Sorted Hash Backup Server for Data Deduplication
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Solution Overview
Problem
Conventional data backup methods face inefficiencies due to the need to store large amounts of redundant data, leading to lengthy backup processes and high storage costs, particularly when backing up data from multiple computer systems, as they often result in dispersed and inefficient storage of hash values in hash tables.
Innovation Solution
The method involves generating hash values for data blocks, sorting them on a backup server, and comparing them against existing hash values in a hash table to identify and discard redundant data, ensuring only unique data is stored, thereby optimizing storage and backup processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional backup methods are used to backup data from multiple computer systems, then data is archived, but redundant data blocks are repeatedly stored leading to large storage volume and high costs
Solution Approach 1:
The patent segments data into fixed-size blocks and computes hash values for each block. This segmentation enables efficient comparison and identification of redundant blocks across multiple backup sources, allowing the system to store only unique data blocks while maintaining complete backup capability.
Solution Approach 2:
The patent transforms the backup approach by changing from storing complete data blocks to storing hash values and pointers. This parameter change in the data representation enables significant storage reduction while maintaining the ability to reconstruct and verify backup data.
2Reliability
If conventional hash table methods are used for data backup, then data blocks are hashed and stored, but hash values become dispersed throughout the hash table causing slow restoration and inefficient storage
Solution Approach 1:
The patent performs preliminary sorting of hash values before final storage. By organizing hash values in sorted order during the backup process, the system prepares the data structure in advance to enable efficient sequential access during restoration, eliminating the need for random access to dispersed hash values.
Solution Approach 2:
The patent merges hash values that correspond to adjacent data blocks into contiguous storage locations in the hash table. This combining of related hash values into sequential positions enables efficient reading and processing during restoration operations.
3Reliability
If full backup is performed first then incremental backups are implemented, then backup coverage is complete, but backup time and storage space are significantly increased
Solution Approach 1:
The patent extracts only the unique portions of data from backup sources by comparing hash values against existing backups. This extraction approach allows the system to maintain complete backup coverage while storing only new or changed data blocks, significantly reducing backup time and storage requirements.
4Quantity of substance
If de-duplication is used to eliminate redundant data blocks, then storage efficiency is improved, but the process of comparing hash values becomes computationally intensive and time-consuming
Solution Approach 1:
The patent uses hash values as copies or representations of the actual data blocks for comparison purposes. By working with these compact hash representations instead of the full data blocks, the system achieves efficient de-duplication with minimal computational overhead while maintaining the ability to verify data integrity.
Data Source
AI summary
A method, system and computer program product for backup and restoration of data. Hash values for data blocks subject to backup are generated. After a number of hashes are accumulated, e.g., on a backup server, these hashes are sorted. Then, the hashes are compared against the hash values in the hash table corresponding to data blocks that have already been backed up. If a hash matches the hash from the hash table, a pointer to the block in the archive is written to the table of pointers to the redundant blocks. Then, this hash value is deleted from a set of the hash values. A check is made if a hash is the last in the group. If the hash is the last in the group, the remaining unique hash values are written into the hash table. Otherwise, the next hash is selected from the group. The redundant data blocks are discarded and only unique data is backed up.