Abstract: Processing a job request for multiple versions of a distributed computing service. The service processing node does this by at least interleavingly (e.g., via time sharing with rapid context switching, or by actually concurrently) running a first runtime library associated with a first service version of the distributed computerized service and a second runtime library associated with a different service version of the distributed computerized service. While running the first runtime library, job requests of a first service version may be at least partially processed using a first set of one or more executables that interact with the first runtime library. While running the second runtime library, job requests of a second service version may be at least partially processed using a second set of one or more executables that interact with the second runtime library.

Abstract: Processing received job requests for a multi-versioned distributed computerized service. For each received job request, the job request is channeled to an appropriate service processing node that depends on the version of the distributed computing service that is to handle the job request. A version of the distributed computing service is assigned to the incoming job request. A service processing node that runs a runtime library for the assigned service version is then identified. The identified service processing node also has an appropriate set of one or more executables that allows the service processing node to plan an appropriate role (e.g., compiler, scheduler, worker) in the distributed computing service. The job request is then dispatched to the identified service processing node.

Abstract: Processing a job request for multiple versions of a distributed computing service. The service processing node does this by at least interleavingly (e.g., via time sharing with rapid context switching, or by actually concurrently) running a first runtime library associated with a first service version of the distributed computerized service and a second runtime library associated with a different service version of the distributed computerized service. While running the first runtime library, job requests of a first service version may be at least partially processed using a first set of one or more executables that interact with the first runtime library. While running the second runtime library, job requests of a second service version may be at least partially processed using a second set of one or more executables that interact with the second runtime library.

Abstract: Processing a job request for multiple versions of a distributed computing service. The service processing node does this by at least interleavingly (e.g., via time sharing with rapid context switching, or by actually concurrently) running a first runtime library associated with a first service version of the distributed computerized service and a second runtime library associated with a different service version of the distributed computerized service. While running the first runtime library, job requests of a first service version may be at least partially processed using a first set of one or more executables that interact with the first runtime library. While running the second runtime library, job requests of a second service version may be at least partially processed using a second set of one or more executables that interact with the second runtime library.

Abstract: Processing received job requests for a multi-versioned distributed computerized service. For each received job request, the job request is channeled to an appropriate service processing node that depends on the version of the distributed computing service that is to handle the job request. A version of the distributed computing service is assigned to the incoming job request. A service processing node that runs a runtime library for the assigned service version is then identified. The identified service processing node also has an appropriate set of one or more executables that allows the service processing node to plan an appropriate role (e.g., compiler, scheduler, worker) in the distributed computing service. The job request is then dispatched to the identified service processing node.

Abstract: Processing a job request for multiple versions of a distributed computing service. The service processing node does this by at least interleavingly (e.g., via time sharing with rapid context switching, or by actually concurrently) running a first runtime library associated with a first service version of the distributed computerized service and a second runtime library associated with a different service version of the distributed computerized service. While running the first runtime library, job requests of a first service version may be at least partially processed using a first set of one or more executables that interact with the first runtime library. While running the second runtime library, job requests of a second service version may be at least partially processed using a second set of one or more executables that interact with the second runtime library.

Abstract: A system and a method for the deferral and incremental performance of integrity processing for data rolled into a table of a data warehouse and for the deferral and incremental performance of maintenance on data rolled out of a table of the data warehouse while permitting general users online access to other data contained in the table. The rolled in and rolled out data are contained within specific data partitions of the table. Each data partition in the table has an associated distinguishable partition ID. Each data partition can also have associated attributes corresponding to containing rolled-in and rolled-out data respectively. Table operation mechanisms are arranged such that general users of the table do not access rolled in data in a partition for which integrity processing has not yet been performed and do not access rolled out data.

Abstract: A system and a method for the deferral and incremental performance of integrity processing for data rolled into a table of a data warehouse and for the deferral and incremental performance of maintenance on data rolled out of a table of the data warehouse while permitting general users online access to other data contained in the table. The rolled in and rolled out data are contained within specific data partitions of the table. Each data partition in the table has an associated distinguishable partition ID. Each data partition can also have associated attributes corresponding to containing rolled-in and rolled-out data respectively. Table operation mechanisms are arranged such that general users of the table do not access rolled in data in a partition for which integrity processing has not yet been performed and do not access rolled out data.

Abstract: A method, apparatus and article of manufacture, implementing the method, accesses rows in a range-clustered table in a database management system. A table stores data that is organized into rows and columns. At least one column is designated as a key column. A particular row in the table is accessed based on a relationship between a specified key value and a predetermined range of key values associated with the key column. The table has slots for storing rows, and the slots are organized in accordance with the values of the predetermined range.

Abstract: A method, apparatus and article of manufacture, implementing the method, accesses rows in a range-clustered table in a database management system. A table stores data that is organized into rows and columns. At least one column is designated as a key column. A particular row in the table is accessed based on a relationship between a specified key value and a predetermined range of key values associated with the key column. The table has slots for storing rows, and the slots are organized in accordance with the values of the predetermined range.