CN111966366A - Cluster deployment method and device of multi-CPU architecture - Google Patents
Cluster deployment method and device of multi-CPU architecture Download PDFInfo
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Abstract
The invention provides a cluster deployment method and device of a multi-CPU architecture, wherein the method comprises the following steps: constructing a code warehouse, and storing source codes of cluster components of different CPU architectures in the code warehouse; compiling corresponding image files according to source codes of cluster components of different CPU architectures, and storing the compiled image files in an image warehouse; compiling a cluster deployment script suitable for a multi-CPU architecture, and storing the script in a code warehouse; in response to receiving the instruction for deploying the cluster, acquiring and running a corresponding script from the code warehouse based on the CPU architecture, so as to call a corresponding program from the code warehouse and the mirror warehouse to execute cluster deployment of the current CPU architecture. By using the scheme of the invention, abnormal deployment or failure caused by personnel error when different CPU architecture environments are deployed can be greatly reduced, and the method has the advantages of repeated multiplexing of one-time configuration, shortened deployment time and improved deployment efficiency.
Description
Technical Field
The field relates to the field of computers, and more particularly to a method and apparatus for cluster deployment for a multi-CPU architecture.
Background
Many of the kubernets cluster components include apiserver, controller-manager, scheduler, kubelet, kube-proxy, and container engine components. The Kubernetes cluster is abbreviated as K8s, and 8 characters "ubernet" are replaced by 8. The Kubernets aims to make the application of container deployment simple and efficient, and provides a mechanism for application deployment, planning, updating and maintenance. The system is a container arrangement engine of Google open source, and supports automatic deployment, large-scale scalable and application containerization management. In addition, the complete cluster service also relies on some additional components, such as kubodns, etc. The current kubernets cluster basically uses a binary deployment scheme in a production environment, and an installation package of each component needs to be respectively copied to a node of the cluster for installation, for example, apiserver, controller-manager and scheduler components need to be installed on a master (main) node in the cluster, and components such as kubelet and kube-proxy need to be installed on node (child) nodes in the cluster. In the installation process, the operations must be performed step by step manually, the installation steps are very complicated, and once a certain deployment link is improper due to manual operation, the kubernets cluster deployment will fail. In addition, when the kubernets cluster deployment of various CPU architectures such as x86, arm and mips64le is adapted, manual deployment is needed for each architecture system, time and labor are wasted, and errors are easy to occur.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method and a device for cluster deployment in a multi-CPU architecture, which can greatly reduce abnormal deployment or failure caused by human error when deploying in different CPU architecture environments, and have the advantages of multiplexing once configuration for many times, shortening deployment time, and improving deployment efficiency.
In view of the above object, an aspect of the embodiments of the present invention provides a method for cluster deployment of multiple CPU architectures, including the following steps:
constructing a code warehouse, and storing source codes of cluster components of different CPU architectures in the code warehouse;
compiling corresponding image files according to source codes of cluster components of different CPU architectures, and storing the compiled image files in an image warehouse;
compiling a cluster deployment script suitable for a multi-CPU architecture, and storing the script in a code warehouse;
in response to receiving the instruction for deploying the cluster, acquiring and running a corresponding script from the code warehouse based on the CPU architecture, so as to call a corresponding program from the code warehouse and the mirror warehouse to execute cluster deployment of the current CPU architecture.
According to an embodiment of the present invention, compiling corresponding image files according to source codes of cluster components of different CPU architectures, and storing the compiled image files in an image repository includes:
compiling the source code of each CPU architecture into a binary file supported by the corresponding CPU architecture by using a go language (a programming language which is developed by Google and has strong static type, compiling type, concurrency type and a garbage recycling function);
and respectively manufacturing all binary files into mirror image files of corresponding CPU architectures, and pushing the mirror image files into a mirror image warehouse for storage.
According to an embodiment of the present invention, further comprising:
and installing a gitlab tool, a go language, a docker engine and an ansable tool on the jenkins tool to construct a cluster deployment environment.
According to one embodiment of the invention, in response to receiving an instruction for deploying a cluster, acquiring and running a corresponding script from a code warehouse based on a CPU (Central processing Unit) architecture, so as to call a corresponding program from the code warehouse and a mirror warehouse to execute cluster deployment of the current CPU architecture, the method comprises the following steps:
acquiring information of a current CPU architecture, and verifying whether the installation environment of the current CPU architecture meets requirements or not;
responding to the requirement met by the installation environment, synchronizing clock information of each node of the cluster, and installing a docker on each node of the cluster;
calling a deployment script of a current CPU from a code repository to distribute cluster certificates;
and pulling the mirror image of the current CPU in the mirror image warehouse to deploy each node component of the cluster.
