Sap Se

Sybase, Inc.

Ivan T Bowman

Lars Dannecker

Robert Schulze

Gordon Gaumnitz

Daniel James Farrar

Dan Farrar

Anil Kumar Goel

John Smirnios

Muhammed Sharique

Henning Zahn

Mohammed Abouzour

Daniil Golod

Juchang Lee

Mihnea Andrei

Norman May

Thorsten Glebe

Yong Sik Kwon

Anisoara Nica

Jonathan Bregler

Jaeyoung Choi

Steven A Kirk

Alexander Boehm

Daniel Kirmse

Meinolf Block

Peter Bumbulis

Frank Renkes

Hyunjun Kim

Eunsang Kim

Nandan Marathe

Martin Schindewolf

Gunes Aluc

Nathan Auch

Patrick Voelker

David Edward DeHaan

Martin Kittel

Holger Mack

Geno Coschi

Uwe Hahn

Mark Delafranier

Pedram Ghodsnia

Sagar Ranadive

Urs Klingsporn

Karim Khamis

Jeffrey J Plager

Vipul Sharma

Ian Roger McHardy

Mumtaz Ahmad

Güneș Aluç

Xi Deng

Robert Veitch

Melanie Handreck

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 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

Disclosed herein are system, method, and computer program product embodiments for disaster resilience of applications managed by Kubernetes operators. An embodiment operates by creating an orchestration and worker cluster, where the worker cluster is coupled to the orchestration cluster by a proxy server. Custom resources are deployed to the orchestration cluster and custom resource controllers are deployed to the worker cluster. The proxy server federates these custom resources between the orchestration cluster and the worker cluster. During disasters, the worker cluster is recreated and reconciled to prevent loss of the federated cluster.

Disaster resilient federated kubernetes operator

Systems and methods include determination of a first logical page number of a first database page to be persisted, identification of a first blockmap page associated with the first logical page number, determination, from the first blockmap page, of a block number associated with the first logical page number, determination that the block number is an object key, determination, in response to the determination, that the block number is an object key, determination of a first object key to associate with the first logical page number, and writing of the first database page to the object store using the first object key.

Cloud-native object storage for page-based relational database

Disclosed is a system and method for improving database memory consumption and performance using compression of time stamp columns. A number of time stamps of a time series is received. The time stamps have a start time, and are separated by an equal increment of time that defines an interval. The start time and interval are stored in a dictionary of a column store of a database. An index is generated in the column store of the database, the index having a number of index vectors. Using the index vectors, each time stamp of the number of time stamps can be calculated from the start time stored in the dictionary and the position in the time series based on the interval stored in the dictionary.

Compressing time stamp columns

A system and method include storing a table of time series data in a database of a data platform, the table of time series data representing a set of time series blocks. Each time series block of the set of time series blocks has a time series of equally-incremented time intervals and a run length. Each time interval of the time series is associated with one or more values. The run length has a starting position with at least one starting value and an ending position with at least one ending value. The starting position and the at least one starting value is stored for each time series block in a column store of the database. Then, a compressed index is generated in the column store of the database for each time series block, the compressed index comprising the starting position and the at least one starting value.

Linear run length encoding: compressing the index vector

Linear run length encoding is described. A system and method include storing a table of time series data in a database of a data platform, the table of time series data representing a set of time series blocks. Each time series block of the set of time series blocks has a time series of equally-incremented time intervals and a run length. Each time interval of the time series is associated with one or more values. The run length has a starting position with at least one starting value and an ending position with at least one ending value. The starting position and the at least one starting value is stored for each time series block in a column store of the database. Then, a compressed index is generated in the column store of the database for each time series block, the compressed index comprising the starting position and the at least one starting value.

Time stamps for time series data can be efficiently compressed by grouping rows in a database table such that time stamp values of the rows in the group are ordered and characterizable by an increment and an offset, which can be stored for the set of rows respectively in an increment column and an offset column such that the time stamp values of the set of rows are represented by a single slope and offset. A run-length compression can be applied to the increment column and offset column for the table.

Formula-encoded time stamps for time series data

Systems, methods and computer program product embodiments for providing table parameterized function (TPF) functionality in a database are disclosed herein. An embodiment includes defining a TPF in a database, wherein the definition of the TPF defines one or more parameters of the TPF, at least one of the parameters being a table parameter. A query is received that operates to call the TPF, wherein the query defines one or more input parameters, the input parameters comprising at least one table input parameter to be used as input for calling the TPF. The query is processed to produce a result, and the result of the query is returned.

Table parameterized functions in database

Systems and methods are presented for auto-starting and auto-stopping databases in a cloud server environment. In some embodiments, a method includes accessing, by an initial server in a network-based system, a request to connect to a target database located in a target server of the network-based system. The method can include determining, by an administrative database residing in the initial server, a location of the target database residing in the target server, switching an execution context from no database in the target server to a copy of the administrative database in the target server, performing an auto-start procedure to auto-start the target database in the target server, switching the execution context from the administrative database in the target server to the target database in the target server, and transmitting a completion acknowledgement indicating the target server is connected to the target database.

Methods and systems for auto-starting and auto-stopping databases

A method and system for transforming a serial schedule of transactions into a parallel schedule of transaction is disclosed. In one example, a computer system stores a list of data transactions in a transaction log. The computer system then reads a respective data transaction from the transaction log. The computer system determines whether the respective data transaction is dependent on any other currently pending data transaction. In accordance with a determination that the respective data transaction is not dependent on any other currently pending data transaction, the computer system applies the data changes to a reconstructed data set. In accordance with a determination that the respective data transaction is dependent on a currently pending second data transaction, the computer system delays commitment of the respective data transaction until the second data transaction has been applied to the reconstructed data set.

Converting a serial transaction schedule to a parallel transaction schedule

Disclosed herein are system, method, and computer program product embodiments for calibrating and using a stable storage model. An embodiment operates by generating, by a central computer, an access request for a stable storage, wherein the access request comprises a plurality of page accesses; measuring a cost to execute the access request on the stable storage; amortizing the cost over the plurality of page accesses; and calibrating, by the central computer, a stable storage model based on the amortized cost.

Estimation of query input/output (I/O) cost in database

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Kitchener, Canada

Ivan T Bowman