Sap Se

Sap Ag

Daniel Baeumges

Christoph Weyerhaeuser

Tobias Mindnich

Gerrit Simon Kazmaier

Johannes Merx

Lars Fricke

Christian Bensberg

Thomas Legler

Franz Faerber

Stefan Dipper

Erich Marschall

Daniel Patejdl

Christian Hensberg

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.

Managing and querying spatial point data in column stores

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.

Columnwise range k-nearest neighbors search queries

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.

Augmented query optimization by data flow graph model optimizer

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.

Split processing paths for a database calculation engine

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.

On-the-fly calculation scenario provision during query runtime

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.

Performing complex operations in a database using a semantic layer

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.

Independent table nodes in parallelized database environments

A select query or a data referencing a calculation scenario is received by a database server from a remote application server. The specified calculation scenario is one of a plurality of calculation scenarios and it defines a data flow model that includes one or more calculation 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 executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set. This data set is then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

Database calculation engine

Columnwise range K-nearest neighbors search queries

A spatial aggregation query of a database is processed by receiving data specifying a maximum bounded rectangle for point data responsive to the query and specifying one or more grid partitions of the maximum bounded rectangle (in which at least one of the partitions is partially aggregated. Thereafter, for each partition, a number of points responsive to the query in each partition and a center of gravity of the points in each partition is computed. Data characterizing the corresponding computed number of points and center of gravity is then provided (e.g., persisted, loaded, transmitted, displayed, etc.). Related apparatus, systems, techniques and articles are also described.

Columnwise spatial aggregation

A query statement is received that specifies a count distinct. Thereafter, a data flow graph that comprises a plurality of nodes for executing the query is generated. The nodes provide aggregation operations, sorting of results on join attributes and vertically appending columns of count distinct results with intermediate results from at least one of the aggregation operations. Thereafter, execution of the query is initiated using the data flow graph. Related apparatus, systems, techniques and articles are also described.

Calculating count distinct using vertical unions

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.

Rule-based extendable query optimizer

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.

Data flow graph optimization using adaptive rule chaining

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.

Growing community of inventors

Viersen, Germany

Daniel Baeumges