Abstract: A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Abstract: A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Abstract: A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Abstract: A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Abstract: A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Abstract: A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Abstract: Methods, systems, and computer-readable media are disclosed for partitioned query execution in event processing systems. A particular method includes receiving a plurality of events via an input stream. The plurality of events is partitioned into one or more groups, and a query application module is instantiated for each of the one or more groups based on a compiled query application plan. Each particular query application module for a particular group is configured to apply a query to events of the particular group to generate partial results. The method includes merging the partial results of each of the query application modules to generate merged output results and providing the output results to an output stream.

Abstract: Systems and methods that eliminate non-qualifying data for queries against data warehouses and improve execution time, via a dynamic filter component(s). In general, such dynamic filter components are derived from data during processing of the query and without being explicitly defined by the users within a query forwarded to the data warehouse. Moreover, an evaluation component can monitor efficiency of filter components (e.g., number of rows that can be eliminated), and dynamically change and/or update the evaluation order of such filters.

Abstract: Methods, systems, and computer-readable media are disclosed for partitioned query execution in event processing systems. A particular method includes receiving a plurality of events via an input stream. The plurality of events is partitioned into one or more groups, and a query application module is instantiated for each of the one or more groups based on a compiled query application plan. Each particular query application module for a particular group is configured to apply a query to events of the particular group to generate partial results. The method includes merging the partial results of each of the query application modules to generate merged output results and providing the output results to an output stream.

Abstract: Systems and methods that eliminate non-qualifying data for queries against data warehouses and improve execution time, via a dynamic filter component(s). In general, such dynamic filter components are derived from data during processing of the query and without being explicitly defined by the users within a query forwarded to the data warehouse. Moreover, an evaluation component can monitor efficiency of filter components (e.g., number of rows that can be eliminated), and dynamically change and/or update the evaluation order of such filters.

Abstract: A method for performing asynchronous statistics updates in a database management system includes receiving a first query against the database, determining if present statistics related to the first query are stale and entering on a queue a request to acquire updated statistics if the present statistics are stale. The queue jobs are executed asynchronously with respect to the query request. As a result, a first query plan may be developed using the present statistics related to the first query. Thus, no delay in processing the query due to statistics updates is incurred. The first query plan may be executed and results given to the requester. At some later time, the request to acquire updated statistics related to the first query is processed asynchronously from the query request. If subsequent queries are received, the queue can delete duplicate requests to update the same statistics. Those subsequent queries can benefit from the updated statistics.