Data Table Cost Analysis Using Dependency-Aware Metrics
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Solution Overview
Problem
Conventional methods for analyzing data tables in cloud computing environments fail to accurately measure processing and use costs due to isolation of computational and storage resource consumption, leading to unfair allocation and inefficient resource utilization.
Innovation Solution
A method and apparatus for calculating processing and use costs of data tables by considering dependence relationships, including extracting processing cost features such as scan, compute, and storage costs, and applying calculation factors to accurately assess costs and optimize data table construction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data processing cost is measured only by computational hardware resources and storage resources consumed during data processing of one current data table in isolation, then the measurement process is simple, but the cost measurement accuracy is low
Solution Approach 1:
The cost measurement is segmented into three distinct components: computational resource consumption (CPU, memory), storage resource consumption (storage medium), and data access frequency. By dividing the overall cost into these separable elements, the system can measure each component independently and then aggregate them, achieving accurate cost measurement without requiring an overly complex unified measurement mechanism.
Solution Approach 2:
The patent introduces an intermediary cost calculation model that considers the dependence relationships between data tables. This intermediary layer processes the raw resource consumption data and transforms it into accurate cost measurements by incorporating factors such as data access frequency and table dependencies, thereby resolving the contradiction between measurement simplicity and accuracy.
2Measurement precision
If data processing cost is allocated equally to various users of the data table, then the allocation process is simple, but the cost allocation fairness is poor
Solution Approach 1:
The patent applies local quality by differentiating cost allocation based on each user's specific data access patterns and frequency. Instead of uniform allocation, each user is charged according to their local usage characteristics, such as the number of times they accessed the data table and the volume of data they processed, achieving fair and accurate cost allocation.
Solution Approach 2:
The system implements feedback mechanisms that track and record each user's data access behavior. This feedback information is then used to dynamically adjust cost allocations, ensuring that users who consume more resources pay accordingly. The feedback loop continuously monitors usage and updates cost distributions, making the allocation process both accurate and adaptive.
3Productivity
If data tables are processed in isolation without considering dependence relationships, then the processing is efficient, but the resource utilization is poor
Solution Approach 1:
The patent merges the processing of related data tables by identifying and utilizing dependence relationships between them. When multiple data tables have hierarchical or dependency relationships, the system combines their processing operations to avoid redundant computations. This merging approach maintains processing efficiency while significantly reducing resource waste through optimized data access patterns.
Solution Approach 2:
The system performs preliminary analysis to identify dependence relationships between data tables before actual processing occurs. By pre-processing the metadata and relationship information, the system can optimize the processing sequence and avoid redundant operations, thereby maintaining high productivity while reducing resource consumption through intelligent preprocessing.
Data Source
AI summary
A method including calculating processing cost data of the general data table of the data common layer; determining a general data table on which the external data table of the non-data common layer is dependent; and calculating use cost data of the external data table according to the processing cost data of the general data table. In evaluation of the cost of each general data table of the data common layer, instead of taking into account the storage and computation consumption of the current data table in isolation, several upstream data tables and multiple siblings of the data table are also taken into account comprehensively. The processing cost of the general data table is reasonably and accurately assessed, thus reflecting the advantages and disadvantages of data model construction of the data common layer, and providing decision-making support for the optimization and operation of the models of the data common layer.