Teradata Us, Inc.

Sanjay Nair

Mohammed Yassin Al-Kateb

Awny Kayed Al-Omari

Grace Kwan-On Au

Paul Laurence Sinclair

Mohamed Ahmed Yassin Eltabakh

Grace K Au

Mohammed Hussien Al-Kateb

Mohamed Yassin Eltabakh

Ming Zhang

Douglas Paul Brown

Louis Martin Burger

Rama Krishna Korlapati

Frank Roderic Vandervort

Kuorong Chiang

Robert Matthew Wehrmeister

Rui Zhang

Kashif Abdullah Siddiqui

Mahbub Hasan

Wellington Marcos Cabrera Arevalo

Bibhu Ranjan Priyadarshi

Lu Ma

B Anantha Subramanian

Yi Xia

Ramakrishna Korlapati

David O Craig

Anantha B Subramanian

Emiran Curtmola

Mamatha Govind Rao

Conrad Tang

Anandh Ravi Kumar

Sandeep Singh Sandha

A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

Correlation-driven query optimization for cloud-based stores

An apparatus, method and computer program product for neural network training over very large distributed datasets, wherein a relational database management system (RDBMS) is executed in a computer system comprised of a plurality of compute units, and the RDBMS manages a relational database comprised of one or more tables storing data. One or more local neural network models are trained in the compute units using the data stored locally on the compute units. At least one global neural network model is generated in the compute units by aggregating the local neural network models after the local neural network models are trained.

Methods and techniques for deep learning at scale over very large distributed datasets

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) storing planning and execution information for QEs from previous queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous queries. Additionally, dynamic join indexes representing QE results are created for high-value QEs selected from the QER and maintained within a DJI repository. During query plan creation for a current or subsequent query, the optimizer searches the QER and DJI repository for DJIs created for high-value QEs corresponding to QEs contained in the current or subsequent query. DJIs corresponding to the matching QEs are used in the query planning phase to rewrite the current or subsequent user query so that stored QE results are used to answer QEs contained in the current or subsequent query.

Query expression result caching using dynamic join index

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

An apparatus, method and computer program product for physical database design and tuning in relational database management systems. A relational database management system executes in a computer system, wherein the relational database management system manages a relational database comprised of one or more tables storing data. A Deep Reinforcement Learning based feedback loop process also executes in the computer system for recommending one or more tuning actions for the physical database design and tuning of the relational database management system, wherein the Deep Reinforcement Learning based feedback loop process uses a neural network framework to select the tuning actions based on one or more query workloads performed by the relational database management system.

Physical database design and tuning with deep reinforcement learning

A database system receives a request from a user. The request invokes a data set function (DSF) and uses a property to be provided by the DSF. The database system determines that a function descriptor is available for the DSF. The function descriptor is expressed as markup language instructions. The function descriptor defines the property of the DSF. The database system uses the function descriptor to define a property for the DSF.

Enabling cross-platform query optimization via expressive markup language

In some examples, a system learns properties of an analytical function based on information of queries invoking the analytical function that have been previously executed, creates a function descriptor for the analytical function based on the learning, and provides the function descriptor for use by an optimizer in generating an execution plan for a received database query that includes the analytical function.

Property learning for analytical functions

A system and method for loading data into column-partitioned database tables. The system and method incorporate a mechanism for buffering data extracted from the rows of a source table in column-oriented fashion within an in-buffer memory, enabling an efficient bulk-write of large arrays of values from the buffer into column-partitioned database tables. The system and method may also include optimizations for grouping columns according to data types and altering the order in which columns are inserted into the database tables.

Buffered data-loading in column-partitioned database tables

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 system may include a storage device to store a plurality of database tables. At least a portion of the database tables may be column-partitioned. The database system may also include a processor in communication with the storage device and a row-column subsystem executable by the processor to receive a request to locate a row of a column-partitioned database table. The row in the request may be used to provide a response to a query. The row-column subsystem may be further executable to determine if referenced column values of the requested row are stored in a cache associated with the row-column subsystem. The row-column subsystem may be further executable to retrieve the referenced column values of the row in the request from the cache in response to the determination that the column values are in the cache. The row-column subsystem may be further executable to provide the referenced column values for evaluation with respect to query conditions of the query. A method and computer-readable medium may also implement the row-column subsystem.

Processing column-partitioned data for row-based operations in a database system

Deriving join cost demographics for a binary join, i.e., a join between two relations each having a join column, includes using a correlation factor related to the ratio of unique join column values stored in each of a plurality of units of storage to the total number of unique values in the join column.

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El Segundo, CA, United States of America

Sanjay Nair