Abstract: Data portions of a database can be grouped and ranked in order of priory for reassignment from one or more maps to another one or more maps. It should be noted that a first map can assign the data portions to a first configuration of processors for processing the data portions, and a second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The data portions are reassigned in groups during an available time (window) for reassignment by taking the first one of the groups can be reassigned (“moved”) in the available, then the second one in the available reaming time, and so on, until no group of data portions can be moved in the remaining time or all of them have been moved.

Abstract: Storage devices and components, including memory components (e.g., non-volatile memory) can be trained by executable code that facilitates and/or performs reads and/or write requests to one or more storage sub-modules of a storage component (e.g., memory configured on a memory channel) made up of multiple storage components (e.g., DIMMs). The executable code can also train multiple storage components at the same time and/or in parallel.

Abstract: An order (or priority) for reassigning data portions of a database from a first map to a second map can be determined. It should be noted that the first map assigns the data portions to a first configuration of processors for processing the data portions, and the second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The order can be determined by grouping the data in multiple groups and ordering the groups in accordance with a determined priority for reassignment In doing so, one or more relationships between the data portions and/or cost of use for each one the groups can be considered.

Abstract: The number of processors that store data can be limited for some data in order to improve efficiency in database system that uses multiple processing units to process data. For example, a map can be provided for one or more sparse database tables (or tables) that are relatively small in view of the number of processors that are available. The map can effectively assign distinct portions of the data, e.g., rows of a database, for processing only to a limited number of processers (or processing units). The database system can use the map to process data accordingly.

Abstract: A database-management system (DBMS) initiates execution of an incoming database query according to an initial query-execution plan that identifies an expected path for execution. Then, at some point after execution of the query has begun, the DBMS concludes that execution has not proceeded along the expected path and, in response, chooses an alternative query-execution plan for continued execution of the query.

Abstract: 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 Cn, computed as the average of a theoretical metric level Cc that would drive a metric to a target T and a theoretical metric level Cr that would drive a rolling average of the metric to the target T.

Abstract: A database system may include a storage device to store a plurality of database tables. At least a portion of the database tables may be column-partitioned. The database system may also include a processor in communication with the storage device and a row-column subsystem executable by the processor to receive a request to locate a row of a column-partitioned database table. The row in the request may be used to provide a response to a query. The row-column subsystem may be further executable to determine if referenced column values of the requested row are stored in a cache associated with the row-column subsystem. The row-column subsystem may be further executable to retrieve the referenced column values of the row in the request from the cache in response to the determination that the column values are in the cache. The row-column subsystem may be further executable to provide the referenced column values for evaluation with respect to query conditions of the query.

Abstract: A database system may include a storage device configured to store a plurality of database tables. The database system may further include a processor in communication with the storage device. The processor may determine a first sampling percentage to be used on a column of a database table. The first sampling percentage may be based on a respective frequency of each column value in the column. The processor may determine a second sampling percentage to be used on the column in generation of the plan to respond to the database query. The second sampling percentage may be based on size of the database table. The processor may select the maximum of the first sampling percentage and the second sampling percentage. The selected sampling percentage may be used to collect statistics on the column. The collected statistics may be used to generate at least one database query response plan associated with the column. A method and computer-readable medium may also be implemented.

Abstract: A method for spooling data for use in joining a small table with a large table in a relational database system. The method analyzes a join condition for combining records from the small and large tables, selects qualified rows from the large table, and writes the qualified rows to a spool file. The spool file includes a first partition containing hash values of all bind terms for the join condition; a second partition including a join column with a best selective bind term; and at least one additional partition including additional join columns used in bind terms. The partitions are grouped together within a container row in the spool file, and multiple container rows are written together within a super-container row in the spool file.

Abstract: A method for performing row qualification in database table retrieval and join operations. This method, referred to as bulk qualification, evaluates conditions on multiple rows in a database table at the same time, providing more efficient utilization of memory bandwidth and CPU throughput.

Abstract: A combination of non-persistent-based and persistent-based schemes are used to effectively manage volatile storages which are conventionally managed solely by using non-persistent schemes (e.g., LRU schemes in cache memory). Generally, the persistent-based schemes can be based on persistent information associated with a non-volatile storage environment (e.g., persistent data temperatures associated with data stored in non-volatile storages). In this context, a persistent-based scheme can, for example, be effectively used in addition or combination with a conventional scheme provided for volatile memory. By way of example, a LRU scheme can be combined with a scheme based on persistent data temperatures in order to more effectively manage cache memory provided to enhance the performance of a system. As another example, a LRU, a LFU aging schemes can be combined with a scheme based on persistent data temperatures.

Abstract: 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.

Abstract: A database request can be processed at least partly based on one or more differences between multiple database systems and/or environments. The differences can, for example, include differences between one or more database capabilities respectively provided by the multiple database systems, differences between the representation of data in the multiple database systems, and differences in the interfaces for accessing the multiple database systems.

Abstract: Methods and systems that implement enhanced user interactions with a grid are described. A method may include generating a grid of cells arranged in a number of rows and columns. Each row may correspond to a data record of a database. The grid may be displayed to a user while identifying one or more cells as editable cells. Input data may be received from the user for each of the editable cells. The input data may be validated using predefined criteria to identify incorrect input data and errors associated with the incorrect input data may be displayed to the user. Additional methods and systems are disclosed.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: Techniques for management of data in multi-storage systems allow managing storage of data in one of the multiple storages (e.g., a HDD) based on the information associated with the storage and/or access of data (e.g., logical hits of data in a cache memory) in another one of the storages (e.g., a cache memory) of the multi-storage system. In one aspect, information associated with storage and/or access of data in a volatile (or non-persistent) storage environment is provided for managing the storage of data in a non-volatile (or persistent) storage environment. By way of example, data access information associated with a volatile storage environment (e.g., logical hits of data in a cache) can be effectively communicated to a non-volatile storage environment, for example, as information that can be indicative of data temperatures. Moreover, the information obtained from a non-volatile storage environment can be persistently stored for and/or in the non-volatile environment (e.g.

Abstract: A system and method for allocating storage devices within a multi-node data storage system. The system maintains a data structure including a generation number indicating an incarnation of the data structure, a highest cylinder ID index value observed to be in use within the data storage system, and a safe index value indicating a lowest cylinder ID index value for use when allocating a new cylinder index. Following receipt of an allocation request, the system assigns a cylinder ID index to the allocation request, the cylinder ID index being greater than the safe index value. The assigned cylinder ID index is compared to the highest cylinder ID index value observed to be in use within said data storage system, and when the cylinder ID index is greater than the highest cylinder ID index value, increments the generation number and highest cylinder ID index value maintained within the data structure.