Sap Se

Peter Steinemann

Thomas Legler

Martin Heidel

Rolando Blanco

Chaitanya Gottipati

Vivek Kandiyanallur

Mihnea Andrei

Colin Florendo

Thorsten Glebe

Michael Muehle

Shiping Chen

Sarika Iyer

Stephan Kottler

Jörn Schmidt

Thomas Peh

Muhammed Sharique

Carsten Thiel

Joern Schmidt

Nirvik Basak

Johannes Haeussler

Liang Song

A method, a system, and a computer program product for performing testing of continuous log replays in a database system. A redo log generated for a data record in a database is received. The redo log is indicative of a change in persistency of the data record in the database. Based on the change in persistency recorded in the redo log, a redo handler in the plurality of handlers is selected for execution of a replay of the received redo log. Using the selected redo log, replay of the redo log is executed to generate a state of the data record prior to the change in persistency recoded in the redo log.

Test continuous log replay

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 and whether the log transaction corresponds to a data manipulation language (DML) query or a data definition language (DDL) query. If the data objects are not in the main memory and the query is a DML query, they are copied from persistence data volumes to the main memory. The data objects in main memory 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.

Replay of redo log records in persistency or main memory of database systems

Objects across multiple database manipulation language (DML) redo log records are cached, during continuous replay of redo log records on a secondary system, for the same table partition to enable reuse of such cached objects. Later, these cached objects can be reused as they are accessed during the sequential processing of DML redo records for a specific table partition.

Caching DML statement context during asynchronous database system replication

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 ValueID corresponding to a value to be searched for in a database column can be determined by finding a match in a dictionary for the database column. A row of an index vector for the database column at which to begin a scan for the ValueID can be identified by reading a ValueID lookup table that maps each unique ValueID to a starting position in the index vector for the database column. The ValueID can not occur in the index vector prior to the starting position. The index vector can be scanned beginning at the starting position to find at least one occurrence of the ValueID corresponding to the value being searched, and a result that includes one or more row locations in the database column corresponding to the at least one occurrence in the index vector of the ValueID corresponding to the value being searched can be returned.

Faster main memory scans in unsorted dictionary-encoded vectors

The present disclosure involves systems, software, and computer implemented methods for providing a bucket skiplist. In one example, a method comprises identifying a value to be searched for within a skiplist comprising a data structure for referencing a plurality of ordered nodes, the data structure having a plurality of linked lists, each node representing at least one memory block, wherein each node is associated with a bucket including a set of ordered values, where the first value in each bucket of each node is the relatively lowest value in the bucket, comparing the value to the first values in each bucket to identify a particular node in which the first value may be located, and, in response to determining that the first value of the particular node is not the same as the value to be searched, comparing the value to be searched with the ordered values in the bucket.

Bucket skiplists

The subject matter disclosed herein provides methods for inserting and retrieving value identifiers from a dictionary encoded database using hash maps. A first value identifier and a first value can be accessed from a dictionary storing one or more value identifiers and one or more values. Each value identifier can correspond to a different value. The hash map and the first value can be used to determine a first index in a bucket list for inserting the first value identifier. The bucket list can have one or more indices. Each index can store at least one value identifier. The hash map can include a vector of one or more pointers. Each pointer can refer to at least one of the indices. Based on the determining, the first value identifier can be inserted at the first index without inserting the first value. Related apparatus, systems, techniques, and articles are also described.

Database value identifier hash map

Disclosed herein are system, method, and computer program product embodiments for bloom filter versioning. A request to add a new value to a first bitmap is received, wherein the first bitmap includes a bit sequence corresponding to a most recently added value. It is determined that the first bitmap has exceeded a threshold. A second bitmap is generated, the second bitmap including more bits than the first bitmap. The first bitmap is deleted based upon a determination that a number of readers accessing the first bitmap is zero.

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Mannheim, Germany

Peter Steinemann