Abstract: Described herein are optimizations and execution strategies for spreadsheet extensions to SQL. The partitioning of data, as specified in a spreadsheet clause, provides a way to parallelize the computation of spreadsheet and to provide and improve scalability. Even if the partitioning is not explicitly specified in the spreadsheet clause, the database optimizer can automatically infer the partitioning in some cases. Efficient hash based access structures on relations can be used for symbolic array addressing, enabling fast computation of formulas. When rewriting SQL statements, formulas whose results are not referenced in outer blocks can be removed from the spreadsheet clause, thus removing unnecessary computations. The predicates from other query blocks can be moved inside query blocks with spreadsheets clauses, thus considerably reducing the amount of data to be processed. Conditions for validity of this transformation are given.

Abstract: Described herein are optimizations and execution strategies for spreadsheet extensions to SQL. The partitioning of data, as specified in a spreadsheet clause, provides a way to parallelize the computation of spreadsheet and to provide and improve scalability. Even if the partitioning is not explicitly specified in the spreadsheet clause, the database optimizer can automatically infer the partitioning in some cases. Efficient hash based access structures on relations can be used for symbolic array addressing, enabling fast computation of formulas. When rewriting SQL statements, formulas whose results are not referenced in outer blocks can be removed from the spreadsheet clause, thus removing unnecessary computations. The predicates from other query blocks can be moved inside query blocks with spreadsheets clauses, thus considerably reducing the amount of data to be processed. Conditions for validity of this transformation are given.

Abstract: Hash aggregation operates in two phases. In the first phase, data read from underlying row source is partitioned on GROUP BY keys with on-the-fly aggregation. It is determined into which partition an entry corresponding to the incoming record should be grouped, and whether a matching entry is already present in the partition, using a hash function and a hash table built for the partition. For aggregation operations, if a match is found, then a measure value from the incoming record is aggregated with a measure value for the entry existing in the partition. If there is not enough system volatile memory to complete the first phase, then a partition is selected for storing persistently. Thus, only one partition at a time is stored persistently, as necessary. In the second phase, one persistently stored partition is processed at a time, whereby aggregation of the data in that partition is completed and results are returned.

Abstract: Methods are provided for efficiently evaluating database queries including multiple rollup operators. With the computation of grouping identifiers to distinguish grouping levels of each rollup operator, evaluation of database queries that include concatenation of rollup operators includes nesting of the rollup operators and includes the grouping identifiers as sorting keys for subsequent processing. Furthermore, to optimize the query evaluation process, the order in which the rollup operators are computed can be determined based on cardinalities that estimate the number of records generated by each rollup operator, and parallel evaluation can be utilized by partitioning data records between rollup operator processing stages based on the grouping keys associated with the rollup operators that are not being processed at the next stage.

Abstract: Described herein are optimizations and execution strategies for spreadsheet extensions to SQL. The partitioning of data, as specified in a spreadsheet clause, provides a way to parallelize the computation of spreadsheet and to provide and improve scalability. Even if the partitioning is not explicitly specified in the spreadsheet clause, the database optimizer can automatically infer the partitioning in some cases. Efficient hash based access structures on relations can be used for symbolic array addressing, enabling fast computation of formulas. When rewriting SQL statements, formulas whose results are not referenced in outer blocks can be removed from the spreadsheet clause, thus removing unnecessary computations. The predicates from other query blocks can be moved inside query blocks with spreadsheets clauses, thus considerably reducing the amount of data to be processed. Conditions for validity of this transformation are given.

Abstract: Described herein are optimizations and execution strategies for spreadsheet extensions to SQL. The partitioning of data, as specified in a spreadsheet clause, provides a way to parallelize the computation of spreadsheet and to provide and improve scalability. Even if the partitioning is not explicitly specified in the spreadsheet clause, the database optimizer can automatically infer the partitioning in some cases. Efficient hash based access structures on relations can be used for symbolic array addressing, enabling fast computation of formulas. When rewriting SQL statements, formulas whose results are not referenced in outer blocks can be removed from the spreadsheet clause, thus removing unnecessary computations. The predicates from other query blocks can be moved inside query blocks with spreadsheets clauses, thus considerably reducing the amount of data to be processed. Conditions for validity of this transformation are given.

Abstract: Techniques are described for optimizing the computation of OLAP ranking functions. The techniques involve push-down of the filtering operation into the window sort operation corresponding to a target ranking function. The push-down technique may be employed when a predetermined set of push-down conditions are met.