Sap Se

Robert Schulze

Colin Florendo

Lars Dannecker

Norman May

Ivan T Bowman

Gordon Gaumnitz

Mihnea Andrei

Thomas Legler

Amit Pathak

Alexander Boehm

Guido Moerkotte

Christian Lemke

Michael Brendle

Michael Grossniklaus

Reza Sherkat

Sebastian Seifert

Dan Farrar

Martin Heidel

Oliver Horn

Daniel James Farrar

Yanhong Wang

Adrian Dragusanu

Guenter Radestock

Rolando Blanco

Amarnadh Sai Eluri

Andreas Tonder

Carsten Thiel

Joern Schmidt

Vivek Kandiyanallur

Panfeng Zhou

Bernhard Scheirle

Pushkar Khadilkar

Zheng-Wei She

Junze Bao

Wei Zhou

Ivan Schreter

Thorsten Glebe

Wolfgang Lehner

Michael Muehle

Thomas Peh

Klaus Otto Mueller

Matthias Boehm

Neeraj Kulkarni

Kai-Uwe Sattler

Janardhan Hungund

Simhachala Sasikanth Gottapu

Sarika Iyer

Robert Lasch

Souvik Pal

Mehul Wagle

Holger Bischoff

Deepak Shrivastava

Amit Saonerker

Anup Ghatage

Randall Hammon

Mahammad Valiyev

Nick Weber

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

A database system detects an operation to move a first database object from an in-memory store to a persistence store, wherein the first database object is in either a page-loadable or column-loadable format in the in-memory store, and wherein the first database object comprises a plurality of subcomponents. Each subcomponent of the first database object is examined separately from other subcomponents of the plurality of subcomponents. In response to determining that a first subcomponent of the plurality of subcomponents has a datatype and a compression type supported by a unified persistence format, the first subcomponent is stored in a unified persistence format in the persistence store. In response to determining that a second subcomponent of the plurality of subcomponents has either a datatype or a compression type which is not supported by the unified persistence format, the second subcomponent is stored in a serial persistence format in the persistence store.

Unified persistence overall feature

According to some embodiments, methods and systems may be associated with a cloud computing environment. A workload prediction framework may receive observed workload information associated with a database in the cloud computing environment (e.g., a DataBase as a Service ('DBaaS')). Based on the observed workload information, a Statement Arrival Rate (“SAR”) prediction may be generated. In addition, a host variable assignment prediction may be generated based on the observed workload information. The workload prediction framework may then use the SAR prediction and the host variable assignment prediction to automatically create a workload prediction for the database. A physical database design advisor (e.g., a table partitioning advisor) may receive the workload prediction and, responsive to the workload prediction, automatically generate a recommended physical layout for the database (e.g., using a cost model, the current physical layout, and an objective function).

Framework for workload prediction and physical database design

A database management system is described that can encode data to generate a plurality of data vectors. The database management system can perform the encoding by using a dictionary. The database management system can adaptively reorder the plurality of data vectors to prepare for compression of the plurality of data vectors. During a forward pass of the adaptive reordering, most frequent values of a data vector of the plurality of data vectors can be moved-up in the data vector. During a backward pass of the adaptive reordering, content within a rest range of a plurality of rest ranges can be rearranged within the plurality of data vectors according to frequencies of the content. The reordering according to frequency can further sort the rest range by value. Related apparatuses, systems, methods, techniques, computer programmable products, computer readable media, and articles are also described.

Adaptive compression optimization for effective pruning

In some example embodiments, there may be provided a method including detecting a query operator of a predetermined type requiring a sorting or a scanning responsive to the query operator at a column-oriented database; determining a sorted dictionary at the column-oriented database is eligible for direct access by at least checking that the column-oriented database provides sorted dictionary access and that dictionary collation and a collation of the query operator are the same; when the sorted dictionary is eligible, reading directly from the sorted dictionary a batch of sorted values; and processing, based on the batch sorted values read from the sorted dictionary, the query operator.

