Server Data Compression by File-Type Sub-Block Selection
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Solution Overview
Problem
Existing server compression methods achieve a relatively low compression ratio for data blocks due to the use of a single preset compression algorithm, which is inadequate for data with varying characteristics.
Innovation Solution
A server method that parses data blocks to identify file types and characteristics, selects appropriate compression algorithms based on these characteristics, and uses them to compress data sub-blocks, thereby improving the compression ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single preset compression algorithm is used to compress data in a data block, then the compression process is simple, but the compression ratio is relatively low
Solution Approach 1:
The data block is divided into multiple data sub-blocks based on different file types (e.g., text files, image files, video files). Each data sub-block is then compressed using a compression algorithm specifically suited to its characteristics, rather than applying a single algorithm to the entire data block. This segmentation allows the system to achieve higher overall compression ratios while managing complexity through organized division of the compression task.
2Quantity of substance
If multiple compression algorithms are used for different data characteristics, then the compression ratio is improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary classification of data blocks into different file types before compression. By identifying the file type (text, image, video, etc.) in advance, the system can pre-select the most appropriate compression algorithm for each data sub-block. This preliminary action simplifies the overall system architecture by establishing a clear workflow: classify first, then compress with the predetermined algorithm, avoiding the need for complex real-time algorithm selection and comparison.
3Quantity of substance
If data is classified by file type and compressed accordingly, then the compression ratio is enhanced, but the processing time increases
Solution Approach 1:
The system changes the parameter of file type classification to organize data into distinct categories (text, image, video, audio, etc.). Each file type parameter corresponds to a pre-configured compression algorithm. This parameter-based organization allows for efficient processing by matching data characteristics with optimized compression methods, achieving high compression ratios without excessive processing time due to the systematic approach.
Data Source
AI summary
A device and a method for compressing data by a device are provided, which relate to the storage field and are used to resolve a prior-art problem that a compression ratio at which data in a data block is compressed by a device is relatively low. The method includes: parsing, by a device, an information block in a data block, to obtain a file type of data in the data block and a data sub-block that is included in the data block; determining a characteristic of data in the data sub-block according to the file type; selecting, according to the characteristic, a target compression algorithm that is used to compress the data in the data sub-block; and compressing the data in the data sub-block by using the target compression algorithm. Embodiments of the present disclosure are used to compress data.