Abstract: A stream computing application may permit one job to connect to a data stream of a different job. As more and more jobs dynamically connect to the data stream, the connections may have a negative impact on the performance of the job that generates the data stream. Accordingly, a variety of metrics and statistics (e.g., CPU utilization or tuple rate) may be monitored to determine if the dynamic connections are harming performance. If so, the stream computing system may be optimized to mitigate the effects of the dynamic connections. For example, particular operators may be unfused from a processing element and moved to a compute node that has available computing resources. Additionally, the stream computing application may clone the data stream in order to distribute the workload of transmitting the data stream to the connected jobs.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: Method, system and computer program product for performing an operation, including providing a plurality of processing elements comprising one or more operators, the operators configured to process streaming data tuples, establishing an operator graph of multiple operators, the operator graph defining at least one execution path in which a first operator is configured to receive data tuples from at least one upstream operator and transmit data tuples to at least one downstream operator, providing each operator a buffer configured to hold data tuples requiring processing receiving a plurality of data tuples in a buffer associated with an operator, the data tuples comprising at least one attribute, determining a backlog exists in the first buffer, selecting at least one data tuple from the first buffer, examining an attribute of the selected data tuples to identify a candidate tuple, and performing a second operation on the candidate tuple.

Abstract: Method, system and computer program product for performing an operation, including providing a plurality of processing elements comprising one or more operators, the operators configured to process streaming data tuples, establishing an operator graph of multiple operators, the operator graph defining at least one execution path in which a first operator is configured to receive data tuples from at least one upstream operator and transmit data tuples to at least one downstream operator, providing each operator a buffer configured to hold data tuples requiring processing receiving a plurality of data tuples in a buffer associated with an operator, the data tuples comprising at least one attribute, determining a backlog exists in the first buffer, selecting at least one data tuple from the first buffer, examining an attribute of the selected data tuples to identify a candidate tuple, and performing a second operation on the candidate tuple.

Abstract: Techniques are described for eliminating backpressure in a distributed system by changing the rate data flows through a processing element. Backpressure occurs when data throughput in a processing element begins to decrease, for example, if new processing elements are added to the operating chart or if the distributed system is required to process more data. Indicators of backpressure (current or future) may be monitored. Once current backpressure or potential backpressure is identified, the operator graph or data rates may be altered to alleviate the backpressure. For example, a processing element may reduce the data rates it sends to processing elements that are downstream in the operator graph, or processing elements and/or data paths may be eliminated. In one embodiment, processing elements and associate data paths may be prioritized so that more important execution paths are maintained.

Abstract: Streams applications may decrypt encrypted data even though the decrypted data is not used by an operator. Operator properties are defined to permit decryption of data within the operator based on a number of criteria. By limiting the number of operators that decrypt encrypted data, the anonymous nature of the data is further preserved. Operator properties also indicate whether an operator should send encrypted or decrypted data to a downstream operator.

Abstract: Streams applications may decrypt encrypted data even though the decrypted data is not used by an operator. Operator properties are defined to permit decryption of data within the operator based on a number of criteria. By limiting the number of operators that decrypt encrypted data, the anonymous nature of the data is further preserved. Operator properties also indicate whether an operator should send encrypted or decrypted data to a downstream operator.

Abstract: Method, system and computer program product for performing an operation, the operation including providing a plurality of processing elements comprising one or more operators, the operators configured to process streaming data tuples. The operation then establishes an operator graph of multiple operators, the operator graph defining at least one execution path in which a first operator of the plurality of operators is configured to receive data tuples from at least one upstream operator and transmit data tuples to at least one downstream operator. The operation then defines a breakpoint, the breakpoint comprising a condition, the condition based on attribute values of data tuples in a window of at least one operator, the window comprising a plurality of data tuples in an operator. The operation, upon detecting occurrence of the condition, triggers the breakpoint to halt processing by each of the plurality of operators in the operator graph.

Abstract: Method product for performing an operation, the operation including providing a plurality of processing elements comprising one or more operators, the operators configured to process streaming data tuples. The operation then establishes an operator graph of multiple operators, the operator graph defining at least one execution path in which a first operator of the plurality of operators is configured to receive data tuples from at least one upstream operator and transmit data tuples to at least one downstream operator. The operation then defines a breakpoint, the breakpoint comprising a condition, the condition based on attribute values of data tuples in a window of at least one operator, the window comprising a plurality of data tuples in an operator. The operation, upon detecting occurrence of the condition, triggers the breakpoint to halt processing by each of the plurality of operators in the operator graph.

