Abstract: An Application Programming Interface (API) processes object storage requests in an object storage format that are received from client applications. A mapper maps the object storage formatted requests into database statements and executes the statements within a database management system. Results returned from the statements are mapped by the mapper to object-storage constructs and provided to the API in object storage format. The object storage formatted requests are provided from the API to the client applications to satisfy the original client applications' requests.

Abstract: A query that is frequently processed to access an object storage is identified. Results from the query returned from the object storage is transformed into a relational database format as a materialized view. When the query is submitted a subsequent time, updated results are managed from the materialized view, other materialized views, and/or the object storage when needed.

Abstract: A data-changing request is received on a first data source system. Metadata for a data source object associated with the request is obtained. The request is executed on a second data source system and results are obtained. A selection between multiple replication processes is made based on the metadata and the results. The selected process is performed on each of the remaining data source systems to synchronize/replicate data changes on the remaining data source systems. In an embodiment, bidirectional and concurrent data connections are permitted to a source object for one data source system and a replicated version of the source object on a different source system. In an embodiment, each data source system includes an agent that establishes an out-of-band connection to a synchronization/replication server, such that synchronization/replication is processed by the server without an affirmative connection being made from the data source systems to the server.

Abstract: A database includes a Value List Compression (VLC) predicate evaluator. A table identified in a query that is being processed is identified as having compressed data values. The predicate evaluator compares a query predicate of the query against actual decompressed values noted in a dictionary for the table and the predicate evaluator maintains a bitmap for selective ones of the actual values that satisfy the query predicate. The matched bitmap positions are processed against an index maintained in the table for the actual values to provide selective decompressed table entries as results for the query.

Abstract: In some examples, a system sends a transaction to a database server to cause storing of data of the transaction in a cache of the database server, where the data in the cache is for inclusion in a backup of data from the database server to a remote data store (e.g., the backup may be in a cloud and may be a snapshot). The system detects a failure associated with the database server, and in response to detecting the failure, requests, from the database server or a replacement database server, transaction information of at least one transaction that was successfully applied to the remote data store, the transaction information based on the backup of data. The system causes replay one or more transactions to recover data at the database server or the replacement database server, to perform recovery of the database server or the replacement database server to a current state.

Abstract: Search spaces for obtaining query execution plans for a query are identified. The search spaces are subdivided into sub-search spaces. Searches are initiated within the sub search spaces and plan costs for competing query execution plans are noted along with search costs associated with continuing to search the sub-search spaces. A decision is made based on the plan costs and search costs for utilizing search resources as to when to terminate the searching and to select the then-existing lowest cost query execution plan as an optimal query execution plan for executing the query.

Abstract: Database processing engines of a single cluster are configured such that each engine is a primary engine and a dedicated fallback engine to one other engine of the cluster. In an embodiment, the cluster includes more than two processing engines.

Abstract: A database system comprises a plurality of processing modules arranged to process data objects from a plurality of data object servers based upon a database query from a client computer system. A control task module is arranged to iteratively dynamically assign data objects from the plurality of data object servers to each of the plurality of processing modules based upon processing activity associated with the processing module during the database query from the client computer system. Alternatively, or in addition to, the control task module is arranged to iteratively dynamically assign data objects from the plurality of data object servers to each of the plurality of processing modules based upon a characteristic of the data objects to be dynamically assigned to the processing module during the database query from the client computer system.

Abstract: Data portions of a database can be grouped and ranked in order of priory for reassignment from one or more maps to another one or more maps. It should be noted that a first map can assign the data portions to a first configuration of processors for processing the data portions, and a second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The data portions are reassigned in groups during an available time (window) for reassignment by taking the first one of the groups can be reassigned (“moved”) in the available, then the second one in the available reaming time, and so on, until no group of data portions can be moved in the remaining time or all of them have been moved.

Abstract: An Application Programming Interface (API) processes object storage requests in an object storage format that are received from client applications. A mapper maps the object storage formatted requests into database statements and executes the statements within a database management system. Results returned from the statements are mapped by the mapper to object-storage constructs and provided to the API in object storage format. The object storage formatted requests are provided from the API to the client applications to satisfy the original client applications' requests.

