Distributed Database Search Request Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In distributed database environments, cached search results can become outdated due to changes in the original data, leading to inefficiencies in maintaining cache validity, especially in systems with limited computational resources where re-computing cached results is computationally expensive and resource-intensive.
Innovation Solution
The method involves separating data records into stable and volatile components, caching stable data pieces, and retrieving volatile data pieces from the live data source as needed, allowing for efficient updating and combining them to maintain valid search results without unnecessary re-computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cached results are continuously re-computed to maintain validity, then cache accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments data records into stable data pieces and volatile data pieces based on their change rates. Stable data pieces are cached and updated less frequently, while volatile data pieces are retrieved from the live data source more frequently. This segmentation allows the system to maintain cache accuracy for the most important data while reducing unnecessary computational resources for data that changes rarely.
Solution Approach 2:
The patent applies different update strategies to different parts of the data based on their local characteristics (change rates). Each data piece is evaluated individually for its volatility, and appropriate caching and update policies are applied locally. This ensures that computational resources are allocated efficiently based on the specific needs of each data element rather than uniformly across all data.
2Speed
If all data is cached to improve response time, then search request processing speed is improved, but data freshness deteriorates
Solution Approach 1:
The patent segments data into stable and volatile components, caching the stable portions to achieve fast response times while retrieving volatile portions from the live data source to maintain freshness. This allows the system to serve both speed and freshness requirements simultaneously by treating different data types differently.
Solution Approach 2:
Instead of caching all data uniformly, the patent applies partial caching only to stable data pieces where it provides the most benefit. For volatile data, the system retrieves fresh copies from the live source as needed. This partial action approach optimizes the balance between response speed and data freshness by applying caching only where appropriate.
3Reliability
If high-frequency updates are applied to all cached data, then data validity is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent evaluates each data piece's change rate and applies appropriate update frequencies locally. Stable data pieces are updated at lower frequencies to maintain computational efficiency, while volatile data pieces receive higher-frequency updates to ensure validity. This local quality approach optimizes the balance between data validity and computational efficiency for each data element.
Solution Approach 2:
The patent changes the update parameter (frequency) based on the data characteristics. By adjusting the update frequency parameter according to each data piece's volatility, the system achieves optimal computational efficiency while maintaining adequate data validity. This parameter adaptation allows the system to avoid unnecessary computations for stable data while ensuring freshness for volatile data.
Data Source
AI summary
Search requests are processed in a distributed database environment which includes a frontend module, one or more clients, a cache, a live data source, and multiple data records. Each data record includes at least one stable data piece stored in the cache and at least one volatile data piece. The frontend module receives a search request from a client and retrieves from the cache a set of data records. Each stable data piece in the set of data records corresponds to search criteria indicated in the search request. Current volatile data pieces, which correspond to the stable data pieces of the retrieved set of data records, are retrieved by the frontend module from the live data source for the retrieved set of data records. A completed set of data records is generated by combining the stable data pieces and the current volatile data pieces, and returned to the requesting client.