Teradata US, Inc.

A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database comprised of one or more tables storing data. A virtual join index provides a new query optimization method based on enhanced join-index-rewrite to achieve cost-based late-materialization benefits without the costs for join index creation, storage and maintenance. The virtual join index is not materialized, but instead is treated by an optimizer as join index with dynamically determined columns to rewrite the query. In the join index rewrite, the columns of the virtual join index are dynamically determined by join columns in the query to produce a partial-covering query execution plan. Then, in the query execution plan, the virtual join index is replaced by a base table to produce the late materialization query execution plan.

A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database storing data in a table having an open table format (OTF). The RDBMS maintains a cache for the data stored in the table, and determines whether the cache is stale using metadata for the table that is stored in the cache. The RDBMS incrementally updates the cache when the cache is stale.

In some examples, a system compresses a data time series by initializing an intermediate data time series by adding a first data point and a second data point to the intermediate data time series, and performing a compression processing loop. In a respective round of a plurality of rounds of the compression processing loop, the system selects a data point to extract from the data time series, the data point selected based on a comparison of distances between a line segment connecting data points in the intermediate data time series and corresponding data points of the data time series, and the system adds the extracted data point to the intermediate data time series. The system outputs the intermediate data time series produced by the compression processing loop as a compressed data time series.

A system may include a storage device and at least one processor in communication with the storage device. The at least one processor may receive a query that comprises a reference to at least one data object. The at least one processor may identify a locally-stored version of the at least one data object. The at least one processor may retrieve a current version of the at least one data object. The at least one processor may compare the locally-stored version and the current version of the at least one data object. The at least one processor may, in response to the locally-stored version being different than the current version, update the locally-stored version to the current version. The at least one processor may execute the query based on the current version of the at least one data object. A method and computer-readable medium are also disclosed.

In some examples, a database system includes processing modules with access to a remote object store and a local database storage associated with the database system. The processing modules perform a database operation that involves use of a plurality of instances of spool data. The processing modules store a first instance of spool data in the remote object store based on a first characteristic of the first instance of spool data, and the processing modules store a second instance of spool data in the local database storage based on a second characteristic of the second instance of spool data.

A system and methods for performing read and write operations in a database system including a managed object store with an open table format. These systems and methods provide throughput/concurrency improvements for write operations employing position deletes by avoiding unnecessary transaction aborts due to possible write conflicts. A probabilistic structure, such as a bloom or cuckoo filter, is utilized to log row updates which are matched with delete file entries to identify conflicts at the row level in write operations. Similarly, improvements in the efficiency of read operations when employing an equity delete process are achieved by using bloom filters for logging object file data, which is matched with delete file entries to eliminate rows from consideration in read operations.

A system includes a storage device and at least one processor in communication with the storage device. The at least one processor receives a query associated with a plurality of data tables stored in the storage device. The at least one processor processes the query using a large language model (“LLM”) trained on semantic mapping information that describes relationships between data elements stored within the plurality of tables. The at least one processor generates, with the LLM, a natural language response to the query based on semantic mapping data generated from the data elements stored withing the tables. A method and computer-readable medium are also disclosed.

Hyperparameter tuning for a machine learning model is performed in a massively parallel database system. A computer system comprised of a plurality of compute units executes a relational database management system (RDBMS), wherein the RDBMS manages a relational database comprised of one or more tables storing data. One or more of the compute units perform the hyperparameter tuning for the machine learning model, wherein the hyperparameters are control parameters used in construction of the model, and the tuning of the hyperparameters is implemented as an operation in the RDBMS that accepts training and scoring data for the model, constructs the model using the hyperparameters and the training data, and generates goodness metrics for the model using the scoring data.

A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

A memory management computer system arranges a set of consumers of a computer memory resource in an inverted tree hierarchy. The memory management computer system allocates the computer memory resource among the scheduling entities. Each of the scheduling entities allocates the computer memory resource among the consumers of the computer memory resource to which it is directly coupled in the hierarchy. One of the scheduling entities in the hierarchy, a modifying scheduling entity, determines that a change is required to the allocation of the computer memory resource to one of the consumers of the computer memory resource for which the modifying scheduling entity is responsible for allocating the computer memory resource. Modifying the allocation of the computer memory resource to the one of the consumers of the computer memory resource without changing the hierarchy or creating a new hierarchy.