Abstract: A database server receives a multi-part query from a remote application server. The multi-part query specifies a calculation scenario defining a data flow model that includes a plurality of calculation nodes that include a main section of nodes and two or more branches of nodes. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the specified calculation scenario and additionally executes the operations defined by the calculation nodes of the main section of the instantiated calculation scenario a single time and executes the operations defined by the calculation nodes of the branches to result in respective responsive data sets corresponding to each branch. The responsive data sets are then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

Abstract: A query associated with a calculation scenario comprising a plurality of calculation nodes can be received by a calculation engine. The calculation scenario is instantiated and the query is transformed into a convex hull filter based on the at least one multiple selection condition that can be executed at any of the calculation nodes. The instantiated calculation scenario and the convex hull filter are executed to provide a result set. Related apparatus, systems, methods, and articles are also described.

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.

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: 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.

Abstract: A dynamic split node defined within a calculation model can receive data being operated on by a calculation plan generated based on the calculation model. A partition specification can be applied to one or more reference columns in a table containing at least some of the received data. The applying can cause the table to be split such that a plurality of records in the table are partitioned according to the partition specification. A separate processing path can be set for each partition, and execution of the calculation plan can continue using the separate processing paths, each of which can be assigned to a processing node of a plurality of available processing nodes.

Abstract: A query is received by a database server from a remote application server. The query is associated with a calculation scenario that defines a data flow model that includes one or more calculation nodes with each calculation node defining one or more operations to execute on the database server. Thereafter, at least one of the calculation nodes is transformed into a star schema. Next, the database server instantiates the calculation scenario with the transformed at least one calculation node. Subsequently, a calculation engine of the database server executes the operations defined by the calculation nodes of the instantiated calculation scenario other than the transformed at least one calculation node and, additionally, an OLAP engine executes the transformed at least one calculation node to collectively result in a responsive data set.

Abstract: A query is received and an initial data flow graph comprising a plurality of nodes is generated for executing the query. The initial data flow graph is optimized using a model optimizer that accesses at least one of a plurality of patterns to identify a matching pattern and executes at least one optimization rule associated with a matching pattern. Execution of the query is then initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query statement is received that requires at least one calculated attribute. Thereafter, a data flow graph is generated that includes a plurality of nodes for executing the query. At least one of the nodes corresponds to the at least one calculated attribute and has at least one level of child nodes. The data flow graph is generated by generating at least one filter for each of the nodes corresponding to the at least one calculated attribute and by pushing down the generated filters to a corresponding child node. Once the data flow graph is generated, execution of the query can be initiated using the generated data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query associated with a calculation scenario comprising a plurality of calculation nodes can be received by a calculation engine. The calculation scenario is instantiated and the query is transformed into a convex hull filter based on the at least one multiple selection condition that can be executed at any of the calculation nodes. The instantiated calculation scenario and the convex hull filter are executed to provide a result set. Related apparatus, systems, methods, and articles are also described.

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 the query for operations requiring special handling. 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: A database server receives a multi-part query from a remote application server. The multi-part query specifies a calculation scenario defining a data flow model that includes a plurality of calculation nodes that include a main section of nodes and two or more branches of nodes. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the specified calculation scenario and additionally executes the operations defined by the calculation nodes of the main section of the instantiated calculation scenario a single time and executes the operations defined by the calculation nodes of the branches to result in respective responsive data sets corresponding to each branch. The responsive data sets are then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received by a database server from a remote application server. The query is associated with a calculation model that defines a data flow model that includes a plurality of calculation nodes that each define one or more operations to execute on the database server. Thereafter, the database server dynamically determine, using at least one attribute of at least one dataset responsive to the query, that intermediate results provided by at least one of the operations specified by at least one of the nodes of the calculation model require partitioning. The database server then modifies the calculation model to partition operations on the at least one dataset based on the dynamic determination. The database server subsequently instantiates the modified calculation model so that it can be executed to generate at least one result set.

Abstract: A query is received which causes an initial data flow graph that includes a plurality of nodes that are used to execute the query is generated. Thereafter, the initial data flow graph is optimized using a model optimizer that includes an optimizer framework and an application programming interface (API). The optimizer framework provides logic to restructure the initial data flow graph and a rules engine for executing one or more optimization rules. The API allows for registration of new optimization rules to be executed by the rules engine. Execution of the query is then initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A dynamic split node defined within a calculation model can receive data being operated on by a calculation plan generated based on the calculation model. A partition specification can be applied to one or more reference columns in a table containing at least some of the received data. The applying can cause the table to be split such that a plurality of records in the table are partitioned according to the partition specification. A separate processing path can be set for each partition, and execution of the calculation plan can continue using the separate processing paths, each of which can be assigned to a processing node of a plurality of available processing nodes.

Abstract: A query is received and an initial data flow graph comprising a plurality of nodes is generated for executing the query. The initial data flow graph is optimized using a model optimizer that accesses at least one of a plurality of patterns to identify a matching pattern and executes at least one optimization rule associated with a matching pattern. Execution of the query is then initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A recipient node of a multi-node data partitioning landscape can receive, directly from a requesting machine without being handled by a master node, a first data request related to a table. A target node of a plurality of processing nodes can be identified to handle the data request. The determining can include the recipient node applying partitioning information to determine a target data partition of the plurality of data partitions to which the data request should be directed and mapping information associating each data partition of the plurality of data partitions with an assigned node of the plurality of processing nodes. The recipient node can redirect the data request to the target node so that the target node can act on the target data partition in response to the data request.

Abstract: A database query of point data among two or more axes of a database is received. The database stores point data in distinct integer vectors with a shared dictionary. Thereafter, the dictionary is scanned to determine boundaries for each axis specified by the query. In response, results characterizing data responsive to the query within the determined boundaries for each axis are returned. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received which causes an initial data flow graph that includes a plurality of nodes that are used to execute the query is generated. Thereafter, the initial data flow graph is optimized using a model optimizer that includes an optimizer framework and an application programming interface (API). The optimizer framework provides logic to restructure the initial data flow graph and a rules engine for executing one or more optimization rules. The API allows for registration of new optimization rules to be executed by the rules engine. Execution of the query is then initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.