Abstract: A database query to be run against a database is received by a processor. The query includes a query predicate. The query predicate includes a condition. The condition applies to a single database table. The condition is parsed to create an input vector. The input vector is submitted to a neural network. The neural network is trained to calculate the selectivity, a number of unique values (NUV) of results of applying predicates to the single database table, and a high mode frequency (HMF) of results of applying predicates to the single database table. The neural network determines the selectivity of the query predicate, an NUV for each column in the result of applying the query predicate to the single database table, and an HMF for each column in the result of applying the query predicate to the single database table.

Abstract: A table reconfiguration command is received. A READ lock is placed on the table while original table data for the table is moved from a first network location to a second network location into a temporary table according to a new map for the table. An EXCLUSIVE lock on the table is obtained, a data dictionary is modified with the new map for the table, the original table data at the first network location is deleted, and the temporary table is renamed to be the table at the second network location; then, the EXCLUSIVE lock is released.

Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER), so that the optimizer learns from previous versions of the queries to improve current and subsequent versions of the queries. The QER stores planning and execution information for QEs from the previous versions of the queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous versions of the queries. The optimizer searches the QER for QEs from the query execution plans, and uses information from the QEs stored in the QER when optimizing the current and subsequent versions of the queries. The optimizer may also reuses results from the QEs stored in the QER.

Abstract: An apparatus, method and computer program product for physical database design and tuning in relational database management systems. A relational database management system executes in a computer system, wherein the relational database management system manages a relational database comprised of one or more tables storing data. A Deep Reinforcement Learning based feedback loop process also executes in the computer system for recommending one or more tuning actions for the physical database design and tuning of the relational database management system, wherein the Deep Reinforcement Learning based feedback loop process uses a neural network framework to select the tuning actions based on one or more query workloads performed by the relational database management system.

Abstract: 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.

Abstract: A method is provided for predicting workload group metrics of a workload management system of a database system. The method comprises predicting a future workload group metric for a plurality of workload groups based upon historical user-load patterns. Each workload group has a priority that is different from priority of other workload groups.

Abstract: Improved techniques for performing Spatial Joins multi-processing computing systems and environments are disclosed. One or more intersection of bounds (or limits) of data sets is determined as a join bounding space. The join bounding space is in a space (Global space or Global universe) where a spatial join between (or for) the data can be performed. The determined join bounding space can be partitioned into sub-partitions of the join bounding space. The sub-partitions of the join bounding space can assigned respectively to multiple processing unit for processing in parallel in. In addition, distribution cost information associated with the cost of distribution of the datasets (and/or their components) to the processing units of a multi-processing system can be provided and/or used to effectively distribute and/or redistribute processing of the Spatial Join between the processing units of a multi-processing system.

Abstract: An apparatus is provided for facilitating a local database system to execute a query function at a foreign database system. The apparatus comprises a processor of the local database system and a storage device communicatively coupled with the processor.

Abstract: A determination is made that a database system is resource bound resulting in a resource bound condition. Signals for the resources being bound in the database system are identified. Events associated with the signals are extracted. Events are correlated temporally to identify a time interval for which an arrival rate meter (ARM) is helpful. Database system segments are selected that effect key performance indicators associated with the identified time interval. Parameters for the selected database system segments to be deferred by the database system are estimated. The estimated parameters are incorporated into an arrival rate meter (ARM). The ARM is put into effect.

Abstract: A system may include a storage device configured to store a plurality of database tables. The system may further include a processor in communication with the storage device. The processor may receive a request to transmit a database table from the plurality of database tables. The database table may have a plurality of rows. The processor may determine if contents of each column row of each row of the database table are eligible to be compressed. For each column row that contains eligible contents, the processor may generate compressed data representative of the contents of a respective column row. The processor may remove the contents of the respective column row from the associated row. The processor may transmit the compressed data and the database table without content of the column rows represented by the compressed data. A method and computer-readable medium may also be implemented.

Abstract: A database table is defined as a load isolated table (LDI table) that supports a current table version number, multiple row versions, and special load protocols. An isolated loading statement on the LDI table is identified in a Data Manipulation Language (DML), causing the table to be placed in an isolated loading state associated with an incremented new value of the table's current version number. A designated group of DML operations in one or more sessions are then used to load/modify rows in the table, and affected rows are marked with the uncommitted new version. Read-committed queries supported for LDIs ignore the uncommitted rows during an isolated load, seeing only previously committed versions of the rows. When an end isolated loading on the table is identified, the table's version is updated to the new incremented value, causing the newly loaded/modified rows to be recognized as committed by subsequent read-committed queries.

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: Database values and their associated indicators can be arranged in multiple “buckets.” Adjacent buckets can be combined into a single bucket successively based one or more criteria associated with the indicators to effectively reduce the number of buckets until a desired number is reached.

Abstract: Database processing engines of a single cluster are configured such that each engine is a primary engine and a dedicated fallback engine to one other engine of the cluster. In an embodiment, the cluster includes more than two processing engines.

Abstract: The execution of the one or more database queries can be optimized by using a multi-platform cost model that take into account the respective cost of execution in each one of the multiple execution platforms. The respective costs of the execution can, for example, include cost associated with one or more operators for executing the one or more database queries in each one of the multiple execution platforms and/or the costs of exchanging data between the multiple execution platforms.

Abstract: As an abstract representation, a set of equivalent logical structures representative of multiple execution plans for execution of a database query can be used to optimize a database query. A logical structure can include one or more logical operators each representing multiple physical operators for executing the database query. Group and Operator Rules can be applied as rules to the set of equivalent logical structures to obtain additional equivalent logical structures and logical operator until no additional logical operators can be obtained. A set of possible implementation plans for the total number of the obtained logical operators can be obtained, for example, based on physical and/or implementation context.

Abstract: A self-organizing list machine is provided for reordering items of a list in a manner that achieves both a constant and minimum time complexity. The self-organizing list machine comprises an array of symbol index generators, wherein (i) each symbol index generator has a unique location in the array between zero and N?1 inclusive, and (ii) N is the total number of unique symbols in a symbol alphabet.

Abstract: A data-warehousing system allows various areas of an enterprise to view data at varying levels of data freshness. The system acquires data that represents an event in the life of a business enterprise, such as a transaction between the enterprise and one of its customers, and loads this data into a database table. The system then makes the data available for retrieval from the table and stores information indicating when the data was made available for retrieval. In some embodiments, the system also acquires data that is related to and more current than the data representing the event and stores the more current data in the database. The system then stores information indicating when the more current data was stored in the database. Such a data warehouse allows decision-makers in the business to see some information (e.g., customer transaction or account data) up-to-the-moment and other information as it stood at some specific point-in-time, such as at the end of the previous month.

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 in-memory hash join processing. This method utilizes bulk processing within the hash join steps, such as performing bulk reads of hash values from tables to be joined, and performing bulk probes of hash values in tables to be joined, thereby providing more efficient utilization of memory bandwidth and CPU throughput, reducing memory accesses in the execution path, and reducing CPU cycles per instruction. Data movement is reduced by reducing load-stores to memory and by performing more operations in CPU cache.