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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,(More)
This paper describes cost-based query transformation in Oracle relational database system, which is a novel phase in query optimization. It discusses a suite of heuristic- and cost-based transformations performed by Oracle. It presents the framework for cost-based query transformation, the need for such a framework, possible interactions among some of the(More)
This paper describes the new architecture and optimizations for parallel SQL execution in the Oracle 10g database. Based on the fundamental shared-disk architecture underpinning Oracle's parallel SQL execution engine since Oracle7, we show in this paper how Oracle's engine responds to the challenges of performing in new grid-computing environments. This is(More)
Large tables are often decomposed into smaller pieces called partitions in order to improve query performance and ease the data management. Query optimizers rely on both the statistics of the entire table and the statistics of the individual partitions to select a good execution plan for a SQL statement. In Oracle 10g, we scan the entire table twice, one(More)
The query engine is the component inside a database system that is responsible for the compilation and execution of every SQL statement submitted by a database user or application. One of the most important steps of query compilation is query optimization. The goal of query optimization is to find the best execution plan based on metadata and statistics(More)
We live in the golden age of distributed computing. Public cloud platforms now offer virtually unlimited compute and storage resources on demand. At the same time, the Software-as-a-Service (SaaS) model brings enterprise-class systems to users who previously could not afford such systems due to their cost and complexity. Alas, traditional data warehousing(More)
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