Abstract: Computer-implemented method, system, and computer program product for processing data in an out-of-order manner in a streams computing environment. A windowing condition is defined such that incoming data tuples are processed within a specified time or count of each other. Additionally, the windowing condition may be based on a specified attribute of the data tuples. If the tuples are not processed within the constraints specified by the windowing condition, the unprocessed tuples may be discarded, i.e., not processed, to optimize operator performance.

Abstract: Computer-implemented method, system, and computer program product for processing data in an out-of-order manner in a streams computing environment. A windowing condition is defined such that incoming data tuples are processed within a specified time or count of each other. Additionally, the windowing condition may be based on a specified attribute of the data tuples. If the tuples are not processed within the constraints specified by the windowing condition, the unprocessed tuples may be discarded, i.e., not processed, to optimize operator performance.

Abstract: Methods and systems for monitoring a stream application are disclosed. The stream application is composed from a plurality of processing elements executing on one or more compute nodes. A graphical user interface display presents a user with at least a portion of an operator graph in a running stream application. The operator graph represents the plurality of processing elements, and links between processing elements, corresponding to a flow of data tuples through the stream application. A monitoring application then monitors user interactions with the presentation of the portion of the operator graph on the graphical user interface display and identifies at least a first modification to the stream application based on a processing state of the stream application and the monitored user interactions.

Abstract: Computer-implemented method, system, and computer program product for processing data in an out-of-order manner in a streams computing environment. A windowing condition is defined such that incoming data tuples are processed within a specified time or count of each other. Additionally, the windowing condition may be based on a specified attribute of the data tuples. If the tuples are not processed within the constraints specified by the windowing condition, the unprocessed tuples may be discarded, i.e., not processed, to optimize operator performance.

Abstract: Computer-implemented method, system, and computer program product for processing data in an out-of-order manner in a streams computing environment. A windowing condition is defined such that incoming data tuples are processed within a specified time or count of each other. Additionally, the windowing condition may be based on a specified attribute of the data tuples. If the tuples are not processed within the constraints specified by the windowing condition, the unprocessed tuples may be discarded, i.e., not processed, to optimize operator performance.

Abstract: Techniques are disclosed for invalidating, at one or more processing elements, data streams containing data tuples. A plurality of tuples is received via a data stream, whereupon the data stream is determined to be invalid based on at least one tuple in the plurality of tuples. The data stream is then invalidated, and a message is issued that causes one or more data streams included in the stream-based computing system and related to the invalidated data stream to also be invalidated.

Abstract: Method, system and computer program product for performing an operation, the operation including providing a plurality of processing elements comprising one or more operators, the operators configured to process streaming data tuples. The operation then establishes an operator graph of multiple operators, the operator graph defining at least one execution path in which a first operator of the plurality of operators is configured to receive data tuples from at least one upstream operator and transmit data tuples to at least one downstream operator. The operation then defines a breakpoint, the breakpoint comprising a condition, the condition based on attribute values of data tuples in a window of at least one operator, the window comprising a plurality of data tuples in an operator. The operation, upon detecting occurrence of the condition, triggers the breakpoint to halt processing by each of the plurality of operators in the operator graph.

Abstract: Streams applications may decrypt encrypted data even though the decrypted data is not used by an operator. Operator properties are defined to permit decryption of data within the operator based on a number of criteria. By limiting the number of operators that decrypt encrypted data, the anonymous nature of the data is further preserved. Operator properties also indicate whether an operator should send encrypted or decrypted data to a downstream operator.

Abstract: Method product for performing an operation, the operation including providing a plurality of processing elements comprising one or more operators, the operators configured to process streaming data tuples. The operation then establishes an operator graph of multiple operators, the operator graph defining at least one execution path in which a first operator of the plurality of operators is configured to receive data tuples from at least one upstream operator and transmit data tuples to at least one downstream operator. The operation then defines a breakpoint, the breakpoint comprising a condition, the condition based on attribute values of data tuples in a window of at least one operator, the window comprising a plurality of data tuples in an operator. The operation, upon detecting occurrence of the condition, triggers the breakpoint to halt processing by each of the plurality of operators in the operator graph.

Abstract: A stream computing application may permit one job to connect to a data stream of a different job. As more and more jobs dynamically connect to the data stream, the connections may have a negative impact on the performance of the job that generates the data stream. Accordingly, a variety of metrics and statistics (e.g., CPU utilization or tuple rate) may be monitored to determine if the dynamic connections are harming performance. If so, the stream computing system may be optimized to mitigate the effects of the dynamic connections. For example, particular operators may be unfused from a processing element and moved to a compute node that has available computing resources. Additionally, the stream computing application may clone the data stream in order to distribute the workload of transmitting the data stream to the connected jobs.