Abstract: A query that requests a filter attribute is received by a database server from a remote application server that is associated with at least one calculation scenario that includes calculation nodes having relational operators and calculation nodes having non-relational operators. The calculation nodes having relational or non-relational operators are optimized by a calculation engine model optimizer and the calculation nodes having relational operators are further optimized by a relational database optimizer. Thereafter, the operations defined by the optimized calculation nodes can be executed to result in a responsive data set. Next, the data set is provided to the application server by the database server.

Abstract: Methods and apparatus, including computer program products, are provided for first and last aggregation. In one aspect, there is provided a method, which may include receiving, by a calculation engine, a query; detecting, by the calculation engine, whether the query includes a first aggregation and/or a last aggregation over at least one group and at least one keyfigure; optimizing the received query, when the detecting indicates the received query includes the first aggregation and/or the last aggregation, wherein the optimizing further comprises initiating execution of the received query by at least: performing a single read of a table, detecting, from the single table read, at least one group, and indicating, in the detected at least one group, the first aggregation in the at least one keyfigure and/or the last aggregation in the at least one keyfigure; and returning, for the at least one detected group, the indicated first aggregation and/or the indicated second aggregation.

Abstract: A query of spatial data is received by a database comprising a columnar data store storing data in a column-oriented structure. Thereafter, a spatial data set is mapped to physical storage in the database using a space-filling curve. The spatial data set is then compacted and such compacted data can be used to retrieve data from the database that is responsive to the query. Related apparatus, systems, techniques and articles are also described.

Abstract: According to an aspect, a system includes a query engine configured to obtain a multi-provider query model for a query specifying a filter to be applied on provider partitions. The obtained multi-provider query model incorporates a pruning plan. The query engine includes a query optimizer configured to execute the pruning plan during optimization of the multi-provider query model. The pruning plan determines if each of the provider partitions includes content that is required to execute the filter based on pruning information. The query optimizer configured to reduce a scope of the query by excluding at least one provider partition that is determined as not required to execute the filter.

Abstract: A range k-nearest neighbor search query of a database is processed by first defining an inner rectangle bounded within a circle around a center point specified by the range k-nearest neighbor search query. Thereafter, a distance to the center point is calculated for each point within the inner rectangle. Query results are returned if k or more points are within the inner rectangle. Otherwise, at least one additional query is executed. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received by a database server from a remote application server that is associated with a calculation scenario that defines a data flow model including one or more calculation nodes including stacked multiproviders. Subsequently, the database server instantiates the calculation scenario and afterwards optimizes the calculation scenario. As part of the optimization, the calculation scenario is optimized by merging the two multiproviders. Thereafter, the operations defined by the calculation nodes of the optimized calculation scenario can be executed to result in a responsive data set. Next, the data set is provided to the application server by the database server.

Abstract: Methods and apparatus, including computer program products, are provided for union node pruning. In one aspect, there is provided a method, which may include receiving, by a calculation engine, a query; processing a calculation scenario including a union node; accessing a pruning table associated with the union node, wherein the pruning table includes semantic information describing the first input from the first data source node and the second input from the second data source node; determining whether the first data source node and the second data source node can be pruned by at least comparing the semantic information to at least one filter of the query; and pruning, based on a result of the determining, at least one the first data source node or the second data source node. Related apparatus, systems, methods, and articles are also described.

Abstract: According to an aspect, a system for moving a filter in a query model to ensure query semantics includes a query engine configured to obtain a query model for a query to be executed on a database. The query engine includes a query optimizer configured to instantiate the query model by merging the query with the query model. The query optimizer includes a filter mover configured to move a filter to a point within the instantiated query model such that semantics for the query within the instantiated query model is ensured.

Abstract: A database server receives a query from a remote application server that is associated with a calculation scenario. The calculation scenario defines a data flow model that includes one or more calculation nodes that each define one or more operations to execute on the database server. A top operator node of the calculation nodes specifies a plurality of attributes and the query requests a subset of the attributes specified by the top operator node; Thereafter, the database server instantiates the calculation scenario so that it is optimized by requesting only the subset of attributes. The database server then executes the operations defined by the calculation nodes of the optimized calculation scenario to result in a responsive data set. The database server then provides the data set to the application server.

Abstract: A database server receives a request to perform a primary query on a table of a database. A first table query can be generated and can include a starting row identifier, ROW A, and a number of rows, n, for generating a data chunk from the table of the database. Multiple table queries can be performed each having a different starting row identifier and each defining the number of rows forming a data chunk. The primary query can be extended with the first table query in preparation for performing the primary query on the first data chunk.

