Custom Index Recommender for Multi-Tenant Database Query Optimization
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Solution Overview
Problem
Conventional database systems face performance issues with slow query processing, especially when queries are inartfully drafted or when a large number of users concurrently access the system, leading to inefficient data retrieval and user experience degradation.
Innovation Solution
The creation of custom indexes in a multi-tenant database environment, where a method is implemented to identify and prioritize queries for index creation based on criteria such as scan size, selectivity, and frequency of use, using a custom index recommender tool to enhance query performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If custom indexes are created to improve query performance, then query execution speed is improved, but storage requirements and system complexity increase
Solution Approach 1:
The system performs preliminary analysis of query patterns before creating indexes. The index recommender tool evaluates historical query data, scan sizes, and selectivity metrics to identify which indexes will provide the most benefit, creating indexes proactively before performance degradation occurs rather than reactively after problems arise
Solution Approach 2:
The system changes parameters by evaluating multiple criteria including scan size thresholds, selectivity ratios, and query frequency weights. These parameter-based evaluations determine which queries qualify for index creation, allowing the system to optimize storage usage by creating indexes only when parameter thresholds indicate sufficient benefit
2Reliability
If multiple custom indexes are created to handle concurrent user access, then query processing reliability is improved, but device complexity increases
Solution Approach 1:
The system provides self-service through an automated index recommender tool that independently analyzes query patterns and recommends appropriate indexes without requiring manual database administrator intervention. The tool automatically evaluates criteria such as scan size, selectivity, and query frequency to generate index recommendations, reducing operational complexity while improving reliability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring query performance metrics and using this information to refine index recommendations. The index recommender tool evaluates actual query behavior against recommended indexes and adjusts future recommendations based on observed performance improvements or lack thereof, creating a self-optimizing system
3Speed
If indexes are created without evaluation criteria, then query performance may be improved, but unnecessary indexes consume excessive storage and resources
Solution Approach 1:
The system applies partial action by creating indexes only for queries that meet specific criteria thresholds rather than for all queries. The index recommender tool evaluates each query against multiple parameters including scan size, selectivity, and frequency, creating indexes only for the partial subset of queries where the benefit clearly outweighs the cost, avoiding excessive index creation
Solution Approach 2:
The system performs preliminary evaluation of query characteristics before committing to index creation. The index recommender tool analyzes historical query data, calculates selectivity ratios, and estimates potential performance improvements before recommending indexes, ensuring that resources are invested only in indexes with demonstrated value
Data Source
AI summary
Methods and systems are described for creating a custom index in a multi-tenant database environment. In one embodiment, a method includes obtaining query for a multi-tenant database that is recommended as a candidate for creating an additional filter, evaluating the query against criteria to determine whether to select the query for creating the additional filter, and creating the additional filter for the query, if the query is selected.