Teradata Us, Inc.

Mohammed Yassin Al-Kateb

Awny Kayed Al-Omari

Grace Kwan-On Au

Sanjay Nair

Mohamed Ahmed Yassin Eltabakh

Mahbub Hasan

Mohamed Yassin Eltabakh

Paul Laurence Sinclair

Rama Krishna Korlapati

Robert Matthew Wehrmeister

Kashif Abdullah Siddiqui

B Anantha Subramanian

Douglas Paul Brown

Eugene Szedenits, Jr

Sanjib Kumar Mishra

Lu Ma

Tri Minh Tran

Allen N Diaz

Wellington Marcos Cabrera Arevalo

Yi Xia

Sanjay Sukumaran

Bin Cao

Christina Pavlopoulou

Chengyang Zhang

Kassem Awada

Chengzhu Zhang

In some examples, the database system maintains metadata for a plurality of data objects, the metadata containing ranges of values of an attribute for the plurality of data objects, where the ranges of values of the attribute comprise a respective range of values of the attribute for each corresponding data object of the plurality of data objects. The database system generates a data structure tracking quantities of ranges of values of the attribute that have a specified relationship with respect to corresponding different values of the attribute. The database system receives a database query comprising a predicate specifying a condition on a given value of the attribute, and computes, for the database query, a selectivity of filtering based on the metadata, the selectivity computed based on the data structure.

Selectivity computation for filtering based on metadata

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

In some examples, a system receives function descriptors for different types of functions to be used when processing database queries, each function descriptor of the function descriptors comprising information relating to a respective function of the different types of functions. The system computes, based on a first function descriptor for a first function of the different types of functions, an estimate of a runtime metric associated with execution of the first function for processing a database query.

Runtime metric estimations for functions

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

Techniques for improving complex database queries are provided. A determination is made whether to adopt a static or dynamic query execution plan for a received database query based on metrics. When a dynamic query execution plan is adopted, the database query is separated into query fragments. A plan fragment is generated for each query fragment and executed to generate feedback for the plan fragment. The feedback from the execution of each plan fragment is used to initiate query rewrite rules to simplify the corresponding query fragments. The rewritten query fragments are combined to generate the dynamic query plan.

Incremental simplification and optimization of complex queries using dynamic result feedback

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

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 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

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 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 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.

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Worchester, MA, United States of America

Mohammed Yassin Al-Kateb