Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system provides users with the ability to specify logical rules in an application. The system processes the logical rules by generating a DAG of transformations representing the logical rules and converts the DAG of transformations into a logical rules Resilient Distributed Dataset (RDD) DAG of transformations. The system processes events in an event batch against the RDD DAG of transformations to generate a set of output results for the user.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system receive updates to an application, processes the updated application against a continuous stream of data, and continuously generates updated results to a user. In certain embodiments, the system reduces delays associated with application deployment and execution when updates to an application are received. The system enables a user to view results pertaining to an updated application quickly without experiencing the typical deployment and execution delays that occur when updates to an application are made.

Abstract: Techniques for processing event streams are provided that retain attributes or fields from a current event or group during stream aggregation. An exemplary technique includes receiving a stream of events having an input shape having a plurality of attributes and executing a query on the stream of events. The executing includes: (i) summarizing data of a first attribute of the plurality of attributes based on an aggregate function to generate values of data for a result-set, (ii) collecting data of events with values that are identical for a second attribute of the plurality of attributes based on a group by function to group the result-set by the second attribute, and (iii) preserving data of a third attribute of the plurality of attributes based on a current function. The technique further includes outputting events having an output shape having the first attribute, the second attribute, and the third attribute.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system receive updates to an application, processes the updated application against a continuous stream of data, and continuously generates updated results to a user. In certain embodiments, the system reduces delays associated with application deployment and execution when updates to an application are received. The system enables a user to view results pertaining to an updated application quickly without experiencing the typical deployment and execution delays that occur when updates to an application are made.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system provides users with the ability to specify logical rules in an application. The system processes the logical rules by generating a DAG of transformations representing the logical rules and converts the DAG of transformations into a logical rules Resilient Distributed Dataset (RDD) DAG of transformations. The system processes events in an event batch against the RDD DAG of transformations to generate a set of output results for the user.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system receive updates to an application, processes the updated application against a continuous stream of data, and continuously generates updated results to a user. In certain embodiments, the system reduces delays associated with application deployment and execution when updates to an application are received. The system enables a user to view results pertaining to an updated application quickly without experiencing the typical deployment and execution delays that occur when updates to an application are made.

Abstract: Techniques for implementing logical queries in a distributed stream processing system using automatic branching and joins. An exemplary technique includes determining a query is a logical query. The logical query includes two or more summaries based on different groups configured to execute in a single query stage of a stream analytics application. The technique further includes converting the logical query into one or more physical queries. The one or more physical queries are separated into individual query stages, and each of the query stages includes a summary from the two or more summaries that is based on an associated group. The technique further includes generating a directed acyclic graph for the one or more physical queries. The directed acyclic graph includes a physical query transformation for each of the individual query stages.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system provides users with the ability to specify logical rules in an application. The system processes the logical rules by generating a DAG of transformations representing the logical rules and converts the DAG of transformations into a logical rules Resilient Distributed Dataset (RDD) DAG of transformations. The system processes events in an event batch against the RDD DAG of transformations to generate a set of output results for the user.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system provides users with the ability to specify logical rules in an application. The system processes the logical rules by generating a DAG of transformations representing the logical rules and converts the DAG of transformations into a logical rules Resilient Distributed Dataset (RDD) DAG of transformations. The system processes events in an event batch against the RDD DAG of transformations to generate a set of output results for the user.

Abstract: A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system receive updates to an application, processes the updated application against a continuous stream of data, and continuously generates updated results to a user. In certain embodiments, the system reduces delays associated with application deployment and execution when updates to an application are received. The system enables a user to view results pertaining to an updated application quickly without experiencing the typical deployment and execution delays that occur when updates to an application are made.

Abstract: Techniques for processing event streams are provided that retain attributes or fields from a current event or group during stream aggregation. An exemplary technique includes receiving a stream of events having an input shape having a plurality of attributes and executing a query on the stream of events. The executing includes: (i) summarizing data of a first attribute of the plurality of attributes based on an aggregate function to generate values of data for a result-set, (ii) collecting data of events with values that are identical for a second attribute of the plurality of attributes based on a group by function to group the result-set by the second attribute, and (iii) preserving data of a third attribute of the plurality of attributes based on a current function. The technique further includes outputting events having an output shape having the first attribute, the second attribute, and the third attribute.

Abstract: Techniques for implementing logical queries in a distributed stream processing system using automatic branching and joins. An exemplary technique includes determining a query is a logical query. The logical query includes two or more summaries based on different groups configured to execute in a single query stage of a stream analytics application. The technique further includes converting the logical query into one or more physical queries. The one or more physical queries are separated into individual query stages, and each of the query stages includes a summary from the two or more summaries that is based on an associated group. The technique further includes generating a directed acyclic graph for the one or more physical queries. The directed acyclic graph includes a physical query transformation for each of the individual query stages.