A Storage Advisor for Hybrid-Store Databases

TL;DR

This paper introduces a storage advisor tool for hybrid-store databases like SAP HANA, which recommends optimal data storage strategies by estimating query costs, thereby improving performance for mixed transactional and analytical workloads.

Contribution

The paper presents a novel storage advisor with a cost model that supports per-table and partitioned data placement decisions in hybrid-store databases.

Findings

The storage advisor accurately predicts optimal storage configurations.

Using the advisor improves query performance significantly.

The tool effectively manages data partitioning for hybrid stores.

Abstract

With the SAP HANA database, SAP offers a high-performance in-memory hybrid-store database. Hybrid-store databases---that is, databases supporting row- and column-oriented data management---are getting more and more prominent. While the columnar management offers high-performance capabilities for analyzing large quantities of data, the row-oriented store can handle transactional point queries as well as inserts and updates more efficiently. To effectively take advantage of both stores at the same time the novel question whether to store the given data row- or column-oriented arises. We tackle this problem with a storage advisor tool that supports database administrators at this decision. Our proposed storage advisor recommends the optimal store based on data and query characteristics; its core is a cost model to estimate and compare query execution times for the different stores. Besides a per-table decision, our tool also considers to horizontally and vertically partition the data and manage the partitions on different stores. We evaluated the storage advisor for the use in the SAP HANA database; we show the recommendation quality as well as the benefit of having the data in the optimal store with respect to increased query performance.