Database Cache Update Adaptation via Probabilistic Accuracy
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Solution Overview
Problem
Existing database systems face challenges in maintaining up-to-date cache results for search queries, leading to inefficient re-computation or re-collection of search results, which consume substantial computational and bandwidth resources, especially when pre-computed results become outdated due to changes in the original data.
Innovation Solution
A method that randomly selects queries to retrieve both cached and original data, adapts a probabilistic model using machine learning to evaluate the accuracy of cached data, and updates the cache only when necessary, thereby reducing unnecessary polling and improving the efficiency of cache updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If re-computation or re-collection of search results is performed frequently to keep cache up-to-date, then the freshness and accuracy of cached data is improved, but the computational resource consumption and bandwidth usage increase substantially
Solution Approach 1:
The system pre-computes and stores search results in a cache before they are actually needed by queries. This preliminary action allows the system to avoid expensive re-computation when queries arrive, as the results are already prepared and stored in the cache memory.
Solution Approach 2:
The patent introduces dynamic strategies for cache updates based on data change detection. Instead of using fixed re-computation schedules, the system dynamically adjusts cache update behavior by monitoring data changes and only re-computing when necessary, thereby optimizing between freshness and computational resource consumption.
2Ease of operation
If manual scheduling of re-computation or re-collection is implemented, then cache update control is simplified, but the response time to queries increases and computational resources are wasted on unnecessary updates
Solution Approach 1:
The system implements self-service cache update mechanisms where the database automatically detects data changes and triggers cache updates without manual intervention. Change detection mechanisms monitor the original data sources and automatically invalidate or re-compute cache entries when changes are detected, eliminating the need for manual scheduling while optimizing update timing.
3Reliability
If the entire cache is re-computed or re-collected when outdated, then the accuracy of search results is maintained, but the bandwidth consumption and computational overhead increase significantly
Solution Approach 1:
The patent segments the cache into individual entries or groups that can be updated independently. Instead of re-computing the entire cache when outdated, the system identifies and updates only the specific cache entries that correspond to changed data, thereby maintaining accuracy while minimizing bandwidth consumption and computational overhead.
Solution Approach 2:
The system applies local quality updates by re-computing or re-collecting only the specific search results that are affected by data changes, rather than treating the entire cache uniformly. This localized approach ensures that accuracy is maintained for affected results while avoiding unnecessary computational resources and bandwidth usage for unaffected entries.
Data Source
AI summary
A database system includes an original data source storing pieces of original data and a cache source storing pieces of cached data, each associated with an accuracy value. A method of processing queries in the system includes: for each randomly selected client query, retrieving a first piece of cached data matching the query, and retrieving a first piece of original data matching the query; for non-selected client queries, retrieving a second piece of cached data matching the query; evaluating the accuracy value of the second piece of cached data; if the accuracy value is below a given threshold, retrieving a second piece of original data matching the query, and updating the second piece of cached data by the second piece of original data; and adapting a probabilistic model based on the first piece of cached data and the first piece of original data using a machine learning algorithm.