Abstract: A column-store database computer system responsive to database requests for the update and retrieval of data from within a stable data table providing for the storage of database tuples within a column-store organized database structure. A positional delta tree data structure is implemented in the memory space of the computer system and is operatively coupled in an update data transfer path between a database engine interface and the stable data table. The positional delta tree data structure includes a differential data storage layer operative to store differential update data values in positionally defined relative reference to database tuples stored by the stable data table.

Abstract: A method, apparatus, and system for join operations of a plurality of relations that are distributed over a plurality of storage locations over a network of computing components.

Abstract: A method, apparatus, and system for join operations of a plurality of relations that are distributed over a plurality of storage locations over a network of computing components.

Abstract: Example data management systems and methods are described. In one implementation, a method identifies multiple files to process based on a received query and identifies multiple execution nodes available to process the multiple files. The method initially creates multiple scansets, each including a portion of the multiple files, and assigns each scanset to one of the execution nodes based on a file assignment model. The multiple scansets are processed by the multiple execution nodes. If the method determines that a particular execution node has finished processing all files in its assigned scanset, an unprocessed file is reassigned from another execution node to the particular execution node.

Abstract: A system and method for improving input/output and memory utilization in a database are described. The system may implement an automatic vertical table decomposition process to achieve the improved input/output, storage and memory utilization in a database.

Abstract: A method includes storing table data for a table in a plurality of partitions and for maintaining approximate or good enough clustering. The method includes creating one or more new partitions based on changes to the table, wherein at least one of the one or more new partitions overlap with each other or previous partitions resulting in a decrease in a degree of clustering of the table. The method includes determining that a degree of clustering of the table data is below a clustering threshold. The method further includes reclustering one or more partitions of the table to improve the degree of clustering of the table in response to one or more of: determining that the degree of clustering has fallen below the clustering threshold, an explicit user command from a user, and/or as part of a DML command. Reclustering may be performed in incremental steps to iteratively improve clustering.

Abstract: A column-store database engine operates in response to database requests for the update and retrieval of data from within a stable data table and provides for the storage of database tuples within a column-store organized database structure. A positional delta tree data structure is implemented in the memory space of the database engine and is operatively coupled in an update data transfer path between a database engine interface and the stable data table. The positional delta tree data structure includes a differential data storage layer operative to store differential update data values in positionally defined relative reference to database tuples stored by the stable data table.

Abstract: A system, apparatus, and method for processing queries wherein the query includes a request to access or delete data and accessing metadata associated with the set of data, the metadata defining data characteristics of the set of data and identifying at least sets of data that need or not need to be accessed or deleted based on the metadata without accessing the actual data in the set of data; also methods to optimize processing of some operations based on the collected metadata on data.

Abstract: Example data management systems and methods are described. In one implementation, a method identifies multiple files to process based on a received query and identifies multiple execution nodes available to process the multiple files. The method initially creates multiple scansets, each including a portion of the multiple files, and assigns each scanset to one of the execution nodes based on a file assignment model. The multiple scansets are processed by the multiple execution nodes. If the method determines that a particular execution node has finished processing all files in its assigned scanset, an unprocessed file is reassigned from another execution node to the particular execution node.

Abstract: Example data management systems and methods are described. In one implementation, a method identifies multiple files to process based on a received query and identifies multiple execution nodes available to process the multiple files. The method initially creates multiple scansets, each including a portion of the multiple files, and assigns each scanset to one of the execution nodes based on a file assignment model. The multiple scansets are processed by the multiple execution nodes. If the method determines that a particular execution node has finished processing all files in its assigned scanset, an unprocessed file is reassigned from another execution node to the particular execution node.

Abstract: Example resource provisioning systems and methods are described. In one implementation, an execution platform accesses multiple remote storage devices. The execution platform includes multiple virtual warehouses, each of which includes a cache to store data retrieved from the remote storage devices and a processor that is independent of the remote storage devices. A resource manager is coupled to the execution platform and monitors received data processing requests and resource utilization. The resource manager also determines whether additional virtual warehouses are needed based on the data processing requests and the resource utilization. If additional virtual warehouses are needed, the resource manager provisions a new virtual warehouse.

Abstract: Example resource provisioning systems and methods are described. In one implementation, an execution platform accesses multiple remote storage devices. The execution platform includes multiple virtual warehouses, each of which includes a cache to store data retrieved from the remote storage devices and a processor that is independent of the remote storage devices. A resource manager is coupled to the execution platform and monitors received data processing requests and resource utilization. The resource manager also determines whether additional virtual warehouses are needed based on the data processing requests and the resource utilization. If additional virtual warehouses are needed, the resource manager provisions a new virtual warehouse.

Abstract: A constant-vector computation system and method are provided that exploit constant-value blocks during data processing in a database. The system and method may be implemented in any data processing system that processes data at a vector or block granularity.

Abstract: A system, apparatus, and method for managing data storage and data access with querying data and filtering value ranges using only a constant amount of computer memory in the implementation of bloom filters based on a first consumption of a relation.

Abstract: Example resource provisioning systems and methods are described. In one implementation, an execution platform accesses multiple remote storage devices. The execution platform includes multiple virtual warehouses, each of which includes a cache to store data retrieved from the remote storage devices and a processor that is independent of the remote storage devices. A resource manager is coupled to the execution platform and monitors received data processing requests and resource utilization. The resource manager also determines whether additional virtual warehouses are needed based on the data processing requests and the resource utilization. If additional virtual warehouses are needed, the resource manager provisions a new virtual warehouse.

Abstract: A method, apparatus, and system for join operations of a plurality of relations that are distributed over a plurality of storage locations over a network of computing components.

Abstract: Example data management systems and methods are described. In one implementation, a method identifies multiple files to process based on a received query and identifies multiple execution nodes available to process the multiple files. The method initially creates multiple scansets, each including a portion of the multiple files, and assigns each scanset to one of the execution nodes based on a file assignment model. The multiple scansets are processed by the multiple execution nodes. If the method determines that a particular execution node has finished processing all files in its assigned scanset, an unprocessed file is reassigned from another execution node to the particular execution node.

Abstract: Example caching systems and methods are described. In one implementation, a method identifies multiple files used to process a query and distributes each of the multiple files to a particular execution node to execute the query. Each execution node determines whether the distributed file is stored in the execution node's cache. If the execution node determines that the file is stored in the cache, it processes the query using the cached file. If the file is not stored in the cache, the execution node retrieves the file from a remote storage device, stores the file in the execution node's cache, and processes the query using the file.

Abstract: Example resource provisioning systems and methods are described. In one implementation, an execution platform accesses multiple remote storage devices. The execution platform includes multiple virtual warehouses, each of which includes a cache to store data retrieved from the remote storage devices and a processor that is independent of the remote storage devices. A resource manager is coupled to the execution platform and monitors received data processing requests and resource utilization. The resource manager also determines whether additional virtual warehouses are needed based on the data processing requests and the resource utilization. If additional virtual warehouses are needed, the resource manager provisions a new virtual warehouse.

Abstract: Example resource management systems and methods are described. In one implementation, a resource manager is configured to manage data processing tasks associated with multiple data elements. An execution platform is coupled to the resource manager and includes multiple execution nodes configured to store data retrieved from multiple remote storage devices. Each execution node includes a cache and a processor, where the cache and processor are independent of the remote storage devices. A metadata manager is configured to access metadata associated with at least a portion of the multiple data elements.