Metadata-Driven Index Querying for Faster Database Retrieval
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Solution Overview
Problem
Current data storage systems require extensive database traversal to process query requests, leading to low data query efficiency.
Innovation Solution
A data processing method that determines specific indexes based on metadata information to directly identify relevant data groups, utilizing multiple threads for querying data from different tables with varying data amounts, and supporting multi-level query requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the storage system traverses all data in the database based on the query request, then it can obtain complete query results, but it takes a long time and data query efficiency is low
Solution Approach 1:
The patent segments the database into multiple tables, each table storing data for a specific index. When a query request arrives, the system identifies the relevant table(s) based on the query fields and retrieves data only from those segmented tables rather than traversing the entire database. This segmentation enables targeted data retrieval and significantly improves query efficiency.
2Productivity
If the database stores data in a single large table, then data storage is simple, but querying requires traversing all data which reduces efficiency
Solution Approach 1:
The database is divided into multiple tables, with each table dedicated to storing data for a specific index. This segmentation creates a more complex database structure but enables efficient queries by allowing the system to access only the relevant table(s) based on the query request, rather than scanning the entire database.
Solution Approach 2:
The patent introduces a new dimension to the database structure by creating multiple tables organized by index types. This dimensional organization allows the system to navigate the database through index dimensions rather than linearly traversing all data, significantly improving query efficiency despite the increased structural complexity.
3Adaptability or versatility
If the system queries data from multiple tables with different data amounts, then it can handle diverse query scenarios, but the query process becomes more complex
Solution Approach 1:
The patent creates a universal query processing mechanism that can handle queries across multiple tables with different data amounts. The system uses a unified approach to identify relevant tables based on query fields and retrieve data from appropriate tables, making the query process adaptable to diverse scenarios while maintaining a consistent processing framework.
Data Source
AI summary
A data processing method and apparatus for data query efficiency is described. The method includes: receiving a query request, determining, based on metadata information, at least one index corresponding to the query request, and querying data from a database based on the at least one index without traversing all data in the database.