Abstract: Techniques are provided for autonomous caching of hierarchical data. In one technique, query log data is stored that comprises multiple entries, each entry (a) corresponding to a different instance of a query that was executed against a database and (b) indicating a tuple (or level grouping) comprising a set of hierarchy levels corresponding to a set of dimensions. Multiple tuples indicated in the query log data are identified. For each tuple: (1) a set of entries that indicate the tuple is identified and the set of entries is associated with the tuple; (2) aggregated performance data is generated for the tuple based on performance data associated with each entry in the set of entries that is associated with the tuple; and (3) based on the aggregated performance data, it is determined whether to create a new auto-cache table or to delete an existing auto-cache table associated with the tuple.

Abstract: Techniques are provided for autonomous caching of hierarchical data. In one technique, query log data is stored that comprises multiple entries, each entry (a) corresponding to a different instance of a query that was executed against a database and (b) indicating a tuple (or level grouping) comprising a set of hierarchy levels corresponding to a set of dimensions. Multiple tuples indicated in the query log data are identified. For each tuple: (1) a set of entries that indicate the tuple is identified and the set of entries is associated with the tuple; (2) aggregated performance data is generated for the tuple based on performance data associated with each entry in the set of entries that is associated with the tuple; and (3) based on the aggregated performance data, it is determined whether to create a new auto-cache table or to delete an existing auto-cache table associated with the tuple.

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.

Abstract: Approaches are disclosed for using a multidimensional database as a surrogate for a run-time hash table in the context of processing a relational query. In an embodiment, an analytic workspace is used to store data, wherein the data is to be evaluated using a by a function in the processing of a MODEL clause. The approaches eliminate the need to use a hash table to store the data that is to be evaluated. The data may initially reside in the analytic workspace, or it may be loaded into the analytic workspace.

Abstract: In one approach disclosed herein, a cache is used to provide cursor isolation. The cache is used to cache data that is updated in a query. Specifically, when a query is initiated, one or more caches may be instantiated in respect of the cursor. When data is updated during the query, the updated data is stored in the cache. By storing the updated data in the cache, the updated data is isolated from other users, from other sessions and from other queries by the same user.

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 being executed on multidimensional data. In response 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 data in the buffer is accessed rather than again extracting the data.

Abstract: Approaches are disclosed for using a multidimensional database as a surrogate for a run-time hash table in the context of processing a relational query. In an embodiment, an analytic workspace is used to store data, wherein the data is to be evaluated using a by a function in the processing of a MODEL clause. The approaches eliminate the need to use a hash table to store the data that is to be evaluated. The data may initially reside in the analytic workspace, or it may be loaded into the analytic workspace.

Abstract: In one approach disclosed herein, a cache is used to provide cursor isolation. The cache is used to cache data that is updated in a query. Specifically, when a query is initiated, one or more caches may be instantiated in respect of the cursor. When data is updated during the query, the updated data is stored in the cache. By storing the updated data in the cache, the updated data is isolated from other users, from other sessions and from other queries by the same user.

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.