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Joerg Schad

Christoph Weyerhaeuser

Tobias Mindnich

Johannes Merx

Julian Schwing

Stefan Dipper

Daniel Patejdl

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

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.

Decoupling filter injection and evaluation by forced pushdown of filter attributes in calculation models

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.

Dynamic resource-based parallelization in distributed query execution frameworks

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. The calculation nodes each define one or more operations to execute by a calculation engine on the database server. Thereafter, the database server instantiates a runtime model of the calculation scenario by accessing a local settings object stored with the calculation scenario that specifies local settings for the calculation scenario. Subsequently, the database server executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set. The data set can then be provided by the database server to the application server.

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Leimen, Germany

Joerg Schad