Embedding Index Retrieval for Low-Latency Backup Data Search
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Solution Overview
Problem
Conventional data platforms struggle with efficiently accessing and leveraging backup data due to high access latencies and complexity in managing large volumes of unstructured 'dark data', missing opportunities for insights and decision-making.
Innovation Solution
A data platform generates an index of embeddings for filtered backup data, scoped to user queries, using machine learning to create a unique filter and apply it to backup data, enabling efficient retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional backup systems store and retain all backup data for compliance and analysis, then data availability and compliance are improved, but data access latency and retrieval complexity increase significantly
Solution Approach 1:
The system creates embeddings and indexes backup data in advance during the backup process itself, so that when queries are executed, the data is already prepared for rapid retrieval. This preliminary processing eliminates the need to scan and analyze raw backup data at query time, significantly reducing access latency while maintaining full data availability.
Solution Approach 2:
The patent introduces an embedding layer as an intermediary between the backup storage system and the query interface. This embedding index acts as a mediator that translates natural language queries into efficient data retrieval operations, reducing the complexity and time required to access backup data while maintaining comprehensive data availability.
2Loss of information
If backup data is collected and collated from distributed systems for analysis, then data completeness is improved, but system complexity and computational resources increase
Solution Approach 1:
The system transforms backup data from its original format into embedding representations, changing the parameter space from raw data to vector space. This transformation enables efficient similarity search and analysis while reducing the computational complexity of querying distributed backup systems, as embeddings can be searched using efficient vector operations rather than traditional data scanning.
3Ease of operation
If traditional indexing methods are used for backup data, then data retrieval is simplified, but the ability to perform semantic search and understand user intent is limited
Solution Approach 1:
The embedding index serves multiple functions simultaneously: it enables traditional keyword-based search, semantic similarity search, and intent-based querying. This multi-functional approach maintains the simplicity of traditional retrieval methods while adding advanced semantic capabilities, allowing the system to handle both simple and complex queries efficiently.
4Adaptability or versatility
If all backup data is made accessible for AI and machine learning applications, then data utility and insights are improved, but data security and access control requirements increase
Solution Approach 1:
The system applies different access control policies to different portions of the embedding index based on data sensitivity and user roles. Rather than uniformly restricting or allowing access to all backup data, the system creates fine-grained access controls that permit AI/ML applications to access only the portions of data they are authorized to use, maintaining security while maximizing data utility.
Data Source
AI summary
In general, techniques for efficient data retrieval from a backup system are described. An example computing system includes one or more storage devices and processing circuitry having access to the one or more storage devices and configured to: process an input to generate a filter, wherein the input indicates a context for one or more queries; apply the filter to backup data to obtain filtered data from the backup data; generate an index of embeddings from the filtered data; process, based on the index of embeddings, a query to generate a response for the query; and output the response.