Abstract: Techniques for a database management system to predict when in the future a materialized view will have a quiet period during which the materialized view will not be stale. This is a followed by an approach that uses the quiet period prediction to determine an optimized schedule for refreshing the materialized view. The approach combines the quiet period prediction with a query rewrite pattern prediction for the materialized view and an estimated refresh duration for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: Techniques for a database management system to estimate a time needed to refresh a materialized view. This is a followed by an approach that uses estimated refresh duration to determine an optimized schedule for refreshing the materialized view. The approach combines the refresh duration estimate with a query rewrite pattern prediction for the materialized view and a quiet period prediction for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: Techniques are provided for bitmap-based computation of a COUNT(DISTINCT) function, where the bitmaps are generated based on ranks of target expression values. According to an embodiment, the ranks are computed using the DENSE_RANK function. The bitmaps may be maintained in a materialized view. Bitmap data that represents the ranks for target expression values occurring in data for a given group is divided across multiple bucket bitmaps, each corresponding to a distinct sub-range of the ranks. According to an embodiment, target expression value ranks are computed relative to partitions of the target expression values. When these partitions correspond to a subset (not necessarily strict) of the target query grouping keys for a query rewrite, the resulting bitmaps allow computation of multiple levels of aggregation from the single set of bitmaps.

Abstract: Techniques are described for storing and maintaining, in a materialized view, bitmap data that represents a bitmap of each possible distinct value of an expression and rewriting a query for a count of distinct values of the expression using the materialized view. The materialized view contains bitmap data that represents a bitmap of each possible distinct value of a first expression, and aggregate values of additional expressions, and is stored in memory or on disk by a database system. The database system receives a query that requests a number of distinct values, of the first expression, and an aggregate value for an additional expression. In response, the database system, rewrites the query to: compute the number of distinct values by counting the bits in the bitmap data of the materialized view that are set to the first value, and obtains the aggregate value for the additional expression in the materialized view.

Abstract: Techniques for a database management system to predict when in the future a materialized view will have a quiet period during which the materialized view will not be stale. This is a followed by an approach that uses the quiet period prediction to determine an optimized schedule for refreshing the materialized view. The approach combines the quiet period prediction with a query rewrite pattern prediction for the materialized view and an estimated refresh duration for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: Techniques for a database management system to predict when in the future a materialized view will be used for query rewrite. This is a followed by an approach that uses the quiet rewrite pattern prediction to determine an optimized schedule for refreshing the materialized view. The approach combines the query rewrite pattern prediction with a quiet period prediction for the materialized view and an estimated refresh duration for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: Techniques for a database management system to estimate a time needed to refresh a materialized view. This is a followed by an approach that uses estimated refresh duration to determine an optimized schedule for refreshing the materialized view. The approach combines the refresh duration estimate with a query rewrite pattern prediction for the materialized view and a quiet period prediction for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: Techniques are provided for bitmap-based computation of a COUNT(DISTINCT) function, where the bitmaps are generated based on ranks of target expression values. According to an embodiment, the ranks are computed using the DENSE_RANK function. The bitmaps may be maintained in a materialized view. Bitmap data that represents the ranks for target expression values occurring in data for a given group is divided across multiple bucket bitmaps, each corresponding to a distinct sub-range of the ranks. According to an embodiment, target expression value ranks are computed relative to partitions of the target expression values. When these partitions correspond to a subset (not necessarily strict) of the target query grouping keys for a query rewrite, the resulting bitmaps allow computation of multiple levels of aggregation from the single set of bitmaps.

Abstract: Techniques are described for storing and maintaining, in a materialized view, bitmap data that represents a bitmap of each possible distinct value of an expression and rewriting a query for a count of distinct values of the expression using the materialized view. The materialized view contains bitmap data that represents a bitmap of each possible distinct value of a first expression, and aggregate values of additional expressions, and is stored in memory or on disk by a database system. The database system receives a query that requests a number of distinct values, of the first expression, and an aggregate value for an additional expression. In response, the database system, rewrites the query to: compute the number of distinct values by counting the bits in the bitmap data of the materialized view that are set to the first value, and obtains the aggregate value for the additional expression in the materialized view.

Abstract: A method, system, and computer program product for validating database table partitioning from partition advisors. The method commences by receiving a workload comprising a plurality of queries, then analyzes the queries to determine stratification buckets based on the usage of tables in the queries. Further analysis of the queries results in assigning the queries into one or more of the stratification buckets from which buckets a number n of queries (n being smaller than the total number of queries in the received workload) are drawn from the stratification buckets to form a representative workload having a confidence interval C and a margin of error M. Now, having a representative workload that is smaller, yet statistically representative of the received workload, a computer evaluates each of a plurality of partition schemes using the representative workload to determine an optimal partitioning scheme. The confidence interval C can be increased or decreased.

Abstract: A method, system, and computer program product for validating database table partitioning from partition advisors. The method commences by receiving a workload comprising a plurality of queries, then analyzes the queries to determine stratification buckets based on the usage of tables in the queries. Further analysis of the queries results in assigning the queries into one or more of the stratification buckets from which buckets a number n of queries (n being smaller than the total number of queries in the received workload) are drawn from the stratification buckets to form a representative workload having a confidence interval C and a margin of error M. Now, having a representative workload that is smaller, yet statistically representative of the received workload, a computer evaluates each of a plurality of partition schemes using the representative workload to determine an optimal partitioning scheme. The confidence interval C can be increased or decreased.