Teradata Us, Inc.

Teradota Us, Inc.

John Mark Morris

Douglas Paul Brown

P Keith Muller

Criselda Samera Carrillo

Anita Richards

Gary Lee Boggs

Peter Ian Frazier

Donn Harold Holtzman

Paul Andersen

Todd A Walter

Scott E Gnau

Bhashyam Ramesh

Donald Raymond Pederson

Paul Rubio

Ronald Yellin

Gregory Howard Milby

William P Ward

Kuorong Chiang

Timothy Brent Kraus

A function reference for a function is identified in a query. A plurality of processing environments that can provide the function is identified. Function costs for the function to process in the processing environments are obtained. Input data transfer costs are acquired for providing input data identified in the query to each of the functions. A specific one of the functions from a specific processing environment is selected based on the function costs and the input data transfer costs. A query execution plan for executing the query with the specific function is generated. The query execution plan is provided to a database engine for execution.

Cost based function optimization for coupled systems

One or more assignments (e.g., one or more maps) can be used for assigning data to processing units of a database system with multiple processing. In other words, assignments can be provided as a more effective solution for assigning data to the processing units of database systems that can operate with multiple processing units. Generally, an assignment can be used to assign data to the processing units for processing, virtually in any desired manner (e.g., virtually any desired function). By way of example, maps can associate data to containers (e.g., buckets) and associate the containers to processing units of database system with multiple processing units.

Map intelligence for mapping data to multiple processing units of database systems

Data can be stored based on one or more indications and one or more other storage criteria. The indications can effectively indicate or identify a storage level for storing data in a multi-storage system. The indications, however, need not be the only basis for storing the data object in a multi-storage system as one or more other storage criteria can also be considered. As a result, the indication can be used to effectively influence data storage but other storage criteria can be used as well to prevent adverse effects caused by undue influence and to ensure the overall efficiency of the system. Also, the one or more other storage criteria can be evaluated or revaluated on a continual basis.

Data storage management based on indicated storage levels and other criteria for multi-level storage systems

Search spaces for obtaining query execution plans for a query are identified. The search spaces are subdivided into sub-search spaces. Searches are initiated within the sub search spaces and plan costs for competing query execution plans are noted along with search costs associated with continuing to search the sub-search spaces. A decision is made based on the plan costs and search costs for utilizing search resources as to when to terminate the searching and to select the then-existing lowest cost query execution plan as an optimal query execution plan for executing the query.

Query plan searching and optimization

Data or one or more operations can be provided, based on one or more characteristics associated with the data and/or operations, to a designated computing group or pool of computing resources designated for handling the data and/or operations with the particular data characteristic(s). The designated computing group can, for example, be one of multiple computing groups in the same system or device. As such, all of the computing groups can still function together in the same system or device, for example, in parallel. However, each one of the multiple computing groups can, for example, be defined or predefined to include one or more computing resources that are more suitable for storing and/or processing data with one or more data characteristics or handle operations with one or more determined characteristics.

Designated computing groups or pools of resources for storing and processing data based on its characteristics

A data store system may include a storage array comprising a plurality of storage devices configured to store data. The data store may further include a processor array comprising a plurality of processors. The processor array may be in communication with the storage array. The processor array may receive workloads to be performed on the data. The processor may further process the workloads at a processing resource usage level of processing resources that is less than maximum available processing resources. The processing resource usage level may be associated with completion of at least a predetermined number of received workloads in accordance with the level of performance that is less than a total number of received workloads. The processor may further process the workloads at the processing resource level in response to presence of predetermined operating conditions. A method and computer-readable medium may also be implemented.

Performance continuity management

Computing capacity of a computing environment can be managed by controlling it associated processing capacity based on a target (or desired) capacity. In addition, fine-grained control over the processing capacity can be exercised. For example, a computing system can change the processing capacity (e.g., processing rate) of at least one processor operating based on a target capacity. The computing system may also be operable to change the processing capacity based on a measured processing capacity (e.g., a measured average of various processing rates of a processor taken over a period of time when a processor may have been operating at different processing rates over that period). By way of example, the processing rate of a processor can be switched between 1/8 and 2/8 of a maximum processing rate to achieve virtually any effective processing rates between them.

