Sap Se

Sap Ag

Franz Faerber

Juchang Lee

Ivan Schreter

Guenter Radestock

Boris Gruschko

Christian Mohr

Martin Strenge

Meinolf Block

Andrew Ross

Jonathan Dees

Peter Sanders

Bernhard Jaecksch

Christian M Bartholomae

Christoph Weyerhaeuser

Tobias Mindnich

Stefan Dipper

Erich Marschall

Carsten Binnig

Jochen Seidel

Christian Bensberg

Daniel Baeumges

Frederik Transier

Lars Fricke

Florian Foebel

Ingo Mueller

Martin Weidner

Kesavaprakash Vasudevan

Stefan Hildenbrand

Cornelius Ratsch

Achim Becker

Norman May

Arne Harren

Donald Kossmann

Andreas Tonder

Roland Sedler

Klaus Nagel

Christian Lemke

Gerd G Forstmann

Thomas Zurek

Stefan Unnebrink

Kai-Uwe Sattler

Markus Ulke

Till Westmann

Martin Richtarsky

Emanuel Ziegler

Tobias Elfner

Maciej Kabala

Stefan Kain

Johannes Woehler

A system and method of query processing in a multi-level storage system having a unified table architecture. A query is received by a common query execution engine connected with the unified table architecture, the query specifying a data record. The common query execution engine performs a look-up for the data record based on the query at the first level storage structure. If the data record is not present at the first level storage structure, the common query execution engine performs separate look-ups in each of the second level storage structure and the main store.

Unified table query processing

In one embodiment, the present invention includes a computer-implemented method comprising storing data in an application using an application custom data type and application custom data structure. The data is stored in a database using the application custom data type and the application custom data structure. In one embodiment, a request is sent to access the data from the application to the database. The data is retrieved from the database in response to the request in the application custom data type and the application custom data structure. In one embodiment, the data is sent from the database to a shared memory in the application custom data type and the application custom data structure and the data is retrieved by the application from the shared memory in the application custom data type and the application custom data structure.

Integrated application server and data server processes with matching data formats

Technologies are provided for scaling applications via application scenarios. Portions of application resource content stored in a database are associated with application scenarios. The portions of the resource content associated with an application scenario are replicated to one or more computing nodes associated with the application scenario. Requests associated with the application scenario are transmitted to the one or more one or more computing nodes, where they are processed using the replicated portions of the resource content associated with the application scenario.

Smart elastic scaling based on application scenarios

A system, medium, and method including modifying a graphical view representation of a first table of a set of data, the first table having at least a first column subject to changes in value; modifying a graphical view representation of a second table of a second set of data; determining a query operation of the view representations of the first table and second table; aggregating values of the view representation of the result of the query operation having a same key; determining, based on the aggregated values, values for the first column of the view representation of the first table; and providing an output of the values for the first column of the view representation of the first table, the output values including changed values.

Data modification in hypothetical planning

Methods and apparatus, including computer program products, for block compression of tables with repeated values. In general, value identifiers representing a compressed column of data may be sorted to render repeated values contiguous, and block dictionaries may be generated. A block dictionary may be generated for each block of value identifiers. Each block dictionary may include a list of block identifiers, where each block identifier is associated with a value identifier and there is a block identifier for each unique value in a block. Blocks may have standard sizes and block dictionaries may be reused for multiple blocks.

Block compression of tables with repeated values

Deleting a data record from the second level storage or main store is disclosed. A look-up is performed for the data record in the first level storage, where the data record is defined by a row identifier. If the row identifier is found in the first level storage, a look-up is performed for an updated row identifier representing an update of the data record in the second level storage and the main store, the update of the data record being defined by an updated row identifier. If the updated row identifier is found in the second level storage, an undo log is generated from the first level storage to invalidate a row identifier of the row identifier. A flag is generated representing an invalid updated row identifier, and a redo log is generated to restore the data record in the first level storage.

Multi-level storage architecture

A system and method of logless atomic data movement. An internal transaction is started within a multi-level storage architecture, the internal transaction to merge data from the first level storage structure to the second level storage structure. Committed data is read from a first level storage structure of the multi-level storage architecture as specified by the internal transaction. The committed data from the first level storage structure is inserted into a second level storage structure in a bulk insertion process, and the committed data is marked as being deleted from the first level storage. The internal transaction is then committed to the multi-level storage architecture when the committed data has been inserted into the second level storage structure.

Logless atomic data movement

Innovations for adaptive compression and decompression for dictionaries of a column-store database can reduce the amount of memory used for columns of the database, allowing a system to keep column data in memory for more columns, while delays for access operations remain acceptable. For example, dictionary compression variants use different compression techniques and implementation options, Some dictionary compression variants provide more aggressive compression (reduced memory consumption) but result in slower run-time performance. Other dictionary compression variants provide less aggressive compression (higher memory consumption) but support faster run-time performance. As another example, a compression manager can automatically select a dictionary compression variant for a given column in a column-store database. For different dictionary compression variants, the compression manager predicts run-time performance and compressed dictionary size, given the values of the column, and selects one of the dictionary compression variants.

Adaptive dictionary compression/decompression for column-store databases

A pattern can be identified in at least part of a query whose definition is received in a query request. The identified pattern can be matched with a set of pre-defined patterns, each of which has associated therewith at least one pre-compiled query execution sub-component of a plurality of pre-compiled query execution sub-components retained in a library. A plan for executing the query can be generated, for example by incorporating the pre-compiled query execution sub-component associated with the matched pattern into the plan based on a pseudo code representation of the plan derived from the definition.

Many-core algorithms for in-memory column store databases

A multi-level storage architecture and method of performing a partial merge are disclosed. A main store is partitioned into a passive main part and an active main part, the active main part being empty at a start of the partial merge, the passive main part storing encoded data records of the main store that are not subject to the partial merge. A values index corresponding to a sorted dictionary of the passive main part is set to a cardinality of n. The data records of a second level storage structure is merged into the active main part, the active main part having a dictionary that starts with a value of n+1, such that the merging into the active main part continues an encoding scheme according to the values index of the passive main part.

Partial merge

Growing community of inventors

Walldorf, Germany

Franz Faerber