Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management

TL;DR

This paper introduces DataSpread, a storage engine that integrates spreadsheets with databases, enhancing scalability and interactivity by representing spreadsheet data efficiently and supporting fast positional access.

Contribution

It develops a flexible storage mechanism for spreadsheet data within databases, addressing the NP-hardness of optimal representation and providing efficient positional access methods.

Findings

Up to 20% reduction in storage requirements.

Up to 50% faster formula evaluation.

Constant time access and modification performance.

Abstract

Spreadsheet software is the tool of choice for interactive ad-hoc data management, with adoption by billions of users. However, spreadsheets are not scalable, unlike database systems. On the other hand, database systems, while highly scalable, do not support interactivity as a first-class primitive. We are developing DataSpread, to holistically integrate spreadsheets as a front-end interface with databases as a back-end datastore, providing scalability to spreadsheets, and interactivity to databases, an integration we term presentational data management (PDM). In this paper, we make a first step towards this vision: developing a storage engine for PDM, studying how to flexibly represent spreadsheet data within a database and how to support and maintain access by position. We first conduct an extensive survey of spreadsheet use to motivate our functional requirements for a storage engine for PDM. We develop a natural set of mechanisms for flexibly representing spreadsheet data and demonstrate that identifying the optimal representation is NP-Hard; however, we develop an efficient approach to identify the optimal representation from an important and intuitive subclass of representations. We extend our mechanisms with positional access mechanisms that don't suffer from cascading update issues, leading to constant time access and modification performance. We evaluate these representations on a workload of typical spreadsheets and spreadsheet operations, providing up to 20% reduction in storage, and up to 50% reduction in formula evaluation time.