Big Data analytics often include complex queries with similar or identical expressions, usually referred to as Common Table Expres sions (CTEs). CTEs may be explicitly defined by users to simplify query formulations, or implicitly included in queries generated by business intelligence tools, financial applications and decision support systems. In Massively Parallel Processing (MPP) database systems, CTEs pose new challenges due to the distributed nature of query processing, the overwhelming volume of underlying data and the scalability criteria that systems are required to meet. In these settings, the effective optimization and efficient execution of CTEs are crucial for the timely processing of analytical queries over Big Data. In this paper, we present a comprehensive framework for the representation, optimization and execution of CTEs in the context of Orca -Pivotal's query optimizer for Big Data. We demonstrate experimentally the benefits of our techniques using industry standard decision support benchmark.
In the context of MPP databases, the work in REF presents a comprehensive framework for the optimization of Common Table Expressions (CTEs) implemented for Orca.
Optimization of Common Table Expressions in MPP Database Systems
PVLDB
PVLDB
Computer Science