COTTAS: Columnar Triple Table Storage for Efficient and Compressed RDF Management
Abstract
One of the main challenges in working with RDF data is its verbosity, as repeated IRIs and IRI prefixes lead to large files that are costly to store and process. HDT, a binary RDF format, addresses this by compressing data while supporting efficient triple pattern evaluation without decompression. However, its performance is highly dependent on index alignment with query patterns. In this paper, we introduce COTTAS, a storage model that encodes RDF graphs directly into the open-source Apache Parquet columnar format. COTTAS represents RDF as a triple table and leverages block range indexes (zone maps) to achieve high compression ratios and fast query execution over compressed data. We also provide pycottas, an open-source Python library that enables compression of RDF data into COTTAS format and supports efficient querying by translating triple patterns into SQL queries over COTTAS files. This implementation facilitates the adoption of COTTAS for managing RDF graphs. Experiments on the WDBench and DBpedia benchmarks show that COTTAS reduces storage requirements by around 50% with respect to HDT and exhibits competitive triple pattern evaluation, with less performance volatility across pattern types.
