Abstract: A technique for generating two or more execution plans for an SQL query within a database system. The system has two or more resources. A first resource utilization profile is defined by associating a first set of numerical utilization values respectively with two or more of the resources. The utilization values represent utilization of the resources. A first execution plan is generated that is optimal assuming utilization of the resources specified in the first resource utilization profile. The technique defines at least one further resource utilization profile by associating at least one further set of numerical utilization values respectively with two or more of the resources, the further utilization values representing utilization of the resources. At least one further execution plan is generated that is optimal assuming utilization of the resources specified in the further resource utilization profile(s).

Abstract: A method, database system, and computer program for collecting statistics about a table are disclosed. The table includes one or more rows and each row includes a respective value. The method includes creating zero or more histogram buckets. Each histogram bucket includes a width representing a respective range of values and a height representing a count of rows having values in the range of values. The method further includes creating one or more high-bias buckets, each high-bias bucket represents one or more values that appear in a minimum percentage of rows.

Abstract: The invention relates to a method of determining the performance of an optimizer program arranged to generate a plan. The method generally comprises the steps of executing the optimizer utilising at least one test case to derive a pre-variation plan; executing the pre-variation plan to generate a first output; varying a strategy of the optimizer program; executing the varied optimizer program utilising the at least one test case to derive a post-variation plan; and executing the post-variation plan to generate a second output. The outputs are then grouped according to at least one element of the plans to assess the performance of the optimizer.

Abstract: A database system receives a transaction specifying one or more conditions to apply to a first table, and inserts data selected in the transaction into a second. The database system further generates a value list used for compressing a column of the second table, where generating the value list comprises one of inheriting entries of the value list from another value list associated with the first table, and deriving the value list based on the one or more conditions to apply on the first table.

Abstract: FIG. 1 illustrates a database system 1 including a storage device 2. Device 2 stores table data 3 indicative of a plurality of rows 4. A source access module 5 is assigned to access one of rows 4, this particular row being designated by reference numeral 6. Module 5 is responsive to a command 7 for reading row 6 from device 2. Subsequently, module 5 writes row 6 to device 2 in a packet accessible by a target module 8, this packet presently taking the form of a shared spool file 9.

Abstract: There is provided a database system 1 including a source cluster, in the form of a source clique 2, for providing a clique shared spool file 3. This spool file is provided for consumption by a target module 4 belonging to a target cluster, in the form of a target clique 5. A node interconnect 6 receives of spool 3, and exports the spool for consumption by module 4.

Abstract: There is provided a parallel database system 1. System 1 includes a storage device 2 for maintaining a plurality of rows 3. System 1 also includes a plurality of access modules 4 for accessing in parallel a respectively assigned one or more of rows 3. An interface 5 receives data 6 indicative of a table 7 defined by a selection 8 of rows 3. A processor 9 is responsive to data 6 for selectively designating table 7 to be a single-module table. In this single module table, selection 8 is collectively assigned to an owning one of modules 4, indicated by reference numeral 10.

Abstract: A method, computer system, and database system for automating a business rule in a database are disclosed. One or more database triggers associated with a business rule state machine are enabled and the business rule state machine is initialized.

Abstract: A method, apparatus, and article of manufacture for directly manipulating a query for a relational database management system (RDBMS). The query is transformed into an operator tree that is displayed on a monitor, wherein the operator tree includes nodes for data sources and operators referenced in the query, and lines between the nodes. The RDBMS alters an execution plan for the query in response to one or more manipulations made to the displayed operator tree by the user. Generally, these manipulations comprise hints for an optimizer function of the RDBMS that an efficient execution plan to be generated for the query. Specifically, the hints influence the optimizer to choose one execution plan over another when there is insufficient information for the optimizer function to make a choice on its own.

Abstract: A method, apparatus, and article of manufacture for optimizing a star join operation in relational database management systems (RDBMS). A cross-product is generated from a plurality of dimension tables referenced by the star join. The join columns of the cross-product are then hashed to create a hash-row value. Using the hash-row value, a Star Map is probed to determine whether a record exists in a fact table that corresponds to the cross-product, wherein a first portion of the hash-row value is used to select a row of the Star Map and a second portion of the hash-row value is used to select a column of the selected row of the Star Map. The fact table is accessed to perform a merge join with the cross-product when the selected column of the selected row of the Star Map indicates that the record exists in the fact table.

Abstract: A method, apparatus, and article of manufacture for optimizing SQL queries in a relational database management system uses hash star join operations. A hash star join operation is performed in place of the star join operation when the query includes the star join operation on a base table and the plurality of dimension tables. The base table is hash partitioned into a plurality of distinct and separate sub-portions thereof. Selecting and projecting rows from all of the dimension tables to separate temporary dimension tables. The query is split into a plurality of sub-queries, wherein each of the sub-queries comprises a hash star join operation on the temporary dimension tables and one or more of the distinct sub-portions of the base table. Each of the sub-queries are then performed independently of one another to create intermediate result tables. The intermediate result tables from each of the sub-queries are then merged together to create an output table.