Database Index Selection via Query Pattern Matching
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Solution Overview
Problem
Database query performance is often suboptimal due to the use of improper index types, which are not efficiently adapted to different query types, leading to deteriorated performance.
Innovation Solution
A mechanism for selecting index types based on performance analysis of historical queries, where context information from received queries is matched to query records in an index knowledge base to determine the best index type for improved query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed index type is used for all query types, then the database system is simple to maintain, but query performance deteriorates for queries with mismatched index types
Solution Approach 1:
The system automatically selects appropriate index types based on query patterns and performance metrics without requiring manual intervention. The index knowledge base self-updates with performance information from executed queries, enabling the system to autonomously optimize query performance while maintaining simplicity in operation
Solution Approach 2:
The system pre-populates an index knowledge base with query patterns, index types, and performance metrics before actual query execution. By analyzing historical query data and pre-determining optimal index selections, the system prepares optimization strategies in advance, improving query performance without adding complexity during runtime
2Reliability
If manual index type selection is used, then index type is precisely matched to query requirements, but the operation becomes complex and time-consuming
Solution Approach 1:
The system implements a feedback mechanism where performance metrics from executed queries are collected and stored in the index knowledge base. This feedback loop allows the system to learn from actual query performance and automatically refine index type selections, achieving high matching accuracy without manual intervention
Solution Approach 2:
The system creates and maintains an index knowledge base that copies and stores performance information from historical queries. By replicating successful query-pattern to index-type mappings in this knowledge base, the system enables automated selection that achieves manual-level accuracy without the operational complexity
3Productivity
If index type is not adapted to different query types, then the system operates consistently, but query performance deteriorates
Solution Approach 1:
The system dynamically adapts index type selection based on the specific query pattern being executed. Rather than using a static fixed index type, the system adjusts the index type in real-time according to the query requirements and historical performance data, enabling both consistency in operation and adaptability to different query types
Solution Approach 2:
The system changes the index type parameter based on query characteristics and performance metrics. By modifying which index type is applied to a query based on the query pattern and stored performance information, the system achieves high query execution efficiency while maintaining operational simplicity through automated parameter adjustment
Data Source
AI summary
One or more computer processors match a query pattern to a received query; context information related to the received query; retrieve a set of query records including the same context information as the obtained context information from an index knowledge base, wherein each query record in the set of query records include context information related to a respective history query, the query pattern, an index type associated with the query pattern, and performance information relating to the query pattern and the index type; determine that a subset of the retrieved query records includes one or more query patterns equivalent to the matched query pattern; select a query pattern and an associated index type from the subset of query records based on associated performance information in the set of query records; and perform the received query by applying the selected query pattern and the associated index type.