According to one embodiment of the present invention, verifying the installation environment includes verifying whether the CPU, the memory, and the hard disk satisfy a preset condition.
In another aspect of the embodiments of the present invention, an apparatus for cluster deployment in a multi-CPU architecture is further provided, where the apparatus includes:
the building module is configured to build a code warehouse and store the source codes of the cluster components with different CPU architectures in the code warehouse;
the compiling module is configured to compile corresponding image files according to source codes of cluster components of different CPU architectures and store the compiled image files in an image warehouse;
the storage module is configured to compile a cluster deployment script suitable for a multi-CPU architecture and store the script in a code warehouse;
and the operation module is configured to respond to the received instruction for deploying the cluster, acquire and operate the corresponding script from the code warehouse based on the CPU architecture, and call the corresponding program from the code warehouse and the mirror image warehouse to execute the cluster deployment of the current CPU architecture.
According to one embodiment of the invention, the compiling module is further configured to:
compiling the source code of each CPU architecture into a binary file supported by the corresponding CPU architecture by using a go language;
and respectively manufacturing all binary files into mirror image files of corresponding CPU architectures, and pushing the mirror image files into a mirror image warehouse for storage.
According to an embodiment of the invention, further comprising a mounting module configured to:
and installing a gitlab tool, a go language, a docker engine and an ansable tool on the jenkins tool to construct a cluster deployment environment.
According to one embodiment of the invention, the execution module is further configured to:
acquiring information of a current CPU architecture, and verifying whether the installation environment of the current CPU architecture meets requirements or not;
responding to the requirement met by the installation environment, synchronizing clock information of each node of the cluster, and installing a docker on each node of the cluster;
calling a deployment script of a current CPU from a code repository to distribute cluster certificates;
and pulling the mirror image of the current CPU in the mirror image warehouse to deploy each node component of the cluster.
According to one embodiment of the present invention, verifying the installation environment includes verifying whether the CPU, the memory, and the hard disk satisfy a preset condition.
The invention has the following beneficial technical effects: according to the cluster deployment method of the multi-CPU architecture provided by the embodiment of the invention, a code warehouse is constructed, and source codes of cluster components of different CPU architectures are stored in the code warehouse; compiling corresponding image files according to source codes of cluster components of different CPU architectures, and storing the compiled image files in an image warehouse; compiling a cluster deployment script suitable for a multi-CPU architecture, and storing the script in a code warehouse; the technical scheme that the corresponding scripts are acquired from the code warehouse and run based on the CPU architecture to execute the cluster deployment of the current CPU architecture by calling corresponding programs from the code warehouse and the mirror image warehouse in response to the received cluster deployment instruction can greatly reduce abnormal deployment or failure caused by personnel error when different CPU architecture environments are deployed, and has the advantages of repeated multiplexing of one-time configuration, shortened deployment time and improved deployment efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a method of cluster deployment for a multi-CPU architecture in accordance with one embodiment of the present invention;
fig. 2 is a schematic diagram of a device deployed by a cluster of a multi-CPU architecture according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following embodiments of the present invention are described in further detail with reference to the accompanying drawings.
In view of the above object, a first aspect of embodiments of the present invention proposes an embodiment of a method for cluster deployment of a multi-CPU architecture. Fig. 1 shows a schematic flow diagram of the method.
As shown in fig. 1, the method may include the steps of:
s1, constructing a code warehouse, and storing source codes of cluster components of different CPU architectures in the code warehouse, wherein the cluster components mainly refer to kubernets cluster components for deploying kubernets clusters, and can also be used for deploying other clusters by using the scheme, the code warehouse can be a gitlab code warehouse, and the source codes installed in the clusters of each CPU architecture are respectively stored in the code warehouse;
s2, compiling corresponding image files according to source codes of cluster components of different CPU architectures, storing the compiled image files in an image warehouse, compiling different image files according to different source codes, and storing the image files in a directory corresponding to the source codes of the image warehouse, wherein the image warehouse can be a warehouse of enterprise-level private docker images constructed based on harbor;
s3 compiling a cluster deployment script suitable for a multi-CPU architecture, storing the script in a code warehouse, judging the CPU architecture needing to deploy the cluster when the script runs, and then running the script code of the CPU architecture deployment cluster to complete the deployment of the cluster;
s4, in response to receiving the instruction for deploying the cluster, acquires and runs a corresponding script from the code repository based on the CPU architecture, so as to call a corresponding program from the code repository and the mirror repository to perform cluster deployment of the current CPU architecture, and a running log is generated at each operation of the script running process.
