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Joern Schmidt

Martin Heidel

Rolando Blanco

Andreas Tonder

Christian Bensberg

Thomas Legler

Robert Schulze

Guenter Radestock

Lars Fricke

Edward-Robert Tyercha

Carsten Thiel

Shiping Chen

Vivek Kandiyanallur

Bernhard Scheirle

Holger Bischoff

Ivan Schreter

Mihnea Andrei

Thomas Peh

Frederik Transier

Christoph Roterring

Peter Steinemann

Stephan Kottler

SungGun Jung

Taesik Yoon

Xin Liu

Liang Song

Sukhyeun Cho

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

In one aspect, there is provided a method. The method may include adding a tuple phrase to a phrase index as part of a lazy creation of the phrase index. The adding may include adding the tuple phrase to a dictionary mapped to the phrase index, adding to the phrase index a document identifier and a positional information for the tuple phrase, and adapting a first threshold and a second threshold value, the first threshold representing a threshold lower estimate of query processing burden associated with a least burdensome tuple phrase in the dictionary and the second threshold representing a top estimate of query processing burden associated with a most burdensome tuple phrase in the dictionary. The method may further include sending a query response including a generated query result corresponding to the tuple phrase.

Self-adapting resource aware phrase indexes

A system includes reception of a query to update a partition key value of a first set of rows of a database table, determination, based on fetched row identifiers, of a first subset of the first rows which are not stored on the first database server node and a second subset of the first rows which are stored on the first database server node, fetching of the first subset of rows from the database server nodes, update of the partition key value of each row of the fetched first subset of rows, instructing of the first database server node to: store the updated rows of the fetched first subset in the first partition stored on the first database server node, and update the partition key value of each of the second subset of rows of the partition stored on the first database server node.

Partition move in case of table update

Disclosed herein are system, method, and computer program product embodiments for a database recovery and optimization with batch processing system. An embodiment operates by retrieving a database log that includes a plurality of operations for modifying data of a database stored across a plurality of tables. From the database log, a plurality of consecutive insert operations for inserting data into the database are identified. The consecutive insert operations are sorted by table. The sorted insert operations are grouped into a batch message. The batch message is transmit to the database for replay. An acknowledgement is received that the replay has completed.

Database recovery and optimization with batch processing

A computer implemented method for manipulating data comprises receiving log transaction information from a primary database system at a secondary database system and parsing the log transaction information to identify data objects that have been modified on the primary database system. The secondary database system determines whether the data objects are present in main memory on the secondary database system. If the data objects are not in the main memory, they are copied from persistence data volumes to the main memory. The data objects are modified by replaying the log transactions on the data objects in the main memory. Then, the data objects in the persistence data volumes are modified. Related apparatus, systems, techniques and articles are also described.

Common runtime and recovery framework for manipulating data in main memory of database systems

A first store and a second store can be configured to store a first partition of a table of a database and a second partition of the table of the database, respectively. A database management system can be configured to at least update at least a first portion of the first partition where the update includes an update to a first primary key column in the first partition and verify that the first primary key column is unique across the first partition and the second partition. A first row having a first primary key value of the first primary key column from the first partition can be deleted, when the first primary key value in the first partition conflicts with a second primary key value of a second primary key column in the second partition.

Synchronized updates across multiple database partitions

An update to a partitioning column for a partitioned table in a database can be performed by handling rows for which the update will not require moving to a different partition separately (e.g. 'locally') for each partition followed by re-inserting those rows that will require moving to a different partition and deleting them from the original partition. The re-inserted rows are directed to a correct partition by application of the partitioning criteria for the table. Methods, articles, and systems are described.

Updating a partitioning column

A database operation is initiated in an in-memory database that stores persistent data in a secondary storage and has at least one associated log. The database operation involves data having a size above a pre-defined threshold. Thereafter, the data is split into a plurality of chunks and the database operation is executed by parallelizing each chunk over a plurality of hierarchically arranged threads. Related systems, apparatus, methods and computer program products are also described.

Maintaining in-memory database consistency by parallelizing persistent data and log entries

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Walldorf, Germany

Joern Schmidt