Abstract: A query that is frequently processed to access an object storage is identified. Results from the query returned from the object storage is transformed into a relational database format as a materialized view. When the query is submitted a subsequent time, updated results are managed from the materialized view, other materialized views, and/or the object storage when needed.

Abstract: A data-changing request is received on a first data source system. Metadata for a data source object associated with the request is obtained. The request is executed on a second data source system and results are obtained. A selection between multiple replication processes is made based on the metadata and the results. The selected process is performed on each of the remaining data source systems to synchronize/replicate data changes on the remaining data source systems. In an embodiment, bidirectional and concurrent data connections are permitted to a source object for one data source system and a replicated version of the source object on a different source system. In an embodiment, each data source system includes an agent that establishes an out-of-band connection to a synchronization/replication server, such that synchronization/replication is processed by the server without an affirmative connection being made from the data source systems to the server.

Abstract: An order (or priority) for reassigning data portions of a database from a first map to a second map can be determined. It should be noted that the first map assigns the data portions to a first configuration of processors for processing the data portions, and the second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The order can be determined by grouping the data in multiple groups and ordering the groups in accordance with a determined priority for reassignment. In doing so, one or more relationships between the data portions and/or cost of use for each one the groups can be considered.

Abstract: Data or one or more operations can be provided, based on one or more characteristics associated with the data and/or operations, to a designated computing group or pool of computing resources designated for handling the data and/or operations with the particular data characteristic(s). The designated computing group can, for example, be one of multiple computing groups in the same system or device. As such, all of the computing groups can still function together in the same system or device, for example, in parallel. However, each one of the multiple computing groups can, for example, be defined or predefined to include one or more computing resources that are more suitable for storing and/or processing data with one or more data characteristics or handle operations with one or more determined characteristics.

Abstract: Search spaces for obtaining query execution plans for a query are identified. The search spaces are subdivided into sub-search spaces. Searches are initiated within the sub search spaces and plan costs for competing query execution plans are noted along with search costs associated with continuing to search the sub-search spaces. A decision is made based on the plan costs and search costs for utilizing search resources as to when to terminate the searching and to select the then-existing lowest cost query execution plan as an optimal query execution plan for executing the query.

Abstract: Database processing engines of a single cluster are configured such that each engine is a primary engine and a dedicated fallback engine to one other engine of the cluster. In an embodiment, the cluster includes more than two processing engines.

Abstract: A database includes a Value List Compression (VLC) predicate evaluator. A table identified in a query that is being processed is identified as having compressed data values. The predicate evaluator compares a query predicate of the query against actual decompressed values noted in a dictionary for the table and the predicate evaluator maintains a bitmap for selective ones of the actual values that satisfy the query predicate. The matched bitmap positions are processed against an index maintained in the table for the actual values to provide selective decompressed table entries as results for the query.

Abstract: Data portions of a database can be grouped and ranked in order of priory for reassignment from one or more maps to another one or more maps. It should be noted that a first map can assign the data portions to a first configuration of processors for processing the data portions, and a second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The data portions are reassigned in groups during an available time (window) for reassignment by taking the first one of the groups can be reassigned (“moved”) in the available, then the second one in the available reaming time, and so on, until no group of data portions can be moved in the remaining time or all of them have been moved.

Abstract: An order (or priority) for reassigning data portions of a database from a first map to a second map can be determined. It should be noted that the first map assigns the data portions to a first configuration of processors for processing the data portions, and the second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The order can be determined by grouping the data in multiple groups and ordering the groups in accordance with a determined priority for reassignment In doing so, one or more relationships between the data portions and/or cost of use for each one the groups can be considered.

Abstract: Data or one or more operations can be provided, based on one or more characteristics associated with the data and/or operations, to a designated computing group or pool of computing resources designated for handling the data and/or operations with the particular data characteristic(s). The designated computing group can, for example, be one of multiple computing groups in the same system or device. As such, all of the computing groups can still function together in the same system or device, for example, in parallel. However, each one of the multiple computing groups can, for example, be defined or predefined to include one or more computing resources that are more suitable for storing and/or processing data with one or more data characteristics or handle operations with one or more determined characteristics.