The technical scheme of the invention is a method for deploying kubernets cluster by multiple CPU architectures such as x86, arm, mips64le and the like based on gitlab, harbor, jenkins and ansable technologies, and the multiple CPU architectures are realized by adopting the same automatic deployment script without manual deployment of each architecture, so that the labor cost is greatly reduced, and errors caused by manual operation are avoided. In addition, the kubernets cluster is automatically deployed, deployment logs and deployment steps can be traced in each step, and once problems occur, the problems can be rapidly checked and solved.
By the technical scheme, abnormal deployment or failure caused by personnel error in the deployment of different CPU architecture environments can be greatly reduced, and the method has the advantages of repeated multiplexing of one-time configuration, shortened deployment time and improved deployment efficiency.
In a preferred embodiment of the present invention, compiling corresponding image files according to source codes of cluster components of different CPU architectures, and storing the compiled image files in an image repository includes:
compiling the source code of each CPU architecture into a binary file supported by the corresponding CPU architecture by using a go language;
and respectively manufacturing all binary files into mirror image files of corresponding CPU architectures, and pushing the mirror image files into a mirror image warehouse for storage. And compiling a corresponding image file by using a go language aiming at the source code of each CPU architecture deployment cluster, then storing each image file in an image warehouse, and calling the image file required to be used by the current CPU architecture deployment cluster when a deployment script runs.
In a preferred embodiment of the present invention, the method further comprises:
and installing a gitlab tool, a go language, a docker engine and an ansable tool on the jenkins tool to construct a cluster deployment environment. The deployment environment of the cluster is a necessary running environment of the method, and the various plug-ins need to be installed in advance. The Jenkins tool is an open source software project, is a continuous integration tool developed based on Java, is used for monitoring continuous and repeated work, aims to provide an open and easy-to-use software platform, enables the continuous integration of software to be possible, and has the functions of continuous software version release, project testing, monitoring work executed by external calls and the like. The Gitlab tool is an open source project for a warehouse management system, a Web service which is built on the basis of the Git as a code management tool can access public or private projects through a Web interface, and source codes can be browsed, defects can be managed, and comments can be managed. The Docker engine is an open-source application container engine, so that developers can package their applications and rely on the packages to a portable image and then distribute the image to any popular Linux or Windows machine, and can also realize virtualization, wherein the containers completely use a sandbox mechanism, and no interface exists between the containers. The Ansible tool is an automatic operation and maintenance tool, is developed based on Python, integrates the advantages of a plurality of operation and maintenance tools, realizes functions of batch system configuration, batch program deployment, batch operation commands and the like, works based on modules, has no capacity of batch deployment, and really has the module which is operated by the Ansible and is deployed in batch.
In a preferred embodiment of the present invention, in response to receiving an instruction to deploy a cluster, acquiring and running a corresponding script from a code repository based on a CPU architecture, so as to call a corresponding program from the code repository and an image repository to perform cluster deployment of a current CPU architecture, including:
acquiring information of a current CPU architecture, and verifying whether the installation environment of the current CPU architecture meets requirements or not;
responding to the requirement met by the installation environment, synchronizing clock information of each node of the cluster, and installing a docker on each node of the cluster;
calling a deployment script of a current CPU from a code repository to distribute cluster certificates;
and pulling the mirror image of the current CPU in the mirror image warehouse to deploy each node component of the cluster.
In a preferred embodiment of the present invention, the verifying the installation environment includes verifying whether the CPU, the memory, and the hard disk satisfy a preset condition. For example, whether the currently deployed CPU is the CPU type covered in the method, whether the memory is sufficient for operation, and the like.
According to the technical scheme, the kubernets cluster adaptive to different architectures can be automatically deployed by adopting jenkins and ansible technologies, the difference between the deployment environments of the different CPU architectures is distinguished through parameterization management, and abnormal deployment or failure caused by personnel errors during deployment of the different CPU architecture environments is greatly reduced. And one-time configuration is repeated for multiple times, so that the time consumed by deployment is shortened, and the deployment efficiency is improved. In addition, automatic deployment can assist user personnel to locate the problem fast, and automatic deployment is traceable, has deployment log and deployment step traceable, once deployment fails, can go on troubleshooting and solve the problem rapidly according to the log.
Furthermore, the method disclosed according to an embodiment of the present invention may also be implemented as a computer program executed by a CPU, and the computer program may be stored in a computer-readable storage medium. The computer program, when executed by the CPU, performs the above-described functions defined in the method disclosed in the embodiments of the present invention.
