Comparing Data Store Performance for Full-Text Search: To SQL or to NoSQL?

George Fotopoulos, Paris Koloveas, Paraskevi Raftopoulou, Christos Tryfonopoulos

Abstract: The amount of textual data produced nowadays is constantly increasing as the number and variety of both new and reproduced textual information created by humans and (lately) also by bots is unprecedented. Storing, handling and querying such high volumes of textual data have become more challenging than ever and both research and industry have been using various alternatives, ranging from typical Relational Database Management Systems to specialised text engines and NoSQL databases, in an effort to cope with the volume. However, all these decisions are, largely, based on experience or personal preference for one system over another, since there is no performance comparison study that compares the available solutions regarding full-text search and retrieval. In this work, we fill this gap in the literature by systematically comparing four popular databases in full-text search scenarios and reporting their performance across different datasets, full-text search operators and parameters.