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 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 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: 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: 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: 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 present disclosure include a computer implemented method, system, and computer program product for implementing an encryption policy in a stream computing environment. The method includes: monitoring a plurality of stream operators within a stream computing environment to collect performance data identifying a point of congestion within the stream computing environment, based on one or more values within the collected performance data satisfying a predefined congestion condition, determining one or more stream operators of the plurality of stream operators associated with the identified point of congestion, redeploying the one or more stream operators to an isolated network within the computing environment, and modifying an encryption policy for the stream computing environment to disable one or more encryption operations on the redeployed one or more stream operators.

Abstract: A streams controller monitors multiple data streams with speech of a conversation with multiple speakers and uses text analytics and diaritization to identify speakers by their role in the conversation. Diaritization is applied to the audio of a data stream to associate a speaker with an SSRC from the data stream and a speaker identification (ID). The streams controller then runs text analytics on a text version of the speaker's speech to determine a speaker role for the speaker. The speaker role can be used to efficiently monitor conversations in a data stream to provide additional services. For example, speaker role can be used to analyze calls in a call center and provide services such as a transcript of conversations or enhanced customer support.

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: Systems, methods, and computer program products to perform an operation comprising locking, by a first one of a database management system (DBMS) and a distributed application, one of a first database element and a first data tuple, wherein the first data tuple is of a plurality of tuples in an operator graph of the distributed application, transmitting an indication of the locking by the first one of the DBMS and the streams computing application to a second one of the DBMS and the distributed application, identifying, by the second one of the DBMS and the distributed application, one of a second database element and a second data tuple corresponding to the one of the first database element and the first data tuple, and locking, by the second one of the DBMS and the streams computing application, the identified one of the second database element and the second data tuple.

Abstract: A streams controller monitors multiple data streams with speech of a conversation with multiple speakers and uses text analytics and diaritization to identify speakers by their role in the conversation. Diaritization is applied to the audio of a data stream to associate a speaker with an SSRC from the data stream and a speaker identification (ID). The streams controller then runs text analytics on a text version of the speaker's speech to determine a speaker role for the speaker. The speaker role can be used to efficiently monitor conversations in a data stream to provide additional services. For example, speaker role can be used to analyze calls in a call center and provide services such as a transcript of conversations or enhanced customer support.

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: 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 streams controller monitors multiple data streams with speech of a conversation with multiple speakers and uses text analytics and diaritization to identify speakers by their role in the conversation. Diaritization is applied to the audio of a data stream to associate a speaker with an SSRC from the data stream and a speaker identification (ID). The streams controller then runs text analytics on a text version of the speaker's speech to determine a speaker role for the speaker. The speaker role can be used to efficiently monitor conversations in a data stream to provide additional services. For example, speaker role can be used to analyze calls in a call center and provide services such as a transcript of conversations or enhanced customer support.

Abstract: A streams controller monitors multiple data streams with speech of a conversation with multiple speakers and uses text analytics and diaritization to identify speakers by their role in the conversation. Diaritization is applied to the audio of a data stream to associate a speaker with an SSRC from the data stream and a speaker identification (ID). The streams controller then runs text analytics on a text version of the speaker's speech to determine a speaker role for the speaker. The speaker role can be used to efficiently monitor conversations in a data stream to provide additional services. For example, speaker role can be used to analyze calls in a call center and provide services such as a transcript of conversations or enhanced customer support.

Abstract: Disclosed aspects relate to post-compilation configuration management in a stream computing environment to process a stream of tuples. An escalation request may be detected pertaining to a post-compilation phase in the stream computing environment. The escalation request may relate to a requested computing configuration for a process in the stream computing environment. An appropriate computing configuration may be determined for the process in the stream computing environment. The appropriate computing configuration may be determined based on the requested computing configuration for the process in the stream computing environment. The appropriate computing configuration may be established using a containerization technique for the process in the stream computing environment.

Abstract: A method, computer program product, and computer system for defining, on at least one processor, one or more potential overflow paths for processing at least a portion of a data load through a plurality of stream operators deployed on a plurality of computing devices. Embodiments may include determining, on the at least one processor, a conditional availability for each computing device of the plurality of computing devices. Embodiments may also include identifying, on the at least one processor, a change in one or more of a configuration of the plurality of computing devices and the at least a portion of the data load. Embodiments may further include dynamically activating, on the at least one processor, one or more potential overflow paths in response to the identified change based upon, at least in part, the conditional availability for each computing device of the plurality of computing devices.

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 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 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.