In view of the above object, a second aspect of the embodiments of the present invention proposes a cluster-deployed device with multiple CPU architectures, as shown in fig. 2, a device 200 includes:
the building module is configured to build a code warehouse and store the source codes of the cluster components with different CPU architectures in the code warehouse;
the compiling module is configured to compile corresponding image files according to source codes of cluster components of different CPU architectures and store the compiled image files in an image warehouse;
the storage module is configured to compile a cluster deployment script suitable for a multi-CPU architecture and store the script in a code warehouse;
and the operation module is configured to respond to the received instruction for deploying the cluster, acquire and operate the corresponding script from the code warehouse based on the CPU architecture, and call the corresponding program from the code warehouse and the mirror image warehouse to execute the cluster deployment of the current CPU architecture.
In a preferred embodiment of the present invention, the compiling module is further configured to:
compiling the source code of each CPU architecture into a binary file supported by the corresponding CPU architecture by using a go language;
and respectively manufacturing all binary files into mirror image files of corresponding CPU architectures, and pushing the mirror image files into a mirror image warehouse for storage.
In a preferred embodiment of the present invention, the mobile terminal further comprises a mounting module configured to:
and installing a gitlab tool, a go language, a docker engine and an ansable tool on the jenkins tool to construct a cluster deployment environment.
In a preferred embodiment of the present invention, the execution module is further configured to:
acquiring information of a current CPU architecture, and verifying whether the installation environment of the current CPU architecture meets requirements or not;
responding to the requirement met by the installation environment, synchronizing clock information of each node of the cluster, and installing a docker on each node of the cluster;
calling a deployment script of a current CPU from a code repository to distribute cluster certificates;
and pulling the mirror image of the current CPU in the mirror image warehouse to deploy each node component of the cluster.
In a preferred embodiment of the present invention, the verifying the installation environment includes verifying whether the CPU, the memory, and the hard disk satisfy a preset condition.
It should be particularly noted that the embodiment of the system described above employs the embodiment of the method described above to specifically describe the working process of each module, and those skilled in the art can easily think that the modules are applied to other embodiments of the method described above.
The embodiments described above, particularly any "preferred" embodiments, are possible examples of implementations and are presented merely to clearly understand the principles of the invention. Many variations and modifications may be made to the above-described embodiments without departing from the spirit and principles of the technology described herein. All such modifications are intended to be included within the scope of this disclosure and protected by the following claims.
Claims (10)
1. A method for cluster deployment of a multi-CPU architecture is characterized by comprising the following steps:
constructing a code warehouse, and storing source codes of cluster components of different CPU architectures in the code warehouse;
compiling corresponding image files according to the source codes of the cluster components with different CPU architectures, and storing the compiled image files in an image warehouse;
compiling a cluster deployment script suitable for a multi-CPU architecture, and storing the script in the code warehouse;
in response to receiving an instruction for deploying the cluster, acquiring and running the corresponding script from the code warehouse based on the CPU architecture, so as to call a corresponding program from the code warehouse and the mirror warehouse to execute cluster deployment of the current CPU architecture.
2. The method of claim 1, wherein compiling the corresponding image file according to the source code of the cluster component of the different CPU architectures and saving the compiled image file in an image repository comprises:
compiling the source code of each CPU architecture into a binary file supported by the corresponding CPU architecture by using a go language;
and respectively manufacturing all the binary files into mirror image files of corresponding CPU architectures, and pushing the mirror image files into a mirror image warehouse for storage.
3. The method of claim 1, further comprising:
and installing a gitlab tool, a go language, a docker engine and an ansable tool on the jenkins tool to construct a cluster deployment environment.
4. The method of claim 1, wherein in response to receiving an instruction to deploy a cluster, retrieving and running the corresponding script from the code repository based on a CPU architecture to call a corresponding program from the code repository and mirror repository to perform cluster deployment of a current CPU architecture comprises:
acquiring information of a current CPU architecture, and verifying whether the installation environment of the current CPU architecture meets requirements or not;
responding to the requirement met by the installation environment, synchronizing clock information of each node of the cluster, and installing a docker on each node of the cluster;
retrieving a deployment script of the current CPU from the code repository to distribute cluster certificates;
and pulling the mirror image of the current CPU in the mirror image warehouse to deploy each node assembly of the cluster.
5. The method of claim 4, wherein verifying the installation environment comprises verifying whether a CPU, a memory, and a hard disk satisfy preset conditions.
