Abstract: There is provided a computer-implemented method of optimizing a query. An exemplary method retrieves a subset of rows from a sample table based on a query for a table associated with the sample table. The query specifies the table. The sample table is generated in a previous optimization of the query. The sample table includes a scrambled, random selection of rows from the table. A cardinality of the table is determined based on the subset. A query plan is generated based on the query and the cardinality.

Abstract: One example discloses a database management system that can comprise a memory for storing computer executable instructions and a processing unit for accessing the memory and executing the computer executable instructions. The computer executable instructions can comprise a query data collector to access a query repository table to determine if a first query plan for a first configuration executed on a relational database has a lowest resource cost of execution between the first query plan and a second query plan for a second configuration. The query repository table can include a plurality of query records with annotated query plans, each annotated query plan comprising text sufficient to generate a query plan of a certain form.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: A method of optimizing a query is provided herein. The method includes determining a cost estimate for a query. The method further includes determining a budget for optimizing the query based on the cost estimate. Additionally, the method includes determining a complexity of the query based on the budget. The method also includes determining a strategy based on the complexity. The strategy specifies a limit to a search space enumerated during optimization of the query. Further, the method includes optimizing the query based on the strategy.

Abstract: An enterprise data warehouse or business intelligence system having a plurality of processors and memory resources. The system includes at least a database server, a workload management system, a query compiler, and a query executor. The database server configured to receive a database query from an application at a database client system. The query compiler configured to prepare the execution plan for the query and compute the number of executive server processes (ESPs) in each ESP layer of the query. The workload management system is configured to generate an affinity value, and the query executor is configured to execute the query. As disclosed herein, placement of the executive server process layers of the query onto processors of the computing system is determined using the affinity value. Other embodiments, aspects and features are also disclosed.

Abstract: There is provided a computer-implemented method of optimizing a query. An exemplary method comprises receiving a first request from an optimizer that specifies a table, a first predicate and a first sample size, wherein the optimizer is optimizing a relational query language statement that specifies the table and the first predicate. The exemplary method also comprises generating a sample table, comprising a first subset of rows from the table, based on the request. The exemplary method also comprises selecting a second subset of rows from the sample table based on the predicate. The exemplary method additionally comprises sending a count of rows in the second subset to the optimizer.

Abstract: There is provided a computer-implemented method of optimizing a query. An exemplary method retrieves a subset of rows from a sample table based on a query for a table associated with the sample table. The query specifies the table. The sample table is generated in a previous optimization of the query. The sample table includes a scrambled, random selection of rows from the table. A cardinality of the table is determined based on the subset. A query plan is generated based on the query and the cardinality.

Abstract: Embodiments of the present invention are directed to query-optimization methods and systems that employ risk premiums assigned to database operations in order to increase the robustness and reliability of the evaluation and selection of query-execution plans for executing high-level database queries. Using risk-premium estimates, selection of query-execution plans that include potentially inefficient database operations and inefficient database-operation-execution sequences, often selected by currently available query-optimization methods and systems, are avoided.

Abstract: There is provided a computer-executable method of executing an outer join on a parallel database management system. An exemplary method comprises receiving an outer skewed values list (SVL). The outer SVL may comprise values that are indicated to be skewed. The exemplary method further comprises receiving an inner SVL. The inner SVL may comprise values that are indicated to be skewed. Additionally, the exemplary method comprises partitioning the outer table and the inner table across a plurality of join instances, based on the outer SVL and the inner SVL. A missing skew value is identified. The missing skewed value may be a value of the inner SVL that is not found in the inner table. The outer join is performed using the plurality of join instances, based on the missing skewed value.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: Systems and methods are provided for performing a nested join operation. Partitioning key values are computed for an outer data source using a partitioning key function used to partition an inner table. A join process is established for each of a plurality of partitions of the inner table (at 204), with a given partition of the inner table representing a plurality of partitioning key values. Each row from the outer data source is routed to a join process according to its associated partitioning key value (at 206). The inner table is probed to return a row from the inner table having the partitioning key value associated with the row from the outer data source (at 208). The row from the outer data source and the row returned from the inner table are joined to form a row in a combined table (at 210).

Abstract: There is provided a computer-implemented method of optimizing a query. An exemplary method comprises determining a maximum cardinality for an operator. The query may comprise the operator. The exemplary method also comprises determining a plurality of potential implementations for the operator. The exemplary method additionally comprises generating a query plan for the query based on the maximum cardinality and the potential implementations.

Abstract: Optimized query plans are partitioned into equivalence groups that each include equivalence classes. Each equivalence group corresponds to a particular compiled, normalized, and parameterized query plan prior to optimization. Each equivalence class within an equivalence group corresponds to a different query plan corresponding to the particular compiled, normalized, and parameterized query plan represented by the equivalence group that has been optimized with respect to the selectivity of one or more predicate clauses of the query that is compiled to produce the particular compiled, normalized, and parameterized query plan. Optimized query plans are cached according to their respective equivalence groups and equivalence classes.

Abstract: One example discloses a database management system that can comprise a memory for storing computer executable instructions and a processing unit for accessing the memory and executing the computer executable instructions. The computer executable instructions can comprise a compiler to amortize the execution resource cost of searching a search space corresponding to a received query over a plurality of searches.

Abstract: One embodiment is a method that determines skew to predict costs for query plans for a database query. The method selects a query plan that accounts for the skew to evenly distribute processing load among multiple processors and then executes the query with the selected query plan.

Abstract: One example discloses a database management system that can comprise a memory for storing computer executable instructions and a processing unit for accessing the memory and executing the computer executable instructions. The computer executable instructions can comprise a query data collector to access a query repository table to determine if a first query plan for a first configuration executed on a relational database has a lowest resource cost of execution between the first query plan and a second query plan for a second configuration. The query repository table can include a plurality of query records with annotated query plans, each annotated query plan comprising text sufficient to generate a query plan of a certain form.

Abstract: There is provided a computer-executable method of executing an outer join on a parallel database management system. An exemplary method comprises receiving an outer skewed values list (SVL). The outer SVL may comprise values that are indicated to be skewed. The exemplary method further comprises receiving an inner SVL. The inner SVL may comprise values that are indicated to be skewed. Additionally, the exemplary method comprises partitioning the outer table and the inner table across a plurality of join instances, based on the outer SVL and the inner SVL. A missing skew value is identified. The missing skewed value may be a value of the inner SVL that is not found in the inner table. The outer join is performed using the plurality of join instances, based on the missing skewed value.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: A method of optimizing a query is provided herein. The method includes determining a cost estimate for a query. The method further includes determining a budget for optimizing the query based on the cost estimate. Additionally, the method includes determining a complexity of the query based on the budget. The method also includes determining a strategy based on the complexity. The strategy specifies a limit to a search space enumerated during optimization of the query. Further, the method includes optimizing the query based on the strategy.

Abstract: Methods and systems receive a normalized query tree and analyze the tree to collect information about join operators and their children, and tables in an associated query. This information is then made available to a rule based optimizer that is configured to produce, from the normalized query tree, an execution plan. In addition, in at least some embodiments, an extensible framework is provided for join order optimization via the use of a multi-join operator and multi-join rules as part of the general framework of a query optimizer.