Oracle International Corporation

Harshad Kasture

Hassan Chafi

Pit Fender

Benjamin Schlegel

Matthias Brantner

Alexander Ulrich

Eric Sedlar

Laurent Daynes

Anantha Kiran Kandukuri

Bastian Hossbach

Juergen Christ

Lucas Braun

Techniques are provided for lazy push optimization, allowing for constant time push operations. A d-heap is used as the underlying data structure for indexing values being inserted. The d-heap is vectorized by storing values in a contiguous memory array. Heapify operations are delayed until a retrieve operation occurs, improving insert performance of vectorized d-heaps that use horizontal aggregation SIMD instructions at the cost of slightly lower retrieve performance.

Lazy push strategies for vectorized D-Heaps

Herein are techniques that extend a software system to embed new guest programing languages (GPLs) that interoperate in a transparent, modular, and configurable way. In embodiments, a computer inserts an implementation of a GPL into a deployment of the system. A command registers the GPL, define subroutines for the GPL, generates a guest virtual environment, and adds a binding of a dependency to a guest module. In an embodiment, a native programing language invokes a guest programing language to cause importing intra- or inter-language dependencies. An embodiment defines a guest object that is implemented in a first GPL and accessed from a second GPL. In an embodiment, dependencies are retrieved from a virtual file system having several alternative implementation mechanisms that include: an archive file or an actual file system, and a memory buffer or a column of a database table.

Database modularization of pluggable guest languages

Techniques are provided for vectorizing Heapsort. A K-heap is used as the underlying data structure for indexing values being sorted. The K-heap is vectorized by storing values in a contiguous memory array containing a beginning-most side and end-most side. The vectorized Heapsort utilizes horizontal aggregation SIMD instructions for comparisons, shuffling, and moving data. Thus, the number of comparisons required in order to find the maximum or minimum key value within a single node of the K-heap is reduced resulting in faster retrieval operations.

Method for vectorizing heapsort using horizontal aggregation SIMD instructions

Herein are machine learning (ML) feature processing and analytic techniques to detect anomalies in parse trees of logic statements, database queries, logic scripts, compilation units of general-purpose programing language, extensible markup language (XML), JAVASCRIPT object notation (JSON), and document object models (DOM). In an embodiment, a computer identifies an operational trace that contains multiple parse trees. Values of explicit features are generated from a single respective parse tree of the multiple parse trees of the operational trace. Values of implicit features are generated from more than one respective parse tree of the multiple parse trees of the operational trace. The explicit and implicit features are stored into a same feature vector. With the feature vector as input, an ML model detects whether or not the operational trace is anomalous, based on the explicit features of each parse tree of the operational trace and the implicit features of multiple parse trees of the operational trace.

Inferring intra package and module dependencies

Herein are computerized techniques for deploying JavaScript and TypeScript stored procedures and user-defined functions into a database management system (DBMS). In an embodiment, a computer generates a SQL call specification for each subroutine of one or more subroutines encoded in a scripting language. The generating is based on a signature declaration of the subroutine. Each subroutine comprises a definition of a stored procedure or a user-defined function. The computer packages the definition and the SQL call specification of each subroutine into a single bundle file. The definition and the SQL call specification of each subroutine are deployed into a DBMS from the single bundle file. Eventually, the SQL call specification of at least one subroutine is invoked to execute the definition of the subroutine in the DBMS.

Deployment of javascript and typescript stored procedures and user-defined functions into database management systems

Dynamic resolution of dependencies for database guest languages

Techniques for maintaining d-heap property and speeding up retrieval operations, such as top or pop, by vectorizing the d-heap and utilizing horizontal aggregation SIMD instructions across the retrieval operations. A d-heap is vectorized by storing it in a contiguous memory array containing a beginning-most side and end-most side. Horizontal aggregation SIMD instructions are utilized to aggregate the values of the vectorized d-heap. Thus, the number of comparisons required in order to find the maximum or minimum key value within a single node of the d-heap is reduced resulting in faster retrieval operations.

Growing community of inventors

Sunnyvale, CA, United States of America

Harshad Kasture