Cross-Database Execution Plan Conversion for OLAP Data Access
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Solution Overview
Problem
Open-source databases like POSTGRESQL, MYSQL, and ORACLE Database perform poorly in complex data analysis scenarios, lacking compatibility with different databases and failing to meet the requirements of online analytical processing (OLAP).
Innovation Solution
An instruction execution apparatus processes first execution plans from various databases, converting them into second execution plans through operator splitting, combining, mapping, and adjusting sequences, and configuring hardware and data formats to enhance data access efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If open-source databases are used for OLAP scenarios, then data storage and basic management are achieved, but data analysis performance is poor and complex analysis requirements cannot be met
Solution Approach 1:
The patent introduces an instruction execution apparatus as an intermediary layer between the client and different open-source databases. This apparatus receives data access instructions, generates execution plans, and executes them across different database systems. The intermediary approach enables complex data analysis operations without requiring deep customization of each individual database, thus improving data analysis performance while maintaining compatibility with multiple database types.
Solution Approach 2:
The instruction execution apparatus is designed with multi-functional capabilities to handle different database types and execution plan formats. It can process execution plans from various open-source databases (PostgreSQL, MySQL, Oracle, etc.) and adapt them for execution. This universal design allows the system to improve data analysis performance across different database platforms without sacrificing adaptability.
2Productivity
If deep customization is performed on a single open-source database to improve data analysis capability, then execution engine performance is improved, but compatibility with different open-source databases is lost
Solution Approach 1:
Rather than customizing individual databases, the patent employs an instruction execution apparatus as a mediator that handles the complexity of execution plan adaptation. This apparatus receives execution plans from different database sources, processes them through standardized operations (splitting, combining, mapping, reordering), and executes them appropriately. This approach achieves high execution engine performance for complex analytics while maintaining broad compatibility with multiple database systems.
Solution Approach 2:
The patent extracts the execution plan processing logic from individual database systems and consolidates it into a separate instruction execution apparatus. By taking out the customization requirements from each database and centralizing them in a dedicated execution engine, the system achieves improved data analysis performance without sacrificing compatibility with different database platforms.
3Productivity
If execution plans are processed through multiple operations (splitting, combining, mapping, reordering), then data access efficiency is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple execution plan processing operations (splitting, combining, mapping, reordering) into a unified instruction execution apparatus. Rather than implementing separate systems for each operation, the apparatus integrates these functions into a cohesive execution engine that processes execution plans through a standardized workflow. This consolidation improves data access efficiency while managing system complexity through unified architecture.
Solution Approach 2:
The instruction execution apparatus dynamically adjusts execution plan parameters based on the specific database type and query requirements. It modifies execution plans by changing parameters such as operator splitting granularity, combining strategies, mapping relationships, and execution ordering. These parameter changes enable optimized data access efficiency across different databases without requiring fundamentally different system architectures for each operation.
Data Source
AI summary
A data access method for a database comprising obtaining a first execution plan that is based on a data access instruction initiated in any database; generating a second execution plan based on the first execution plan; and accessing data in the database based on the second execution plan.