Abstract: A system and method for compressing data. The system and method employ a static compression dictionary, or look-up table, containing a predetermined number of uncompressed data values and corresponding compressed code values for replacing uncompressed data values with their corresponding compressed code values to reduce data storage requirements. The system and method further employ a dynamic compression dictionary, to which uncompressed data values and corresponding compressed code values are added as required to compress uncompressed data values not contained within the static compression dictionary.

Abstract: Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Abstract: The number of iterations or self joins required to execute a recursive database query can be estimated. It will be appreciated that this estimation can be used to plan the execution of the recursive query and can be made in various ways and for various applications. By way of example, an estimated number of iterations or self joins required to execute a recursive database query (e.g., 12) can be used as a basis to determine or plan an optimal execution plan. For example, given an estimated twelve (12) iterations, an execution plan can be determined for executing at least the first three (3) iterations or for executing every three (3) iterations, whereas for an estimated twenty (21) iterations required to complete a recursive database query, an execution plan can be determined for the first five (5) or six (6) iterations, and so on.

Abstract: Based on one or more predicated results (e.g., estimations of the actual results) associated with one or more iterations and/or sub-queries of an iterative or recursive database query, it can be determined whether to use an execution plan (e.g., a current execution plan used to execute one or more iterations) to execute one or more other iterations and/or sub-queries of the iterative or recursive database query. Consequently, use of the execution plan to execute the one or more other iterations and/or sub-queries of the iterative or recursive database query can be disallowed. As a result, the iterative or recursive database query can be executed more efficiently. The determination of whether to use an execution plan can, for example, be performed by determining whether an actual result obtained by executing one or more iterations differs or diverges more than a determined value from an estimated result of the same iteration(s).

Abstract: Demographics for data are determined. A compression ratio (“CR”) is determined for each of a plurality of compression techniques. CR is a size of the data before compression divided into a predicted size of the data after compression. The predicted size of the data after compression is determined as a function of the determined demographics. An access efficiency of each of the compression techniques is determined as a function of the determined demographics. The compression techniques are ranked by CR and access efficiency. A compression technique is selected based on the ranking. The data is compressed using the selected compression technique. The compressed data is stored.

Abstract: Embodiments of the present invention provide a hardware accelerator that assists a host database system in processing its queries. The hardware accelerator comprises special purpose processing elements that are capable of receiving database query/operation tasks in the form of machine code database instructions, execute them in hardware without software, and return the query/operation result back to the host system.

Abstract: A database system may implement resource management for redistribution of stored database information in additional database resources. Resource management may include detection of the presence of additional database resources, such as storage and processing resources. Based on conditions associated with the additional resources and database information selected for potential redistribution, resource management may include a determination as to whether or not redistribution of the database information is to be performed. Resource management may include a cost-benefit analysis to determine if redistribution of database information is to be performed. The cost-benefit analysis may be based on factors related to both the additional resources and the database information being considered for redistribution.

Abstract: Techniques for guided access to an external distributed file system (DFS) from a database management system (DBMS) are provided. A user access a graphical user interface (GUI) to interactively define a search on an external DFS. The search and any filtering conditions are processed on the external DFS and results are imported to the DBMS and mapped to a table in the DBMS for manipulation and access by the user within the DBMS.

Abstract: A system, method, and computer-readable medium that facilitate the optimization of frequently executed queries via automated index creation are provided. Standard optimizer strategies for dynamic index creation are utilized with query frequency information taken from a system's request cache. The described mechanisms provide a process of determining what indexes would be beneficial to query performance based on decisions and information from the optimizer that is ultimately responsible for the choice of index usage. Further, the plan cache is utilized in a manner that provides reliable information about the historical and anticipated frequency of specific queries.

Abstract: 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.

