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A flattened table (FT) of a database of a database management system (DBMS) is defined. The FT logically materializes a number of columns over a number of partitions. The columns include normalized columns, denormalized columns from a plurality of source tables of the database, as well as an aggregate column defining an aggregation of a selected normalized column over a selected denormalized column. A live-aggregate projection (LAP) is defined on the FT of the database. The LAP corresponds to the aggregate column and physically materializes the aggregation of the selected denormalized column over the selected denormalized column, as defined by the aggregate column. The FT is refreshed on-demand, on a per-column, per-partition basis. Responsive to the FT being refreshed on-demand, the LAP is automatically refreshed.

Automatic live-aggregate projection refresh responsive to on-demand flattened table refresh

In an example, data from a source location is merged into a target location containing existing data, in which the source location and the target location contain tuples of data. For each tuple in the source location, during a single operation, a determination is made as to whether there is a matched tuple in the target location that satisfies a predetermined condition. For each matched tuple that satisfies the predetermined condition, the matched tuple in the target location is updated with a count value that is equal to a count of the matched tuple in the source location and the target location. In addition, for each tuple that does not have a matched tuple that satisfies the predetermined condition, the unmatched tuple is inserted into the target location.

Merging data from a source location into a target location

In an example, a top-k function is associated with a top-k projection for a data storage system. Input data to be loaded into the data storage system is divided and ranked according to the top-k function and stored in the top-k projection.

Top-k projection

According to an example, in a method for managing storage of data from an input table, a projection definition that includes an expression to be executed on data contained in a column of the input table may be accessed. The expression on the data contained in the column of the input table may be executed to obtain results data, which may be stored in an expression projection, in which the expression projection includes a column that provides physical storage for the results data. The results data may be stored in the expression projection, segmented, and encoded. In addition, the expression projection may be stored in a node.

Managing storage of data

In an example, an aggregate function is associated with an aggregate projection for a data storage system. Input data to be loaded into the data storage system is aggregated according to the aggregate function and stored in the aggregate projection.

Aggregate projection

According to an example, database constraint generation may include receiving data related to a table in a database, analyzing the data to determine a row count for a column of the table, and analyzing the data to determine a number of distinct values for the column of the table. A comparison value may be determined by comparing the row count to the number of distinct values. The database constraint generation may further include determining if the comparison value is within a threshold. If the comparison value is within the threshold, an annotation may be added to the column of the table such that the column is considered unique during a cardinality estimation process involving the table.

Database table column annotation

In an example, data from a source location is merged into a target location containing existing data, in which both the source location and the target location contain tuples of data. For each tuple in the source location, during a single operation, a determination is made as to whether there is a matched tuple in the target location that satisfies a predetermined condition. For each matched tuple that satisfies the predetermined condition, the matched tuple in the target location is updated with a count value that is equal to a count of the matched tuple in the source location and the target location. In addition, for each tuple that does not have a matched tuple that satisfies the predetermined condition, the unmatched tuple is inserted into the target location.

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