Abstract: One or more assignments (e.g., one or more maps) can be used for assigning data to processing units of a database system with multiple processing. In other words, assignments can be provided as a more effective solution for assigning data to the processing units of database systems that can operate with multiple processing units. Generally, an assignment can be used to assign data to the processing units for processing, virtually in any desired manner (e.g., virtually any desired function). By way of example, maps can associate data to containers (e.g., buckets) and associate the containers to processing units of database system with multiple processing units.

Abstract: A system and method for loading data into column-partitioned database tables. The system and method incorporate a mechanism for buffering data extracted from the rows of a source table in column-oriented fashion within an in-buffer memory, enabling an efficient bulk-write of large arrays of values from the buffer into column-partitioned database tables. The system and method may also include optimizations for grouping columns according to data types and altering the order in which columns are inserted into the database tables.

Abstract: In some examples, a system stores data in a logically disconnected data store. In response to a query for data in the data store, the system accesses metadata of objects stored in the data store, the metadata including information of a respective range of values of at least one clustering attribute in data contained in each respective object of the objects. The system partitions the objects across the plurality of processing engines based on the information of the respective ranges of values of the at least one clustering attribute in the data contained in the objects. The system assigns, based on the partitioning, the objects to respective processing engines of the plurality of processing engines.

Abstract: Disclosed are systems and methods for implementing contract-based polymorphic and parallelizable user-defined scalar and aggregate functions. The systems and methods can include receiving a query including a plurality of user-defined functions, parsing the query into a plurality of nodes (e.g., basic operation unit or atomic operator), generating an execution plan that minimizes data transfer between the plurality of nodes, and executing the plan in a distributed environment. Each of the plurality of user-defined functions can correspond to one of a plurality of nodes.

Abstract: An apparatus, method and computer program product for estimating as-a-Service (aaS) query prices in a relational database management system (RDBMS). An optimizer of the RDBMS inserts an EXPLAIN modifier into a query, wherein the EXPLAIN modifier results in the optimizer generating a summary of a query execution plan for the query that includes one or more cost estimates for the RDBMS to perform the query. A price estimate for the query is then generated based on the cost estimates, wherein the price estimate is generated using one or more configurable pricing formulae. The price estimate is merged into the summary of the query execution plan for the query. Moreover, a price guarantee may be generated for the price estimate, wherein the price guarantee is honored when the query is subsequently invoked for execution by the RDBMS.

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 relational database management system (RDBMS) optimizes limit queries over analytical functions, wherein the limit queries include an output clause comprising a LIMIT, TOP and SAMPLE clause with an expression specifying a limit that is a number K or a percentage ? %. The optimizations of the limit queries include: (1) static compile-time optimizations, and (2) dynamic run-time optimizations, based on semantic properties of “granularity” and “input-to-output cardinality” for the analytical functions.

Abstract: A first query execution plan generated for a query on a second time the query was processed by a database is compared against a dynamically generated second query plan generated based on statistics only dynamic feedback for the second time the query is processed by the database. A determination is made on the second time as to whether to cache the first query execution plan, the second query execution plan, or no plan for third or more times the query is processed by the database. The query can be non-parameterized or parameterized.

Abstract: A system may include at least one processor. The at least one processor may receive data from a plurality of independent data sources. The data from each respective data source is received at a rate determined by the respective data source. The at least one processor may further write the received data to at least one data store at a rate independent of the respective rates at which data from the plurality of independent data sources is received. A method and computer-readable medium are also disclosed.

Abstract: A query having a Union All view is identified. A logical join between Union AH view/derived table and other tables is broken down into multiple physical joins. The physical joins are pushed to the branches. Cost-based processing statistics are obtained for the branches. An optimal plan for the joins is selected based on the statistics; representing an optimal query execution for the query. The optimal query execution plan is provided to a database engine for executing the optimal query execution plan against a data warehouse.

Abstract: A system may include a server and a data store system. The server may include at least one storage device and at least one processor. The server may execute an application and may store an encrypted password. The data store system may include at least one persistent storage device configured to store a data store. The data store system may further include a plurality of processing nodes configured to operate on the data store. The data store system may receive the encrypted password from the application with one of the plurality of processing nodes and may decrypt the encrypted password with the one of the plurality of processing nodes. The data store system may authenticate the decrypted password with the one of the processing nodes and provide the decrypted password to other processing nodes. Each processing node that has the decrypted password may be accessible to the application to operate on the data store. A method and computer-readable medium may also be implemented.

