Vector Embedding Data Storage for Distributed Availability
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Solution Overview
Problem
Existing data storage systems face challenges in efficiently managing data across multiple storage nodes and ensuring high availability and reliability, particularly in cloud-based storage systems.
Innovation Solution
The proposed solution involves a data storage system that utilizes vector embeddings to store and retrieve data, allowing for efficient data management across multiple storage nodes. This system includes a storage cluster with multiple storage nodes, each equipped with non-volatile solid state storage units, and a cloud-based storage system that leverages cloud services providers for enhanced data management and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored across multiple storage nodes in a distributed system, then availability and reliability are improved, but system complexity increases
Solution Approach 1:
The patent segments data into multiple shards that are distributed across different storage nodes. Each shard is independently stored and can be accessed, allowing the system to maintain high availability while managing complexity through modular data organization. The segmentation principle is applied to divide large datasets into manageable units that can be independently managed across the distributed system.
Solution Approach 2:
The patent introduces an intermediary indexing system that maps data identifiers to their physical locations across storage nodes. This intermediary layer simplifies access operations by handling the complexity of data location resolution, allowing clients to access distributed data through simple key-based operations without directly managing the underlying distribution complexity.
2Productivity
If vector embeddings are used to store unstructured data, then data retrieval efficiency is improved, but storage space requirements increase
Solution Approach 1:
The patent creates compressed representations (copies) of unstructured data in the form of vector embeddings. Instead of storing the original large unstructured data, the system stores compact vector representations that capture the essential semantic information. These vector copies enable efficient similarity searches and data retrieval while significantly reducing the storage space required compared to storing the complete original data.
3Adaptability or versatility
If cloud-based storage services are leveraged, then scalability is improved, but dependency on external services increases
Solution Approach 1:
The patent designs a storage system that can operate with multiple backend storage services, making the system universal and adaptable to different cloud providers or on-premises storage solutions. The architecture allows the same data management logic to work across different storage backends, providing scalability benefits while reducing dependency on any single external service through multi-provider compatibility.
Data Source
AI summary
Storing and retrieving data using vector embeddings, including: receiving a request to store a data object in an object store; generating, based on the data object, a vector embedding for the data object; and storing, in the object store, the data object and the vector embedding for the data object.