Unified Access Layer for Hybrid Multi-Cloud Database Query Optimization
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Solution Overview
Problem
Managing database query execution in a hybrid multi-cloud environment is challenging due to the heterogeneity of database implementations and computing platforms, leading to performance inefficiencies and increased costs.
Innovation Solution
A scalable query engine and unified access layer that use machine learning models to analyze query performance metrics, optimize query processing, and automatically generate indexes, views, and recommend data sources, while managing resources and policies to improve query performance across multiple cloud platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple database implementations are used across hybrid multi-cloud platforms, then data storage capacity and accessibility are improved, but query performance and system complexity deteriorate
Solution Approach 1:
The patent introduces a unified access layer as an intermediary component between the query engine and multiple heterogeneous database implementations. This access layer translates and standardizes queries across different database systems (Oracle, SQL, MySQL, IBM DB2, Snowflake), enabling efficient query execution without requiring changes to the underlying diverse database infrastructure. The intermediary handles platform-specific optimizations and data format conversions, maintaining high query performance while supporting multiple database types.
2Quantity of substance
If multiple database implementations are used across hybrid multi-cloud platforms, then data storage capacity and accessibility are improved, but system complexity increases
Solution Approach 1:
The unified access layer is designed with universal functionality to handle multiple database implementations through a single interface. It provides multi-functional capabilities including query translation, result set normalization, metadata management, and platform-specific optimization routines. This universal component eliminates the need for separate access logic for each database type, reducing system complexity while maintaining support for diverse database systems across hybrid cloud environments.
3Ease of manufacture
If traditional query management is used in heterogeneous database environments, then implementation simplicity is maintained, but resource utilization and performance optimization deteriorate
Solution Approach 1:
The system implements dynamic query optimization by analyzing query characteristics, data distribution, and resource availability in real-time. The query engine dynamically adjusts execution strategies, such as pushing down filters to appropriate database platforms, selecting optimal join algorithms, and redistributing data access patterns based on current system state. This dynamic adaptation maximizes resource utilization across heterogeneous databases while maintaining ease of implementation through automated decision-making.
Data Source
AI summary
A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.