Abstract: A query is received, and in response, an initial data flow graph is generated that includes a plurality of nodes for executing the query with at least one of the nodes having at least one associated hint. The initial data flow graph is subsequently optimized using a model optimizer having a rules engine using a plurality rules to optimize the initial data flow graph. The at least one associated hint is used by the model optimizer to change how at least one of the plurality of rules is applied. Thereafter, execution of the query is initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query that requests a filter attribute is received by a database server from a remote application server that is associated with a calculation scenario that defines a data flow model including one or more calculation nodes. Subsequently, the database server instantiates the calculation scenario. As part of the instantiation, the calculation scenario is optimized by (i) pushing down a filter attribute from a first node to a lowest available child node, (ii) removing the filter attribute from the first node, and (iii) removing non-required join-attributes from the instantiated calculation scenarios. Thereafter, the operations defined by the calculation nodes of the instantiated calculation scenario can be executed to result in a responsive data set. Next, the data set is provided to the application server by the database server.

Abstract: A stacked calculation view, defining a calculation scenario, having calculation views associated with high-level programming logic which can include complex analytical privileges. Resolving, using a high-level programming logic layer, a calculation view having associated high-level programming logic with the next calculation view in the stack to generate a sub-query. Converting the sub-query into a format readable by a database system and separating the part of the sub-query provided by the high-level programming logic from the sub-query to form a high-level programming logic filter in the database system format. Merge the high-level programming logic filter into the calculation scenario to provide a calculation scenario including high-level programming logic information in the database system format.

Abstract: A calculation engine is described that executes calculation scenarios comprising a plurality of calculation nodes that each specify operations to be performed to execute the query. One of the nodes can be a semantic node that is used to modify a higher-level programming language query that stacks the calculation scenario on top of another semantic node based calculation scenario for operations that call for processing of non-processable key figures. Non-processable key figures being key figures that produce incorrect semantics when processed by higher-level calculation scenarios. Related apparatus, systems, methods, and articles are also described.

Abstract: As part of query processing within a distributed execution environment framework, available resources taken into account when generating an execution plan and/or executing an execution plan to determine whether to parallelize any operations. Related apparatus, systems, methods and articles are also described.

Abstract: Based on received input, a vertex table of the database layer and a vertex key column in the vertex table can be identified. The vertex key column defines vertices of a graph representation. Also based on the received input, an edge table of the database layer, an edge key column in the edge table, a source column in the edge table, and a target column in the edge table can be identified. The source column and target column define relationships between vertices of the plurality of vertices. Within a graph workspace object, graph relationships linking the vertices defined by the vertex key column via edges defined by the source column and target column in the edge table can be assigned, and the graph workspace can be generated in a higher level programming layer based on the graph workspace object.

Abstract: A calculation engine is described that executes calculation scenarios comprising a plurality of calculation nodes that specify operations to be performed to execute the query. One of the nodes can be a semantic node that is used to modify the query for operations requiring special handling including handling of hierarchy views. Related apparatus, systems, methods, and articles are also described.

Abstract: A query is received by a database server from a remote application server. The query encapsulates an on-the-fly calculation scenario that defines a data flow model that includes one or more calculation nodes. Thereafter, the database server instantiates the on-the-fly calculation scenario. The database server then executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set so that the database server can provide the data set to the application server. Related apparatus, systems, methods, and articles are also described.

Abstract: According to an aspect, a database system for integrating calculation models into execution plans includes a first engine configured to parse a query to be applied on a database. The first engine is configured to invoke a second engine during query compilation. The second engine is configured to instantiate a calculation model based on the query, and the second engine is configured to derive a converted calculation model by converting the calculation model into a format compatible with the first engine. The first engine is configured to incorporate the converted calculation model into an execution plan during the query compilation and execute the query on the database according to execution plan.

Abstract: The present invention includes a database with a semantic layer for performing standard SQL and complex operations custom to one or more application. In one embodiment the present invention includes receiving a data request from an application in a database, the data request specifying standard SQL operations and custom application operations to be performed for said application. The data request is processed in a semantic layer of the database. The semantic layer processes the standard SQL operations and the custom application operations and generates a logical execution plan comprising the standard SQL operations and the custom application operations. The logical execution plan is converted into a physical execution plan, which is executed by the database to generate a data result. The data result may be sent back to the application.