Abstract: A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database comprised of one or more tables storing data. The RDBMS executes a query with a semi-join operation comprising an inclusion join and/or an exclusion join performed against at least an outer table and an inner table, wherein the inclusion join returns a row from the outer table when there is a match with a row in the inner table, and the exclusion join returns a row from the outer table when there is no match with a row in the inner table. The RDBMS performs a rewrite of the query to avoid spooling and/or sorting of the inner table, when the inner table is larger than the outer table and a cost after the rewrite is lower than before the rewrite.

Abstract: Techniques for ordering predicates in column partitioned databases for query optimization. Predicates on a single CP table within a query are organized to predicate-CP nodes with various sets of column partitions. The predicates within each predicate-CP node and the predicate-CP nodes as a whole are ordered in ascending order of cost which is determined by CPU/IO cost and predicate selectivity. The reorganized query is then executed.

Abstract: A query having a Union All view is identified. A logical join between Union AH view/derived table and other tables is broken down into multiple physical joins. The physical joins are pushed to the branches. Cost-based processing statistics are obtained for the branches. An optimal plan for the joins is selected based on the statistics; representing an optimal query execution for the query. The optimal query execution plan is provided to a database engine for executing the optimal query execution plan against a data warehouse.

Abstract: A data store system includes an array of persistent storage devices configured to store a plurality of data store tables. The data store system includes a processor in communication with the storage device. The processor may receive a query comprising an aggregate function and identify structure of an argument of the aggregate function. The subset of data store tables may be associated with the argument. The processor may partially-execute the aggregate function on each data store table in the subset involved in the argument of the aggregate function to create partially-executed results for each data store table of the subset of data store tables. The processor may join the partially-executed results based on join conditions contained in the aggregate function. The processor may complete execution of the aggregate function on the partially-executed results to generate a final result of the aggregate function. A method and computer-readable medium are also disclosed.

Abstract: One or a soft correlation of a database can be adjusted (e.g., modified, replaced, overwritten) for use with respect to one or more record(s) of the database associated with the soft correlation, by considering at least one or more violations of the soft correlations in the one or more of records database records associated with the soft correlation. In addition, an adjusted soft correlation can be stored and used for optimizations of database queries pertaining to one or more records associated with the adjusted soft correlation. Typically, the adjusted soft correlation is adjusted by at least considering the violations of an original soft correlation in the one or more records relating to the database queries.

Abstract: A database query to be run against a database is received by a processor. The query includes a query predicate. The query predicate includes a condition. The condition applies to a single database table. The condition is parsed to create an input vector. The input vector is submitted to a neural network. The neural network is trained to calculate the selectivity, a number of unique values (NUV) of results of applying predicates to the single database table, and a high mode frequency (HMF) of results of applying predicates to the single database table. The neural network determines the selectivity of the query predicate, an NUV for each column in the result of applying the query predicate to the single database table, and an HMF for each column in the result of applying the query predicate to the single database table.

Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER), so that the optimizer learns from previous versions of the queries to improve current and subsequent versions of the queries. The QER stores planning and execution information for QEs from the previous versions of the queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous versions of the queries. The optimizer searches the QER for QEs from the query execution plans, and uses information from the QEs stored in the QER when optimizing the current and subsequent versions of the queries. The optimizer may also reuses results from the QEs stored in the QER.

Abstract: One or a soft correlation of a database can be adjusted (e.g., modified, replaced, overwritten) for use with respect to one or more record(s) of the database associated with the soft correlation, by considering at least one or more violations of the soft correlations in the one or more of records database records associated with the soft correlation. In addition, an adjusted soft correlation can be stored and used for optimizations of database queries pertaining to one or more records associated with the adjusted soft correlation. Typically, the adjusted soft correlation is adjusted by at least considering the violations of an original soft correlation in the one or more records relating to the database queries.

Abstract: Multiple cost models (e.g., a sub-operations costing model and logical-operations costing model) can be used to make cost estimations of execution of database queries in one and each one of the multiple heterogeneous database systems. As a result, a “hybrid” cost estimating mode can be used whereby two or more cost models can be used in a single database system in to order maximize the advantages and minimize the disadvantages of each of the cost models, thereby striving to achieve an optimal balance. In addition, cost estimation can be switched between a hybrid cost estimating mode and a single cost estimating mode. The switch can, for example, be made as a part of tuning phase, as more information about actual costs of execution of database queries becomes more available, or as a result of changes to the database system and/or it operations, and so on. As a result, a flexible cost estimating mechanism can also be provided.

