Tenant-Selective Query Optimization in Multi-Tenant Databases
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Solution Overview
Problem
Conventional query optimizers in multi-tenant database systems are inefficient as they fail to account for the unique data characteristics of each tenant, leading to suboptimal query performance due to assumptions based on aggregate statistics that do not reflect individual tenant data distributions.
Innovation Solution
The implementation of tenant-selective query optimization methods that utilize tenant-level metadata and contextual information to generate improved queries, customizing the query syntax and index usage based on specific tenant data patterns, thereby overcoming the limitations of system-wide aggregate statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If system-wide aggregate statistics are used for query optimization, then the complexity of the optimization system is reduced, but the query performance accuracy deteriorates because it does not reflect individual tenant data distributions
Solution Approach 1:
The patent segments the query optimization process by creating separate index structures and statistics for each tenant. Instead of using a single system-wide aggregate statistics approach, the system generates tenant-specific metadata and custom indices that reflect individual tenant data distributions, thereby improving measurement precision without significantly increasing overall system complexity through modular architecture.
2Productivity
If tenant-specific query optimization is implemented, then query performance is improved by reflecting individual tenant data distributions, but the device complexity increases due to multiple index structures
Solution Approach 1:
The patent implements a universal custom index structure that serves multiple functions: it stores tenant-specific data distributions, maintains statistics for query optimization, and supports multiple tenants through a standardized interface. This multi-functional design allows tenant-specific optimization without proportionally increasing complexity, as the same index mechanism serves all tenants.
Solution Approach 2:
The system applies local quality by creating customized index structures tailored to each tenant's specific data characteristics and query patterns. Each tenant receives optimized indices based on their local data distribution requirements, while the overall system maintains a manageable complexity through on-demand generation rather than pre-configuring all possible combinations.
3Ease of operation
If conventional query optimizers are used, then the ease of operation is maintained, but the query execution time increases due to suboptimal data access paths
Solution Approach 1:
The system performs preliminary actions by pre-generating custom indices and tenant-specific metadata before query execution. This advance preparation creates optimized data access paths that are ready when needed, reducing query execution time without requiring complex real-time optimization decisions, thereby maintaining ease of operation while improving performance.
Data Source
AI summary
Methods and systems for query optimization for a multi-tenant database system are provided. Some embodiments comprise receiving, at a network interface of a server computer in a multi-tenant database system, an original query transmitted to the multi-tenant database system by a user associated with a tenant, wherein the original query is associated with data accessible by the tenant, and wherein the multi-tenant database system includes a first two-column custom index. Metadata associated with the data accessible by the tenant is retrieved, using a processor of the server computer, wherein at least a portion of the data accessible by the tenant is stored in a common table within the multi-tenant database system. A tenant-selective query syntax is determined, using the processor, wherein determining comprises analyzing at least one of metadata generated from information about the tenant or metadata generated from the data accessible by the tenant. Finally, using the processor, an improved query is generated using the query syntax, wherein the improved query is based at least in part upon the original query and data stored in the first two-column custom index.