Abstract: Queries are monitored in a database which receives input from a stream computing application to identify data of interest. Parameters defining the data of interest, which are preferably expressed as a logical query, are sent to the stream computing application, which then processes the in-flight streamed data satisfying the parameters in some special manner. In some embodiments, the stream computing application increases the processing priority of in-flight data satisfying the parameters. In some embodiments, the stream computing application applies additional processing steps to the in-flight data satisfying the parameters to provide enhanced data or metadata.

Abstract: Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.

Abstract: Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.

Abstract: A method, system and computer program product for staggering a stream application's deployment, by: providing one or more configuration settings that define one or more delays for instantiation or initialization of at least one target processing element of the stream application based on the stream application's run-time conditions or events; and instantiating or initializing the target processing element of the stream application when the delays defined by the configuration settings have been satisfied. The configuration settings may define the delays to wait until a specified period of time has lapsed after one or more other processing elements have been instantiated or initialized, or to wait until a specified amount of data has been processed by one or more other processing elements, or to wait until one or more signals is received from one or more other processing elements, before instantiating or initializing the target processing element.

Abstract: Disclosed aspects relate to window management in a stream computing environment. A set of computing resources may be detected with respect to the stream computing environment. Based on the set of computing resources, a set of window configurations in the stream computing environment may be determined. In response to determining the set of window configurations in the stream computing environment, the set of window configurations may be established in the stream computing environment.

Abstract: A database which receives input from a data streaming application may include in-flight streamed data within the scope of a command which alters database data. Preferably, the streaming application produces data tuples for input to at least one table of the database. A user submitting modify data commands to apply to multiple tuples meeting some specified logical parameters may define the command scope to include in-flight data in the streaming application, causing the database manager to forward the command to the streaming application. A streaming application manager invokes database agents in respective nodes of the streaming application, which identify in-flight data meeting the specified logical parameters of the command, and apply the modify data operation to such data.

Abstract: The present invention provides a computer implemented method, system, and computer program product of assigning processing elements to stream computing servers with respect to external computing resources.

Abstract: A method, system and computer program product for staggering a stream application's deployment, by: providing one or more configuration settings that define one or more delays for instantiation or initialization of at least one target processing element of the stream application based on the stream application's run-time conditions or events; and instantiating or initializing the target processing element of the stream application when the delays defined by the configuration settings have been satisfied. The configuration settings may define the delays to wait until a specified period of time has lapsed after one or more other processing elements have been instantiated or initialized, or to wait until a specified amount of data has been processed by one or more other processing elements, or to wait until one or more signals is received from one or more other processing elements, before instantiating or initializing the target processing element.

Abstract: Queries are monitored in a database which receives input from a stream computing application to identify data of interest. Parameters defining the data of interest, which are preferably expressed as a logical query, are sent to the stream computing application, which then processes the in-flight streamed data satisfying the parameters in some special manner. In some embodiments, the stream computing application increases the processing priority of in-flight data satisfying the parameters. In some embodiments, the stream computing application applies additional processing steps to the in-flight data satisfying the parameters to provide enhanced data or metadata.

Abstract: Disclosed aspects relate to operation efficiency management in a shared pool of configurable computing resources. A first set of processing operations of a first application may be detected. A second set of processing operations of a second application may be detected. The first set of processing operations of the first application may be compared with the second set of processing operations of the second application. A substantial match of the first and second processing operations of the first and second applications may be determined. A single set of processing operations for both the first and second applications may be established.

Abstract: Disclosed aspects relate to operation efficiency management in a shared pool of configurable computing resources. A first set of processing operations of a first application may be detected. A second set of processing operations of a second application may be detected. The first set of processing operations of the first application may be compared with the second set of processing operations of the second application. A substantial match of the first and second processing operations of the first and second applications may be determined. A single set of processing operations for both the first and second applications may be compiled.

Abstract: Queries are monitored in a database which receives input from a stream computing application to identify data of interest. Parameters defining the data of interest, which are preferably expressed as a logical query, are sent to the stream computing application, which then processes the in-flight streamed data satisfying the parameters in some special manner. In some embodiments, the stream computing application increases the processing priority of in-flight data satisfying the parameters. In some embodiments, the stream computing application applies additional processing steps to the in-flight data satisfying the parameters to provide enhanced data or metadata.

Abstract: A streams analysis tool allows a user to define one or more buckets according to a specified tuple collection criteria for each bucket. The specified tuple collection criteria for each bucket defines some way to distinguish one data tuple from another. The specified tuple collection criteria for each bucket is therefore used to distinguish data tuples that satisfy the specified tuple collection criteria from data tuples that do not satisfy the specified tuple collection criteria. When a data tuple satisfies the specified tuple collection criteria for a bucket, the data tuple is stored in the bucket. In addition, data tuples preceding or succeeding the data tuple may also be stored in the bucket, as determined by the specified tuple collection criteria. The data tuples in each bucket are analyzed, and based on the analysis a streams manager can change how future data tuples are processed by the streaming application.

Abstract: An operator locking tool allows a user to define a lock profile for one or more operators in a streaming application. The lock profile preferably specifies lock criteria and one or more corresponding lock actions that are taken when the lock criteria is satisfied. The lock criteria can include operator performance, resource utilization, events, and user-defined triggers. The lock actions can include blocking tuple entry, blocking tuple exit, halting tuple processing, processing high-priority tuples while not processing other tuples, allowing tuples to exit an operator only when specified exit criteria is satisfied, and enabling bypass of an operator. Locking can be done for individual operators or for a group of operators.

Abstract: An operator locking tool allows a user to define a lock profile for one or more operators in a streaming application. The lock profile preferably specifies lock criteria and one or more corresponding lock actions that are taken when the lock criteria is satisfied. The lock criteria can include operator performance, resource utilization, events, and user-defined triggers. The lock actions can include blocking tuple entry, blocking tuple exit, halting tuple processing, processing high-priority tuples while not processing other tuples, allowing tuples to exit an operator only when specified exit criteria is satisfied, and enabling bypass of an operator. Locking can be done for individual operators or for a group of operators.

Abstract: Disclosed aspects relate to tuple attribute expiry management in a stream computing environment to process a stream of tuples. A tuple may be received. The tuple may include a tuple attribute that has a first attribute value. An expiry criterion may be determined for the first attribute value of the tuple attribute. An achievement of the expiry criterion for the first attribute value of the tuple attribute may be detected. The tuple attribute related to the first attribute value may be modified. The modification of the tuple attribute may occur in response to detecting the achievement of the expiry criterion for the first attribute value of the tuple attribute.

Abstract: Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.

Abstract: Disclosed aspects relate to window management in a stream computing environment. A set of computing resources may be detected with respect to the stream computing environment. Based on the set of computing resources, a set of window configurations in the stream computing environment may be determined. In response to determining the set of window configurations in the stream computing environment, the set of window configurations may be established in the stream computing environment.

Abstract: Disclosed aspects relate to window management in a stream computing environment. A set of computing resources may be detected with respect to the stream computing environment. Based on the set of computing resources, a set of window configurations in the stream computing environment may be determined. In response to determining the set of window configurations in the stream computing environment, the set of window configurations may be established in the stream computing environment.

Abstract: Disclosed aspects relate to window management in a stream computing environment. An indication of congestion may be detected with respect to the stream computing environment. Based on the indication of congestion, a set of window configurations in the stream computing environment may be determined. In response to determining the set of window configurations in the stream computing environment, the set of window configurations may be established in the stream computing environment.