Abstract: An embodiment includes analyzing data associated with an original flow graph comprising a plurality of operators of a stream computing application, including identifying a secure network connection between a first operator and a second operator that uses encryption. The embodiment fuses the first operator with the second operator such that a first logical function of the first operator is combined with a second logical function of the second operator. The embodiment then generates a modified flow graph as a modification of the original flow graph that combines the first operator and the second operator and lacks encryption between the first operator and the second operator.

Abstract: Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.

Abstract: A stream of tuples is processed by a stream application. The stream application includes a plurality of processing elements that operate on one or more compute nodes, each processing element includes one or more stream operators. One or more databases that are capable of communicating with the stream application are monitored during the processing of the stream of tuples. A potential performance condition of a first database of the one or more databases is detected based on the monitoring. An output adjustment is performed, in response to the potential performance condition.

Abstract: An embodiment includes monitoring a distributed computing application at runtime for occurrence of a condition, where the condition includes occurrence of a degradation condition on a processing element (PE). The embodiment also includes starting a provisional PE on a second node at runtime while maintaining runtime operation of the PE. The embodiment compares operation of the PE to operation of the provisional PE based on a performance metric associated with the computing resource. The embodiment connects the provisional PE to replace the stream of tuples from the PE to a downstream PE with a stream of tuples from the provisional PE to the downstream PE.

Abstract: Certain aspects of the present disclosure provide techniques for identifying temporal data in data streams to create a temporal database for a stream(s) application to query for temporal data. An example technique includes receiving streams of data at a streams engine and processing the streams of data according to a priority order. The streams engine identifies whether the database is a temporal database and identifies temporal data in each stream of data based on frame analysis, natural language processing techniques, metadata, and optical character recognition. Further, the streams engine generates context data corresponding to the temporal data. The streams engine generates a temporal data record based on the temporal data and context data, and the streams engine generates a reliability factor. The temporal data record and reliability factor are stored in the temporal database for a stream application to query regarding temporal information at a later point in time.

Abstract: An embodiment includes monitoring a distributed computing application at runtime for occurrence of a condition, where the condition includes occurrence of a degradation condition on a processing element (PE). The embodiment also includes starting a provisional PE on a second node at runtime while maintaining runtime operation of the PE. The embodiment compares operation of the PE to operation of the provisional PE based on a performance metric associated with the computing resource. The embodiment connects the provisional PE to replace the stream of tuples from the PE to a downstream PE with a stream of tuples from the provisional PE to the downstream PE.

Abstract: An embodiment includes analyzing data associated with an original flow graph comprising a plurality of operators of a stream computing application, including identifying a secure network connection between a first operator and a second operator that uses encryption. The embodiment fuses the first operator with the second operator such that a first logical function of the first operator is combined with a second logical function of the second operator. The embodiment then generates a modified flow graph as a modification of the original flow graph that combines the first operator and the second operator and lacks encryption between the first operator and the second operator.

Abstract: Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.

Abstract: Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.

Abstract: A computer system controls processing elements associated with a stream computing application. A stream computing application is monitored for the occurrence of one or more conditions. One or more processing element groups are determined to be restarted based on occurrence of the one or more conditions, wherein the processing element groups each include a plurality of processing elements associated with the stream computing application. Each processing element of the determined one or more processing element groups is concurrently restarted. Embodiments of the present invention further include a method and program product for controlling processing elements within a stream computing application in substantially the same manner described above.

Abstract: Systems, methods, and articles of manufacture are disclosed for processing element (PE) deployment placement in a streaming data system. In particular, PEs of a stream computing application are grouped across compute nodes in a dynamic manner that improves over manually specified groupings of PEs on particular compute nodes. A stream manager deploys PEs according to rules in deployment instructions specifying whether to collocate PEs on a single compute node and/or ex-collocate PEs across compute nodes based on dynamically-determined information relating to the PEs themselves, compute nodes on which PEs are to run, and/or the streams runtime. For example, the stream manager may profile code and deploy PEs on compute nodes based on such profiling, deploy PEs together on compute nodes meeting predefined system characteristics or streams runtime conditions or metrics, and/or deploy PEs on a compute node within a predefined proximity of an external source.

