Overlay Spreadsheets

Efforts to scale spreadsheets either follow a 'virtual' strategy that layers a spreadsheet interface on top of an existing database engine or a 'materialized' strategy based on reengineering a spreadsheet engine. Because databases are not optimized for spreadsheet access patterns, the materialized approach has better performance. However, the virtual approach offers several advantages that can not be easily replicated in the materialized approach, including the ability to re-apply user interactions to an updated input dataset. We propose the overlay update model, a hybrid approach that overlays user updates on an existing dataset (as in the virtual approach) and indexes user updates (as in the materialized approach). A key feature of our approach is storing updates generated by bulk operations (e.g., copy/paste) as compact "patterns" that can be leveraged to reduce execution costs. We implement an overlay spreadsheet over Apache Spark and demonstrate that, compared to DataSpread (a materialized spreadsheet), it can significantly reduce execution costs.