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.

This paper describes enhanced subquery optimizations in Oracle relational database system. It discusses several techniques -- subquery coalescing, subquery removal using window functions, and view elimination for group-by queries. These techniques recognize and remove redundancies in query structures and convert queries into potentially more optimal forms. The paper also discusses novel parallel execution techniques, which have general applicability and are used to improve the scalability of queries that have undergone some of these transformations. It describes a new variant of antijoin for optimizing subqueries involved in the universal quantifier with columns that may have nulls. It then presents performance results of these optimizations, which show significant execution time improvements.

Commercial relational database systems lack support for complex business modeling. ANSI SQL cannot treat relations as multidimensional arrays and define multiple, interrelated formulas over them, operations which are needed for business modeling. Relational OLAP (ROLAP) applications have to perform such tasks using joins, SQL Window Functions, complex CASE expressions, and the GROUP BY operator simulating the pivot operation. The designated place in SQL for calculations is the SELECT clause, which is extremely limiting and forces the user to generate queries with nested views, subqueries and complex joins. Furthermore, SQL query optimizers are preoccupied with determining efficient join orders and choosing optimal access methods and largely disregard optimization of multiple, interrelated formulas. Research into execution methods has thus far concentrated on efficient computation of data cubes and cube compression rather than on access structures for random, interrow calculations. This has created a gap that has been filled by spreadsheets and specialized MOLAP engines, which are good at specification of formulas for modeling but lack the formalism of the relational model, are difficult to coordinate across large user groups, exhibit scalability problems, and require replication of data between the tool and RDBMS. This article presents an SQL extension called SQL Spreadsheet , to provide array calculations over relations for complex modeling. We present optimizations, access structures, and execution models for processing them efficiently. Special attention is paid to compile time optimization for expensive operations like aggregation. Furthermore, ANSI SQL does not provide a good separation between data and computation and hence cannot support parameterization for SQL Spreadsheets models. We propose two parameterization methods for SQL. One parameterizes ANSI SQL view using subqueries and scalars, which allows passing data to SQL Spreadsheet. Another method presents parameterization of the SQL Spreadsheet formulas. This supports building stand-alone SQL Spreadsheet libraries. These models are then subject to the SQL Spreadsheet optimizations during model invocation time.

Data in a relational data warehouse is usually sparse. That is, if no value exists for a given combination of dimension values, no row exists in the fact table. Densities of 0.1-2% are very common. However, users may want to view the data in a dense form, with rows for all combination of dimension values displayed even when no fact data exists for them. For example, if a product did not sell during a particular time period, users may still want to see the product for that time period with zero sales value next to it. Moreover, analytic window functions [1] and the SQL model clause [2] can more easily express time series calculations if data is dense along the time dimension because dense data will fill a consistent number of rows for each period.Data densification is the process of converting spare data into dense form. The current SQL technique for densification (using the combination of DISTINCT, CROSS JOIN and OUTER JOIN operations) is extremely unintuitive, difficult to express and inefficient to compute. Hence, we propose an extension to the ANSI SQL join operator, referred to as "PARTITIONED OUTER JOIN", which allows for a succinct expression of densification along the dimensions of interest. We also present various algorithms to evaluate the new join operator efficiently and compare it with existing methods of doing the equivalent operation. We also define a new window function "LAST_VALUE (IGNORE NULLS)" which is very useful with partitioned outer join.

One of the critical deficiencies of SQL is lack of support for n-dimensional array-based computations which are frequent in OLAP environments. Relational OLAP (ROLAP) applications have to emulate them using joins, recently introduced SQL Window Functions [18] and complex and inefficient CASE expressions. The designated place in SQL for specifying calculations is the SELECT clause, which is extremely limiting and forces the user to generate queries using nested views, subqueries and complex joins. Furthermore, SQL-query optimizer is pre-occupied with determining efficient join orders and choosing optimal access methods and largely disregards optimization of complex numerical formulas. Execution methods concentrated on efficient computation of a cube [11], [16] rather than on random access structures for inter-row calculations. This has created a gap that has been filled by spreadsheets and specialized MOLAP engines, which are good at formulas for mathematical modeling but lack the formalism of the relational model, are difficult to manage, and exhibit scalability problems. This paper presents SQL extensions involving array based calculations for complex modeling. In addition, we present optimizations, access structures and execution models for processing them efficiently. 1