Sap Se

Sap Ag

Norman May

Guido Moerkotte

Alexander Boehm

Robert Schulze

Michael Brendle

Ismail Oukid

Donald Kossmann

Tobias Scheuer

Anastasia Ailamaki

Juchang Lee

Jaeyoung Choi

Wolfgang Lehner

Robert Brunel

Carsten Binnig

Martin Kaufmann

Florian Wolf

Robert Lasch

Michael Grossniklaus

Kai-Uwe Sattler

Patrick Voelker

Iraklis Psaroudakis

Tiemo Bang

Christian Bensberg

Mihnea Andrei

Paul Willems

Thomas Legler

Sung Heun Wi

Lars Fricke

Andreas Tonder

Ruediger Karl

Thomas Seufert

Philipp Grosse

Yong Sik Kwon

Ki Hong Kim

Chul Won Lee

Hyunjun Kim

Hannes Jakschitsch

Christian Lemke

Eunsang Kim

Yongjae Chuh

Sang Il Song

Changgyoo Park

Uwe Hahn

Jeong Ae Han

Stefan Noll

Hyogi Jung

Georgios Psaropoulos

Axel Hertzschuch

Junze Bao

Wei Zhou

Siomara Schulz

Robin Joy

Urs Klingsporn

Jens Thilo Teubner

Franz Faerber

Anil Kumar Goel

Daniel Ritter

Kyu Hwan Kim

Andreas Kemmler

Thorsten Glebe

Jonathan Bregler

Yongsik Yoon

Meinolf Block

Lars Dannecker

Werner Thesing

Ivan T Bowman

Frank Renkes

Chaitanya Gottipati

Andre Schefe

Henning Zahn

Martin Schindewolf

Kai Morich

Bernhard Scheirle

Prasanta Ghosh

Jeong Hee Won

Jian Luo

Nirvik Basak

Souvik Pal

Martin Kittel

Holger Mack

Kai Mueller

Christian Kraus

Stefan Hildenbrand

Chang Ge

Abdelkader Sellami

Amit Saonerkar

Elias Yousefi Amin Abadi

Christoph Glania

Armin Risch

Amin Amiri Manjili

Mahammad Valiyev

Nick Weber

Andreas Luethi

Puspanantha Shanmuganathan

Andre Dossinger

Melanie Handreck

Peter Lysakovski

Diego Havenstein

Gabriele Steidl

Alexander Martin Böhm

Systems and methods include establishment of a first authenticated database session with a first database user of a database instance hosting a plurality of tenants, determination of a first tenant of the plurality of tenants based on a mapping of the first database user to the first tenant, determination of a first workload class based on the first tenant, and management of resource consumption of the first authenticated database session based on the first workload class.

Mapping of tenant-specific database sessions to workload classes

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

A stream of database transactions are received and conflicts between transaction types are determined. When a conflict is determined, job components forming a database architecture are configured to execute a first transaction, and in response to completing the first transaction, job components forming the database architecture are reconfigured to execute a second transaction.

Stream-based transaction processing

A method, a system, and a computer program product for decompressing data. One or more compressed blocks in a set of stored compressed blocks responsive to a request to access data in the set of stored compressed blocks are identified. String prefixes inside the identified compressed blocks are decompressed using front coding. String suffixes inside the identified compressed blocks are decompressed using a re-pair decompression. Uncompressed data is generated.

Advanced database decompression

Provided are systems and methods for creating histograms with distinct value sketches integrated therein and for query processing based on the histograms with distinct value sketches. In one example, the method may include storing a histogram that comprises a representation of a bucket of data from a database and that includes a distinct value sketch with a distinct value attribute that identifies an estimated number of distinct values within the bucket of data, receiving a database query, generating a query execution plan for the database query based on the distinct value attribute of the bucket within the distinct value sketch embedded within the histogram, and executing the database query on the bucket of data from the database based on the generated query execution plan.

Histogram with integrated distinct value sketches

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

Systems and methods include reception of an instruction to recover a first database tenant of a first database instance to a first point in time, the first database tenant associated with first database artifacts of the first database instance, and the first database instance including two or more database tenants and, in response to the instruction, creation of a second database instance, selection of backup data of the first database instance based on the first point in time, execution of a recovery of the backup data on the second database instance, export of second database artifacts of the first database tenant from the second database instance to a storage, truncation of the first database artifacts from the first database instance, import of the second database artifacts from the storage to the first database instance, and deletion of the second database instance.

Tenant-level database recovery

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

A system includes determination of a query comprising z predicates, determination of known selectivities of each of a set of conjuncts of the z predicates, and estimation of selectivities of all conjuncts of the z predicates based on the known selectivities and a recursive determination of Cx, where x represents selectivities of all complete conjuncts of the z predicates and C is a complete design matrix complete design matrix (c)=1 if i⊆j, else 0, having indices [0, 2−1].

Selectivity estimation of conjunctive predicates

Embodiments of the present disclosure include techniques for cooperative memory management. In one embodiment, a database management system includes a memory manager for managing a memory buffer. The memory buffer is configured to allocate memory to table data and temporary data dynamically. In some embodiments, table data memory allocations are reduced and temporary data memory allocations are increased to process queries. Temporary data memory allocations may be increased to store temporary data associated with one or more portions of a query. The memory buffer reallocates table data memory allocations and temporary data memory allocations as needed to more efficiently use available memory.

Cooperative memory management in database management systems

A method, a system, and a computer program product for providing a compression service, such as a Compression as a Service (Caas). In some implementations, there is provided a method including receiving, at a compression service, metric information regarding one or more columns of a database server; in response to receiving the metric information, processing, by the compression service, the metric information to identify at least one column as a candidate for compression by the compression service; in response to identifying the at least one column, compressing by the compression service, the at least one column, wherein the compression service uses a hardware accelerator to execution a compression algorithm that compresses the at least one column; and notifying, by the compression service, the database server of the at least one column compressed by the compression service. Related systems, methods, and articles of manufacture are also disclosed.

Compression service using FPGA compression

A transaction processing protocol for serverless database management systems can use a transaction scheduler to guarantee consistent serializable execution though analysis of the access pattern of transaction types and appropriate ordering of the transaction's events at runtime. A transaction topology is determined for each type of transaction and these are combined and used to generate a serialization graph. Cycles in the serialization graph are identified and breaking transaction types which may break the cycles are determined. When transaction requests are received, a breaking type of transaction is scheduled as a last transaction in the current epoch and later transactions not having the breaking transaction type are scheduled to execute in the next epoch.

Techniques and solutions are described for implementing virtual domains. Computing resources in a computing environment are determined and assigned to one or more virtual domains. One or more data structures can be located in a given virtual domain. The computing resources assigned to a virtual domain can be dynamically reconfigured without affecting processes that submit tasks to be performed on data structures in the virtual domains. Tasks can be submitted to a dispatcher, which can determine the appropriate virtual domain for the task and forward the task to the determined virtual domain. Tasks are received by virtual domains and assigned to worker threads, which can access a data structure specified for a given task.

Data structure execution framework using virtual computing domains

Systems and methods include establishment of a first database session with a first database user, determine first session variable values associated with the first database session, the first session variable values including a first tenant value, determine a first workload class based on the first tenant value and on zero or more of the other first session variable values, the first workload class including first parameter values specifying first resource consumption limits, and manage resource consumption of the first database session based on the first parameter values.

Workload classes for tenant-level resource isolation

Growing community of inventors

Karlsruhe, Germany

Norman May