Teradata Us, Inc.

Ncr Corporation

Grace Kwan-On Au

Mohammed Yassin Al-Kateb

Paul Laurence Sinclair

Sanjay Nair

Rama Krishna Korlapati

Lu Ma

Ahmad Said Ghazal

Kuorong Chiang

Sung Jin Kim

Haiyan Chen

Frederick Stuart Kaufmann

Arthur Vargas Lopes

Yinuo Zhang

Stephen Sheldon

May Pederson

Bibhu Ranjan Priyadarshi

Mohamed Yassin Eltabakh

Vijay Sarathy

Yi Xia

Ming Zhang

Venkata Ramakrishna Tirunagari

Anantha B Subramanian

Richard Leon Kimball, Iii

Sang-Luen Helen Fan

Conrad Tang

Mohammed Hussien Al-Kateb

Xiaobin Ma

Sanjay Sukumaran

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.

Cost-based semi-join rewrite

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.

Techniques for ordering predicates in column partitioned databases for query optimization

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.

Cost-based optimizations for join queries with union all views and derived tables

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.

Multi-table aggregation through partial-group-by processing

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.

Management of soft correlation for databases and optimization of database queries

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.

Deep learning for optimizer cardinality estimation

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.

Reducing skew for database operation processing with randomization

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.

Techniques for improving the performance of complex queries

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.

Remote nested join between primary access module processors (AMPs)

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.

Techniques for updating join indexes

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.

Techniques for three-step join processing on column partitioned tables

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.

Techniques for join processing on column partitioned tables

A method, computer program, and database system are disclosed for processing a database query that includes one or more expressions. The method includes resolving columns in one or more of the expressions. Expression optimization is performed on one or more of the expressions. Afterward, further query optimization is performed.

Performing SQL query optimization by simplifying sub-expressions

Techniques for independent column detection in selectivity estimations are provided. Values for database statistics are sampled and two independent tests are performed against the values for a first column and a second column. When each test determines that the first and second columns are independent, the first and second columns are said to be independent columns for selectivity estimation.

Independent column detection in selectivity estimation

A method for processing a trigger are described. The trigger is associated with a subject table in a relational database and defines a triggering statement and one or more triggered actions. The method includes determining that a triggering statement of a trigger will execute on a subject table row of a subject table and requesting a transition table in response to determining that the triggering statement will execute. The transition table includes a transition table row. The transition table row includes a first value associated with the subject table row and a second value associated with the subject table row. The method further includes reading the transition table row from the transition table and identifying a processing unit to receive the transition table row and a triggered action of the trigger. The method also includes transmitting the transition table row and the triggered action to the identified processing unit to be processed.

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Rancho Palos Verdes, CA, United States of America

Grace Kwan-On Au