Abstract: According to an example, a system may receive a refresh column command for a column-oriented database. The refresh column command may identify a table in the column-oriented database and a denormalized column in the table. The system may determine column definitions for the column, generate a query to populate values from a data source associated with the column into the column based on the column definitions, and may execute the query to replace entire set of existing column values with the data source values.

Abstract: A user-defined function (UDF) is received. Then, execution of the UDF is distributed into a plurality of phases. Lastly, each of the phases is executed separately on a relational database including an aggregate projection that stores an aggregate value calculated from a table column of a table using an aggregate function.

Abstract: Examples herein involve providing a first query to an optimizer; identifying a first execution plan selected by the optimizer based on the first query; generating an annotated query based on the selected first execution plan, the annotated query comprising hints for executing the selected first execution plan; and storing the annotated query in a database, the annotated query to cause the optimizer to generate a second execution plan corresponding to the first execution plan based on the hints.

Abstract: According to an example, a system may receive a refresh column command for a column-oriented database. The refresh column command may identify a table in the column-oriented database and a denormalized column in the table. The system may determine column definitions for the column, generate a query to populate values from a data source associated with the column into the column based on the column definitions, and may execute the query to replace entire set of existing column values with the data source values.

Abstract: A user-defined function (UDF) is received. Then, execution of the UDF is distributed into a plurality of phases. Lastly, each of the phases is executed separately on a relational database including an aggregate projection that stores an aggregate value calculated from a table column of a table using an aggregate function.

Abstract: In an example, data from a source location is merged into a target location, in which both the source location and the target location contain tuples of data. A matched tuple in the target table that matches a The deleting of the tuple in the target table includes executing a semi inner join operation between the source table and the target table to identify the matched tuple. Each tuple contained in the source table may be inserted into the target table as a new tuple.

Abstract: For a database query that defines a plurality of separate snowflake schemas, a query optimizer computes separately for each of the snowflake schemas a logical access plan for obtaining from that schema's tables a respective record set that includes the data requested from those tables by that query. The query optimizer also computes a logical access plan for obtaining the query's results from the record sets in which execution of the logical access plans thus computed will result.

Abstract: Computer programs encoded on a computer storage medium provide for a modular query optimizer. In one aspect, a computer program product includes selecting one or more projections from a set of projections for each table in a database query wherein each of the selected projections for the table has leads to an estimated lower execution cost for the query as compared to non-selected projections; generating join orders for the query based on data distribution of one or more of the selected projections among sites in a computer network wherein the join orders reflect different combinations of data distribution operations applied to the output of one or more of the query's joins; and selecting a join order from the join orders based on evaluation of the join orders using a cost model.

Abstract: Computer programs encoded on a computer storage medium provide for a modular query optimizer. In one aspect, a computer program product includes selecting one or more projections from a set of projections for each table in a database query wherein each of the selected projections for the table has leads to an estimated lower execution cost for the query as compared to non-selected projections; generating join orders for the query based on data distribution of one or more of the selected projections among sites in a computer network wherein the join orders reflect different combinations of data distribution operations applied to the output of one or more of the query's joins; and selecting a join order from the join orders based on evaluation of the join orders using a cost model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modular query optimizer. In one aspect, a method includes selecting one or more projections from a set of projections for each table in a database query wherein each of the selected projections for the table has leads to an estimated lower execution cost for the query as compared to non-selected projections; generating join orders for the query based on data distribution of one or more of the selected projections among sites in a computer network wherein the join orders reflect different combinations of data distribution operations applied to the output of one or more of the query's joins; and selecting a join order from the join orders based on evaluation of the join orders using a cost model.

Abstract: Methods, program products and systems for determining, for a database query that does not represent a snowflake schema, a graph comprising vertices each representing a table joined in the query, a directed edge between each pair of vertices of which a first vertex represents a first table and a second vertex represents a second table that is joined in the query with the first table, each of the edges representing one of an outer join and an inner join. Further determining, for the graph, a directed spanning tree that represents an ordering of joins in the query and includes all outer join edges in the graph.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modular query optimizer. In one aspect, a method includes selecting one or more projections from a set of projections for each table in a database query wherein each of the selected projections for the table has leads to an estimated lower execution cost for the query as compared to non-selected projections; generating join orders for the query based on data distribution of one or more of the selected projections among sites in a computer network wherein the join orders reflect different combinations of data distribution operations applied to the output of one or more of the query's joins; and selecting a join order from the join orders based on evaluation of the join orders using a cost model.

Abstract: For a database query that defines a plurality of separate snowflake schemas, a query optimizer computes separately for each of the snowflake schemas a logical access plan for obtaining from that schema's tables a respective record set that includes the data requested from those tables by that query. The query optimizer also computes a logical access plan for obtaining the query's results from the record sets in which execution of the logical access plans thus computed will result.