Sap Ag

Sap Se

Guenter Radestock

Andrew Ross

Franz Faerber

Olaf Meincke

Jens-Peter Dittrich

Carsten Thiel

Erich Marschall

Christian M Bartholomae

Bernhard Scheirle

Mihnea Andrei

Thomas Legler

Stefan Dipper

Rolando Blanco

Klaus Nagel

Stefan Unnebrink

Ivan Schreter

Anil Kumar Goel

Robert Schulze

Martin Heidel

Andreas Tonder

Oliver M Steinau

Joern Schmidt

Stefan Biedenstein

Colin Florendo

Daniel Booss

Reza Sherkat

Wolfgang Stephan

David Wein

Sebastian Seifert

Muhammed Sharique

Christian Lemke

Thomas Zurek

Mehul Wagle

Surendra Vishnoi

Martin Richtarsky

Franz-X Faerber

A method may include receiving a first transaction inserting a record into a database and a second transaction deleting the record from the database. A validity period for the record may be determined based on a first commit time at which the first transaction is committed and a second commit time at which the second transaction is committed. A current table and/or a history table of a system versioned table may be updated to include the record based on the validity period of the record. One or more temporal operations may be performed based on the system versioned table. For example, a time travel operation may be performed to retrieve, based on the system versioned table, one or more records that are valid at a given point in time. Related systems and computer program products are also provided.

System versioned table with transactional system time

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

A system includes storage, in a volatile random access memory, of a first data block comprising an array of distinct values of a database table column, and first header data comprising a first pointer to the first data block, determination of a memory size associated with the first header data and the first data block, allocation of a first memory block of the non-volatile random access memory based on the determined memory size, determination of an address of the non-volatile random access memory associated with the allocated first memory block, and writing of an indicator of the number of distinct values of the array and a binary copy of the first data block at the address of the non-volatile random access memory.

Storage of database dictionary structures in non-volatile memory

A system in which first header data and a first logical array are stored in a volatile random access memory, the first logical array stored in a first contiguous memory block, with each array position of the first logical array representing a position in a database table and storing a value identifier of a value associated with the position in the database table, and with the first header data indicating a number of bits used to encode the value identifiers and comprising a first pointer to the first contiguous memory block. A memory size associated with the first header data and the first contiguous memory block is determined, a first memory block of the non-volatile random access memory is allocated based on the determined memory size, an address of the non-volatile random access memory associated with the allocated first memory block is determined, and the number of bits and a binary copy of the first contiguous memory block are written at the address of the non-volatile random access memory.

Storage of database column data in non-volatile memory

A system in which first header data, second header data, a first logical array and a second logical array are stored in volatile random access memory. Each array position of the first logical array represents an identifier of a database column value and stores an offset value associated with an array position of the second logical array, and each array position of the second logical array stores a row position of the database column value. A memory size associated with the first header data, the second header data, the first logical array and the second logical array is determined, a first memory block of a non-volatile random access memory is allocated based on the determined memory size, an address of the non-volatile random access memory associated with the allocated first memory block is determined, and a portion of the first header data, a portion of the second header data, a binary copy of the first logical array and a binary copy of the second logical array are written at the address of the non-volatile random access memory.

Storage of an inverted index in non-volatile memory

A system includes a volatile random access memory storing first header data and a first data block, the first header data including descriptive information and a first pointer to the first data block, and a non-volatile random access memory. The system includes determination of a memory size associated with the first header data and the first data block, allocation of a first memory block of the non-volatile random access memory based on the determined memory size, determination of an address of the non-volatile random access memory associated with the allocated first memory block, and writing of the descriptive information and a binary copy of the first data block at the address of the non-volatile random access memory.

Storage of data structures in non-volatile memory

A compression manager may store, within an index vector, a plurality of value identifiers (IDs), each value ID representing a value within a database. A page generator may designate a number of the value IDs as defining a page within the index vector, so that the index vector includes a plurality of pages, each page including the number of value IDs. The page generator may store the index vector in a secondary memory of a main memory database. An iterator may access a requested value ID, and a page loader may load a corresponding page of the index vector that contains the requested value ID into the main memory database.

Main memory database management using page index vectors

The subject matter disclosed herein provides methods for the dual storage of data using an in-memory array and an on-disk page structure. An in-memory array holding a column of data can be maintained. One or more pages can be maintained. Each of the one or more pages can have one or more rows for storing the column of data. Random access can be provided to a subset of the one or more rows by at least loading the subset of rows from the one or more pages to the in-memory array without loading all of the rows from the one or more pages. Related apparatus, systems, techniques, and articles are also described.

Dual data storage using an in-memory array and an on-disk page structure

Methods and apparatus, including computer program products, for compression of tables based on occurrence of values. In general, a number representing an amount of occurrences of a frequently occurring value in a group of adjacent rows of a column is generated, a vector representing whether the frequently occurring value exists in a row of the column is generated, and the number and the vector are stored to enable searches of the data represented by the number and the vector. The vector may omit a portion representing the group of adjacent rows. The values may be dictionary-based compression values representing business data such as business objects. The compression may be performed in-memory, in parallel, to improve memory utilization, network bandwidth consumption, and processing performance.

Compression of tables based on occurrence of values

Instances of a call stack executing in a database management system and separated by a sampling interval can be compared and upwardly traversed until reaching a point of difference between the instances. A call counter can be incremented for each new frame identified in the second instance of the call stack since the point of difference such that relative call count frequencies of multiple frames (which can include function calls) can be determined. Systems, methods, and computer program products are described.

Call count profiling for estimation of relative hotness of function call frequency

In business systems, one or more methods can be used to reduce an amount of redundant data. In one implementation, a method to reduce redundancy within a data model in a database, in which the data model is represented by at least one table, includes determining a number of distinct values of partial keys in a table. Each partial key represents at least one row in the table. The method includes reordering one or more columns of the table by cardinality of partial keys, in which the cardinality of a partial key represents a number of distinct values of the partial key. The method further includes determining whether pairs of partial keys are functionally dependent and eliminating one or more columns having functional dependencies from the table.

Automatic elimination of functional dependencies between columns

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Karlsruhe, Germany

Guenter Radestock