Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for balancing database workload in a cloud database system employing multiple database instances, wherein intermediate results are shared between database instances via object store or cloud storage system accessible by the multiple database instances. During sharing of intermediate results between database instances having different node sizes or processing unit sizes, load balancing is achieved by evaluating the target and source database instance sizes to select an appropriate load balancing strategy. Data and metadata contained within the intermediate results shared between source and target database instances are organized to provide optimal load balancing on the target database instance.

Abstract: A system and method for balancing database workload in a cloud database system employing multiple database instances, wherein intermediate results are shared between database instances via object store or cloud storage system accessible by the multiple database instances. During sharing of intermediate results between database instances having different node sizes or processing unit sizes, load balancing is achieved by evaluating the target and source database instance sizes to select an appropriate load balancing strategy. Data and metadata contained within the intermediate results shared between source and target database instances are organized to provide optimal load balancing on the target database instance.

Abstract: An apparatus, method and computer program product for neural network training over very large distributed datasets, wherein a relational database management system (RDBMS) is executed in a computer system comprised of a plurality of compute units, and the RDBMS manages a relational database comprised of one or more tables storing data. One or more local neural network models are trained in the compute units using the data stored locally on the compute units. At least one global neural network model is generated in the compute units by aggregating the local neural network models after the local neural network models are trained.

Abstract: A database system analyzes a query to determine the resources required to execute the query by identifying the department that issued the query (the “source department”) and identifying query-recited tables and determining the department by which each of the query-recited tables is controlled (the “accessed departments”). The database system determines that the resources required to execute the query are controlled by more than one department: the source department and one or more accessed departments. For each accessed department, a cost of a first strategy involving a SQL rewrite and a cost of a second strategy not involving a SQL rewrite are determined by preparing a query execution plan, determining the cost of the resources, and considering whether data controlled by the accessed department is not accessible except to the accessed department. The first strategy is chosen for at least one of the accessed departments because the first-strategy cost is lower.

Abstract: Techniques for transitioning between code-based and data-based execution forms (or models) are disclosed. The techniques can be used to improve the performance of computing systems by allowing the execution to transition from one of the execution models to another one of the execution models that may be more suitable for carrying out the execution or effective processing of information in a computing system or environment. The techniques also allow switching back to the previous execution model when that previous model is more suitable than the execution model currently being used. In other words, the techniques allow transitioning (or switching) back and forth between a data-based and code-based execution (or information processing) models.

Abstract: Improved techniques for management of access in computing environments and systems are disclosed. An object-level data access mechanism can be provided. to effectively provide an object-level locking mechanism for locking data objects of database tables, individually, as individual data objects. Furthermore, the object-level data access mechanism can be provided as a safe and efficient filtering mechanism (e.g., cuckoo filter) that effectively provide an object-level locking mechanisms for locking data objects of a database table, individually (i.e., as individual locks placed on individual data objects). For example, a set of filters (e.g., write cuckoo and read cuckoo) can be provided for a database table to facilitate concurrent database operations in a safe but efficient manner.

Abstract: A data request that references an external data environment object (foreign object) is identified. A Data Manipulation Language (DML) statement for accessing the object is traversed in a defined order to identify foreign servers having the foreign object. Connections are attempted to foreign servers in the defined order and a selection to one of the foreign servers is made based on server and/or data conditions. The selected server is used for the request to process the portion of the request that includes the foreign object. In an embodiment and during execution of data request, the server and/or the data conditions can be dynamically overridden to change selection criteria for the selected server.

Abstract: Various techniques may be employed in a system, method, and computer-readable medium to allow declarative database syntax language to accommodate matrix multiplication.

Abstract: In a database system, wherein data is stored as objects within an object storage system, a system and method for estimating object cardinality, determining query execution plan costs, and selecting a query plan for execution by the database system. Multiple object cardinality estimation approaches for estimating the number of objects to be accessed for a given query condition on a column of a relation composed of a set of objects, where each object maintains the minimum value and the maximum value of individual columns are presented. A set of global statistics is also maintained, consisting of the total number of objects and the minimum and maximum values of individual columns. The object cardinality estimation is determined based on the global statistics without retrieving individual object-level statistics.

Abstract: In some examples, in response to a join query to join a plurality of tables, a first processing engine retrieves tuples of a first table from a subset of objects of a data store, and adds content of the retrieved tuples to an in-memory table, where the objects are range partitioned across a plurality of processing engines based on respective ranges of values of at least one join attribute in the join query. The first processing engine retrieves, from the data store, tuples of a second table of the plurality of tables based on a range of values of the at least one join attribute in the retrieved tuples of the first table. The first processing engine performs an in-memory join of the plurality of tables based on the retrieved tuples of the second table and the in-memory table.

Abstract: A method, apparatus and computer program product for estimating resource consumption for steps in a query execution plan for a query performed by a relational database management system (RDBMS) in a computer system. Past execution data for the steps are used to train a machine learning (ML) model and its model parameters to predict execution times for the steps. A prediction module comprised of the ML model configured by the model parameters predicts an execution time for a current step of the query execution plan for the query, based on current step information and current system load. A boosting module boosts the current step either up or down for processing by the RDBMS to meet a service level goal (SLG) for the query, based on the predicted execution time for the current step, as well as an elapsed query time, a query SLG time, and/or a query CPU time.

Abstract: A multi-parameter data type framework can, among other things, provide a more comprehensive, systematic, and/or formal mechanisms for determining an appropriate data type for a data set. For example, the multi-parameter data type framework can be used to allow analytic tools to virtually automatically figure out an appropriate data type for a set of data values.

Abstract: In a cloud database system employing multiple types of storage, such as external object store, managed object store. block storage, and compute node memory, each type of storage having different kinds of file organization, different types of data organization, different forms of storage access, and different latency and throughput costs, a system and method for caching different data transformations created during query executions involving different data stores. Transformed versions of data read from external object storage are saved to a multi-layered warehouse cache for use in subsequent query executions.