Abstract: Techniques for selectively aggregating records based on a downstream function to be applied to the records are disclosed. A system obtains an instruction corresponding to a set of records and a function to be applied to the set of records. The system determines whether the function meets a particular criteria for aggregating records prior to transmitting the records to an application for executing the function on the records. If the system determines that the function does meet the records-aggregation criteria, the system stores a set of records in a buffer prior to sending the set of records to the function-executing application. The system sends the set of records to the application together as a group with an instruction to generate a set of function results that includes a separate value for each record in the set of records.

Abstract: Techniques for generating recommendations based on the predicted performance of an execution plan are disclosed. A system predicts the future characteristics of a set of data objects associated with a set of structured query language (SQL) statements. The system predicts how the changes to the set of data objects will result in changes to a query execution plan associated with the SQL statements. The system predicts a set of performance metrics for the changed query execution plan. Based on the predicted performance, the system generates recommendations for modifying data, applications, or database server operations to improve performance.

Abstract: Techniques for selectively aggregating records based on a downstream function to be applied to the records are disclosed. A system obtains an instruction corresponding to a set of records and a function to be applied to the set of records. The system determines whether the function meets a particular criteria for aggregating records prior to transmitting the records to an application for executing the function on the records. If the system determines that the function does meet the records-aggregation criteria, the system stores a set of records in a buffer prior to sending the set of records to the function-executing application. The system sends the set of records to the application together as a group with an instruction to generate a set of function results that includes a separate value for each record in the set of records.

Abstract: Generative artificial intelligence-based techniques are disclosed herein to conduct conversations of various types with multiple Large Language Model Services at once. In one aspect, a method is provided that includes identifying large language model services that qualify to take part in the conversation based, at least in part, on a conversation type and profile information provided by a user, user-selected terms selected by the user specific to the conversation, or both, rendering a conversation screen in a graphical user interface, receiving a prompt input into a dialog box of the conversation screen, communicating the prompt input to each of the large language model services, receiving responses from the large language model services based on the prompt input, and rendering the responses in a dialog box of the conversation screen with an indication of which of the large language model services provided each of the responses.

Abstract: Techniques for selectively aggregating records based on a downstream function to be applied to the records are disclosed. A system obtains an instruction corresponding to a set of records and a function to be applied to the set of records. The system determines whether the function meets a particular criteria for aggregating records prior to transmitting the records to an application for executing the function on the records. If the system determines that the function does meet the records-aggregation criteria, the system stores a set of records in a buffer prior to sending the set of records to the function-executing application. The system sends the set of records to the application together as a group with an instruction to generate a set of function results that includes a separate value for each record in the set of records.

Abstract: Techniques for generating recommendations based on the predicted performance of an execution plan are disclosed. A system predicts the future characteristics of a set of data objects associated with a set of structured query language (SQL) statements. The system predicts how the changes to the set of data objects will result in changes to a query execution plan associated with the SQL statements. The system predicts a set of performance metrics for the changed query execution plan. Based on the predicted performance, the system generates recommendations for modifying data, applications, or database server operations to improve performance.

Abstract: Techniques are disclosed hereafter for efficiently enforcing a “WITHOUT OVERLAP” range constraint by confirming primary-key integrity for a new or modified row (the “target row”) by checking just two neighboring index entries, using a new “two-sided halted range scan” of the same key index on entities which have range-endpoints data as part of their primary key. Techniques are described for reducing search time and resources in situations where a query specifies an entity and a point within a non-overlapping range. Techniques are also described for optimized handling of queries that do not specify a primary key but have both a range condition and a filter on a non-key column.

Abstract: Techniques are disclosed hereafter for efficiently enforcing a “WITHOUT OVERLAP” range constraint by confirming primary-key integrity for a new or modified row (the “target row”) by checking just two neighboring index entries, using a new “two-sided halted range scan” of the same key index on entities which have range-endpoints data as part of their primary key. Techniques are described for reducing search time and resources in situations where a query specifies an entity and a point within a non-overlapping range. Techniques are also described for optimized handling of queries that do not specify a primary key but have both a range condition and a filter on a non-key column.

Abstract: Systems and methods for organizing a structured query language statement execution plan to increase the efficiency of a computer device in executing the statement. A data structure is created in computer memory that encodes information about a database statement execution plan (e.g., an SQL statement). The information is arranged in a specific logical structure and may be displayed graphically as a query diagram. The data structure is used in optimizing the SQL statement execution plan so as to merge joined tables to enable efficient optimization. Furthermore optimized SQL execution plans in accordance with the present invention proportionally scale with the number of rows returned from a query, do not depend unnecessarily on potentially unavailable central resources (e.g., sort space), scale in the event that optimizer statistics and assumptions are poor, and make use of processing and memory resources.

Abstract: In a database table with rows each having respective key values, the locking of a key value to enforce key-value uniqueness is accomplished by (a) mapping the key value into a set of hash values to produce a resulting hash value, and (b) locking all key values (i.e., preventing writing of key values in new rows and preventing updates creating new key values in existing rows) that map into the resulting hash value. This provides a compromise between the high-granularity and high-cost solution of indexing, and the low-cost but very low-granularity solution of table-level locking. One useful application of this technique lies in the enforcement of uniqueness of multi-column keys in the table without incurring the cost of indexing all possible combinations of column values that might comprise key values.

Abstract: A method of constructing an aid to tuning of database statements comprises a data structure (stored in a memory on a computer system) which compactly represents that information which is needed about a database statement to determine the optimal series of operations (e.g., table data fetches) needed to execute the statement. The information includes the relationships between tables joined in the statement and the selectivities of logical conditions applied to rows in those tables.