Custom Index Generation for Multi-Tenant Database Query Optimization
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Solution Overview
Problem
Conventional database systems face performance issues due to poorly indexed queries, especially in multi-tenant environments where a large variety of queries can lead to slow performance and timeouts, affecting user experience and system efficiency.
Innovation Solution
A system and method for determining query candidates for custom indexes, which involves capturing and analyzing slow queries, identifying potential candidates through runtime analysis, and generating custom indexes to improve query performance by utilizing a Query Identifier, Filter Analyzer, Selectivity Tester, User Interface, and Custom Index Generator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional database systems process queries without custom indexes, then device complexity remains low, but query execution speed decreases significantly
Solution Approach 1:
The system automatically analyzes query patterns and creates custom indexes without requiring manual intervention from database administrators. The query analyzer identifies slow queries, determines optimal index structures, and implements them automatically, allowing the database system to self-optimize its performance characteristics.
Solution Approach 2:
The system dynamically adjusts database parameters by creating custom indexes based on analyzed query patterns. It monitors query execution times and modifies the database schema by adding, modifying, or dropping indexes to optimize performance for specific query workloads, thereby changing system parameters adaptively.
2Adaptability or versatility
If a large variety of queries are supported in multi-tenant environments, then adaptability increases, but query performance decreases due to lack of optimization
Solution Approach 1:
The system performs preliminary analysis of query patterns and creates custom indexes in advance before performance degradation occurs. By continuously monitoring query execution and proactively creating optimized indexes based on observed patterns, the system prepares the database structure ahead of time to handle diverse query types efficiently.
Solution Approach 2:
The system dynamically adapts the database schema by creating custom indexes based on real-time query analysis. It adjusts the index structure according to the specific query patterns observed in each multi-tenant environment, allowing the same database to be optimized for different workloads and query types without manual reconfiguration.
3Manufacturing precision
If custom indexes are created manually, then manufacturing precision of query optimization is high, but ease of operation decreases
Solution Approach 1:
The system automatically analyzes query patterns and creates custom indexes without requiring manual intervention from database administrators. The query analyzer identifies slow queries, determines optimal index structures, and implements them automatically, allowing the database system to self-optimize its performance characteristics.
Data Source
AI summary
Methods and systems are described for determining candidates for a custom index in a multi-tenant database environment. In one embodiment, a method includes, capturing a query that is directed to a multi-tenant database, determining whether the captured query is a candidate for an additional filter, determining operators used by the captured query if the query is a candidate, determining data types of the database used by the captured query if the query is a candidate, determining whether there is a current filter for the operator and data types used by the captured query if the query is a candidate, selecting the captured query based on the determined operators, data types, and the determined current filters, and generating a custom index for the selected query.