Query Resource Caching by Cumulative Execution Time
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Solution Overview
Problem
Existing caching systems in multi-tenant cloud architectures are inefficient in caching resources accessed by queries, leading to increased execution times during peak usage and resource overload, without effectively utilizing cache memory.
Innovation Solution
A resource caching system that selectively caches resources based on cumulative execution times of queries, caching those with execution times exceeding predefined thresholds to optimize cache usage and reduce memory consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If resources are cached for all queries, then query execution time is reduced, but cache memory is wasted on frequently accessed low-cost queries
Solution Approach 1:
The system changes the parameter of cache selection by introducing execution time as a threshold parameter. Queries are evaluated against a threshold execution time parameter, and only those exceeding the threshold are cached. This parameter-based filtering optimizes cache memory utilization by storing only the queries that will benefit from caching, avoiding waste on already-efficient queries.
Solution Approach 2:
Instead of caching all queries (excessive action), the system applies partial caching only to queries that meet specific criteria (execution time exceeds threshold). This partial action approach ensures cache memory is used effectively for high-cost queries while avoiding unnecessary storage of low-cost queries that don't benefit from caching.
2Quantity of substance
If no resources are cached, then cache memory is conserved, but query execution times increase during peak usage
Solution Approach 1:
The system uses execution time as a dynamic parameter to determine caching eligibility. By monitoring and comparing actual query execution times against a threshold parameter, the system automatically identifies which queries should be cached, balancing memory conservation with execution time optimization.
Solution Approach 2:
The system implements feedback by monitoring query execution times and using this information to make caching decisions. Queries that exceed the execution time threshold trigger caching actions, creating a feedback loop that continuously optimizes performance based on actual runtime behavior.
3Quantity of substance
If selective caching based on execution time is implemented, then cache efficiency is improved, but system complexity increases
Solution Approach 1:
The system manages complexity by using a single clear parameter (execution time threshold) for caching decisions. This parameter-based approach simplifies the caching logic compared to more complex multi-factor selection algorithms, while still achieving efficient cache utilization.
Solution Approach 2:
The system performs self-service by automatically monitoring query execution times and making caching decisions without external intervention. The threshold-based mechanism autonomously identifies candidates for caching, reducing the need for complex manual configuration or external control systems.
Data Source
AI summary
Operations include determining whether to cache resources accessed by a query based on the execution time of the query. The system identifies a set of executions of a same query. The system determines a cumulative execution time for the set of executions of the same query. If the cumulative execution time exceeds a threshold value, then the system caches a resource used for execution of the query.