Abstract: A data store system includes an array of persistent storage devices configured to store a plurality of data store tables. The data store system includes a processor in communication with the storage device. The processor may receive a query comprising an aggregate function and identify structure of an argument of the aggregate function. The subset of data store tables may be associated with the argument. The processor may partially-execute the aggregate function on each data store table in the subset involved in the argument of the aggregate function to create partially-executed results for each data store table of the subset of data store tables. The processor may join the partially-executed results based on join conditions contained in the aggregate function. The processor may complete execution of the aggregate function on the partially-executed results to generate a final result of the aggregate function. A method and computer-readable medium are also disclosed.

Abstract: A data-changing request is received on a first data source system. Metadata for a data source object associated with the request is obtained. The request is executed on a second data source system and results are obtained. A selection between multiple replication processes is made based on the metadata and the results. The selected process is performed on each of the remaining data source systems to synchronize/replicate data changes on the remaining data source systems. In an embodiment, bidirectional and concurrent data connections are permitted to a source object for one data source system and a replicated version of the source object on a different source system. In an embodiment, each data source system includes an agent that establishes an out-of-band connection to a synchronization/replication server, such that synchronization/replication is processed by the server without an affirmative connection being made from the data source systems to the server.

Abstract: A method may include receiving a stored procedure associated with data stored in a plurality of data stores. The stored procedure may include a plurality of executable statements. The method may further include identifying a first executable statement of the plurality of executable statements to be executed by the processor and a second executable statement of the plurality of executable statements that is executable by at least one of a plurality of other processors. The other processors each may have access to only a respective one of the plurality of copies of the data. The method may further include executing the first executable statement. A system and computer-readable medium may also be implemented.

Abstract: Techniques for processing relational data with a user-defined function (UDF) are provided. Relational input data being requested by the UDF, from within a relational database system, is intercepted and normalized. The UDF is called with the normalized input data and as the UDF produces output data in response to the normalized input data that output data is captured and normalized. In an embodiment, the normalized output data is used to dynamically update a data model within the relational database for the input data.

Abstract: A single storage allocation unit of a distributed storage is mapped to multiple different data temperature metric values (metric value), and each metric value calculated and produced by a data temperature generator. Each generator is designed to provide a metric value used to optimize performance of a specific data management feature for the storage subsystem. At any given point in time, the storage allocation unit has multiple up-to-date metric values available for managing the underlying data of the storage allocation unit by storage subsystems and storage management processes. Based on changing performance characteristics associated with the underlying data, a current metric value being used to manage the storage allocation unit can be dynamically switched to a different up-to-date metric value. This automatically causes the underlying storage subsystems/storage management processes to optimize arrangement and organization of the underlying data for a desired data management feature.

Abstract: Improved techniques for reducing the size of data of the multidimensional data are disclosed. The improved techniques are highly suitable for processing large-scale data and various large-scale data applications (e.g., large-scale Data Mining (DM), large-scale data analysis (LSDA) in computing systems (e.g., Data Information Systems, Database Systems). Redundancy-reduced data can be provided (RRDS) thereby providing data that can be used more efficiently by various applications, especially, large-scale data applications. At least one assumption about the distribution of a multi-dimensional data set (MDDS) and its corresponding set of responses (Y) can be made in order to reduce the multi-dimensional data set (MDDS). It should be noted that the assumption can be made after effectively combining multi-dimensional data set (MDDS) and its corresponding set of responses (Y) so that the set of responses (Y) can also be in considered in effectively reducing the size of the multi-dimensional data set (MDDS).

Abstract: A system and method for determining optimal query plans within distributed database system employing table operators for performing analytic operations for storing and processing multi-structured data. The optimization of a query plan proceeds through a collaborative exchange between a database system optimizer, or planner, and a table operator, wherein multiple communications between said optimizer and said table operator are conducted to exchange input and output information relevant to optimizing execution of the query and table operator.

Abstract: A User-Defined Function (UDF) provided by a user and accessed through a user query is identified. A wrapper for the UDF is generated. The wrapper represents a customized instance of the UDF. The wrapper, during execution, selectively calls the UDF provided by the user. Any query that references the UDF is rewritten to process the wrapper instead of the UDF.

Abstract: Data can be stored based on one or more indications and one or more other storage criteria. The indications can effectively indicate or identify a storage level for storing data in a multi-storage system. The indications, however, need not be the only basis for storing the data object in a multi-storage system as one or more other storage criteria can also be considered. As a result, the indication can be used to effectively influence data storage but other storage criteria can be used as well to prevent adverse effects caused by undue influence and to ensure the overall efficiency of the system. Also, the one or more other storage criteria can be evaluated or revaluated on a continual basis.