Abstract: Control versioning of records in a temporal table is provided to reduce data redundancy. New Data Definition Language (DDL) syntax is provided to make individual columns within a table sensitive or insensitive to whether new row versions are generated when Database Manipulation Language (DML) statements operate on the table. The database parser and back-end data processors are configured to create the table with the user-defined versioning attributes and to manage versioning of the rows without requiring additional programming.

Abstract: Control versioning of records in a temporal table is provided to reduce data redundancy. New Data Definition Language (DDL) syntax is provided to make individual columns within a table sensitive or insensitive to whether new row versions are generated when Database Manipulation Language (DML) statements operate on the table. The database parser and back-end data processors are configured to create the table with the user-defined versioning attributes and to manage versioning of the rows without requiring additional programming.

Abstract: A method for facilitating join operations between a first database table and a second database table within a database system. The first database table and the second database table share at least one common index column. The method includes creating a new index column in the second database table that is populated with a limited number of distinct calculated values for the purpose of increasing the overall number of distinct values collectively assumed by the columns common between the two tables. An intermediate table is created, the intermediate table including the common columns of the first database table, the second database table, and the new index column. An index is defined of the intermediate table to be the column(s) common between the first and second tables. An index is defined of the second table to be the column(s) common between the first database table, the second database table and the new index column.

Abstract: A method for determining when a database system query optimizer should employ join skew avoidance steps. The method includes dynamically calculating the worst-case anticipated frequency distribution for a particular relation along a particular set of join column(s) at query execution time. The calculated frequency distribution value is compared to a skew threshold, the skew threshold representing the number of rows on the same distinct value that would lead to avoidable processing inefficiencies. It is then determined that the database system query optimizer should employ join skew avoidance steps if the calculated frequency distribution value exceeds the skew threshold.

Abstract: A method for facilitating join operations between a first database table and a second database table within a database system. The first database table and the second database table share at least one common index column. The method includes creating a new index column in the second database table that is populated with a limited number of distinct calculated values for the purpose of increasing the overall number of distinct values collectively assumed by the columns common between the two tables. An intermediate table is created, the intermediate table including the common columns of the first database table, the second database table, and the new index column. An index is defined of the intermediate table to be the column(s) common between the first and second tables. An index is defined of the second table to be the column(s) common between the first database table, the second database table and the new index column.

Abstract: A method for determining when a database system query optimizer should employ join skew avoidance steps. The method includes dynamically calculating the worst-case anticipated frequency distribution for a particular relation along a particular set of join column(s) at query execution time. The calculated frequency distribution value is compared to a skew threshold, the skew threshold representing the number of rows on the same distinct value that would lead to avoidable processing inefficiencies. It is then determined that the database system query optimizer should employ join skew avoidance steps if the calculated frequency distribution value exceeds the skew threshold.