Abstract: A database system may process multiple column-oriented tasks in parallel for a database being stored according to a row-partitioning protocol. The database system may determine when the query should process the column-oriented task serially or in parallel. For parallel processing, the database system may generate processing tasks for each unique column-oriented task contained in the query used to retrieve column data and to process the column data according to the column-oriented tasks requested. A method of operating the database system may determine that multiple column-oriented tasks included in a query are to be processed in parallel. The method may further include generating a processing task for each unique column-oriented task included in the query. The method may further include performing the column-oriented tasks in parallel based on the processing threads. The method may implement various considerations in determining to process to the column-oriented tasks in parallel.

Abstract: A virtual regulator implemented to provide closed-loop system management (CLSM) functionality to a database system and without a need to substantially adapt a given CLSM technology to account for specific nuances and requirements of an operating system. For example, the regulator may be implemented with either or both of process model operating systems and thread model operating systems. Common examples of these include UNIX, LINUX, MP-RAS, MS WINDOWS or WINDOWS NT.

Abstract: A method, system, apparatus, and article of manufacture provide the ability to maintain multiple versions of structured views of data in a computer system. A relational database management system (RDBMS) is executed that stores master data in the computer system in master RDBMS tables. The master data is hierarchical in nature and hierarchy metadata for the master data is stored in the RDBMS tables. As part of a process and framework, a series of business rules and process workflows are maintained to manage the master data. Version tables are created in the RDBMS that correspond to each of the master RDBMS tables. Each of the version tables includes an attribute denoting version information. Versions of the master data are defined by replicating the master data and hierarchy metadata into the corresponding version tables. The version tables are used to graphically visualize, manage, and manipulate the versions of the master data.

Abstract: A method of processing a transaction request at a database load balancer. The method comprises receiving the transaction request, where the transaction request is comprised of one or more operations; analyzing the transaction request to determine the one or more operations; associating one or more database locks with each of the one or more operations; analyzing one or more of the database locks to determine the one or more sequence numbers associated with each of the one or more operations; and transmitting the one or more operations with the associated database locks and the sequence numbers to one or more databases servers accessible to the database load balancer.

Abstract: A system and method for managing soft reserve space within a database system including fast solid state storage devices (SSDs) and traditional hard drive storage device (HDDs). The system and method monitors actual spool use within the database system and dynamically modifies the soft-reserve space as a percentage of total storage space in response to changes in spool use.

Abstract: An optimization technique is provided that optimizes data access by mapping each table, or row and/or column in a table, to a particular query, depending on whether the data in said table, row or column is ‘hot’ or ‘cold’ data. In one aspect, the invention features a method for optimizing the access time of an SQL query to a database including data. The method includes receiving, for each query type, a data frequency access measure and associating each query type with a processing rule set, according to the received data frequency access measure.

Abstract: Table rows are mapped to corresponding characters, where the mapping produces a collection of the characters. A query is received to identify a pattern in the table rows. The collection of the characters is accessed to process the query.

Abstract: Techniques for time-bound batch status rollup for logged events are provided. A status for each action defined in a database log is resolved during a configured interval of time. The statuses for the actions are aggregated at the end of the interval of time and then joined back into the log.

Abstract: The subject mater herein relates to requesting data from a database and, more particularly, to database query table substitution. Various embodiments provide systems, methods, and software to evaluate requests for data from a database and make table substitutions in the request to reduce a number of tables queried. Some embodiments include receiving a data request including data from two or more database table columns and building a list of all database tables specified in the data request. Some such embodiments further include, for each column in the data request, identifying each table from the list of all tables within which the column is present and identifying a least number of one or more tables that together include all of the columns of the data request. The data request may then be modified by changing the table of one or more of the columns in the received data requests.

Abstract: A system and method for allocating storage devices within a database system including fast solid state storage devices (SSDs) and traditional hard drive storage device (HDDs). The method repetitively collects temperature and queue depth data for each storage device, and determines from the collected data points a service time for each storage device. The method then selects, in response to an allocation request, a storage device having a minimum total wait time determined from the current queue depth and service time for each storage device.