Abstract: A respective volatility attribute associated with each of one or more tables of a computerized database is used in any of various aspects to (a) determine how table data is stored in a physical storage device; (b) regulate the use of a materialized query table using database table data; and/or (c) influence circumstances under which indexes are created or advised by database analytic software. Various optional additional uses of a volatility attribute to manage a database are disclosed. Preferably, database parameters are automatically monitored over time and database table volatility state is automatically determined and periodically adjusted.

Abstract: A query is received. Based on statistics for a database, a first query access plan is generated for executing the query on the database. A first result set for the query is obtained by implementing the first query access plan on the database. Based on at least one trend in the statistics, a target time after which implementing the first query access plan will fail a set of criteria is forecasted. In response to the forecasting and based on the statistics, a replacement query access plan is generated prior to the target time. After the target time, a second result set for the query is obtained by implementing the replacement query access plan rather than the first query access plan.

Abstract: Systems, methods, and computer program products to perform an operation comprising identifying a first access to a first subset of a first database table on a first storage node, of a plurality of storage nodes, identifying a second access to a second subset of the first database table on a second storage node, of the plurality of storage nodes, and performing a predefined operation to restrict access to the first database table on the plurality of storage nodes based on the first and second accesses.

Abstract: Method and apparatus for query optimization using metadata. A first query associated with a first metadata is received. Upon receiving the first query, the first metadata is compared to predefined criteria, to determine that the first query is delayable. Upon determining that the first query is delayable, a first query plan is generated for the first query based on the first metadata. Upon generating the first query plan, a first schedule for executing the first query plan is generated. The first execution query plan may be scheduled to be executed at a later point in time based on the first metadata. Finally, the first query plan is executed according to the first schedule.

Abstract: Disclosed aspects relate to query hint learning in a database management system. A hint in a first query is detected by the database management system. The first query has a first signature. A second query is received by the database management system. The second query has a second signature. A determination is made by the database management system that the second signature correlates with the first signature. A query plan is established by the database management system. The query plan is based on the second query and uses the hint in the first query. In response to receiving the second query, the database management system processes the query plan.

Abstract: Disclosed aspects relate to query hint learning in a database management system. A hint in a first query is detected by the database management system. The first query has a first signature. A second query is received by the database management system. The second query has a second signature. A determination is made by the database management system that the second signature correlates with the first signature. A query plan is established by the database management system. The query plan is based on the second query and uses the hint in the first query. In response to receiving the second query, the database management system processes the query plan.

Abstract: Disclosed aspects relate to query hint management for a database management system. With respect to a hint for a query, a set of hint profile data is established. The database management system detects a triggering event. Based on the set of hint profile data, the database management system determines to perform a hint action in response to the triggering event. Performance of the hint action is initiated.

Abstract: Embodiments of the preset disclosure include a method, a system, and a computer program product for rearranging a set of filters within a data streams environment. The method includes setting a first filter ordering of two or more filter processing elements. The method includes determining a filtering efficiency of the first filter ordering. The determining of the filtering efficiency includes monitoring a runtime, one or more tuples, and an amount of tuples retained after filtering of the first filter ordering. The method includes creating a second filter ordering of the two or more filter processing elements. The method includes comparing the filtering efficiency of the second filter ordering to the first filter ordering. The method includes selecting the second filter ordering in response to comparing the filtering efficiency of the second filter ordering to the filtering efficiency of the first filter ordering.

Abstract: A database table is monitored to automatically determine its volatility, and at least one volatility attribute expressing volatility state is saved. The volatility attribute is used to manage the database, e.g., to determine an optimum query execution strategy for a query involving data in the table, and/or for determining whether to re-optimize a previously saved query execution strategy and/or for managing storage and retrieval of table data. Preferably, parameters of the database table are captured at regular sampling intervals and a figure of merit representing volatility is computed from the parameter values. Preferably, the database supports the SQL database language, and the volatility attribute is or includes the SQL VOLATILE attribute. In an additional embodiment, the database manager may express volatility of a database table as a value within a range.

Abstract: Methods to perform an operation comprising upon determining that a received query requests values of sensitive data stored in a secure database table of a database, computing a security score for the received query based on a determined specificity of a selection predicate of the received query, and upon determining that the security score exceeds a security threshold, performing a predefined operation to restrict access to the requested values of the sensitive data.

