CN114138414A - Incremental compression method and system for container mirror image - Google Patents
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Abstract
The invention discloses an incremental compression method of a container mirror image, which comprises the following steps: analyzing the container mirror image layer, decompressing the mirror image layer, temporarily decompressing the files of each layer, and generating a hash value for each file; comparing and analyzing the difference files, comparing the hash values of the files, deleting the files with the same hash value, adding placeholders into a history file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders; deep analysis of container mirror images, repackaging mirror image layers, reading a history file library, deleting files with completely same placeholders, packaging files with high similarity in mirror image files, and merging and exporting the difference parts of the files; and optimizing an offline scene, and in an offline exporting scene without the Internet, exporting the mirror image file and further comparing and analyzing the mirror image file. The invention solves the problems of high container mirror image redundancy and high required resource cost in the prior art.
Description
Technical Field
The invention relates to the technical field of virtual machines, in particular to an incremental compression method and system for container images.
Background
In the existing container technology, the size of a container image almost completely depends on the personal skill of a user, a single image is often hundreds of MB in size, and a single image is often thousands of MB in size, and a plurality of images may be uploaded in one-time system release. The deployment of the original light weight becomes heavier and more complex, and particularly under the project of high-speed iteration, the resource cost becomes a burden. The common solution is to adopt a dockerhub public cloud mode for management, but no solution is provided for the private cloud. Therefore, the prior container has high mirror image redundancy and large resource cost.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problems that the redundancy of the prior container mirror image is high and the cost of required resources is high, the invention provides an incremental compression method of the container mirror image, which reduces the size of the capacity mirror image by a method of difference comparison between the historical mirror image and a plurality of mirror images.
The technical scheme is as follows: a method of incremental compression of a container image, comprising the steps of:
(1) analyzing the container mirror image layer, decompressing the mirror image layer, temporarily decompressing the files of each layer, and generating a hash value for each file;
(2) comparing and analyzing the difference files, comparing the hash values of the files, deleting the files with the same hash value, adding placeholders into a history file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders;
(3) deep analysis of container mirror images, repackaging mirror image layers, reading a history file library, deleting files with completely same placeholders, packaging files with high similarity in mirror image files, and merging and exporting the difference parts of the files;
(4) optimizing an offline scene, wherein in an offline export scene without the Internet, image files are exported for further comparison and analysis, all image file lists used by a target deployed server are compared, the file list is ensured to have at least more than one comparison and analysis, and the same file character streams or highly similar file character streams in the last comparison and analysis in the file list are subjected to differential comparison;
(5) and (4) image decompression, off-line copy and image loading, processing all image layers and loading into the container system.
The hash value generated for each file in the step (1) is an MD5 hash value.
The difference analysis of the step (2) is to set a storage capacity D1 value, if the file size exceeds a D1 value, the repeated character streams between the individual files are compared to generate a repetition D2 value, wherein the D1 value and the D2 value are dynamically adjusted thresholds, and if the set value is exceeded, the file is represented by a placeholder and links the difference deployment of the files.
The step (5) is as follows:
(5.1) importing a mirror image and decompressing a mirror image layer;
(5.2) checking whether the mirror image file has a placeholder;
(5.3) if there is a placeholder, restoring the placeholder to actual file content;
(5.4) if no placeholder exists, reading the file content, generating a hash value and recording the hash value in a special history library for comparing and analyzing the placeholder at the next time;
(5.5) processing all the mirror image layers, and finally restoring to the original mirror image entity before compression;
(5.6) the Docker system runs a mirror image.
A container mirrored incremental compression system comprising: the system comprises a container mirror image layer analysis module, a difference file comparison analysis module, a container mirror image depth analysis module, an offline scene optimization module and a mirror image decompression module;
the container mirror image layer analysis module decompresses the mirror image layers, temporarily decompresses the files of each layer, and generates a hash value for each file;
the difference file comparison and analysis module is used for comparing hash values of files, deleting the files with the same hash value, adding placeholders into the historical file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders;
the container mirror image depth analysis module repacks mirror image layers, reads a historical file library, deletes files with completely the same placeholders, packs files with high similarity in mirror image files, and combines and exports difference parts of the files;
the offline scene optimization module is used for further comparison and analysis before exporting the image file in an offline exporting scene without the Internet, comparing all the image file lists used by the server deployed by a target once, ensuring that the file lists have at least one comparison and analysis, and performing differential comparison on the same file character stream or the file character stream with high similarity existing in the last comparison and analysis in the file lists;
and the mirror image decompression module is used for off-line copying and loading mirror images, processing all mirror image layers and loading the mirror image layers into the container system.
The hash value generated for each file in the container mirror layer analysis module is an MD5 hash value.
The difference analysis in the difference file comparison analysis module is to set a storage capacity D1 value, if the file size exceeds a D1 value, the repeated character streams between the individual files are compared to generate a duplication degree D2 value, wherein the D1 value and the D2 value are dynamically adjusted thresholds, and if the file size exceeds the set value, the file is represented by a placeholder and links the differential deployment of the file.
