Abstract: A computing system receives a program. The program is in a first computer language and specifies computer operations on stored data. The computing system is configured to partition the stored data into sets of partitioned data for performing parallel execution on each of the sets of partitioned data. The computing system determines whether the program comprises a thread program component. The computing system, responsive to determining that the program comprises a thread program component, generates computer-generated computer instructions. The computer-generated computer instructions are in a second computer language. The computer-generated computer instructions are dependent on whether the thread program component specifies information for partitioning and grouping the stored data; whether the program comprises a data program component; or whether the data program component specifies information for partitioning and grouping the output data of the thread program component.

Abstract: A computing system receives a program. The program is in a first computer language and specifies computer operations on stored data. The computing system is configured to partition the stored data into sets of partitioned data for performing parallel execution on each of the sets of partitioned data. The computing system determines whether the program comprises a thread program component. The computing system, responsive to determining that the program comprises a thread program component, generates computer-generated computer instructions. The computer-generated computer instructions are in a second computer language. The computer-generated computer instructions are dependent on whether the thread program component specifies information for partitioning and grouping the stored data; whether the program comprises a data program component; or whether the data program component specifies information for partitioning and grouping the output data of the thread program component.

Abstract: Described herein are techniques for reading data from a distributed storage system and for writing data to a distributed storage system. The disclosed techniques make use of efficient computing task and thread usage to minimize or reduce overhead and improve read or write efficiency. For example, read or write tasks may handle multiple read or write operations instead of just a single operation, which may reduce overhead associated with task creation and termination. Additionally, operations within a single task may be processed in parallel. For example, the disclosed techniques provide MapReduce implementations useful in Apache Hadoop that perform better than previous MapReduce implementations.

Abstract: Described herein are techniques for reading data from a distributed storage system and for writing data to a distributed storage system. The disclosed techniques make use of efficient computing task and thread usage to minimize or reduce overhead and improve read or write efficiency. For example, read or write tasks may handle multiple read or write operations instead of just a single operation, which may reduce overhead associated with task creation and termination. Additionally, operations within a single task may be processed in parallel. For example, the disclosed techniques provide MapReduce implementations useful in Apache Hadoop that perform better than previous MapReduce implementations.

Abstract: Described herein are techniques for reading data from a distributed storage system and for writing data to a distributed storage system. The disclosed techniques make use of efficient computing task and thread usage to minimize or reduce overhead and improve read or write efficiency. For example, read or write tasks may handle multiple read or write operations instead of just a single operation, which may reduce overhead associated with task creation and termination. Additionally, operations within a single task may be processed in parallel. For example, the disclosed techniques provide MapReduce implementations useful in Apache Hadoop that perform better than previous MapReduce implementations.

Abstract: Described herein are techniques for reading data from a distributed storage system and for writing data to a distributed storage system. The disclosed techniques make use of efficient computing task and thread usage to minimize or reduce overhead and improve read or write efficiency. For example, read or write tasks may handle multiple read or write operations instead of just a single operation, which may reduce overhead associated with task creation and termination. Additionally, operations within a single task may be processed in parallel. For example, the disclosed techniques provide MapReduce implementations useful in Apache Hadoop that perform better than previous MapReduce implementations.

Abstract: The invention comprises a new and useful process, and appurtenant apparatus, for developing and using workflow applications with decision support. Specifically, the invention includes a decision support engine and a programmatic interface thereto. The decision support engine is a middleware computer program that receives queries from a workflow application, connects to external data sources, executes queries, and returns query results to the workflow application. The programmatic interface allows a developer to integrate business logic and queries into a workflow application that supports decision-making.