Abstract: An apparatus, method and computer program product for estimating as-a-Service (aaS) query prices in a relational database management system (RDBMS). An optimizer of the RDBMS inserts an EXPLAIN modifier into a query, wherein the EXPLAIN modifier results in the optimizer generating a summary of a query execution plan for the query that includes one or more cost estimates for the RDBMS to perform the query. A price estimate for the query is then generated based on the cost estimates, wherein the price estimate is generated using one or more configurable pricing formulae. The price estimate is merged into the summary of the query execution plan for the query. Moreover, a price guarantee may be generated for the price estimate, wherein the price guarantee is honored when the query is subsequently invoked for execution by the RDBMS.

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: An apparatus, method and computer program product for estimating as-a-Service (aaS) query prices in a relational database management system (RDBMS). An optimizer of the RDBMS inserts an EXPLAIN modifier into a query, wherein the EXPLAIN modifier results in the optimizer generating a summary of a query execution plan for the query that includes one or more cost estimates for the RDBMS to perform the query. A price estimate for the query is then generated based on the cost estimates, wherein the price estimate is generated using one or more configurable pricing formulae. The price estimate is merged into the summary of the query execution plan for the query. Moreover, a price guarantee may be generated for the price estimate, wherein the price guarantee is honored when the query is subsequently invoked for execution by the RDBMS.

Abstract: A system and method for determining optimal query plans within a distributed processing system. The system includes a query optimizer for receiving a query and generating a query plan for execution by the distributed processing system; a repository including statistics related to prior executed query plans; a plan execution engine for receiving and executing the query plan, collecting statistics corresponding to the query plan, and storing the statistics and optimizer usage metadata corresponding to the query plan and prior executed query plans in the repository. The system further includes a statistics advisory tool for mining the metadata to generate a collection of histogram statistics and storing said histogram statistics in the repository. When generating a query plan, the query optimizer searches the statistics repository for statistics, including histogram statistics, useful to the generation of the current query plan and uses selected statistics from the repository to generate the query plan.

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: 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: Data of a database (e.g., database tables) can be reassigned from a first map to a second map in a database system that uses maps to assign data for processing to multiple processing units of a database system in accordance with one or more distributions schemes. Data portions can be selected in groups and moved in the selected groups in an efficient manner. The selection and/or movement of the data portions can be automated without requiring input for users of database systems.

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: 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).

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: 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: Data of a database (e.g., database tables) can be reassigned from a first map to a second map in a database system that uses maps to assign data for processing to multiple processing units of a database system in accordance with one or more distributions schemes. Data portions can be selected in groups and moved in the selected groups in an efficient manner. The selection and/or movement of the data portions can be automated without requiring input for users of database systems.

Abstract: A system and method for determining optimal query plans within a distributed processing system. The system includes a query optimizer for receiving a query and generating a query plan for execution by the distributed processing system; a repository including statistics related to prior executed query plans; a plan execution engine for receiving and executing the query plan, collecting statistics corresponding to the query plan, and storing the statistics and optimizer usage metadata corresponding to the query plan and prior executed query plans in the repository. The system further includes a statistics advisory tool for mining the metadata to generate a collection of histogram statistics and storing said histogram statistics in the repository. When generating a query plan, the query optimizer searches the statistics repository for statistics, including histogram statistics, useful to the generation of the current query plan and uses selected statistics from the repository to generate the query plan.

Abstract: A temporary computer-stored first-unit intermediate result is generated while executing a first unit of work on a computer-based database management system. The first-unit intermediate result has properties. It is determined that the properties of the first-unit intermediate result are similar to properties of a second-unit intermediate result to be generated for a second unit of work when the second unit of work is executed on the computer-based database management system. In response, the first-unit intermediate result is saved for use in executing the second unit of work.

Abstract: In an example embodiment, a method is illustrated to determine multi-column indexes, the method including receiving one or more structured query language (SQL) queries, the one or more SQL queries defining a workload. A database index analysis tool may be used to determine a plurality of index configurations for executing the workload. Further, leading index configurations may be determined, leading index configurations including a subset of the plurality of index configurations. Also, a statistics database may be updated with complete statistics for the leading index configurations.

Abstract: In an example embodiment, a method is illustrated to determine multi-column indexes, the method including receiving one or more structured query language (SQL) queries, the one or more SQL queries defining a workload. A database index analysis tool may be used to determine a plurality of index configurations for executing the workload. Further, leading index configurations may be determined, leading index configurations including a subset of the plurality of index configurations. Also, a statistics database may be updated with complete statistics for the leading index configurations.