Abstract: A storage system communicatively coupled to a database management system (DBMS performs storage-side scanning of data sources that are not stored in native database storage format of the DBMS. Data sources for external tables are accessible in a storage system referred to as a distributed data access system (DDAS), e.g. a Hadoop Distributed File System. To execute a query that references an external table, a DBMS first generates an execution plan. The DDAS supplies the DBMS with information that specifies each portion of the data source, and specifies which data node to use to access the portion. The DBMS sends a request for each portion to the respective data node, requesting that the data node generate rows from data in the portion. The request may specify scanning criteria, specifying one or more columns to project and/or filter on, and code modules for the data node to execute to generate records.

Abstract: An approach, referred to herein as parallelized-external-table access, generates rows from a single external table in parallel for a given query. Under parallelized-external-table access, an execution plan generated for the query includes multiple work granules that generate rows for a single external table from a data source. Such work granules are referred to herein as external work granules. Each external work granule of the execution plan may be assigned to a slave process, which executes the external work granule in parallel with another slave process executing another external work granule. External tables are accessible on a cluster of data nodes in a distributed data access system (e.g. Hadoop Distributed File System) connected to a DBMS.

Abstract: A storage system communicatively coupled to a DBMS performs storage-side scanning of data sources that are not stored in the native database storage format of the DBMS. Data sources for external tables are accessible in a storage system referred to herein as a distributed data access system, e.g. a Hadoop Distributed File System. To execute a query that references an external table, a DBMS first generates an execution plan. The distributed data access system supplies the DBMS with information that specifies each portion of the data source, and specifies which data node to use to access the portion. The DBMS sends a request for each portion to the respective data node, the request requesting that the data node generate rows from data in the portion. The request may specify scanning criteria, specifying one or more columns to project and/or filter on. The request may also specify code modules for the data node to execute to generate rows or records and columns.

Abstract: An approach, referred to herein as parallelized-external-table access, generates rows from a single external table in parallel for a given query. Under parallelized-external-table access, an execution plan generated for the query includes multiple work granules that generate rows for a single external table from a data source. Such work granules are referred to herein as external work granules. Each external work granule of the execution plan may be assigned to a slave process, which executes the external work granule in parallel with another slave process executing another external work granule. External tables are accessible on a cluster of data nodes in a distributed data access system (e.g. Hadoop Distributed File System) connected to a DBMS.

Abstract: Embodiments of the invention enable data to be moved from one or more external sources into a database system. A plurality of processes are executed concurrently. Each of the processes are executable to move data from at least a portion of the one or more external sources into the database system, so that executing the plurality of processes concurrently moves data from different portions of the one or more external sources into the database system in parallel. Execution of the plurality of processes are coordinated from the database system.

Abstract: Methods of inserting data in parallel into a partitioned data storage object are disclosed. Multiple processes are used to insert the data, and multiple processes may insert data into the same partition at the same time. Write conflicts are avoided through use of buffers and a brokering scheme to manage the data flows into the partitioned data storage object. Also disclosed are methods to optimize the size of the buffers to minimize bottlenecks.

Abstract: Methods of inserting data in parallel into a partitioned data storage object are disclosed. Multiple processes are used to insert the data, and multiple processes may insert data into the same partition at the same time. Write conflicts are avoided through use of buffers and a brokering scheme to manage the data flows into the partitioned data storage object. Also disclosed are methods to optimize the size of the buffers to minimize bottlenecks.

Abstract: A method and apparatus for parallelizing operations that change a database is provided. A coordinator process receives a statement that requires data to be written into the database. In response to the statement, the coordinator process assigns granules of work to multiple processes. Each of the multiple processes executes the granule of work by writing to the database a distinct portion of the set of data that is to be added to the database. The various portions of data are then merged to update the set of data to be added to the database.