Shadow Query Engine for Multi-Tenant Cloud Service Optimization
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Solution Overview
Problem
In multi-tenant cloud services, optimizing query execution is challenging due to the increasing volume of structured, semi-structured, and unstructured data, which demands efficient data retrieval techniques to reduce compute resources and maintain response times without disrupting existing workloads.
Innovation Solution
A method involving a shadow query engine is used to determine optimal configuration parameter values by categorizing service queries, recording metadata, and correlating performance data to apply these values to future queries, optimizing query execution without affecting regular response times or resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If query execution is optimized by testing different configuration parameters, then query performance is improved, but compute resources and response time are increased during the optimization process
Solution Approach 1:
The patent creates a shadow query engine that is a copy of the production query engine. This shadow engine executes test queries with different configuration parameters to determine optimal settings without affecting production workloads. The shadow engine replicates the production engine's behavior and resource characteristics, allowing realistic performance testing while isolating optimization experiments from actual user queries.
Solution Approach 2:
The shadow query engine serves as an intermediary between the optimization process and the production system. It receives test queries and configuration parameters, executes them in isolation, and returns performance results without directly impacting production query execution. This intermediary layer enables safe experimentation and parameter tuning.
2Reliability
If a shadow query engine is used for optimization, then production workloads are not disrupted, but system complexity increases
Solution Approach 1:
Instead of modifying the production query engine to add optimization capabilities, the patent creates a separate shadow copy. This copy has the same interface and behavior but is dedicated to optimization testing. The shadow engine can be independently configured, started, and stopped without affecting the production system.
Solution Approach 2:
The system is segmented into distinct components: the production query engine for handling user queries and the shadow query engine for optimization testing. This segmentation allows each component to have specialized functionality and be managed independently, reducing the complexity burden on the production system.
3Productivity
If multiple configuration parameters are tested simultaneously, then optimization speed is improved, but resource consumption and result analysis complexity increase
Solution Approach 1:
The optimization process segments the parameter testing into controlled batches. The shadow query engine tests multiple configuration parameters systematically, and the system correlates results to identify which parameters have the most significant impact on performance. This segmented approach allows parallel testing while managing analysis complexity.
Solution Approach 2:
The system implements feedback loops where query performance results from the shadow engine are analyzed and used to guide subsequent parameter testing. The correlation analysis provides feedback on which parameters are most influential, allowing the optimization process to focus on the most promising configurations and avoid exhaustive testing of all possible parameter combinations.
Data Source
AI summary
Aspects of the present invention disclose a method, computer program product, and system for query execution in a multi-tenant cloud service. The method includes one or more processors determining category classes for service queries. The method further includes sending for execution, a selected number of service queries from one of the determined category classes to a shadow query engine. Respective service queries of the categorically classified service queries comprise a different set of configuration parameter values for the shadow query engine. The method further includes recording metadata for the selected number of service queries of the one category class executed on said shadow query engine. The method further includes determining correlations between the recorded metadata. The method further includes determining, from the determined correlations, optimal configuration parameter values comprising optimal configuration parameters for executing the selected number of service queries of the one category classes.