that use such data layouts, rather than rule-based approaches as in traditional query optimizers. We evaluated Chestnut on four open-source Rails database applications. The result shows that it can reduce average query processing time by over 3.6X (and up to 42X), as compared to other in-memory relational database engines.