A Graph-Based Database Partitioning Method for Parallel OLAP Query Processing

As the amount of data to process increases, a scalable and efficient horizontal database partitioning method becomes more important for OLAP query processing in parallel database platforms. Existing partitioning methods have a few major drawbacks such as a large amount of data redundancy and not supporting join processing without shuffle in many cases despite their large data redundancy. We elucidate the drawbacks arise from their tree-based partitioning schemes and propose a novel graph-based database partitioning method called GPT that improves query performance with lower data redundancy. Through extensive experiments using three benchmarks, we show that GPT significantly outperforms the state-of-the-art method in terms of both storage overhead and query performance.