Sap Se

Jeong Ae Han

Sung Heun Wi

Yongsik Yoon

Norman May

Christoph Weyerhaeuser

Tobias Mindnich

Johannes Merx

Ki Hong Kim

Alexander Boehm

Juchang Lee

Christian Bensberg

Kyu Hwan Kim

Andreas Kemmler

Chul Won Lee

Sang Il Song

Kai Morich

Christoph Glania

Armin Risch

A method may include receiving a query for data to be provided by a database server, wherein the query includes an indication of a maximum lag. The method may further include determining whether a hint is available to apply to the query, wherein the hint affects an execution of the query. When no hint is available, a baseline database server may be selected to be the database server. When the hint is available, a replication server or a cache server may be selected to be the database server based on the hint and the maximum lag. The query may be processed at the selected database server.

Query hints for caching and replication

Embodiments relate to view caching techniques that cache for a limited time, some of the (intermediate) results of a previous query execution, in order to avoid expensive re-computation of query results. Particular embodiments may utilize a cache manager to determine whether information relevant to a subsequent user request can be satisfied by an existing cache instance or view, or whether creation of an additional cache instance is appropriate. At design time, cache defining columns of a view are defined, with user input parameters automatically being cache defining. Cache instances are created for each tuple of literals for the cache defining columns, and for each explicit or implicit group by clause. Certain embodiments may feature enhanced reuse between cache instances, in order to limit memory footprint. Over time a cache instances may be evicted from memory based upon implementation of a policy such as a Least Recently Used (LRU) strategy.

Cached views

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.

Integration of calculation models into SQL layer

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 nodes that each define one or more operations for execution by a calculation engine on the database server. Thereafter, the database server instantiates a runtime model of the calculation scenario based on the nodes of the instantiated calculation scenario. Subsequently, one or more of the nodes are identified as being convertible into a relational database format. These nodes are then used to form a container node. An execution plan of the runtime model of the calculation scenario including the container node is built that is executed by the database server to result in a data set which is provided by the database server to the application server.

Integration of optimization and execution of relational calculation models into SQL layer

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Seoul, South Korea

Jeong Ae Han