Inline Post-Process Data Deduplication File System
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Solution Overview
Problem
Existing file systems inefficiently manage redundant data during write and read operations, leading to unnecessary storage of redundant data, which occupies memory space and causes wear on storage devices.
Innovation Solution
A method that employs inline and post-process data deduplication by segmenting data into chunks, using hash values to identify duplicates, and either removing or referencing duplicate chunks to prevent redundant writes and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is written to the data store without deduplication, then write speed is improved, but memory space is wasted and storage devices experience wear
Solution Approach 1:
The system performs preliminary hash value comparison during the write operation to identify duplicate data chunks before they are written to the data store. By checking against the memory index in advance, the system determines whether data already exists in the data store, preventing redundant writes and optimizing both write speed and memory space utilization.
Solution Approach 2:
The data is divided into discrete chunks that can be independently hashed and compared. This segmentation allows the system to process and deduplicate data in manageable units, improving write efficiency by only writing unique chunks while maintaining comprehensive deduplication coverage.
2Quantity of substance
If inline data deduplication is performed during write operation, then memory space usage is reduced, but processing time increases
Solution Approach 1:
The hash value computation and comparison are performed preliminarily during the write operation setup phase. By preparing hash values and checking the memory index before actual data writing, the system identifies duplicates early, allowing for efficient space optimization without significantly impacting overall write performance.
3Measurement precision
If hash value comparison is performed for every data chunk, then deduplication accuracy is improved, but computational overhead increases
Solution Approach 1:
The system replaces complex full-data comparison with hash value comparison. Instead of comparing entire data chunks which would be computationally expensive, the system uses hash functions to generate compact representations that can be quickly compared against the memory index, achieving high deduplication accuracy with minimal computational overhead.
Data Source
AI summary
Deduplication, including inline deduplication, of data for a file system can be implemented and managed. A data management component (DMC) can control inline and post-process deduplication of data during write and read operations associated with memory. DMC can determine whether inline data deduplication is to be performed to remove a data chunk from a write operation to prevent the data chunk from being written to a data store based on a whether a hash associated with the data chunk matches a stored hash stored in a memory index and associated with a stored data chunk stored in a shadow store. If there is a match, DMC can perform a byte-by-byte comparison of the data chunk and stored data chunk to determine whether they match. If they match, DMC can perform inline data deduplication to remove the data chunk from the write operation.