Oracle International Corporation

Ekrem S C Soylemez

Martin Roth

Adam Kociubes

Albert A Hopeman, Iv

Shasank Kisan Chavan

Garret Frederick Swart

Aurosish Mishra

Raghuram Venkatasubramanian

Scott Feinstein

James Hartsing

Vinita Subramanian

Anne Murphy

Eugene Karichkin

Techniques related to cache storage formats are disclosed. In some embodiments, a set of values is stored in a cache as a set of first representations and a set of second representations. For example, the set of first representations may be a set of hardware-level representations, and the set of second representations may be a set of non-hardware-level representations. Responsive to receiving a query to be executed over the set of values, a determination is made as to whether or not it would be more efficient to execute the query over the set of first representations than to execute the query over the set of second representations. If the determination indicates that it would be more efficient to execute the query over the set of first representations than to execute the query over the set of second representations, the query is executed over the set of first representations.

Storage formats for in-memory caches

Herein are techniques for automatically leveraging metadata of an analytic view to accelerate a relational query. In an embodiment, a computer stores model metadata that defines an analytic view that contains a join operation that is based on a dimension column of a dimension table and a join column of a fact table. The analytic view also contains a measure that is based on an aggregation operation and a measure column of the fact table. Also stored is denormalization metadata that defines a transparency view that is based on the analytic view. In operation, a query that references the transparency view is received. The query does not reference the analytic view. The query that references the transparency view is executed based on: a) the denormalization metadata that defines the transparency view, b) the model metadata that defines the analytic view, and c) the measure that is based on the aggregation operation and the measure column of the fact table.

Method for generating views based on a semantic model, that allows for autonomous performance improvements and complex calculations

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.

Group determination based on multi-table dictionary codes

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.

Densely grouping dimensional data

Aggregating dimensional data using dense containers

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Lexington, MA, United States of America

Ekrem S C Soylemez