Scaling up to Billions of Cells with DATASPREAD : Supporting Large Spreadsheets with Databases

Mangesh Bendre,Vipul Venkataraman,Xinyan Zhou,Kevin Chen-Chuan Chang,Aditya Parameswaran

semanticscholar（2017）

Cited 0|Views5

Abstract

Spreadsheet software is the tool of choice for ad-hoc tabular data management, manipulation, querying, and visualization with adoption by billions of users. However, spreadsheets are not scalable, unlike database systems. We develop DATASPREAD, a system that holistically unifies databases and spreadsheets with a goal to work with massive spreadsheets: DATASPREAD retains all of the advantages of spreadsheets, including ease of use, ad-hoc analysis and visualization capabilities, and a schema-free nature, while also adding the scalability and collaboration abilities of traditional relational databases. We design DATASPREAD with a spreadsheet front-end and a regular relational database back-end. To integrate spreadsheets and databases, in this paper, we develop a storage and indexing engine for spreadsheet data. We first formalize and study the problem of representing and manipulating spreadsheet data within a relational database. We demonstrate that identifying the optimal representation is NP-HARD via a reduction from partitioning of rectangles; however, under certain reasonable assumptions, can be solved in PTIME. We develop a collection of mechanisms for representing spreadsheet data, and evaluate these representations on a workload of typical data manipulation operations. We augment our mechanisms with novel positionally-aware indexing structures that further improve performance. DATASPREAD can scale to billions of cells, returning results for common operations within seconds. Lastly, to motivate our research questions, we perform an extensive survey of spreadsheet use for ad-hoc tabular data management.

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