Polyglot Database Dependency Discovery via Introspection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current polyglot database systems lack a clear and consistent method to discover and manage dependencies between guest modules, which are essential for software development lifecycle support across multiple programming languages, leading to transparency and security concerns for database administrators.
Innovation Solution
The implementation of techniques that extend a database management system to automatically discover and report a hierarchy of dependencies for guest modules by using introspection instrumentation and metadata extraction, providing a language-neutral format for dependency metadata aggregation and reporting through database views and table functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate products are used to implement specific database models for different applications, then each application can benefit from optimized data model support, but system complexity and integration difficulty increase
Solution Approach 1:
The patent merges multiple database models (relational, document, key-value, graph) into a single database management system. This allows different applications to use the same DBMS while maintaining their specific data model requirements, thereby reducing the number of separate products needed while preserving adaptability across different workloads.
Solution Approach 2:
The DBMS is designed with universal capabilities to handle multiple data models simultaneously. The system provides a unified interface that can accommodate various data models (OLTP, OLAP, document, key-value) within a single product, enabling one system to perform multiple functions that previously required separate specialized products.
2Adaptability or versatility
If polyglot database systems support multiple programming languages, then programming flexibility improves, but dependency management and security monitoring become difficult
Solution Approach 1:
The patent introduces dependency metadata as an intermediary layer between guest modules and the DBMS core. This metadata structure standardizes dependency information across different programming languages, allowing the system to uniformly track, discover, and manage dependencies regardless of the underlying language without compromising programming flexibility.
Solution Approach 2:
The patent replaces manual dependency tracking mechanisms with automated introspection instrumentation. The system automatically extracts dependency information from guest modules through standardized metadata interfaces, eliminating the need for manual configuration and enabling comprehensive dependency discovery across multiple programming languages.
3Reliability
If database administrators need to monitor all guest modules for security, then security coverage improves, but requiring administrator privileges creates access control complexity
Solution Approach 1:
The patent enables guest modules to self-report their own dependency information through introspection instrumentation. This allows modules to provide security-relevant metadata about their own composition and dependencies without requiring external administrator intervention, thereby maintaining comprehensive security monitoring while simplifying access control.
Solution Approach 2:
The system implements a feedback mechanism where guest modules automatically provide dependency information back to the DBMS through standardized metadata interfaces. This continuous feedback loop enables real-time security monitoring and dependency tracking without requiring administrators to manually inspect each module, reducing access control complexity while maintaining security coverage.
Data Source
AI summary
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.