Teradata Us, Inc.

Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Kashif Abdullah Siddiqui

Awny Kayed Al-Omari

Robert Matthew Wehrmeister

Mohammed Yassin Al-Kateb

QiFan Chen

Gregory S Battas

Michael James Hanlon

Mahbub Hasan

Mohamed Yassin Eltabakh

B Anantha Subramanian

Hansjorg Zeller

Sanjay Nair

Subbarao Kakarlamudi

Zbigniew Omanski

Peter Friedenbach

Sergei Perfilov

Christina Pavlopoulou

Robert M Wehrmeister

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

Execution of a query invoking an analytical function (AF) is optimized. The query includes a join operation between an AF table and an AuxiliaryTable. A determination is made that the AF includes a plurality of AF properties. Query-level properties about the query are inferred. A determination is made to change an order of the join operation from the plurality of AF properties and query-level properties.

Optimizing the execution order between analytical functions and joins in SQL queries

The execution of the one or more database queries can be optimized by using a multi-platform cost model that take into account the respective cost of execution in each one of the multiple execution platforms. The respective costs of the execution can, for example, include cost associated with one or more operators for executing the one or more database queries in each one of the multiple execution platforms and/or the costs of exchanging data between the multiple execution platforms.

Multi-platform optimization

As an abstract representation, a set of equivalent logical structures representative of multiple execution plans for execution of a database query can be used to optimize a database query. A logical structure can include one or more logical operators each representing multiple physical operators for executing the database query. Group and Operator Rules can be applied as rules to the set of equivalent logical structures to obtain additional equivalent logical structures and logical operator until no additional logical operators can be obtained. A set of possible implementation plans for the total number of the obtained logical operators can be obtained, for example, based on physical and/or implementation context. An optimization request can be effectively propagated through an implantation plan in a top-down manner, for example, recursively for each child of physical operators, where only new contexts are optimized, in order to generate an optimized structure, for example, in consideration of, implementation details, costs, physical properties, etc. One of the optimized structures can be selected as an optimal plan.

Optimization of database queries for database systems and environments

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.

Data skew insensitive parallel join scheme

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.

Optimizing a query

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.

Allocation of resources for concurrent query execution via adaptive segmentation

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.

Growing community of inventors

Round Rock, TX, United States of America

Kashif Abdullah Siddiqui