Efficient computation of order by, order by with limit, min, and max in column-oriented databases

A system and method for faster access for compressed time series data. A set of blocks are generated based on a table stored in a database of the data platform. The table stores data associated with multiple sources of data provided as consecutive values, each block containing index vectors having a range of the consecutive values. A block index is generated for each block having a field start vector representing a starting position of the block relative to the range of consecutive values, and a starting value vector representing a value of the block at the starting position. The field start vector of the block index is accessed to obtain the starting position of a field corresponding to a first block and to the range of the consecutive values of the first block. The starting value vector is then determined from the block index to determine an end and a length of the field of the first block.

Faster access for compressed time series data: the block index

The present disclosure involves systems, software, and computer implemented methods for improved design and implementation of data access metrics for automated physical database design. An example method includes identifying a database workload for which index advisor access counters are to be tracked. Each SQL statement in the database workload is executed. For each SQL statement, attribute sets are determined for which a selection predicate filters a result for an SQL statement. An output cardinality of each selection predicate is determined. A logarithmic counter for an attribute set corresponding to the selection predicate is determined based on the output cardinality of the selection predicate. The determined logarithmic counter is incremented. Respective values for logarithmic counters of the determined attributes are provided to an index advisor. The index advisor determines attribute sets for which to propose an index based on the logarithmic counters of the respective attribute sets.

Implementation of data access metrics for automated physical database design

According to some embodiments, methods and systems may be associated with a cloud computing environment. A workload prediction framework may receive observed workload information associated with a database in the cloud computing environment (e.g., a DataBase as a Service ('DBaaS')). Based on the observed workload information, a Statement Arrival Rate ('SAR') prediction may be generated. In addition, a host variable assignment prediction may be generated based on the observed workload information. The workload prediction framework may then use the SAR prediction and the host variable assignment prediction to automatically create a workload prediction for the database. A physical database design advisor (e.g., a table partitioning advisor) may receive the workload prediction and, responsive to the workload prediction, automatically generate a recommended physical layout for the database (e.g., using a cost model, the current physical layout, and an objective function).

A method, a system, and a computer program product for placement or storage of data structures in memory/storage locations. A type of a data structure for storing data and a type of data access to the data structure are determined. The type of data access includes a first and a second type of data access. A frequency of each type of access to each type of data structure accessed by a query is determined. Using the determined frequency, a number of first type of data accesses to the data structure is compared to a number of second type of accesses to the data structure. The numbers of first and second types of data access are compared to a predetermined threshold percentage of a total number of data accesses to the data structure. Based on the comparisons, a physical memory location for storing data is determined.

Storage of data structures

System, method, and various embodiments for providing a data access and recommendation system are described herein. An embodiment operates by identifying a column access of one or more data values of a first column of a plurality of columns of a table of a database during a sampling period. A count of how many of the one or more data values are accessed during the column access are recorded. A first counter is incremented by the count. The sampling period is determined to have expired. A load recommendation on how to load data values into the first column based on the first counter is computed. The load recommendation for implementation into the database for one or more subsequent column accesses is provided.

Data access and recommendation system

Disclosed herein are system, method, and computer-program product embodiments for generating a paged and in-memory representation of a database object. An embodiment operates by maintaining in-memory and paged form primitives unique to the database object or a substructure thereof in a database such that the in-memory and paged form primitives are capable of providing the in-memory and paged representations of the database objects, respectively. Thereafter, a load configuration for the database object is determined. Based on the load configuration, the in-memory and/or paged representations of the database object are generated using the in-memory form primitive or the paged form primitive unique to the database object, respectively. Subsequently, the in-memory and/or paged representations of the database object are stored in the database.

Hybrid column store providing both paged and memory-resident configurations

System, method, and various embodiments for providing a hybrid data storage and load system are described herein. An embodiment operates by receiving a lookup request comprising a row identifier (ROWID) corresponding to a row of a table of a database. Which of a first set of pages corresponds to a sorted position of the ROWID is identified. The identified page of the first set of pages is loaded from the disk storage to the memory storage. A sorted row position of the ROWID is identified from a memory block of the loaded page from the first set of pages. One of a second set of pages that includes the sorted row position is identified. The identified one of the second set of pages is loaded from the disk storage into memory. The loaded identified one of the second set of pages is searched for the unsorted position of the ROWID.

Hybrid data storage and load system with ROWID lookup

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Walldorf, Germany

Robert Schulze