Bit Vector Database for Storage Entity Signature Mapping
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Solution Overview
Problem
The estimation of unique space in storage systems is resource-intensive and complex due to the need for significant processor time and resources, especially when dealing with block sharing, clones, and snapshots, which complicates query processing and increases costs.
Innovation Solution
A storage system uses a database with a bit vector data structure to map entities to sampled signatures, allowing for faster query processing and minimal I/O resources by storing only signature information, which facilitates bulk data retrieval and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to estimate unique space in storage systems, then comprehensive data analysis is achieved, but processor time and resources are significantly consumed
Solution Approach 1:
The patent extracts only the essential signature information from sampled data units and stores it in a database, rather than storing or processing the complete data units. This extraction approach maintains the ability to estimate unique space accurately while dramatically reducing the computational resources and processor time required for query processing.
Solution Approach 2:
The patent applies local quality by using different representation methods for different parts of the data. Sampled data units are represented by compact signatures in the database, while the actual data units remain on storage devices. This localized optimization reduces overall system resource consumption while maintaining estimation accuracy.
2Measurement precision
If complete data unit information is stored for analysis, then accurate entity-related data retrieval is achieved, but database size and I/O resources increase
Solution Approach 1:
The patent extracts only the signature information from sampled data units and stores it in a database, rather than storing or processing the complete data units. This extraction approach maintains the ability to estimate unique space accurately while dramatically reducing the computational resources and processor time required for query processing.
Solution Approach 2:
The patent uses lightweight signature representations instead of complete data units in the database. These signature objects are computationally inexpensive to store, retrieve, and process, enabling efficient query processing while minimizing database size and I/O resource requirements.
Data Source
AI summary
Example implementation relates to generating a database for a set of entities associated with sampled data units of a storage system. A first data structure in the database maps, in a bit vector, each entity with a sampled signature of a set of sampled signatures. The set of sampled signatures are associated with the sampled data units. The set of entities associated with the set of sampled signatures are managed using the first data structure.