Abstract: A method for performing multiple merge operations with a source data structure and a plurality of destination data structures, in which the source data structure is scanned one time in order to obtain source data that is then used for multiple merge operations. The source data is then used consecutively in order to merge the source data into the one or more destination data structures. Each merge is performed using the same scan of the source data structure and within the same execution of a database command.

Abstract: Techniques are described herein for partitioning a database table using a virtual column as the partitioning key. A virtual column is defined by the database to contain results of a defining expression. Partitioning can be performed using a variety of partitioning schemes, such as range partitioning and hash partitioning. The partitions are automatically updated to reflect changes to the values in the base columns upon which the virtual column depends. Partition pruning and parallel-wise partition joins can be performed on database tables which have been partitioned on virtual columns.

Abstract: Techniques are described herein for performing column functions on virtual columns in database tables. A virtual column is defined by the database to contain results of a defining expression. Statistics are collected and maintained for virtual columns. Indexing is performed on virtual columns. Referential integrity is maintained between two tables using virtual columns as keys. Join predicate push-down operations are also performed using virtual columns.

Abstract: Techniques are described herein for performing column functions on virtual columns in database tables. A virtual column is defined by the database to contain results of a defining expression. Statistics are collected and maintained for virtual columns. Indexing is performed on virtual columns. Referential integrity is maintained between two tables using virtual columns as keys. Join predicate push-down operations are also performed using virtual columns.

Abstract: Techniques are described herein for replacing a part of an expression in a query to a database with a virtual column. A virtual column is defined by the database to contain results of a defining expression. A part of an expression in a query is replaced with a virtual column whose defining expression is equal to the part of the expression in the query. The part of an expression in a query can be the entire expression. When an expression in a query can be replaced with more than one replacement expression involving virtual columns, the replacement expression with the least cost of query processing is selected.

Abstract: Techniques are described herein for supporting pipelining operations involving DML (Data manipulation Language) and query. In an embodiment, a DML operation is performed for the purpose of changing data in a database system from a first state to a second state. Before the data in the database system is changed by the DML operation, the DML operation has an in-memory representation of such data. In an embodiment, a subsequent query operation that logically depends on the data in the database system in the second state is granted access to the in-memory representation of such data in the second state before the data in the database system is changed by the DML operation to the second state. As a result, operations may be executed in a pipelined fashion with minimum blocking.

Abstract: A method for performing multiple merge operations with a source data structure and a plurality of destination data structures, in which the source data structure is scanned one time in order to obtain source data that is then used for multiple merge operations. The source data is then used consecutively in order to merge the source data into the one or more destination data structures. Each merge is performed using the same scan of the source data structure and within the same execution of a database command.

Abstract: A system for providing real-time cloud computing. The system includes a plurality of computing nodes, each node including a CPU, a memory, and a hard disk. The system includes a central intelligence manager for real-time assigning of tasks to the plurality of computing nodes. The central intelligence manager is configured to provide CPU scaling in parallel. The central intelligence manager is configured to provide a concurrent index. The central intelligence manager is configured to provide a multi-level cache. The central intelligence manager is configured to provide direct disk reads to the hard disks. The central intelligence manager is configured to utilize UDP for peer-to-peer communication between the computing nodes.

Abstract: A technique for migrating (e.g., “copying” or “moving”) temporary data from one session to another session is provided. While executing a workload associated with a particular session, session state is created. A subset of the session state comprises large objects that are referred to as “temporary data.” Thus, temporary data is stored in a database (instead of in the database server) and is normally deleted when the particular session is deleted. To avoid this deletion when the session state is migrated to a different session, the temporary data, in one embodiment, is saved in an external storage that is separate from the database. An external table is created in the external storage and stores the temporary data. Location information to access the external table is provided to the other session.

Abstract: Techniques are described herein for supporting pipelining operations involving DML (Data manipulation Language) and query. In an embodiment, a DML operation is performed for the purpose of changing data in a database system from a first state to a second state. Before the data in the database system is changed by the DML operation, the DML operation has an in-memory representation of such data. In an embodiment, a subsequent query operation that logically depends on the data in the database system in the second state is granted access to the in-memory representation of such data in the second state before the data in the database system is changed by the DML operation to the second state. As a result, operations may be executed in a pipelined fashion with minimum blocking.

Abstract: Techniques are described herein for partitioning a database table using a virtual column as the partitioning key. A virtual column is defined by the database to contain results of a defining expression. Partitioning can be performed using a variety of partitioning schemes, such as range partitioning and hash partitioning. The partitions are automatically updated to reflect changes to the values in the base columns upon which the virtual column depends. Partition pruning and parallel-wise partition joins can be performed on database tables which have been partitioned on virtual columns.

Abstract: Techniques are described herein for performing column functions on virtual columns in database tables. A virtual column is defined by the database to contain results of a defining expression. Statistics are collected and maintained for virtual columns. Indexing is performed on virtual columns. Referential integrity is maintained between two tables using virtual columns as keys. Join predicate push-down operations are also performed using virtual columns.

Abstract: Techniques are described herein for replacing a part of an expression in a query to a database with a virtual column. A virtual column is defined by the database to contain results of a defining expression. A part of an expression in a query is replaced with a virtual column whose defining expression is equal to the part of the expression in the query. The part of an expression in a query can be the entire expression. When an expression in a query can be replaced with more than one replacement expression involving virtual columns, the replacement expression with the least cost of query processing is selected.

Abstract: A technique for migrating (e.g., “copying” or “moving”) temporary data from one session to another session is provided. While executing a workload associated with a particular session, session state is created. A subset of the session state comprises large objects that are referred to as “temporary data.” Thus, temporary data is stored in a database (instead of in the database server) and is normally deleted when the particular session is deleted. To avoid this deletion when the session state is migrated to a different session, the temporary data, in one embodiment, is saved in an external storage that is separate from the database. An external table is created in the external storage and stores the temporary data. Location information to access the external table is provided to the other session.

Abstract: A method for performing multiple merge operations with a source data structure and a plurality of destination data structures, in which the source data structure is scanned one time in order to obtain source data that is then used for multiple merge operations. The source data is then used consecutively in order to merge the source data into the one or more destination data structures. Each merge is performed using the same scan of the source data structure and within the same execution of a database command.

Abstract: A method for performing multiple merge operations with a source data structure and a plurality of destination data structures, in which the source data structure is scanned one time in order to obtain source data that is then used for multiple merge operations. The source data is then used consecutively in order to merge the source data into the one or more destination data structures. Each merge is performed using the same scan of the source data structure and within the same execution of a database command.

Abstract: A source data structure may be scanned one time in order to obtain source data that is then used for multiple merge operations in order to merge source data into one or more destination data structures. Each merge operation is then performed using the same scan of the source data structure and a scan of the destination data structure.

Abstract: A source data structure may be scanned one time in order to obtain source data that is then used for multiple merge operations in order to merge source data into one or more destination data structures. Each merge operation is then performed using the same scan of the source data structure and a scan of the destination data structure.

Abstract: A source data structure may be scanned one time in order to obtain source data that is then used for multiple merge operations in order to merge source data into one or more destination data structures. Each merge operation is then performed using the same scan of the source data structure and a scan of the destination data structure.

Abstract: A source data structure may be scanned one time in order to obtain source data that is then used for multiple merge operations in order to merge source data into one or more destination data structures. Each merge operation is then performed using the same scan of the source data structure and a scan of the destination data structure.