6. An apparatus for cluster deployment of a multi-CPU architecture, the apparatus comprising:
a build module configured to build a code repository and to store source codes of cluster components of different CPU architectures in the code repository;
the compiling module is configured to compile corresponding image files according to the source codes of the cluster components of different CPU architectures and store the compiled image files in an image warehouse;
the storage module is configured to compile a cluster deployment script applicable to a multi-CPU architecture and store the script in the code warehouse;
the running module is configured to respond to the received instruction for deploying the cluster, acquire and run the corresponding script from the code warehouse based on the CPU architecture, and call corresponding programs from the code warehouse and the mirror image warehouse to execute cluster deployment of the current CPU architecture.
7. The device of claim 6, wherein the compiling module is further configured to:
compiling the source code of each CPU architecture into a binary file supported by the corresponding CPU architecture by using a go language;
and respectively manufacturing all the binary files into mirror image files of corresponding CPU architectures, and pushing the mirror image files into a mirror image warehouse for storage.
8. The apparatus of claim 6, further comprising an installation module configured to:
and installing a gitlab tool, a go language, a docker engine and an ansable tool on the jenkins tool to construct a cluster deployment environment.
9. The device of claim 6, wherein the execution module is further configured to:
acquiring information of a current CPU architecture, and verifying whether the installation environment of the current CPU architecture meets requirements or not;
responding to the requirement met by the installation environment, synchronizing clock information of each node of the cluster, and installing a docker on each node of the cluster;
retrieving a deployment script of the current CPU from the code repository to distribute cluster certificates;
and pulling the mirror image of the current CPU in the mirror image warehouse to deploy each node assembly of the cluster.
10. The apparatus of claim 9, wherein verifying the installation environment comprises verifying whether a CPU, a memory, and a hard disk satisfy preset conditions.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112506613A (en) * | 2020-12-11 | 2021-03-16 | 四川长虹电器股份有限公司 | Method for automatically identifying Maven change submodule and pushing docker mirror image by Gitlab-ci |
| CN112558991A (en) * | 2020-12-18 | 2021-03-26 | 北京华胜天成科技股份有限公司 | Mirror image management method and system, cloud management platform and storage medium |
| CN112711575A (en) * | 2021-01-15 | 2021-04-27 | 山东云海国创云计算装备产业创新中心有限公司 | Deployment method, system and related device of database cluster |
| CN112764766A (en) * | 2021-01-22 | 2021-05-07 | 苏州浪潮智能科技有限公司 | Method, device, equipment and storage medium for butting k8s cluster and storage |
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| CN114003302A (en) * | 2021-10-12 | 2022-02-01 | 浪潮云信息技术股份公司 | Resource operation and maintenance management method and system based on kubernets |
| CN115051846A (en) * | 2022-06-07 | 2022-09-13 | 北京天融信网络安全技术有限公司 | Deployment method of K8S cluster based on super fusion platform and electronic equipment |
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| CN112506613A (en) * | 2020-12-11 | 2021-03-16 | 四川长虹电器股份有限公司 | Method for automatically identifying Maven change submodule and pushing docker mirror image by Gitlab-ci |
| CN112558991A (en) * | 2020-12-18 | 2021-03-26 | 北京华胜天成科技股份有限公司 | Mirror image management method and system, cloud management platform and storage medium |
| CN112711575A (en) * | 2021-01-15 | 2021-04-27 | 山东云海国创云计算装备产业创新中心有限公司 | Deployment method, system and related device of database cluster |
| CN112764766A (en) * | 2021-01-22 | 2021-05-07 | 苏州浪潮智能科技有限公司 | Method, device, equipment and storage medium for butting k8s cluster and storage |
| CN113504967A (en) * | 2021-06-28 | 2021-10-15 | 浪潮云信息技术股份公司 | Face recognition method based on container platform |
| CN113391827A (en) * | 2021-08-17 | 2021-09-14 | 湖南省佳策测评信息技术服务有限公司 | Application software publishing method and system based on automation script |
| CN113391827B (en) * | 2021-08-17 | 2021-11-02 | 湖南省佳策测评信息技术服务有限公司 | Application software publishing method and system based on automation script |
| CN114003302A (en) * | 2021-10-12 | 2022-02-01 | 浪潮云信息技术股份公司 | Resource operation and maintenance management method and system based on kubernets |
| CN115051846A (en) * | 2022-06-07 | 2022-09-13 | 北京天融信网络安全技术有限公司 | Deployment method of K8S cluster based on super fusion platform and electronic equipment |
| CN115051846B (en) * | 2022-06-07 | 2023-11-10 | 北京天融信网络安全技术有限公司 | K8S cluster deployment method based on super fusion platform and electronic equipment |
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