Abstract: A database operation is performed in a file system residing on a plurality of processing modules. The file system includes a first relation having a plurality of first-relation entries. Each of the plurality of first-relation entries has a first-relation attribute that is of interest in the database operation. A value of a distribution attribute in each of the first-relation entries is set to a unique value selected from among a domain of unique values. The first-relation entries of the first relation are redistributed among the plurality of processing modules based on the first-relation attribute and the distribution attribute. The computational operation is performed to produce a result.

Abstract: A query having a Union All view is identified. A logical join between Union AH view/derived table and other tables is broken down into multiple physical joins. The physical joins are pushed to the branches. Cost-based processing statistics are obtained for the branches. An optimal plan for the joins is selected based on the statistics; representing an optimal query execution for the query. The optimal query execution plan is provided to a database engine for executing the optimal query execution plan against a data warehouse.

Abstract: Techniques for improving complex database queries are provided. A determination is made whether to adopt a static or dynamic query execution plan based on metrics. When the dynamic query execution plan is used, a request fragment of the request is planned and the corresponding plan fragment is executed. The processed fragment provides feedback related to its processing to the remaining request and the process is repeated on the remaining request until the request is completed.

Abstract: A query optimizer identifies a condition that supports a remote nested join. A first Access Module Processor (AMP) is sent the condition to obtain a first set of rows that satisfy the condition from a first table and the first AMP is instructed to perform the remote nested join with a second remote AMP to acquire a second set of rows that satisfy the condition from a second table. The first AMP joins the first set of rows and the second set of rows as a join result, which is returned to the query optimizer.

Abstract: A query optimizer identifies a condition that supports a remote nested join. A first Access Module Processor (AMP) is sent the condition to obtain a first set of rows that satisfy the condition from a first table and the first AMP is instructed to perform the remote nested join with a second remote AMP to acquire a second set of rows that satisfy the condition from a second table. The first AMP joins the first set of rows and the second set of rows as a join result, which is returned to the query optimizer.

Abstract: Techniques for updating join indexes are provided. A determination is made to update date criteria in a join index query statement. The join index is parsed for current date and current time criteria. The join index is revised based on the location of the current date and current time criteria as they appear in the original join index. The revisions include new criteria that minimize the effort in maintaining and using the join index.

Abstract: Techniques for processing joins on column partitioned tables are provided. A query includes a first-Column Partition (CP) table joined with a second-CP table. The query is decomposed into a three-step process and rewritten and processed.

Abstract: Techniques for processing joins on column partitioned tables are provided. A join operation having a column partitioned table within a query is decomposed into a two-step process. The first process performs the join condition on the column portioned table with optional filtering conditions and a non-column partitioned table and spools the resulting columns to a spooled table. The spooled table is then rowid joined back to the column partitioned table to acquire remaining columns not present in the spooled table. Both the first and second steps can be separately resolved for costs when determining a query execution plan.

Abstract: Techniques for improving complex database queries are provided. A determination is made whether to adopt a static or dynamic query execution plan based on metrics. When the dynamic query execution plan is used, a request fragment of the request is planned and the corresponding plan fragment is executed. The processed fragment provides feedback related to its processing to the remaining request and the process is repeated on the remaining request until the request is completed.

Abstract: Techniques for ordering predicates in column partitioned databases for query optimization. Predicates on a single CP table within a query are organized to predicate-CP nodes with various sets of column partitions. The predicates within each predicate-CP node and the predicate-CP nodes as a whole are ordered in ascending order of cost which is determined by CPU/IO cost and predicate selectivity. The reorganized query is then executed.

Abstract: Techniques for processing joins on column partitioned tables are provided. A join operation having a column partitioned table within a query is decomposed into a two-step process. The first process performs the join condition on the column portioned table with optional filtering conditions and a non-column partitioned table and spools the resulting columns to a spooled table. The spooled table is then rowid joined back to the column partitioned table to acquire remaining columns not present in the spooled table. Both the first and second steps can be separately resolved for costs when determining a query execution plan.