This paper showcases some of the newly introduced parallel execution methods in Oracle RDBMS. These methods provide highly scalable and adaptive evaluation for the most commonly used SQL operations - joins, group-by, rollup/cube, grouping sets, and window functions. The novelty of these techniques is their use of multi-stage parallelization models, accommodation of optimizer mistakes, and the runtime parallelization and data distribution decisions. These parallel plans adapt based on the statistics gathered on the real data at query execution time. We realized enormous performance gains from these adaptive parallelization techniques. The paper also discusses our approach to parallelize queries with operations that are inherently serial. We believe all these techniques will make their way into big data analytics and other massively parallel database systems.

An XML publish/subscribe system needs to filter a large number of queries over XML streams. Most existing systems only consider filtering the simple XPath statements. In this paper, we focus on filtering of the more complex Generalized-Tree-Pattern (GTP) queries. Our filtering mechanism is based on a novel Tree-of-Path (TOP) encoding scheme, which compactly represents the path matches for the entire document. First, we show that the TOP encodings can be efficiently produced via a shared bottom-up path matching. Second, with the aid of this TOP encoding, we can 1) achieve polynomial time and space complexity for post processing, 2) avoid redundant predicate evaluations, 3) allow an efficient duplicate-free and merge join-based algorithm for merging multiple encoded path matches and 4) simplify the processing of GTP queries. Overall our approach maximizes the sharing opportunity across queries by exploiting the suffix as well as prefix sharing. At the same time, our TOP encodings allow efficient post processing for GTP queries. Extensive performance studies show that our GFilter solution not only achieves significantly better filtering performance than state-of-the-art algorithms, but also is capable of efficiently filtering the more complex GTP queries.

An XML range query may impose predicates on the numerical or textual contents of the elements and/or their respective path structures. In order to handle content and structure range queries efficiently, an XML query processing engine needs to incorporate effective indexing and summarization techniques to efficiently partition the XML document and locate the results. In this paper, we propose a dynamic summarization and indexing method, FLUX, based on Bloom filters and B+-trees to tackle these problems. The results of our extensive experimental evaluations indicated the efficiency of the proposed system.