State Operating System for Distributed Data Storage
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Solution Overview
Problem
Conventional file systems are inefficient for storing and managing both small and large data objects, as they impose excessive overhead for small data units and cannot split or merge large data objects like video files, and relational databases are not optimal for variable length data and do not scale well with modern storage and access requirements.
Innovation Solution
A new data storage model and system using State Transformation Units (STUs) that are immutable, self-identifiable, and distributed, allowing for efficient storage and retrieval of data across various sized enterprises, with a State Operating System (State OS) that synchronizes and manages STUs across multiple servers, enabling scalable and flexible data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a file system is used to store data, then data can be stored in a simple manner, but the overhead for small data units is excessive and large data objects cannot be split or merged
Solution Approach 1:
The patent segments large data objects into smaller chunks that can be independently stored and managed. Each chunk is assigned a unique identifier and can be stored separately, allowing efficient retrieval of specific portions without loading entire files. This segmentation enables both small and large data objects to be stored efficiently without excessive overhead.
Solution Approach 2:
The patent extracts the file system overhead (metadata, directory structures, permission management) from the actual data storage mechanism. By separating concerns between file management and data storage, the system eliminates redundant overhead for each data object while maintaining the ability to manage both small and large files efficiently.
2Stability of the object's composition
If relational databases are used to store data, then structured data can be organized with fixed relationships, but variable length data is not optimal and the system does not scale well
Solution Approach 1:
The patent implements a dynamic storage system where data chunks can be freely created, merged, and reorganized based on access patterns and storage requirements. Unlike fixed-schema relational databases, this system adapts its structure dynamically, allowing variable-length data to be stored efficiently without pre-defining relationships or fixed formats.
Solution Approach 2:
The patent creates a universal storage mechanism that handles both structured and unstructured data, small and large objects, and various data types through a single chunk-based interface. This multi-functional approach eliminates the need for separate optimization for different data types while maintaining scalability across diverse workloads.
3Ease of operation
If data is stored as monolithic files, then file management is simple, but the files cannot be split into smaller files or merged into larger files
Solution Approach 1:
The patent automatically segments large data objects into manageable chunks that can be independently stored, retrieved, and managed. This segmentation is transparent to users who continue to interact with data as complete objects, while the system handles the splitting and merging operations automatically in the background.
Solution Approach 2:
The patent introduces a chunk management layer as an intermediary between the user and the physical storage system. This mediator handles the complexity of splitting, merging, and managing data chunks automatically, allowing users to work with simple file-like interfaces while the system optimizes storage by dividing and conquering large objects.
4Quantity of substance
If traditional storage systems are used, then storage capacity is limited, but distributed storage across multiple servers increases scalability and flexibility
Solution Approach 1:
The patent segments the storage system itself into distributed chunks across multiple servers, with each server managing a portion of the overall data storage. This segmentation allows linear scaling of storage capacity by simply adding more servers to the distributed network.
Solution Approach 2:
The patent implements a distributed coordination mechanism where servers continuously exchange information about data chunk locations, availability, and storage status. This feedback loop enables automatic load balancing, failover, and optimization across the distributed system while maintaining a unified view for users.
Data Source
AI summary
Methods and systems for distributed data storage provide automatically expandable and configurable state servers and a state operating system for storing data closer to its natural format.