Abstract: An example method includes receiving a data load request requesting loading and partitioning of an unknown quantity of user data for storage at a data storage system. The user data including a partitioning key; a total data size of the user data; a plurality of rows, each row of the plurality of rows associated with a value defined by the partitioning key; and one or more columns. The method also includes identifying one or more storage constraints for the data storage system. The method further includes, after receiving the user data, determining a plurality of partitioning quantiles defining respective ranges of values of the partitioning key based on the user data and the one or more storage constraints for the data storage system; and range partitioning each row of the user data into files based on the value associated with the row defined by the partitioning key, and the respective ranges of the values of the partitioning key defined by the plurality of partitioning quantiles.

Abstract: A method for execution-time dynamic range partitioning includes receiving user data including a partitioning key and a clustering key. The user data includes a respective number of total rows defining a total data size for the user data. The method also includes identifying storage constraints for the data storage system. The storage constraints include a target file size and a target number of rows per file. The method further includes determining a plurality of split points for the user data based on the storage constraints. The method also includes generating partitioning quantiles from the plurality of split points that define a range between each split point of the plurality of split points. The method further includes range partitioning each row of the user data into files using the partitioning quantiles.

Abstract: A method for execution-time dynamic range partitioning includes receiving user data including a partitioning key and a clustering key. The user data includes a respective number of total rows defining a total data size for the user data. The method also includes identifying storage constraints for the data storage system. The storage constraints include a target file size and a target number of rows per file. The method further includes determining a plurality of split points for the user data based on the storage constraints. The method also includes generating partitioning quantiles from the plurality of split points that define a range between each split point of the plurality of split points. The method further includes range partitioning each row of the user data into files using the partitioning quantiles.

Abstract: An extensible markup language (XML) expression included in a query is parsed to yield an abstract syntax tree. The abstract syntax tree is then transformed into a unified tree including XML algebra operations. The unified tree is then converted into a relational tree including enhanced relational algebra operations. The relational tree is then forwarded to a query processor for optimization and execution of the query at the query processor.

Abstract: When two or more scalar subtrees in an execution plan are directed towards the same data set, but request different data from the data set, the two subtrees are replaced in the execution plan with a new subtree which queries the data set only once. This new query to the data set covers all data required by the different subtrees. A pivoting step then is used to separate the data which would have been sought by each subtree. The pivoting step also yields data in the same form as would have resulted from the original subtrees.

Abstract: An improved type inference may be calculated for a path expression that may be classified into a series of input expressions followed by steps. For each such series, the inference may be calculated based on input including a type for the input expression, an axis for the step, and a node test for the step. The cardinality of the input expression type is preserved for the calculation of the step type. Also, a set of one or more matching node types may be identified within the type repository. These matching node types are node types within the axis of the step that match the node test of the step. These matching node types are identified without calculating the full content type implied by the axis. Avoiding the calculation of the full content type of the axis may reduce the processing time required to perform the inference.

Abstract: An extensible markup language (XML) expression included in a query is parsed to yield an abstract syntax tree. The abstract syntax tree is then transformed into a unified tree including XML algebra operations. The unified tree is then converted into a relational tree including enhanced relational algebra operations. The relational tree is then forwarded to a query processor for optimization and execution of the query at the query processor.