Sap Se

Sap Ag

Christoph Weyerhaeuser

Tobias Mindnich

Johannes Merx

Daniel Baeumges

Julian Schwing

Gerrit Simon Kazmaier

Daniel Patejdl

Alena Bakulina

Joerg Schad

Franz Faerber

Thomas Legler

Isil Pekel

Davide Corradi

Yongsik Yoon

Sung Heun Wi

Stefan Dipper

Jeong Ae Han

Michael Ludwig

Boris Gruschko

Christian Mohr

Martin Strenge

Helmut Prestel

Reiner Singer

Cornelia Kinder

Romans Kasperovics

Sebastian Seifert

Christian Lemke

Rolf Hammer

Kai Baumgarten

Erich Marschall

Thomas Fischer

Kai-Uwe Sattler

Sigrid K Wortmann

Yu Su

Philipp Grosse

Veit Spaegele

Timo Hochberger

Thomas Elvers

Junhui Liu

Juan Ge

Jianfeng Yan

Matthias Vigelius

Berthold Von Haaren

A calculation engine of a database management system is described that determines a multiprovider includes a first data source and a second data source that each require different approaches for operation optimization. The calculation engine can split the multiprovider into a first node corresponding to a first operation compatible with the first data source and a second node corresponding to a second operation compatible with the second data source. The calculation engine can perform the first operation at the first data source to produce a first result and perform the second operation at the second data source to produce a second result. The calculation engine can then merge the first result and the second result according to a third operation, and perform such third operation at the first data source.

Advanced multiprovider optimization

Described herein includes a calculation scenario of a calculation engine that efficiently partitions data for processing at separate hosts, including in parallel, and unions intermediate results from such separate processing when required for further processing. Such parallel processing of partitions can allow for faster processing times, and such unioning of data only when required for further processing can limit the transferring of data that results in slower processing.

Partition aware partial query processing

A method, a system, and a computer program product for executing a query. A query associated with a calculation scenario defining a data flow model that includes one or more calculation nodes is received. Each calculation node corresponds to an operation being performed on one or more database tables stored at a database. The calculation nodes include one or more nodes specifying a window function operation. The window function operation including one or more first attributes and one or more second attributes. A calculation engine executes the calculation scenario by performing, using at least one of the first and second attributes, the window function operation on the database tables stored at the database. Based on the execution of the calculation scenario, a result data set is generated and provided by the database server to the application server.

Database calculation engine with dynamic top operator

A calculation engine of a database management system is described. The calculation engine may receive a query associated with a calculation scenario that defines a data flow model. The data flow model may include one or more calculation nodes, each of which corresponding to an operation performed on one or more database tables stored at a database. The one or more calculation nodes may include at least one calculation node corresponding to a ranking filter operation. The calculation engine may execute the query including by executing the calculation scenario. The executing of the calculation scenario may include performing the ranking filter operation to generate a result corresponding to at least a portion of rows included in a first partition of a database table stored at the database. Related systems, methods, and articles of manufacture are provided.

Ranking filter operation for calculation scenarios

Described herein includes a calculation scenario of a calculation engine that efficiently filters and joins data for processing. The calculation engine enhances the performance of the join operations by allowing join inputs to be pre-filtered more effectively. Such join operations can allow for faster processing times, and a reduction in the amount of data to be joined, resulting in more efficient processing.

Filter and join operations in query processing

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.

Integrated execution of relational and non-relational calculation models by a database system

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. Related apparatus, systems, methods, and articles are also described.

First/last aggregation operator on multiple keyfigures with a single table scan

Described herein is a top operator of a calculation scenario of a calculation engine that can process and rank data according to certain logical criterion requested in a query. Based on the ranked data, the top operator can provide the data results in response to the query. The top operator can provide additional useful data in the data results when certain conditions are met. The top operator can provide the data results in a useful and efficient manner.

Ranking operations in query processing

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 calculation engine of a database management system is described. In some implementations, the calculation engine receives a calculation scenario including a plurality of calculation views comprising one or more relational operations. The calculation engine determines whether a first calculation view includes a second calculation view configured as an operand of one of the relational operations of the first calculation view, and also determines whether the second calculation view comprises a non-relational operation. The calculation engine further converts the plurality of calculation views into a calculation plan via merging the first calculation view with the second calculation view when the first calculation view is determined to comprise the second calculation view as an operand, and replacing the second calculation view with a view search operation when the second calculation view is determined to comprise the non-relational operation. Related systems, methods, and articles of manufacture are also described.

Integration of relational calculation views into a relational engine

A calculation engine of a database management system is described. In some implementations, the calculation engine receives a calculation scenario including a plurality of join operations defining an intersection between at least two nodes. The calculation engine optimizes a first join which is of a certain cardinality, and for which no attributes are requested, other than a join attribute. The optimization includes determining whether a static filter is present for a first node or a second node of the first join, and pruning the first node and/or the second node from the hierarchical join when the attribute is not requested from the first node or the second node and/or when the static filter is not present for the first node or the second node. Related systems, methods, and articles of manufacture are also described.

Calculation engine optimizations for join operations utilizing automatic detection of forced constraints

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.

Data driven multi-provider pruning for query execution plan

A method may include executing a calculation scenario at a database. The calculation scenario may include a parent multiprovider aggregating data from a child multiprovider. The calculation scenario may be executed by mapping a first count keyfigure of the child multiprovider to a first sum keyfigure of the parent multiprovider if the child multiprovider aggregates data from part providers but not from multiproviders. A second sum keyfigure of the child multiprovider may be mapped to the first sum keyfigure of the parent multiprovider if the child multiprovider aggregates data from multiproviders but not from any part providers. The first count keyfigure of the child multiprovider may be mapped to a second count keyfigure of the parent multiprovider and the second sum keyfigure of the child multiprovider may be mapped to the first sum keyfigure of the parent multiprovider if the child multiprovider aggregates data from part providers and multiproviders.

Merging multiproviders in a database calculation scenario

Described herein includes finding and replacing, including automatically, at least one null value in a table for allowing processing of the table with an ABAP-based application. In some implementations, the null values are replaced with replacement values that are compatible with further processing of the data in the table. For example, a replacement value can be a string providing information or a value, such as for performing a calculation.

Null replacing attributes

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Heidelberg, Germany

Christoph Weyerhaeuser