Query Filter Data Structures for Storage Server Load Reduction
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Solution Overview
Problem
Existing query systems face inefficiencies due to overburdened storage servers and difficulty in providing specific data age requirements, as they struggle to predict when to access storage servers and refresh query results, leading to decreased operational efficiency and user experience.
Innovation Solution
A query system utilizing query filter data structures to determine whether to fetch live or aged data from a storage server based on user-defined data age parameters, reducing storage server queries and adjusting dataset refresh frequencies accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the query system fetches live query result data from the storage server for every user query, then the data freshness is ensured, but the storage server becomes overburdened and operational efficiency decreases
Solution Approach 1:
The system performs preliminary actions by creating datasets with predetermined data age periods in advance. Query filter data structures are pre-configured with subject data age periods that define when data becomes stale. This allows the system to proactively manage data freshness without requiring active queries to determine data age requirements, reducing storage server burden while maintaining appropriate data freshness.
Solution Approach 2:
The system dynamically adjusts dataset refresh frequencies based on data age indications from user queries and query frequency patterns. The query filter data structures enable dynamic determination of whether to serve aged data from datasets or fresh data from storage servers, optimizing the balance between data freshness and server load based on actual usage patterns.
2Reliability
If the query system accesses the storage server frequently to ensure data freshness, then reliable data is provided, but the complexity of predicting when to access the storage server increases
Solution Approach 1:
The system changes the parameter of data age by using query filter data structures with subject data age periods. Instead of complex predictions about when data becomes stale, the system uses explicit age period parameters defined in the query filters. This simplifies the decision-making process by providing clear criteria (data age periods) for determining when to refresh datasets from the storage server.
Solution Approach 2:
The system implements feedback mechanisms by monitoring query frequencies and data age indications from user queries. This feedback informs dynamic adjustments to dataset refresh frequencies, creating a closed-loop system that automatically optimizes storage server access patterns based on actual usage, reducing the need for complex predictive algorithms.
3Speed
If the system serves aged query result data from datasets, then query speed increases, but the ability to satisfy user data age requirements decreases
Solution Approach 1:
The system segments query results by creating multiple datasets with different data age characteristics. Query filter data structures are segmented by subject and data age period, allowing the system to serve different aged data for different query subjects. This segmentation enables both fast serving of aged data when appropriate and fresh data retrieval when required, maintaining query speed while satisfying diverse data age requirements.
Solution Approach 2:
The system applies local quality by allowing different data age periods for different query subjects through subject-specific query filter data structures. Instead of a uniform data freshness policy, each query subject can have its own data age period requirements, enabling the system to optimize query speed for appropriate subjects while maintaining data freshness where required.
4Loss of time
If the system creates query filter data structures with subject data age periods, then data age management is improved, but the device complexity increases
Solution Approach 1:
The system introduces query filter data structures as intermediaries between user queries and the data storage/retreival system. These filters act as mediators that encapsulate data age requirements, allowing the complex logic of data age management to be contained within the filter structures rather than scattered throughout the query processing system. This intermediary approach improves data age management while managing overall system complexity.
Data Source
AI summary
A query scheme is presented in which a query filter data structure is created and utilized to determine whether to obtain live query data from a storage server or aged data within an associated dataset. The number of queries to the storage server may be reduced and overall efficiency of the query system is increased. Another query scheme is presented to adjust the frequency of a dataset refresh based upon a user defined data age parameter indication that is contained a subsequent query and/or based upon whether a frequency of storage server queries is above a predetermined threshold. The propensity of obtaining query results from a dataset is increased while the propensity of queries to the storage server decreases, thereby increasing query system efficiency.