Abstract: A database table is monitored to automatically determine its volatility, and at least one volatility attribute expressing volatility state is saved. The volatility attribute is used to manage the database, e.g., to determine an optimum query execution strategy for a query involving data in the table, and/or for determining whether to re-optimize a previously saved query execution strategy and/or for managing storage and retrieval of table data. Preferably, parameters of the database table are captured at regular sampling intervals and a figure of merit representing volatility is computed from the parameter values. Preferably, the database supports the SQL database language, and the volatility attribute is or includes the SQL VOLATILE attribute. In an additional embodiment, the database manager may express volatility of a database table as a value within a range.

Abstract: A first database management system (DBMS) running on first computer having a first set of computing resources determines that with respect to the first set of computing resources a first query access plan for executing a query satisfies a set of query optimization criteria. A determination is then made that with respect to a second set of computing resources the first query access plan fails a set of preliminary criteria. In response to this failure determination, a second query access plan for executing the query is generated. Prior to having a second DBMS running on a second computer having the second set of computing resources receive a request to execute the query, a determination is made that with respect to the second set of computing resources the second query access plan satisfies the set query optimization criteria.

Abstract: A first database management system (DBMS) running on first computer having a first set of computing resources determines that with respect to the first set of computing resources a first query access plan for executing a query satisfies a set of query optimization criteria. A determination is then made that with respect to a second set of computing resources the first query access plan fails a set of preliminary criteria. In response to this failure determination, a second query access plan for executing the query is generated. Prior to having a second DBMS running on a second computer having the second set of computing resources receive a request to execute the query, a determination is made that with respect to the second set of computing resources the second query access plan satisfies the set query optimization criteria.

Abstract: Aspects include obtaining a pattern that describes an event in a natural language format. The pattern that describes the event is converted into at least one identified pattern key value pair. A database transaction is monitored and converted into a least one database transaction key value pair. A similarity score is calculated by scoring similarities between the identified pattern key value pair(s) and the database transaction key value pair(s). Based on similarity score meeting a threshold, an alert is output to indicate that the database transaction matches the pattern that describes the event.

Abstract: Aspects include obtaining a pattern that describes an event in a natural language format. The pattern that describes the event is converted into at least one identified pattern key value pair. A database transaction is monitored and converted into a least one database transaction key value pair. A similarity score is calculated by scoring similarities between the identified pattern key value pair(s) and the database transaction key value pair(s). Based on similarity score meeting a threshold, an alert is output to indicate that the database transaction matches the pattern that describes the event.

Abstract: A respective volatility attribute associated with each of one or more tables of a computerized database is used to adjust an extension file size value associated with a database table file space. Various optional additional uses of a volatility attribute to manage a database are disclosed. Preferably, database parameters are automatically monitored over time and database table volatility state is automatically determined and periodically adjusted.

Abstract: A computer-implemented method for reusing deleted database records includes, receiving, by a database manager executing within a computing device, a first query request to insert, into a database table, a first set of database records. The database manager identifies a first set of entries within a journal log bundle. The database manager also determines, in response to identifying a first set of entries, that there is a third set of deleted database records located on a same page as a second set of database records. The database manager also, in response to determining that there is a third set of deleted database records located on a same page as the second set of database records, inserts into the database table the first set of database records using the third set of deleted database records.

Abstract: A smart tuple manager includes a mechanism for splitting a smart tuple, and for automatically generating one or more classes from existing classes when a smart tuple is split. When a first smart tuple is split into second and third new smart tuples, classes for the second and third smart tuples are automatically generated from the class for the first smart tuple. The classes for the second and third smart tuples are subsets of the data elements and code segments in the first class. After a class is automatically generated, new code segments may be added to the class as needed.

Abstract: A respective volatility attribute associated with each of one or more tables of a computerized database is used to adjust an extension file size value associated with a database table file space. Various optional additional uses of a volatility attribute to manage a database are disclosed. Preferably, database parameters are automatically monitored over time and database table volatility state is automatically determined and periodically adjusted.

Abstract: A computer-implemented method for determining a query execution plan based on transaction state may include determining that a first set of database values in a database table are in a committed transaction state and that a second set of database values in the database table are in an uncommitted transaction state. In response to the determining, the first set of database values may have a first set of statistics generated and the second set of database values may have a second set of statistics generated. The first and second set of statistics may be for use in determining a query execution plan.