SheetReader: Efficient Specialized Spreadsheet Parsing
Spreadsheets are widely used for data exploration. Since spreadsheet systems have limited capabilities, users often need to load spreadsheets to other data science environments to perform advanced analytics. However, current approaches for spreadsheet loading suffer from either high runtime or memory usage, which hinders data exploration on commodity systems. To make spreadsheet loading practical on commodity systems, we introduce a novel parser that minimizes memory usage by tightly coupling decompression and parsing. Furthermore, to reduce the runtime, we introduce optimized spreadsheet-specific parsing routines and employ parallelism. To evaluate our approach, we implement prototypes for loading Excel spreadsheets into R and Python environments. Our evaluation shows that our novel approach is up to 3x faster while consuming up to 40x less memory than state-of-the-art approaches.
