Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: A system and method for selecting a speech-to-text engine are disclosed. The method includes selecting, by an engine selection component, at least two speech-to-text engines to decode a portion of computer-readable speech data. The portion of speech data can be decoded simultaneously by the selected speech-to-text engines for a designated length of time. In some embodiments portions of the speech data can be simultaneously decoded with selected speech-to-text engines at periodic intervals. An accuracy of decoding can be determined for each selected speech-to-text engine by an accuracy testing component. Additionally, the relative accuracies and speeds of the selected speech-to-text engines can be compared by an output comparison component. The engine selection component can then select the most accurate speech-to-text engine accurate to decode a next portion of speech data. Further, the engine selection module may select a speech-to-text engine that meets or exceeds a speed and/or accuracy threshold.

Abstract: An operator split mechanism analyzes code in a streaming application according to specified split criteria to determine when an operator in the streaming application can be split. At compile-time, when an operator satisfies the split criteria, the operator split mechanism splits the operator according to the split criteria. In an integrated development environment (IDE), the operator split mechanism determines when an operator satisfies the split criteria, and splits the operator according to the split criteria. The operator split mechanism can operate in an automatic mode where operators are split without further input from the user, or in a more interactive mode where the operator split mechanism provides recommendations and options to a user, who makes appropriate selections, and the operator split mechanism then functions according to the selections by the user.

Abstract: A latency reconfiguration mechanism in a streams manager detects latency mismatches in a streaming application that affect the performance of the streaming application, and reconfigures the streaming application to minimize the latency mismatches. The reconfiguration can include fusing adjacent processing elements into a single processing element, co-locating adjacent processing elements on the same machine, moving processing elements and/or network links, adding parallel processing elements, and rearranging processing elements and/or network links to create more consistent latency paths. By minimizing latency mismatches and providing more consistent latency paths, the performance of a streaming application is increased by not having to perform significant reordering of data tuples.

Abstract: A first set of code origination data which corresponds to a first computing object and a second set of code origination data which corresponds to a second computing object may be detected for utilization to develop a streaming application in the stream computing environment. Based on the first and second sets of code origination data, a code assembly arrangement with respect to the first and second computing objects may be determined. Based on the first and second sets of code origination data, a consistent region may be determined to utilize for the code assembly arrangement. The code assembly arrangement may be established, in the consistent region, with respect to the first and second computing objects to develop the streaming application.

Abstract: Disclosed aspects relate to checkpointing a set of stream computing data with respect to a stream computing environment having a set of windowed stream operators including both a first windowed stream operator and a second windowed stream operator. It may be identified that the first windowed stream operator has a first subset of the set of stream computing data. It may be identified that the second windowed stream operator has the first subset of the set of stream computing data. It may be determined to checkpoint the first subset of the set of stream computing data without a redundant checkpoint related to the first and second windowed stream operators. The set of stream computing data may be checkpointed without the redundant checkpoint of the first subset of the set of stream computing data.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Aspects of the disclosure relate to managing code origination data for a stream computing environment. A first set of code origination data which corresponds to a first computing object and a second set of code origination data which corresponds to a second computing object may be detected for utilization to develop a streaming application in the stream computing environment. Based on the first and second sets of code origination data, a code assembly arrangement with respect to the first and second computing objects may be determined. The code assembly arrangement may be established with respect to the first and second computing objects to develop the streaming application.

Abstract: Disclosed aspects relate to managing a set of development data for a stream computing environment. A set of development data related to a computing object may be detected. The set of development data may be derived from application development for utilization in the stream computing environment. The set of development data may be established in association with the computing object. A computing artifact which has the computing object in association with the set of development data may be compiled.

Abstract: A latency reconfiguration mechanism in a streams manager detects latency mismatches in a streaming application that affect the performance of the streaming application, and reconfigures the streaming application to minimize the latency mismatches. The reconfiguration can include fusing adjacent processing elements into a single processing element, co-locating adjacent processing elements on the same machine, moving processing elements and/or network links, adding parallel processing elements, and rearranging processing elements and/or network links to create more consistent latency paths. By minimizing latency mismatches and providing more consistent latency paths, the performance of a streaming application is increased by not having to perform significant reordering of data tuples.

