Knowledge Driven Query Sharding

We present the idea of an approach to database query sharding that makes use of knowledge about data structure and purpose. It is based on a case study for a database system that contains information about documents. By making use of knowledge about the data structure and the specific top-k queries to be processed we demonstrate a method for avoiding costly and unnecessary steps in query answering. We also demonstrate how the knowledge of data structure may be used to perform sharding and how such sharding may improve performance. We propose generalization of our findings that could lead to self-optimization and self-tuning in RDBMS engines, especially for column-based solutions.