Teradata Us, Inc.

Ncr Corporation

Teradat Us, Inc.

Thomas Patrick Julien

Louis Martin Burger

Douglas Paul Brown

Frank Roderic Vandervort

Anita Richards

Jeffrey S Shelton

Paul Laurence Sinclair

Jeetendra Chaware

Arthur Vargas Lopes

John Lawrence Perkins

John Jeffrey Mayrack

John Chapra

Sriram K Somayajula

A data request that references an external data environment object (foreign object) is identified. A Data Manipulation Language (DML) statement for accessing the object is traversed in a defined order to identify foreign servers having the foreign object. Connections are attempted to foreign servers in the defined order and a selection to one of the foreign servers is made based on server and/or data conditions. The selected server is used for the request to process the portion of the request that includes the foreign object. In an embodiment and during execution of data request, the server and/or the data conditions can be dynamically overridden to change selection criteria for the selected server.

Automatic query routing

Capacity of a database system and/or a computing system that includes a database can be effectively changed from a current computing capacity to another computing capacity. This can be achieved by causing usage capacity of at least one of resource to be changed when the database is active. By way of example, capacity of a database system can be regulated by delaying the execution of the database work based on a target capacity. As a result, database work can take relatively longer to complete when the capacity of a database is effectively regulated to be below its full capacity. In effect, a portion of available capacity (excess capacity) can be made inaccessible to the database. However, excess capacity can be used to manage various services of the database in accordance with one or more service criteria (e.g., Service Level Goals or Agreements).

Regulating capacity and managing services of computing environments and systems that include a database

A database system monitors a metric for a Workload Definition (WD) and adjusts a metric throttle for the WD to a new metric throttle level C, computed as the average of a theoretical metric level Cthat would drive a metric to a target T and a theoretical metric level Cthat would drive a rolling average of the metric to the target T.

Managing workloads in a database system using a plurality of health and demand metrics

Excess capacity (or excess resources) can be used in a capacity controlled environment to effectively manage data storage of a database. In this context, excess capacity can, for example, be controlled used as needed, as configured and/or on a temporary basis. Management of data storage in a database can, for example, include operations associated with moving or relocating data of the database. Relocation of data can, for example, be performed to relocate data deemed to be 'hot' data to a location that can provide a faster access time in order to improve the overall efficiency of the database system.

Data storage management by using excess capacity in a capacity controlled environment

Excess capacity of a database system can be used for learning activities in a controlled computing environment. In this context, excess capacity of a database system can be used as needed and/or on a temporary basis. Furthermore, learning activities can be performed without requiring the use of the capacity that is configured or has been allotted for various other database operations, including those deemed, especially by the users of database system, to serve a main function or a more important purpose. As a result, learning can be performed without adversely affecting other operations deemed to be more critical, especially by the users of databases. Learning activities associated with a database environment can, for example, include learning operations directed to optimization of database queries, for example, by using a basic feedback or an expanded or active learning.

Learning by using excess capacity in a capacity controlled environment

Excess capacity available to a database system in a capacity controlled environment can be effectively managed. In particular, excess capacity that is not made available for normal operations of a database system can be used to manage errors, especially situations that may hinder expected performance of the database system. In addition, excess capacity can be used to optimize or further optimize database queries, especially those that meet a criterion (e.g., not fully optimize, not optimized as expected).

Managing excess capacity of database systems in a capacity controlled computing environment

A system, method, and computer-readable medium for grouping of database object access counts per processing module in a parallel processing system. Each processing module manages an intermediate cache allocated thereto that stores database object access counts local to the respective processing module. The processing modules are configured in a hierarchical configuration. The intermediate caches managed by the processing modules at the lowest level of the configuration are forwarded to a parent processing module and merged with the intermediate cache allocated to the parent processing module. A root processing module merges the intermediate caches received from one or more parent processing modules with the root processing module's intermediate cache to generate a final cache. An objects access count data structure is then generated by the root processing module.

System, method, and computer-readable medium for grouping database level object access counts per processing module in a parallel processing system

A computer-implemented method, apparatus and article of manufacture for optimizing a database query. Resource usage of one or more resources of the computer system are monitored. A condition code representative of the monitored resource usage is stored. A cost model is modified using the stored condition code. A query execution plan is generated for the database query using the modified cost model. The query execution plan is then executed in order to retrieve data from a database stored on the computer system, wherein the retrieved data is presented to a user for subsequent use and analysis.

Actively managing resource bottlenecks in a database system

Requests to be executed in the database system are received, where a plurality of the requests are provided in a queue for later execution. Priority indicators are calculated for assignment to corresponding ones of the plurality of requests in the queue, where the priority indicators are calculated based on delay times and predefined priority levels of the requests. The requests in the queue are executed in order according to the calculated priority indicators.

Calculating priority indicators for requests in a queue

A computer-implemented method, apparatus and article of manufacture for optimizing a database query. A query execution plan is generated for the database query, which may be stored in an in-line query capture database. Execution of the query execution plan may be monitored in order to generate performance information, which also may be stored in the in-line query capture database. The data stored in in-line query capture database may be used during the initial generation of query execution plans, as well as for the re-optimization of a query execution plan, during the execution of the query execution plan.

Parallel, in-line, query capture database for real-time logging, monitoring and optimizer feedback

A computer-implemented method, apparatus and article of manufacture for optimizing a database query. A query execution plan for the database query is generated using estimated cost information; one or more steps of the query execution plan are executed to retrieve data from a database stored on the computer system. Actual cost information is generated for each of the executed steps, and the estimated cost information is re-calculated using the actual cost information. One or more resource allocation rules defined on one or more steps of the query execution plan are executed, based on the estimated cost information, wherein the resource allocation rules include one or more defined actions. The estimated cost information may be re-calculated using the actual cost information when confidence in the estimated cost information is low, but the estimated cost information may not be re-calculated when confidence in the estimated cost information is high. In addition, the estimated cost information may be re-calculated using the actual cost information, only when the step has one or more resource allocation rules defined thereon.

Accurate and timely enforcement of system resource allocation rules

A filter receives a request to perform an operation in a database system, and the filter receives a resource estimate relating to the request from an optimizer. The filter determines whether the resource estimate exceeds an available resource of the database system, and if so, the filter rejects the request prior to execution of the request.

Rejecting a request in a database system

Excess capacity available to a database system in a capacity controlled environment can be effectively managed for maintenance and/or tuning activities. In this context, excess capacity can be used as needed and/or on a temporary basis. Furthermore, maintenance and/or tuning activities can be performed without requiring the use of the configured or allotted capacity. As a result, these operations can be performed without adversely affecting other operations which may be deemed more critical by the users of databases. System maintenance and tuning (or fine tuning) of a database environment can, for example, include operations associated with 'Physical Tuning,' and 'Index Tuning'. Some exemplary operations include collecting Statistics, analyzing a database workload, sampling, analyzing an execution plan, creating indexes.

System maintenance and tuning of databases by using excess capacity in capacity controlled environment

An improved workload management system and method for a database system. The improved workload management system evaluates certain workload management rules dynamically at key points during database query execution rather than relying on enforcement prior to query activation. In the case of product join operations, the workload management system evaluates the potential impact on system performance by the product join operation after input tables have been populated, but prior to the execution of the join operation. Delaying workload management enforcement to this point ensures that the sizes of inputs and outputs are reliably known and improves system performance impact estimates at a point early enough to prevent any serious performance degradation from an inefficient product join.

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San Marcos, CA, United States of America

Thomas Patrick Julien