Abstract: A variable checkpoint mechanism in a streams manager checkpoints a streaming application based on periodic time periods for checkpoints. The variable checkpoint mechanism can take a checkpoint before a periodic time period ends when a spike is coming, or can take a checkpoint after the periodic time period ends when there is backpressure in a consistent region of the streaming application. When there is no anticipated spike coming and when there is no backpressure in a consistent region of the streaming application, the checkpoint is performed at the normal end of the periodic time period for checkpoints. In this manner the checkpoint timing of the variable checkpoint mechanism can be adjusted real-time to minimize the negative impact of checkpointing on the performance of the streaming application.

Abstract: A variable checkpoint mechanism in a streams manager checkpoints a streaming application based on periodic time periods for checkpoints. The variable checkpoint mechanism can take a checkpoint early before a periodic time period ends or late after the periodic time period ends based on predicted size of one or more tuple windows in the streaming application. The time for taking the checkpoint can be selected based on multiple checkpoint timing criteria, which include storage requirement for the checkpoint and predicted backpressure in the flow graph. In this manner the checkpoint timing of the variable checkpoint mechanism can be adjusted real-time to minimize the negative impact of checkpointing on the performance of the streaming application.

Abstract: A system and method for selecting a speech-to-text engine are disclosed. The method includes selecting, by an engine selection component, at least two speech-to-text engines to decode a portion of computer-readable speech data. The portion of speech data can be decoded simultaneously by the selected speech-to-text engines for a designated length of time. In some embodiments portions of the speech data can be simultaneously decoded with selected speech-to-text engines at periodic intervals. An accuracy of decoding can be determined for each selected speech-to-text engine by an accuracy testing component. Additionally, the relative accuracies and speeds of the selected speech-to-text engines can be compared by an output comparison component. The engine selection component can then select the most accurate speech-to-text engine accurate to decode a next portion of speech data. Further, the engine selection module may select a speech-to-text engine that meets or exceeds a speed and/or accuracy threshold.

Abstract: A method, computer program product, and computer system for tracking tuples by ID as the tuples progress through an operator of a chain of operators within a processing element. Dynamic loading may be utilized to load a processing code for the operator in response to invoking fusion for the operator. Ownership of network I/O may be shifted from the operator to a final operator of the chain of operators within the processing element. A tuple ID may be rolled back to a last ID processed by the operator being added into the processing element.

Abstract: A streaming application processes data by processing tuples via operators. Bottleneck operators are identified. A degree of streaming speed is maintained, even when non-bottleneck operators participate in increased resiliency operations.

Abstract: Aspects of the disclosure relate to managing code origination data for a distributed computing environment having a set of compute nodes which includes a first compute node. A first set of code origination data which corresponds to a first computing object may be detected for utilization to develop a distributed application in the distributed computing environment. Using the first set of code origination data, the first compute node may be identified to host the first computing object for the distributed application. The first computing object may be deployed to the first compute node to develop the distributed application.

Abstract: Aspects of the disclosure relate to managing code origination data for a stream computing environment. A first set of code origination data which corresponds to a first computing object and a second set of code origination data which corresponds to a second computing object may be detected for utilization to develop a streaming application in the stream computing environment. Based on the first and second sets of code origination data, a code assembly arrangement with respect to the first and second computing objects may be determined. The code assembly arrangement may be established with respect to the first and second computing objects to develop the streaming application.

Abstract: A method, computer program product, and computer system for tracking tuples by ID as the tuples progress through an operator of a chain of operators within a processing element. Dynamic loading may be utilized to load a processing code for the operator in response to invoking fusion for the operator. Ownership of network I/O may be shifted from the operator to a final operator of the chain of operators within the processing element. A tuple ID may be rolled back to a last ID processed by the operator being added into the processing element.