Dynamic Cache Update Logic for Database Query Response
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Solution Overview
Problem
Database technologies face challenges in maintaining short response times for complex and resource-intensive queries, particularly when pre-computed query results become outdated, leading to inefficient re-computation and resource consumption.
Innovation Solution
A method is implemented where a computing machine determines whether to update cached data based on polling factors such as query type, data validity, and resource availability, allowing for efficient updating of cached data to maintain its validity and reduce unnecessary re-computations, by inhibiting updates if subsequent queries are expected within a given time period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-computed query results are maintained in a cache to shorten response times, then query response speed is improved, but the cached data becomes outdated and requires frequent re-computation which consumes substantial computation resources
Solution Approach 1:
The patent implements dynamic cache update decision-making by evaluating multiple polling factors (data age, query frequency, resource availability, data criticality) to determine whether to update cached data. This dynamic approach allows the system to adapt update frequency based on current conditions, updating only when necessary to maintain data validity while avoiding unnecessary re-computation resource consumption.
Solution Approach 2:
The system changes the parameter of cache update timing from fixed periodic updates to variable updates based on multiple factors including data age thresholds, query frequency patterns, available computation resources, and data criticality levels. This parameter change enables optimization of both response speed and resource consumption by updating cache data only when the combination of factors indicates necessity.
2Reliability
If cached data is updated frequently to maintain data validity, then data freshness is improved, but the number of cache updates increases leading to higher computation resource consumption
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors query patterns, cache hit rates, and resource usage to inform update decisions. The polling factors include query frequency analysis and cache performance metrics that feed back into the update decision logic, allowing the system to learn from past behavior and optimize update timing to maintain data freshness while minimizing unnecessary updates.
Solution Approach 2:
The system applies partial action by updating only the specific cache entries that require updates based on the polling factor evaluation, rather than performing full cache re-computation. This selective approach maintains necessary data freshness while avoiding excessive computation resource consumption on already-valid cached data.
3Reliability
If the system processes queries using the primary database to ensure data accuracy, then data accuracy is improved, but response time increases due to the resource-intensive nature of primary database processing
Solution Approach 1:
The patent implements preliminary action by pre-computing and caching query results in advance based on predicted query patterns and popularity analysis. The system proactively prepares and stores results for frequently requested queries before they are actually needed, allowing immediate retrieval when queries arrive and eliminating the need for time-consuming primary database processing while maintaining data accuracy through controlled update mechanisms.
Data Source
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AI summary
Cached data is updated by a computing machine coupled to a primary database maintaining original data and to a cache maintaining data corresponding to the data in the primary database. In response to receiving a first database query relating to a first data, the computing machine determines whether to process the first database query by utilizing the cache or based on the primary database. In response to concluding processing the first database query, the computing machine determines whether to update the first data in the cache, and based on the result of the determining, updates the first data in the cache based on original first data in the primary database.