Efficient Specialized Spreadsheet Parsing for Data Science

TL;DR

This paper presents a new spreadsheet parser that significantly reduces memory usage and runtime, enabling efficient data loading for data science tasks on standard computers.

Contribution

The authors introduce a novel, memory-efficient, and faster spreadsheet parser that couples decompression with parsing and employs parallelism, outperforming existing methods.

Findings

Up to 3x faster loading times.

Up to 40x less memory consumption.

Effective for Excel spreadsheets in R environments.

Abstract

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 spreasheet 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 a prototype for loading Excel spreadsheets into R 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.