Partitioned B-tree Index for Data Deduplication
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Solution Overview
Problem
Current data storage systems face inefficiencies in managing and processing data, particularly in scalable and redundant data environments, where existing techniques like single instance storage (SIS) struggle with data deduplication and storage scalability, leading to bottlenecks and resource mismanagement.
Innovation Solution
A method utilizing a B-tree index structure partitioned into ranges of token values, with each data portion having a unique entry and reference count, allowing for efficient allocation and storage across multiple data storage systems, and employing a data pull model with affinity caching to optimize data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If single instance storage (SIS) technology is used to eliminate redundant data, then storage efficiency is improved, but system complexity increases due to additional software requirements
Solution Approach 1:
The patent segments the index structure into multiple partitioned B-trees, where each partition handles a specific range of token values. This segmentation allows the system to manage large volumes of deduplicated data efficiently while maintaining manageable complexity through modular organization of index structures.
Solution Approach 2:
The patent introduces an intermediary allocation space that acts as a buffer between index structure operations and the underlying storage system. This intermediary layer absorbs allocation fluctuations and simplifies the interface between the SIS software and storage operations, reducing overall system complexity.
2Quantity of substance
If data storage systems are scaled to handle larger volumes of data, then storage capacity is improved, but processing bottlenecks increase
Solution Approach 1:
The patent divides the index structure into multiple partitioned B-trees that can be processed independently and in parallel. Each partition handles a specific range of token values, enabling concurrent processing operations across multiple partitions, thereby maintaining high throughput as storage capacity scales.
Solution Approach 2:
The patent performs preliminary allocation of index entries in an allocation space before actual storage operations. This pre-allocation strategy prepares the index structure in advance, reducing processing bottlenecks during data ingestion and enabling smoother scaling operations without compromising throughput.
3Measurement precision
If index structures are made more detailed to improve data access precision, then access accuracy is improved, but memory usage increases
Solution Approach 1:
The patent segments the detailed index structure into partitioned B-trees distributed across multiple storage units. Each partition maintains precise indexing for its specific token value range, ensuring accurate data access while distributing memory requirements across the system rather than concentrating them in a single large structure.
Solution Approach 2:
The patent transitions from a single-dimensional index structure to a multi-dimensional partitioned structure organized by token value ranges. This dimensional transformation allows precise access within each partition while the overall system manages memory efficiently through distributed organization across multiple storage units.
Data Source
AI summary
Described are techniques for managing an index structure used to access data portions. A token having a token value for a data portion is provided. An entry in said index structure for said data portion is provided. The index structure is accessed using said token value as a key to determine said entry in said index structure for said data portion. The entry is allocated to have a location in accordance with said token value.