Centralized Data Storage Service for Dynamic Object Routing
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Solution Overview
Problem
Current data storage systems lack an efficient method to dynamically manage data objects across multiple data stores based on the characteristics of the objects and the capabilities of the stores, leading to suboptimal access speeds, costs, and storage efficiency.
Innovation Solution
A centralized service determines the appropriate data store for each data object by applying rules that consider the object's characteristics, such as size and access frequency, and the capabilities of available data stores, allowing for dynamic storage and retrieval based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data objects are stored in a single data store based on simple criteria, then storage management is simple, but access speed and storage efficiency are suboptimal
Solution Approach 1:
The system segments data objects into different groups based on their characteristics (size, access frequency, sensitivity) and assigns them to different data stores. This segmentation allows each data store to be optimized for specific types of data, improving overall access speed while maintaining manageable complexity through automated classification rules.
Solution Approach 2:
The system dynamically determines data store assignments by evaluating multiple characteristics of data objects including size, access frequency, and sensitivity. This dynamic approach allows the system to adapt storage assignments based on actual data properties rather than using static, simple criteria, thereby improving access speed and storage efficiency.
2Speed
If data objects are stored in high-performance data stores, then access speed is maximized, but storage costs increase
Solution Approach 1:
The system applies local quality by matching specific data object characteristics with appropriate data store capabilities. Frequently accessed, small data objects are placed in high-performance data stores for optimal access speed, while less frequently accessed or larger objects are stored in cost-effective data stores. This localized optimization ensures high performance where needed while minimizing overall storage costs.
Solution Approach 2:
The system changes storage parameters dynamically by evaluating multiple characteristics of data objects (size, access frequency, sensitivity) and selecting data stores based on these parameters. This parameter-based approach allows the system to optimize the balance between access speed and storage cost by placing objects in the most appropriate data store for their specific requirements.
3Device complexity
If all data objects are stored in the same data store, then storage management is simplified, but storage efficiency and cost-effectiveness decrease
Solution Approach 1:
The system achieves universality by implementing a centralized service that handles multiple data stores with different characteristics. This service provides a unified interface for storing and retrieving data objects while internally managing the complexity of multiple data stores. The system evaluates various characteristics of data objects and automatically selects the most appropriate data store, providing multi-functional storage management that improves efficiency without exposing complexity to users.
4Productivity
If data objects are dynamically assigned to different data stores based on characteristics, then access speed and cost optimization improve, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically evaluating the characteristics of incoming data objects and determining the most appropriate data store without requiring manual intervention. The centralized service autonomously applies classification rules based on object characteristics (size, access frequency, sensitivity) and performs the storage assignment, thereby achieving storage optimization while keeping operational complexity manageable through automation.
Data Source
AI summary
Described are techniques for storing data objects heterogeneously, among multiple data stores, based on the values associated with one or more data object characteristics. A central device, entity, or network may receive data objects and determine a data store in which to store each data object. One or more rules that correspond to a received data object may be determined, a rule including expressions that associate characteristics of data objects to threshold values. The rules may specify particular data stores in which to store data objects based on the outcome of the expressions. The central device may generate a communication configured to access the determined data store(s) based on data store characteristics specific to the data store(s) and provide the data objects to the determined data stores. Data objects may be moved to other data stores responsive to a modification to a rule or to characteristics of a data object.