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 analyze tenant-specific metadata to generate improved queries by identifying optimal index intersections and adjusting query syntax, thereby customizing query paths based on tenant-level statistics and data distribution patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional query optimizers use aggregate statistics for query optimization, then the system can maintain simplicity and ease of operation, but query performance deteriorates due to inability to account for individual tenant data distributions
Solution Approach 1:
The patent segments the query optimization process by creating separate index structures for each tenant (tenant-specific indexes) rather than using a single aggregate index. This allows the system to maintain simple optimizer operations while achieving tenant-specific query performance optimization through customized index structures that reflect individual tenant data distributions.
Solution Approach 2:
The patent applies local quality by tailoring index structures to each specific tenant's data characteristics. Each tenant receives customized index structures that reflect their unique data distribution patterns, allowing the query optimizer to achieve high performance for each tenant's specific queries without compromising operational simplicity.
2Productivity
If the system creates separate index structures for each tenant, then query performance improves through tenant-specific optimization, but device complexity increases due to multiple index structures
Solution Approach 1:
The patent applies universality by creating a unified index management framework that handles multiple tenant-specific indexes through a common interface and process. The system maintains multiple indexes but manages them through universal operations, reducing the perceived complexity while preserving the performance benefits of tenant-specific optimization.
Solution Approach 2:
The patent merges the management of multiple tenant-specific indexes into a unified index structure that can be processed together. By combining index maintenance operations and using shared metadata structures, the system reduces overall complexity while maintaining the ability to perform tenant-specific query optimization.
3Ease of manufacture
If conventional optimizers assume uniform data distribution, then the system maintains ease of manufacture and deployment, but manufacturing precision deteriorates due to inaccurate query path selection
Solution Approach 1:
The patent changes the fundamental parameter of data distribution assumptions from uniform (conventional) to tenant-specific non-uniform distributions. By collecting and utilizing actual data distribution statistics for each tenant, the system achieves accurate query path selection that reflects real-world data patterns while maintaining straightforward deployment through automated index creation processes.
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 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 at least a first index and a second index. Metadata associated with the data is retrieved, wherein at least a portion of the data is stored in a common table within the multi-tenant database system. A tenant-selective query syntax is determined by analyzing at least one of metadata generated from information about the tenant or metadata generated from the data accessible by the tenant. An improved query is then generated using the query syntax, wherein the improved query is based at least in part upon the original query and a result of a join between a first number of rows associated with the first index and a second number of rows associated with the second index.