Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped field included in a collection of documents. Each untyped field is configured to store one or more scalar data values. The system generates histograms for scalar data values stored in untyped fields. Responsive to receiving a query statement for fetching or updating data value stored in one of the untyped fields, the system assigns a cost to each operator associated with the query statement based on the histogram, compares the costs assigned to the operators, and generates a query execution plan based on the comparison, and query the database using the query execution plan.

Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Abstract: A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Abstract: In a method for table statistics update, in response to a request to modify a row in a target table, a database management system inspects a log record for the modified row in the target table, determines that the target table is configured for continuous statistics evaluation, and determines whether a mini-bin related to the modified row exists. When the mini-bin related to the modified row does not exist, the database management system creates the mini-bin. When the mini-bin related to the modified row exists, the database management system aggregates the modified row into the mini-bin, and when the mini-bin is determined to have reached a threshold size, stores the mini-bin to a disk. The database management system then consolidates a plurality of mini-bins stored in the disk and corresponding to the target table into a set of bins.

Abstract: In a method for table statistics update, in response to a request to modify a row in a target table, a database management system inspects a log record for the modified row in the target table, determines that the target table is configured for continuous statistics evaluation, and determines whether a mini-bin related to the modified row exists. When the mini-bin related to the modified row does not exist, the database management system creates the mini-bin. When the mini-bin related to the modified row exists, the database management system aggregates the modified row into the mini-bin, and when the mini-bin is determined to have reached a threshold size, stores the mini-bin to a disk. The database management system then consolidates a plurality of mini-bins stored in the disk and corresponding to the target table into a set of bins.

Abstract: In a method for table statistics update, in response to a request to modify a row in a target table, a database management system inspects a log record for the modified row in the target table, determines that the target table is configured for continuous statistics evaluation, and determines whether a mini-bin related to the modified row exists. When the mini-bin related to the modified row does not exist, the database management system creates the mini-bin. When the mini-bin related to the modified row exists, the database management system aggregates the modified row into the mini-bin, and when the mini-bin is determined to have reached a threshold size, stores the mini-bin to a disk. The database management system then consolidates a plurality of mini-bins stored in the disk and corresponding to the target table into a set of bins.

Abstract: In a method for table statistics update, in response to a request to modify a row in a target table, a database management system inspects a log record for the modified row in the target table, determines that the target table is configured for continuous statistics evaluation, and determines whether a mini-bin related to the modified row exists. When the mini-bin related to the modified row does not exist, the database management system creates the mini-bin. When the mini-bin related to the modified row exists, the database management system aggregates the modified row into the mini-bin, and when the mini-bin is determined to have reached a threshold size, stores the mini-bin to a disk. The database management system then consolidates a plurality of mini-bins stored in the disk and corresponding to the target table into a set of bins.

Abstract: A method and system for accurately estimating a result size of a Group-By operation in a relational database. The estimate utilizes the probability of union of the columns involved in the operation, as well as the relative cardinality of each column with respect to the other columns in the operation. In addition, the estimate incorporates the use of table filters when indicated such that table filters are applied prior to determining the size of the tables in the operation, as well as including equivalent columns into the list of columns that are a part of the Group-By operation. Accordingly, the estimate of the result size of the operation includes influencing factors that provide an accurate estimation of system memory requirements.

Abstract: An approach is provided in which a sample point system allocates sample point identifiers to a root node included an index tree that includes multiple leaf nodes. The sample point system distributes the sample point identifiers to the root node's child nodes, and recursively traverses through the index tree's hierarchical index levels and distributes the sample point identifiers from the child nodes to a subset of the index tree's leaf nodes. In turn, the sample point system collects sample data from the subset of the plurality of leaf nodes corresponding to the distributed sample point identifiers.

Abstract: An approach is provided in which a sample point system allocates sample point identifiers to a root node included an index tree that includes multiple leaf nodes. The sample point system distributes the sample point identifiers to the root node's child nodes, and recursively traverses through the index tree's hierarchical index levels and distributes the sample point identifiers from the child nodes to a subset of the index tree's leaf nodes. In turn, the sample point system collects sample data from the subset of the plurality of leaf nodes corresponding to the distributed sample point identifiers.

Abstract: An approach is provided in which a sample point system allocates sample point identifiers to a root node included an index tree that includes multiple leaf nodes. The sample point system distributes the sample point identifiers to the root node's child nodes, and recursively traverses through the index tree's hierarchical index levels and distributes the sample point identifiers from the child nodes to a subset of the index tree's leaf nodes. In turn, the sample point system collects sample data from the subset of the plurality of leaf nodes corresponding to the distributed sample point identifiers.

Abstract: An approach is provided in which a sample point system allocates sample point identifiers to a root node included an index tree that includes multiple leaf nodes. The sample point system distributes the sample point identifiers to the root node's child nodes, and recursively traverses through the index tree's hierarchical index levels and distributes the sample point identifiers from the child nodes to a subset of the index tree's leaf nodes. In turn, the sample point system collects sample data from the subset of the plurality of leaf nodes corresponding to the distributed sample point identifiers.

Abstract: In accordance with aspects of the present invention, provided are systems and computer program products for incrementally estimating the cardinality of a derived relation including statistically correlated partially applicable predicates for a range-partitioned table. During the generation of a QEP a cardinality estimate is calculated in which one or more partially applicable predicates is correlated to another partially applicable predicate and/or to one or more fully applicable predicates. The cardinality includes a number of rows expected to be returned by the QEP and is computed in an incremental fashion for each operator of the QEP.

Abstract: A method and system for accurately estimating a result size of a Group-By operation in a relational database. The estimate utilizes the probability of union of the columns involved in the operation, as well as the relative cardinality of each column with respect to the other columns in the operation. In addition, the estimate incorporates the use of table filters when indicated such that table filters are applied prior to determining the size of the tables in the operation, as well as including equivalent columns into the list of columns that are a part of the Group-By operation. Accordingly, the estimate of the result size of the operation includes influencing factors that provide an accurate estimation of system memory requirements.

Abstract: A method and system for accurately estimating a result size of a Group-By operation in a relational database. The estimate utilizes the probability of union of the columns involved in the operation, as well as the relative cardinality of each column with respect to the other columns in the operation. In addition, the estimate incorporates the use of table filters when indicated such that table filters are applied prior to determining the size of the tables in the operation, as well as including equivalent columns into the list of columns that are a part of the Group-By operation. Accordingly, the estimate of the result size of the operation includes influencing factors that provide an accurate estimation of system memory requirements.

Abstract: In accordance with aspects of the present invention, provided are systems and computer program products for incrementally estimating the cardinality of a derived relation including statistically correlated partially applicable predicates for a range-partitioned table. During the generation of a QEP a cardinality estimate is calculated in which one or more partially applicable predicates is correlated to another partially applicable predicate and/or to one or more fully applicable predicates. The cardinality includes a number of rows expected to be returned by the QEP and is computed in an incremental fashion for each operator of the QEP.