Fine-grained capacity management of computing environments that may support a database

Data can be stored based on one or more indications and one or more other storage criteria. The indications can effectively indicate or identify a storage level for storing data in a multi-storage system. The indications, however, need not be the only basis for storing the data object in a multi-storage system as one or more other storage criteria can also be considered. As a result, the indication can be used to effectively influence data storage but other storage criteria can be used as well to prevent adverse effects caused by undue influence and to ensure the overall efficiency of the system. Also, the one or more other storage criteria can be evaluated or reevaluated on a continual basis.

Data storage management based on indicated storage levels and other criteria for multilevel storage systems

A system, method, and computer-readable medium that facilitate efficient use of cache memory in a massively parallel processing system are provided. A residency time of a data block to be stored in cache memory or a disk drive is estimated. A metric is calculated for the data block as a function of the residency time. The metric may further be calculated as a function of the data block size. One or more data blocks stored in cache memory are evaluated by comparing a respective metric of the one or more data blocks with the metric of the data block to be stored. A determination is then made to either store the data block on the disk drive or flush the one or more data blocks from the cache memory and store the data block in the cache memory. In this manner, the cache memory may be more efficiently utilized by storing smaller data blocks with lesser residency times by flushing larger data blocks with significant residency times from the cache memory. The disclosed cache management mechanisms are effective for many workloads and are adaptable to various database usage scenarios without requiring detailed studies of the particular data demographics and workload.

System, method and computer-readable medium for spool cache management

A system includes a multi-system database management system having a plurality of database systems. An index selection subsystem combines sets of query information from respective ones of the plurality of database systems into a workload. The index selection subsystem then generates candidate indexes from the workload, and selects recommended indexes from the candidate indexes based on one or more criteria.

Index selection in a multi-system database management system

A query is executed on a multi-database system. The multi-database system includes a first system database and a second system database. The query is partially executed on a first system database by generating a spool. It is determined that it is more efficient to store the spool on the second system database than to store the spool on the first system database, and in response the spool is stored on the second system database. The query execution is continued by accessing the spool on the second system database, completing execution of the query to produce a result, and storing the result.

Spool management and checkpointing in a multi-database system

A system and method for managing one or more database systems, wherein the database systems perform database queries to retrieve data stored by the database systems. One or more regulators are used for managing the database systems, wherein the regulators monitor workload priority influenced data temperature in order to allocate resources for the systems. The data temperature is a measure of physical accesses to logical data, and the workload priority is used to further define data temperature, in order to optimize data storage placement and data access decisions.

Workload priority influenced data temperature

A technique for use in performing snap imaging of a plurality of data objects and associated metadata involves placing the plurality of data objects in a consistent state. While the data objects are in a consistent state, a copy is created of the metadata associated with the plurality of data objects. The data objects are then taken out of the consistent state. The temperature value associated with one or more of the data objects is/are compared with a threshold value. A copy is created of at least some of the data objects for which the associated temperature value exceeds the threshold value.

Method of performing snap imaging using data temperature for making anticipatory copies

A join plan creates an intermediate table from two or more database tables within a database. In doing so, at least one projected and compressible column within one of the database tables is identified, where the column contains data values to which the application of a compression data transformation results in a reduction in the size of the data values. The frequency of occurrences of at least some unique values in the compressible column is identified, and then the two or more database tables are joined. The results of the join are then stored in the intermediate table such that the compression data transformation is applied to at least some of the unique values in the compressible column for which the frequency has been identified.

Executing a join plan using data compression

Optimizing the execution of a query in a multi-database system includes identifying a region within a table, the table being referenced in the query. The region is stored on data-storage devices on first and second system databases in the multi-database system. A first access plan for the query is developed, the first access plan comprising accessing the version of the region stored on the first system database. A second access plan for the query is developed, the second access plan comprising accessing the version of the region stored on the second system database. A selection is made between the first access plan and the second access plan to execute the query. The query is executed using the selected access plan to produce a result.

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San Diego, CA, United States of America

John Mark Morris