Abstract: Techniques related to group determination based on multi-table dictionary codes are disclosed. In some embodiments, one or more non-transitory storage media store a sequence of instructions which, when executed by one or more computing devices, cause performance of a method. The method comprises storing a fact table and a dimension table that share a domain dictionary. The fact table and the dimension table each have a column of encoded join keys that is decodable using the shared domain dictionary. A query may specify one or more row groups for the dimension table. To efficiently process the query, one or more group identifiers are assigned to the one or more row groups. Each row group corresponds to a different group identifier. This enables a code-to-group-identifier mapping to be generated. The code-to-group-identifier mapping correlates the encoded join keys to the one or more group identifiers.

Abstract: Techniques related to group determination based on multi-table dictionary codes are disclosed. In some embodiments, one or more non-transitory storage media store a sequence of instructions which, when executed by one or more computing devices, cause performance of a method. The method comprises storing a fact table and a dimension table that share a domain dictionary. The fact table and the dimension table each have a column of encoded join keys that is decodable using the shared domain dictionary. A query may specify one or more row groups for the dimension table. To efficiently process the query, one or more group identifiers are assigned to the one or more row groups. Each row group corresponds to a different group identifier. This enables a code-to-group-identifier mapping to be generated. The code-to-group-identifier mapping correlates the encoded join keys to the one or more group identifiers.

Abstract: Methods, computer systems, and stored instructions are described herein for densely grouping dimensional data and/or aggregating data using a data structure, such as one that is constructed based on dimensional data. When smaller tables are joined with a larger table, a server may analyze the smaller tables first to determine actual value combinations that occur in the smaller tables, and these actual value combinations are used to more efficiently process the larger table. A dense data structure may be generated by processing dimensional data before processing data from fact table. The dense data structure may be generated by compressing ranges of values that are possible in dimensions into a range of values that actually occurs in the dimensions. The compressed range of values may be represented by dense set identifiers rather than the actual compressed range of values.

Abstract: Methods, computer systems, and stored instructions are described herein for densely grouping dimensional data and/or aggregating data using a data structure, such as one that is constructed based on dimensional data. When smaller tables are joined with a larger table, a server may analyze the smaller tables first to determine actual value combinations that occur in the smaller tables, and these actual value combinations are used to more efficiently process the larger table. A dense data structure may be generated by processing dimensional data before processing data from fact table. The dense data structure may be generated by compressing ranges of values that are possible in dimensions into a range of values that actually occurs in the dimensions. The compressed range of values may be represented by dense set identifiers rather than the actual compressed range of values.

Abstract: Predicate abduction involves identifying, from a database statement, predicates on attributes of dimensional data, and applying those predicates to corresponding measure data so that only measure values are fetched and returned for dimension values that satisfy the predicate. In contrast to processing joins of relational data, the subset of the measure data is determined without needing to join a fact table with a dimension table and, therefore, without processing every fact record to determine which measure values are associated with records that satisfy the condition. Buffer reuse involves storing in buffers the “domains” of queries executing on multidimensional data. Responsive to detecting that execution of a database statement requires multiple iterations of extracting the same multidimensional data, the data is extracted during the first iteration and stored in a buffer so that, for each subsequent iteration, the buffer-stored data is accessed rather than re-extracting the data.

Abstract: In processing a query on multidimensional data in a multidimensional schema, a multidimensional database server performs various processes to limit the amount of data that is extracted from the database and presented to a relational database server in response to the request. A subset of data is identified to extract from the multidimensional data, based on the query. In various embodiments, cell-filtering, measure-filtering, and column-filtering criteria identified from the query are used to filter the subset of data, in order to return a minimized set of multidimensional data values from the subset of data. Therefore, computational resources are conserved with respect to further processing by the relational database server in response to the query.