Teradata Us, Inc.

Mahbub Hasan

Awny Kayed Al-Omari

Mohammed Yassin Al-Kateb

Robert Matthew Wehrmeister

Kashif Abdullah Siddiqui

Mohamed Yassin Eltabakh

B Anantha Subramanian

Sanjay Nair

Mohamed Ahmed Yassin Eltabakh

Allen N Diaz

Wellington Marcos Cabrera Arevalo

Christina Pavlopoulou

Kassem Awada

A relational database management system (RDBMS) accepts a workload comprised of one or more queries against a relational database. The RDBMS evolves a default cost profile into a plurality of cost profiles using fixed or dynamic evolution, wherein each of the cost profiles captures one or more cost parameters for the workload. The cost profiles are represented by a multi-dimensional matrix that has one or more dimensions, and each of the dimensions represents one of the cost parameters. The RDBMS dynamically determines which of the cost profiles is an optimal cost profile for the workload by mapping the cost profiles to the workload using a random walk scoring algorithm or a biased walk scoring algorithm that searches the multi-dimensional matrix to identify the optimal cost profile. The RDBMS selects and performs one or more query execution plans for the workload based on the optimal cost profile for the workload.

Dynamically learning optimal cost profiles for heterogenous workloads

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

A relational database management system (RDBMS) optimizes limit queries over analytical functions, wherein the limit queries include an output clause comprising a LIMIT, TOP and SAMPLE clause with an expression specifying a limit that is a number K or a percentage &#x3b1; %. The optimizations of the limit queries include: (1) static compile-time optimizations, and (2) dynamic run-time optimizations, based on semantic properties of 'granularity' and 'input-to-output cardinality' for the analytical functions.

Optimizing limit queries over analytical functions

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

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Mahbub Hasan