Abstract: System and methodology for automatic tuning of database query optimizer is described.

Abstract: System, method, computer program product embodiments and combinations and sub-combinations thereof for tuning query execution performance in a database management system are described. In an aspect, query optimization in a database management system (DBMS) with memory I/O (input/output) awareness includes determining compensated physical I/O (PIO) values indicative of memory I/O operations, including an approximate cache occupancy level, of an executing query in a DBMS, and utilizing the compensated PIO values as feedback to a query optimizer of the DBMS for optimization of future query execution.

Abstract: System, method, computer program product embodiments and combinations and sub-combinations thereof for tuning query execution performance in a database management system are described. In an aspect, query optimization in a database management system (DBMS) with awareness of limited resource usage includes identifying limited resource usage for each possible plan being considered for query execution by a query engine. A determination of whether any possible plan has limited resource usage that does not exceed a threshold level is made, and when at least one possible plan does not exceed the threshold level, the identified limited resource usage is included as a consideration during plan selection by the query engine. In an embodiment, temporary database usage is identified for each possible plan.

Abstract: System, method, computer program product embodiments and combinations and sub-combinations thereof for enhanced utilization of query optimization in a database system are provided. In an embodiment, query optimization of the database system is augmented to include statistics on temporary tables through an implicit update statistics option. Further, a statistics update is generated in the database system on a first optimizable query referencing at least one optimizable column of a temporary table when statistics for the at least one optimizable column are not already available.

Abstract: System, method, computer program product embodiments and combinations and sub-combinations thereof for enhanced utilization of query optimization in a database system are provided. In an embodiment, query optimization of the database system is augmented to include statistics on temporary tables through an implicit update statistics option. Further, a statistics update is generated in the database system on a first optimizable query referencing at least one optimizable column of a temporary table when statistics for the at least one optimizable column are not already available.

Abstract: System, method, computer program product embodiments and combinations and sub-combinations thereof for tuning query execution performance in a database management system are described. In an aspect, query optimization in a database management system (DBMS) with awareness of limited resource usage includes identifying limited resource usage for each possible plan being considered for query execution by a query engine. A determination of whether any possible plan has limited resource usage that does not exceed a threshold level is made, and when at least one possible plan does not exceed the threshold level, the identified limited resource usage is included as a consideration during plan selection by the query engine. In an embodiment, temporary database usage is identified for each possible plan.

Abstract: System, method, computer program product embodiments and combinations and sub-combinations thereof for tuning query execution performance in a database management system are described. In an aspect, query optimization in a database management system (DBMS) with memory I/O (input/output) awareness includes determining compensated physical I/O (PIO) values indicative of memory I/O operations, including an approximate cache occupancy level, of an executing query in a DBMS, and utilizing the compensated PIO values as feedback to a query optimizer of the DBMS for optimization of future query execution.

Abstract: Database system and methods are described for improving execution speed of database queries (e.g., for decision support). A multi-attribute selectivity optimization methodology is described that provides a more accurate estimate of the cost of a query execution plan, so that the predicted performance of the final execution plan will be more accurate. The densities by how much the selectivity deviates from a single attribute density and by how much the multi-attribute densities differ from one another are used as a basis for multi-selectivity estimates. The multi-attribute densities are used to scale estimates between extremes of total independence and total dependence. By taking into account how well attributes are correlated, the approach is able to provide more accurate multi-selectivity estimates. As a result, the database system can formulate better query plans and, thus, provide better performance.

Abstract: Database system and methods are described for improving execution speed of database queries (e.g., for decision support) by provides methods employing spline histograms for improving the determination of selectivity estimates. The general approach improves histogram-based cost estimates as follows. The constant associated with a predicate (e.g., in r.a>5, the constant is "5") is used to do a binary search in an array of histogram boundary values, for determining a particular histogram cell. Once a cell has been found, the system employs interpolation to find out how much of the cell has been selected. Once this interpolation value is found, it is used with a cell weighting and a spline value or weighting to estimate the selectivity of the predicate value, which takes into account how data values are distributed within the cell. As a result of increased accuracy of estimates, the system can formulate better query plans and, thus, provides better performance.