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Thomas Legler

Ivan Schreter

Robert Schulze

Rolando Blanco

Peter Steinemann

Mihnea Andrei

Norman May

Christoph Weyerhaeuser

Tobias Mindnich

Guenter Radestock

Daniel Baeumges

Andreas Tonder

Klaus Otto Mueller

Bernhard Scheirle

Anil Kumar Goel

Martin Heidel

David Wein

Oliver Horn

Chaitanya Gottipati

Carsten Thiel

Joern Schmidt

Kai-Uwe Sattler

Robert Lasch

Anastasia Ailamaki

Georgios Psaropoulos

Juchang Lee

Steffen Geissinger

Muhammed Sharique

Example methods and systems are directed to the enabling of object history tracking in an object-oriented programming environment. According to some examples, a tracking class includes a tracking function and a tracked class includes a tracked function. A system receives user input to link the tracked function to the tracking function. Executable code is executed to create a target object of the tracked class and to execute the tracked function with respect to the target object. Execution of the tracked function triggers the tracking function to obtain tracking data relating to the target object. The tracking data is stored as part of the target object. According to some examples, the system receives an indication of an error that occurred during execution of the executable code, retrieves the tracking data from the target object, and causes presentation of the tracking data at a user device.

Object history tracking

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

Example methods and systems are directed to inverted indexes. According to some examples, an inverted index is generated based on source data and a posting list threshold. The inverted index comprises one or more restricted posting lists. Each restricted posting list has a maximum size corresponding to the posting list threshold. The method may include receiving a search query comprising a value that identifies a restricted posting list of the one or more restricted posting lists. The value may be used to retrieve and return one or more record identifiers from the identified restricted posting list. A record identifier uniquely identifies one of the plurality of records in the source data.

Adjustable inverted indexes

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 may include determining a threshold quantity of attempts to optimistically perform a first transaction operating data stored in a database. The threshold quantity of attempts may be determined based on an expected workload of the first transaction and/or a workload at the database. The first transaction may be performed optimistically including by tracking cache lines accessed by the first transaction and detecting, based on a second transaction writing to a cache line accessed by the first transaction, a conflict between the first transaction and the second transaction. If the first transaction is not successful performed after the threshold quantity of attempts to optimistically perform the first transaction, the first transaction may be performed in a fallback mode including by acquiring a lock to prevent the second transaction from accessing a same data in the database as the first transaction. Related systems and articles of manufacture are also provided.

Adaptive hardware transactional memory based concurrency control

In some example embodiments, there may be provided a method including detecting a query operator of a predetermined type requiring a sorting or a scanning responsive to the query operator at a column-oriented database; determining a sorted dictionary at the column-oriented database is eligible for direct access by at least checking that the column-oriented database provides sorted dictionary access and that dictionary collation and a collation of the query operator are the same; when the sorted dictionary is eligible, reading directly from the sorted dictionary a batch of sorted values; and processing, based on the batch sorted values read from the sorted dictionary, the query operator.

Efficient computation of order by, order by with limit, min, and max in column-oriented databases

A method for optimizing memory access for database operations is provided. The method may include performing a first database operation by at least executing a first instruction stream associated with the first database operation. The first database operation may be defined as a coroutine to at least enable the execution of the first instruction stream to be interleaved with an execution of a second instruction stream. In response to determining that data required to continue the execution of the first instruction stream is absent from a cache, the execution of the first instruction stream may be suspended and the second instruction stream may be executed instead. Related systems and articles of manufacture, including computer program products, are also provided.

Determining an optimum quantity of interleaved instruction streams of defined coroutines

A method, a system, and a computer program product for placement or storage of data structures in memory/storage locations. A type of a data structure for storing data and a type of data access to the data structure are determined. The type of data access includes a first and a second type of data access. A frequency of each type of access to each type of data structure accessed by a query is determined. Using the determined frequency, a number of first type of data accesses to the data structure is compared to a number of second type of accesses to the data structure. The numbers of first and second types of data access are compared to a predetermined threshold percentage of a total number of data accesses to the data structure. Based on the comparisons, a physical memory location for storing data is determined.

Storage of data structures

A system for unloading tables of a database is provided. In some aspects, the system performs operations including determining that a number of accesses to a table occurring within a time period has satisfied an access threshold. The operations may further include identifying, in response to the determining, a first timestamp indicating a most recent access to the table. The operations may further include determining whether a difference between a current timestamp and the first timestamp satisfies a first time threshold. The operations may further include comparing, in response to the difference satisfying the first time threshold, a ratio of the difference and a size of the table to a ratio threshold. The operations may further include unloading, in response to satisfying the ratio threshold, the table. The operations may further include adjusting, based on the feedback, the first time threshold and/or the ratio threshold.

Auto unload

A system includes storage of data into a target memory location allocated to a target leaf node of a tree-based index structure, the target leaf node being a child node of a parent node of the tree-based index structure, where the tree-based index structure comprises one or more other leaf nodes which are child nodes of the parent node, and each of the target leaf node and the one or more other leaf nodes is associated with a plurality of allocated memory locations, incremental identification of all unused allocated memory locations between a first allocated memory location of a left-most one of the target leaf node and the one or more other leaf nodes and a last used allocated memory location of a right-most one of the target leaf node and the one or more other leaf nodes, and movement of data stored in the target leaf node and the one or more other leaf nodes into the identified unused allocated memory locations.

Partial compression of tree-based index structure

There is provided a method for compressing a first tree data structure. The method includes determining, by a processor, to compress a first tree data structure associated with a dictionary of a database management system. The method further includes compressing the first tree data structure to generate a compressed tree data structure. The compressing includes traversing, by the processor and in response to the determining, the first tree data structure on a lowest level. The compressing further includes identifying, by the processor and in response to traversing, empty nodes on the lowest level. The compressing further includes removing the identified empty nodes to compress the lowest level. The compressing further includes constructing, in response to the removing, a second level of the compressed tree data structure based on the compressed lowest level, the second level higher in the compressed tree data structure than the compressed lowest level.

Automated optimization for in-memory data structures of column store databases

A system for cache efficient reading of column values in a database is provided. In some aspects, the system performs operations including pre-fetching, asynchronously and in response to a request for data in a column store database system, a plurality of first values associated with the requested data. The request may identify a row of the column store database system associated with the requested data. The plurality of first values may be located in the row. The operations may further include storing the plurality of first values in a cache memory. The operations may further include pre-fetching, asynchronously and based on the plurality of first values, a plurality of second values. The operations may further include storing the plurality of second values in the cache memory. The operations may further include reading, in response to the storing the plurality of second values, the requested data from the cache memory.

Cache efficient reading of result values in a column store database

Disclosed herein are system, method, and computer program product embodiments for generating a data structure and responding to an aggregation query. An embodiment operates by a computer implemented method that includes receiving, by at least one processor, a request to enter data in a block in a data structure. The method further includes reading, by the at least one processor, a first aggregated value stored in the data structure. The first aggregated value represents a sum over at least values associated with data that is stored in the block in the data structure. The method further includes calculating, by the at least one processor, a second aggregated value by adding the first aggregated value and a value associated with the data to be entered in the data structure. The method further includes storing, by the at least one processor, at least part of the data to be entered and the second aggregated value in the block in the data structure. After the storing, the second aggregated value represents the sum over the at least values associated with the data that is stored in the block in the data structure.

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Walldorf, Germany

Thomas Legler