Abstract
Motivation Biologists commonly store data in tabular form with observations as rows, attributes as columns, and measurements as values. Due to advances in high-throughput technologies, the sizes of tabular datasets are increasing. Some datasets contain millions of rows or columns. To work effectively with such data, researchers must be able to efficiently extract subsets of the data (using filters to select specific rows and retrieving specific columns). However, existing methodologies for querying tabular data do not scale adequately to large datasets or require specialized tools for processing. We sought a methodology that would overcome these challenges and that could be applied to an existing, text-based format.
Results In a systematic benchmark, we tested 10 techniques for querying simulated, tabular datasets. These techniques included a delimiter-splitting method, the Python pandas module, regular expressions, object serialization, the awk utility, and string-based indexing. We found that storing the data in fixed-width formats provided excellent performance for extracting data subsets. Because columns have the same width on every row, we could pre-calculate column and row coordinates and quickly extract relevant data from the files. Memory mapping led to additional performance gains. A limitation of fixed-width files is the increased storage requirement of buffer characters. Compression algorithms help to mitigate this limitation at a cost of reduced query speeds. Lastly, we used this methodology to transpose tabular files that were hundreds of gigabytes in size, without creating temporary files. We propose coordinate-based, fixed-width storage as a fast, scalable methodology for querying tabular biological data.
Contact stephen_piccolo{at}byu.edu
