Sap Se

Alexander Boehm

Norman May

Guido Moerkotte

Mihnea Andrei

Robert Schulze

Michael Brendle

Michael Grossniklaus

Christian Bensberg

Jaeyoung Choi

Frank Renkes

Jochen Becker

Yong Sik Lee

Jin Young Kook

Juchang Lee

Stefan A Baeuerle

Anisoara Nica

Reza Sherkat

Ki Hong Kim

Sung Heun Wi

Marco H Kroll

Adrian Dragusanu

Patrick Voelker

Jeong Ae Han

Lutz Gericke

Michael Perscheid

Ivan Schreter

Thomas Willhalm

Kyu Hwan Kim

Andreas Kemmler

Thorsten Glebe

Yong Sik Kwon

Daniel Booss

Jonathan Bregler

Yongsik Yoon

Xun Cheng

Meinolf Block

Martin Heidel

Lars Dannecker

Rolando Blanco

Peter Bumbulis

Chul Won Lee

Werner Thesing

Ivan T Bowman

Torsten Strahl

Hyunjun Kim

Eunsang Kim

Henning Zahn

Torsten Ziegler

Martin Schindewolf

Sang Il Song

Kai Morich

Frank-Martin Haas

Johannes Alberti

Oliver Rebholz

Florian Wolf

Martin Kittel

Stefan Noll

Uwe Hahn

Holger Mack

Urs Klingsporn

Jens Thilo Teubner

Abdelkader Sellami

Christian Hansen

Lukas Landgraf

Melanie Handreck

Xun Chen

Gunter Radestock

Christoph Glania

Armin Risch

Yaeyoung Choi

Mahammad Valiyev

Nick Weber

Jens Teubner

Systems and methods include creation of a first instance of a tenant object in a database instance, association of the first instance of the tenant object with a first plurality of database artifacts including first data associated with the first instance of the tenant object, creation of a second instance of the tenant object in the database instance, association of the second instance of the tenant object with a second plurality of database artifacts including second data associated with the second instance of the tenant object, and reception and response to queries on the first data associated with the first instance of the tenant object and to queries on the second data associated with the second instance of the tenant object.

Native multi-tenancy for database system

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

The present disclosure relates to computer-implemented methods, software, and systems for providing extension application mechanisms. Memory is allocated for a virtual environment to run in an address space of an application that is to be extended with extension logic in a secure manner. The virtual environment is configured for execution of commands related to an extension functionality of the application. A virtual processor for an execution of a command of the commands is initialized at the virtual environment. The virtual processor is operable to manage one or more guest operating systems (OS). A first guest OS is loaded at the allocated memory and application logic of the extension functionality is copied into the allocated memory. The virtual environment is started to execute the first guest OS and the application logic of the extension functionality in relation to associated data of the application in the allocated memory.

Extension application mechanisms through intra-process operation systems

A system includes reception of a query execution plan associated with a plurality of query execution pipelines, estimated execution costs and estimated intermediate result cardinalities, determination of one or more precedence relationships of the plurality of query execution pipelines, determination of an execution order of the plurality of query execution pipelines based on the estimated execution costs, the estimated intermediate result cardinalities, and the one or more precedence relationships, and providing of the execution order of the plurality of query execution pipelines and the query execution plan to a query execution engine.

Scheduling of query pipeline execution

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).

The present disclosure involves systems, software, and computer implemented methods for efficient shared bulk loading into optimized storage. One example method includes identifying a data set on a client to load to a server. Load steps that can be performed either on the client or the server are identified. Values for load factors are determined. Load rules are determined that are based on load factors and are for determining processing locations for the load steps. A processing location of either the client or the server is determined for each load step, based on the load rules. Load steps that have a processing location of the client are executed on the data set, on the client. The data set is transferred from the client to the server, over a network. Load steps that have a processing location of the server are executed on the data set, at the server.

Efficient shared bulk loading into optimized storage

A computer-implemented method for database management is provided. The method comprises: receiving, from a client device , first data to be stored in a database system that comprises first data storage configured to store a data table and a deletion history table; storing the first data in second data storage that is external to the database system and that is in communication with the database system via a network; obtaining a link that enables access, via the network, to the first data stored in the second data storage; storing the link in the data table; and performing a deletion operation of the first data, in response to a request from the client device to delete the first data from the database system, wherein the deletion operation comprises: deleting the link from the data table without deleting the first data from the second data storage; and storing the link in the deletion history table with a timestamp corresponding to a point in time when the link is deleted from the data table.

Computer-implemented method for database management, computer program product and database system

Techniques and solutions are described for partitioning data among different types of computer-readable storage media, such as between RAM and disk-based storage. A measured workload can be used to estimate data access for one or more possible partition arrangements. The partitions arrangements can be automatically enumerated. Scores for the partition arrangements can be calculated, where a score can indicate how efficiently a partition arrangement places frequently accessed data into storage specified for frequently-accessed data and placed infrequently accessed data into storage specified for infrequently accessed data.

Workload aware data partitioning

Disclosed herein are system, method, and computer program product embodiments for determining a constraint data statistics object is consistent. An embodiment operates by receiving a query for a database table. The embodiment determines a constraint data statistic object associated with a partition of the database table. The embodiment determines the constraint data statistics object is consistent with a data state of the partition of the database table based on consistency metadata of the constraint data statistics object. The embodiment processes the query for the database table based on the determining the constraint data statistics object is consistent with the data state of the partition of the database table.

Synopsis based advanced partition elimination

Persisting data in logical tiers of an in-memory database based on aging rules of the database system. The database system can be configured to age data by moving the data from a first logical tier supporting high performance data access to a second logical tier supporting lower performance data access, thereby improving the operational efficiency of the database system.

Providing database storage to facilitate the aging of database-accessible data

A system for cardinality estimation for database query planning is provided. In some implementations, the system performs operations comprising determining, based on a first bounds and a second bounds for a combination of query predicates, whether a solution for the combination of the query predicates exists which includes both the first and second bounds. When the solution is determined to exist, the operations can further comprise determining at least one selectivity estimate for the combination of the query predicates. A query plan for execution of a database query comprising the query predicates can be generated based on the at least one selectivity estimate, and the database query can be executed through execution of the query plan. Related systems, methods, and articles of manufacture are also described.

Selectivity estimation for database query planning

A system includes a non-volatile random access memory storing a column store main fragment of a column of a database table, and a processing unit to read the column store main fragment from the non-volatile random access memory. A volatile random access memory storing a column store delta fragment of the column of the database table may also be included, in which the processing unit is to write to the column store delta fragment. According to some systems, the stored column store main fragment is byte-addressable, and is copied from the volatile random access memory to the non-volatile random access memory without using a filesystem cache.

Column store main fragments in non-volatile RAM and the column store main fragments are merged with delta fragments, wherein the column store main fragments are not allocated to volatile random access memory and initialized from disk

Growing community of inventors

Sandhausen, Germany

Alexander Boehm