Abstract: Certain aspects of the present disclosure provide techniques for identifying temporal data in data streams to create a temporal database for a stream(s) application to query for temporal data. An example technique includes receiving streams of data at a streams engine and processing the streams of data according to a priority order. The streams engine identifies whether the database is a temporal database and identifies temporal data in each stream of data based on frame analysis, natural language processing techniques, metadata, and optical character recognition. Further, the streams engine generates context data corresponding to the temporal data. The streams engine generates a temporal data record based on the temporal data and context data, and the streams engine generates a reliability factor. The temporal data record and reliability factor are stored in the temporal database for a stream application to query regarding temporal information at a later point in time.

Abstract: Systems, methods, and articles of manufacture are disclosed for processing element (PE) deployment placement in a streaming data system. In particular, PEs of a stream computing application are grouped across compute nodes in a dynamic manner that improves over manually specified groupings of PEs on particular compute nodes. A stream manager deploys PEs according to rules in deployment instructions specifying whether to collocate PEs on a single compute node and/or ex-collocate PEs across compute nodes based on dynamically-determined information relating to the PEs themselves, compute nodes on which PEs are to run, and/or the streams runtime. For example, the stream manager may profile code and deploy PEs on compute nodes based on such profiling, deploy PEs together on compute nodes meeting predefined system characteristics or streams runtime conditions or metrics, and/or deploy PEs on a compute node within a predefined proximity of an external source.

Abstract: A computer system controls processing elements associated with a stream computing application. A stream computing application is monitored for the occurrence of one or more conditions. One or more processing element groups are determined to be restarted based on occurrence of the one or more conditions, wherein the processing element groups each include a plurality of processing elements associated with the stream computing application. Each processing element of the determined one or more processing element groups is concurrently restarted. Embodiments of the present invention further include a method and program product for controlling processing elements within a stream computing application in substantially the same manner described above.

Abstract: Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.

Abstract: A program product, an apparatus, and method of autonomically adjusting when performance data from a call stack is collected during a trace. In particular, the sampling interval between call stack collections may be autonomically adjusted while a trace is executing based upon the call stack, various performance metrics, and/or previous call stack collections.

Abstract: The activity of a database is monitored, and index split options are dynamically determined from the monitored database activity. Appropriate action may then be taken based on the dynamically determined index split options. For example, the index may be automatically split using the dynamically determined index split options. A user command that specifies index split options may be compared to the dynamically determined index split options, and if the user command is not compatible with the dynamically determined index split options, the command may be inhibited, or the user may receive a warning message in response to the command. In addition, existing index use may be monitored, and if the monitored index use indicates the index is split poorly, the index may be re-generated using the dynamically determined index split options.

Abstract: A method and apparatus for monitoring the performance of a computer system with one or more active programs. A periodic sampling of the call stack is obtained. The sampled call stack is examined to infer the system performance similar to that obtained using prior art event based profiling. Embodiments also are directed to a combination approach to describing the system performance using a historical sampling to infer additional detail to fill in the gaps of the sampled data.

Abstract: A master table and one or more slave tables are linked together by different referential integrity constraints. A database engine maintains a separate data structure that includes information about which slave tables include corresponding records that may be affected by a constraint. Accordingly, when an operation is performed on the master table, the data structure is referenced to determine which slave tables has records which are affected as well. The data structure may be a bitmap, an encoded vector index, or separate fields within the master database. As a result of this data structure, unnecessary I/O operations are avoided involving slave tables without a corresponding record.

Abstract: A method, system and article of manufacture for processing aggregation queries that are executed against a database and, more particularly, for managing aggregation data structures associated with tables of a database. One embodiment provides a method for managing creation of aggregation data structures for tables of a database.