The mirror image decompression module is as follows: importing a mirror image and decompressing a mirror image layer; checking whether the mirror image file has a placeholder; if the placeholder exists, restoring the placeholder into the actual file content; if no placeholder exists, reading the file content, generating a hash value and recording the hash value in a historical special library for comparing and analyzing the placeholder at the next time; processing all the mirror image layers, and finally restoring to an original mirror image entity before compression; the Docker system runs a mirror image.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
through difference comparison between the historical mirror image and the multi-mirror image, the cost of storage capacity and the IO cost during deployment are greatly reduced; the reduction of the overall deployment file can increase the transmission efficiency of deployment and greatly reduce the cost of network bandwidth; and the offline scene optimization enables the containerization scheme to be easier to implement and deploy in the scenes without Internet and secret.
Drawings
FIG. 1 is a flow chart of a method of incremental compression of a container image.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
Example 1:
as shown in fig. 1, an incremental compression method for a container image includes the following steps:
(1) analyzing the container mirror image layer, decompressing the mirror image layer, temporarily decompressing the files of each layer, and generating a hash value for each file;
(2) comparing and analyzing the difference files, comparing the hash values of the files, deleting the files with the same hash value, adding placeholders into a history file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders;
(3) deep analysis of container mirror images, repackaging mirror image layers, reading a history file library, deleting files with completely same placeholders, packaging files with high similarity in mirror image files, and merging and exporting the difference parts of the files; after analysis, the mirror image is repackaged, files with completely the same placeholders are excluded, and for files with highly similar attributes in the mirror image file, such as class binary files in a jar program, which are 99% similar to the class binary file of another jar program, the second file will only generate 1% of difference content, but not all file content.
(4) Optimizing an offline scene, wherein in an offline export scene without the Internet, image files are exported for further comparison and analysis, all image file lists used by a target deployed server are compared, the file list is ensured to have at least more than one comparison and analysis, and the same file character streams or highly similar file character streams in the last comparison and analysis in the file list are subjected to differential comparison;
(5) and (4) image decompression, off-line copy and image loading, processing all image layers and loading into the container system.
The hash value generated for each file in the step (1) is an MD5 hash value.
The difference analysis of the step (2) is to set a storage capacity D1 value, if the file size exceeds a D1 value, the repeated character streams between the individual files are compared to generate a repetition D2 value, wherein the D1 value and the D2 value are dynamically adjusted thresholds, and if the set value is exceeded, the file is represented by a placeholder and links the difference deployment of the files.
Where the D1 and D2 values are dynamically adjusted thresholds, such as D1-1 MB and D2-60%.
The step (5) is as follows:
(5.1) importing a mirror image and decompressing a mirror image layer;
(5.2) checking whether the mirror image file has a placeholder;
(5.3) if there is a placeholder, restoring the placeholder to actual file content;
(5.4) if no placeholder exists, reading the file content, generating a hash value and recording the hash value in a special history library for comparing and analyzing the placeholder at the next time;
(5.5) processing all the mirror image layers, and finally restoring to the original mirror image entity before compression;
(5.6) the Docker system runs a mirror image.
Example 2:
a container mirrored incremental compression system comprising: the system comprises a container mirror image layer analysis module, a difference file comparison analysis module, a container mirror image depth analysis module, an offline scene optimization module and a mirror image decompression module;
the container mirror image layer analysis module decompresses the mirror image layers, temporarily decompresses the files of each layer, and generates a hash value for each file;
the difference file comparison and analysis module is used for comparing hash values of files, deleting the files with the same hash value, adding placeholders into the historical file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders;
the container mirror image depth analysis module repacks mirror image layers, reads a historical file library, deletes files with completely the same placeholders, packs files with high similarity in mirror image files, and combines and exports difference parts of the files; after analysis, the mirror image is repackaged, files with completely the same placeholders are excluded, and for files with highly similar attributes in the mirror image file, such as class binary files in a jar program, which are 99% similar to the class binary file of another jar program, the second file will only generate 1% of difference content, but not all file content.
The offline scene optimization module is used for further comparison and analysis before exporting the image file in an offline exporting scene without the Internet, comparing all the image file lists used by the server deployed by a target once, ensuring that the file lists have at least one comparison and analysis, and performing differential comparison on the same file character stream or the file character stream with high similarity existing in the last comparison and analysis in the file lists;
and the mirror image decompression module is used for off-line copying and loading mirror images, processing all mirror image layers and loading the mirror image layers into the container system.
The hash value generated for each file in the container mirror layer analysis module is an MD5 hash value.
The difference analysis in the difference file comparison analysis module is to set a storage capacity D1 value, if the file size exceeds a D1 value, the repeated character streams between the individual files are compared to generate a duplication degree D2 value, wherein the D1 value and the D2 value are dynamically adjusted thresholds, and if the file size exceeds the set value, the file is represented by a placeholder and links the differential deployment of the file.
Where the D1 and D2 values are dynamically adjusted thresholds, such as D1-1 MB and D2-60%.
