Abstract: A computer-based method includes receiving, at a compute device, a collected set of system measurement facility (SMF) data from a mainframe operating system. The method includes, retrieving from a memory operatively coupled to the compute device, a set of SMF field-type identifiers. Each SMF field-type identifier having a one-to-one logical relation with a conditional value from a set of conditional values. The method includes selecting SMF field values from the collected set of SMF data, based on the set of conditional values. The SMF field values is a subset of SMF field values included in the collected set of SMF data. The method further includes executing a flattening process to produce a flattened record that includes at least one flattened SMF field value converted into a target format, and inserting the flattened record into a data repository compatible with the target format.

Abstract: A computer-based method includes receiving, at a compute device, a collected set of system measurement facility (SMF) data from a mainframe operating system. The method includes, retrieving from a memory operatively coupled to the compute device, a set of SMF field-type identifiers. Each SMF field-type identifier having a one-to-one logical relation with a conditional value from a set of conditional values. The method includes selecting SMF field values from the collected set of SMF data, based on the set of conditional values. The SMF field values is a subset of SMF field values included in the collected set of SMF data. The method further includes executing a flattening process to produce a flattened record that includes at least one flattened SMF field value converted into a target format, and inserting the flattened record into a data repository compatible with the target format.

Abstract: Constraint processing for a relational database generates primary (e.g., based on primary key values) and constraint index records (e.g., based on foreign key values) during table load operations that are then sorted in a manner that rapidly and unambiguously identifies rows that fail the specified constraint test. Rows so identified may be deleted to maintain the constraint (e.g., referential) integrity of a child table. In one case, child table row data may be processed in constraint key order, eliminating the need first load the child table with row data and then delete those rows that subsequently fail the integrity test.

Abstract: A time-efficient means for identifying and processing cascading deletes due to referential constraint violations includes: logging, to an error file, all primary key (“PK”) errors detected during table load operations; building a foreign key (“FK”) index for each child table; recursively probing each relevant FK index to identify all loaded rows that violate a referential constraint due to a PK error; logging all identified FK errors to the error file; and using the (preferably sorted) error file contents to identify, mark and physically delete table rows that violate a referential constraint. The described cascade delete processing methods make only a single pass through the table data, using ordinary computer files to track and organize rows identified for deletion. Use of error files rather than tablescans (multiple passes through the loaded table data) can provide a significant reduction in table load times, especially for large or intricately “related” tables.

Abstract: A time-efficient means for identifying and processing cascading deletes due to referential constraint violations includes: logging, to an error file, all primary key (“PK”) errors detected during table load operations; building a foreign key (“FK”) index for each child table; recursively probing each relevant FK index to identify all loaded rows that violate a referential constraint due to a PK error; logging all identified FK errors to the error file; and using the (preferably sorted) error file contents to identify, mark and physically delete table rows that violate a referential constraint. The described cascade delete processing methods make only a single pass through the table data, using ordinary computer files to track and organize rows identified for deletion. Use of error files rather than tablescans (multiple passes through the loaded table data) can provide a significant reduction in table load times, especially for large or intricately “related” tables.

Abstract: A method and system for inputing data to a sort utility from a user program. In one embodiment, first parameters may facilitate optimization for both programs, by either the user program or the sort utility. The sort utility may process the first parameters to optimize sorting by: returning second parameters to the user program; and supporting a multi-record interface between the two programs. The sort utility may process the data records into the multi-record interface by constructing reformatted data records and inserting the reformatted data records into the multi-record interface, or by utilizing an exit routine to construct and insert the reformatted data records into the multi-record interface. The sort utility may sort the data records (e.g., by directly processing them), optimizing memory usage by the sort utility and/or the user program. The processing and sorting actions of the sort utility program may be coordinated and overlapped.

Abstract: A method and system for inputing data to a sort utility from a user program. In one embodiment, first parameters may facilitate optimization for both programs, by either the user program or the sort utility. The sort utility may process the first parameters to optimize sorting by: returning second parameters to the user program; and supporting a multi-record interface between the two programs. The sort utility may process the data records into the multi-record interface by constructing reformatted data records and inserting the reformatted data records into the multi-record interface, or by utilizing an exit routine to construct and insert the reformatted data records into the multi-record interface. The sort utility may sort the data records (e.g., by directly processing them), optimizing memory usage by the sort utility and/or the user program. The processing and sorting actions of the sort utility program may be coordinated and overlapped.