Deduplication Index Management via Dynamic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data storage systems face inefficiencies in managing deduplication, particularly in handling large datasets where duplicate data segments are not effectively identified and stored, leading to storage capacity issues and increased complexity in maintaining deduplication indices.
Innovation Solution
A method is introduced that computes unique values for data segments using hash functions, generates identifiers with metadata indicating storage locations, and manages an index that dynamically adjusts by removing least recently used entries to maintain a predetermined size, ensuring efficient deduplication and storage optimization by preventing duplicate storage of identical segments across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If deduplication is implemented to reduce storage capacity, then storage efficiency is improved, but index management complexity increases
Solution Approach 1:
The patent segments the index into multiple partitions or zones based on data access patterns and temporal characteristics. Hot data segments are separated from cold data segments, allowing differential management strategies. This segmentation reduces the complexity of managing the entire index as a single structure while maintaining deduplication effectiveness.
Solution Approach 2:
The patent implements dynamic index structures that automatically adjust their organization and management based on data access patterns, temporal characteristics, and workload demands. Index entries are dynamically reorganized, merged, or pruned based on usage frequency and data age, reducing management complexity while preserving deduplication benefits.
2Measurement precision
If the index grows to store all data segments, then deduplication accuracy is improved, but index size and maintenance overhead increase
Solution Approach 1:
The patent applies different quality levels and management strategies to different portions of the index based on local characteristics. Frequently accessed or recently added data segments receive more rigorous indexing and validation, while older or less accessed segments use compressed or simplified index entries. This local differentiation maintains high deduplication accuracy for critical data while reducing overall index size.
Solution Approach 2:
The patent dynamically changes index parameters such as entry granularity, hash table size, and validation depth based on data characteristics, access patterns, and system resource availability. These parameter adjustments allow the index to maintain adequate deduplication accuracy while adapting its size and complexity to current workload requirements.
3Loss of substance
If all duplicate segments are identified and stored once, then storage redundancy is reduced, but processing time for data ingestion increases
Solution Approach 1:
The patent performs preliminary hashing and initial duplicate detection during data ingestion using lightweight hash functions and Bloom filters. This preliminary action identifies obvious duplicates early in the process, allowing the system to skip expensive full comparison operations for clearly duplicate segments, thus reducing overall processing time while maintaining deduplication effectiveness.
Solution Approach 2:
The patent implements skip-lists or similar probabilistic data structures that allow the system to quickly skip over segments that are unlikely to be duplicates. By using filtering mechanisms and statistical sampling during data ingestion, the system rushes through obvious non-duplicates without performing full verification, reducing ingestion time while still identifying actual duplicates with high accuracy.
Data Source
AI summary
In one aspect, in general, a method for managing data in a data storage system comprises receiving data to be stored in the data storage system, computing values corresponding to different respective portions of the received data, generating identifiers corresponding to different respective portions of the received data, with an identifier corresponding to a particular portion of data including the computed value corresponding to the particular portion of data and metadata indicating a location where the particular portion of data is being stored in the data storage system, and storing at least some of the identifiers in an index until the index reaches a predetermined size.