The mirror image decompression module is as follows: importing a mirror image and decompressing a mirror image layer; checking whether the mirror image file has a placeholder; if the placeholder exists, restoring the placeholder into the actual file content; if no placeholder exists, reading the file content, generating a hash value and recording the hash value in a historical special library for comparing and analyzing the placeholder at the next time; processing all the mirror image layers, and finally restoring to an original mirror image entity before compression; the Docker system runs a mirror image.
The method is suitable for a scene of repeated iteration, for a system of a mainstream internet, iteration is carried out for many times in a week or a month, file sources of historical analysis are extremely many, the difference comparison efficiency is higher, for example, a certain app only updates a new interface, the whole program is 100MB, 99.9MB is possibly the same, other systems of a container are related to 2000MB, the series of files hardly occupy any space, and the whole compression rate can reach hundreds to tens of thousands of times.
Claims (8)
1. A method of incremental compression of a container image, comprising the steps of:
(1) analyzing the container mirror image layer, decompressing the mirror image layer, temporarily decompressing the files of each layer, and generating a hash value for each file;
(2) comparing and analyzing the difference files, comparing the hash values of the files, deleting the files with the same hash value, adding placeholders into a history file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders;
(3) deep analysis of container mirror images, repackaging mirror image layers, reading a history file library, deleting files with completely same placeholders, packaging files with high similarity in mirror image files, and merging and exporting the difference parts of the files;
(4) optimizing an offline scene, wherein in an offline export scene without the Internet, image files are exported for further comparison and analysis, all image file lists used by a target deployed server are compared, the file list is ensured to have at least more than one comparison and analysis, and the same file character streams or highly similar file character streams in the last comparison and analysis in the file list are subjected to differential comparison;
(5) and (4) image decompression, off-line copy and image loading, processing all image layers and loading into the container system.
2. The method for incremental compression of a container image according to claim 1, wherein the hash value generated for each file in step (1) is an MD5 hash value.
3. The incremental compression method for a container image as claimed in claim 1, wherein the difference analysis of step (2) is to set a storage capacity D1 value, if the file size exceeds a D1 value, then compare the repeated character stream between the individual files to generate a repetition D2 value, wherein the D1 value and the D2 value are dynamically adjusted thresholds, and if the set value is exceeded, then the file will be represented by a placeholder and link the differential deployment of the file.
4. A method of incremental compression of a container image according to claim 1, wherein said step (5) is:
(5.1) importing a mirror image and decompressing a mirror image layer;
(5.2) checking whether the mirror image file has a placeholder;
(5.3) if there is a placeholder, restoring the placeholder to actual file content;
(5.4) if no placeholder exists, reading the file content, generating a hash value and recording the hash value in a special history library for comparing and analyzing the placeholder at the next time;
(5.5) processing all the mirror image layers, and finally restoring to the original mirror image entity before compression;
(5.6) the Docker system runs a mirror image.
5. A system for incremental compression of a container image, comprising: the system comprises a container mirror image layer analysis module, a difference file comparison analysis module, a container mirror image depth analysis module, an offline scene optimization module and a mirror image decompression module;
the container mirror image layer analysis module decompresses the mirror image layers, temporarily decompresses the files of each layer, and generates a hash value for each file;
the difference file comparison and analysis module is used for comparing hash values of files, deleting the files with the same hash value, adding placeholders into the historical file library, deleting the same parts of the highly similar files by adopting difference analysis for the files with different hash values, and replacing the different parts of the highly similar files with the placeholders;
the container mirror image depth analysis module repacks mirror image layers, reads a historical file library, deletes files with completely the same placeholders, packs files with high similarity in mirror image files, and combines and exports difference parts of the files;
the offline scene optimization module is used for further comparison and analysis before exporting the image file in an offline exporting scene without the Internet, comparing all the image file lists used by the server deployed by a target once, ensuring that the file lists have at least one comparison and analysis, and performing differential comparison on the same file character stream or the file character stream with high similarity existing in the last comparison and analysis in the file lists;
and the mirror image decompression module is used for off-line copying and loading mirror images, processing all mirror image layers and loading the mirror image layers into the container system.
6. The system of claim 5, wherein the hash value generated for each file in the container image layer analysis module is an MD5 hash value.
7. The incremental compression system for container images as claimed in claim 5, wherein the difference file comparison analysis module performs difference analysis to set a storage capacity D1 value, if the file size exceeds a D1 value, then compares the repeated character stream between the individual files to generate a repetition D2 value, wherein the D1 value and the D2 value are dynamically adjusted thresholds, and if the file size exceeds the set value, then the file is represented by a placeholder and links the difference deployment of the files.
8. The incremental compression system for a container image of claim 5, wherein the image decompression module is to: importing a mirror image and decompressing a mirror image layer; checking whether the mirror image file has a placeholder; if the placeholder exists, restoring the placeholder into the actual file content; if no placeholder exists, reading the file content, generating a hash value and recording the hash value in a historical special library for comparing and analyzing the placeholder at the next time; processing all the mirror image layers, and finally restoring to an original mirror image entity before compression; the Docker system runs a mirror image.
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