CN111324356A - Software automation deployment method and system - Google Patents
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- CN111324356A CN111324356A CN201811542898.XA CN201811542898A CN111324356A CN 111324356 A CN111324356 A CN 111324356A CN 201811542898 A CN201811542898 A CN 201811542898A CN 111324356 A CN111324356 A CN 111324356A
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Abstract
The invention discloses a software automation deployment method and system. The disclosed method comprises: the method comprises the following steps that a first host automatically downloads software to be deployed from a cloud server; the method comprises the steps that a first host automatically sends software to be deployed to a second host, and the software to be deployed is automatically deployed on the second host, wherein the first host is a host in a server cluster or a host outside the server cluster, and the second host is a host in the server cluster. The technical scheme disclosed can greatly reduce the burden of operation and maintenance personnel, reduce the repetitive work of software deployment, improve the efficiency and reduce the misoperation rate.
Description
Technical Field
The invention relates to the technical field of computer network technology and computer software, in particular to a software automatic deployment method and system.
Background
When a computer network software service provider tests and delivers software products, the service needs to be deployed on a cloud server of the other party. Since the deployment configuration of the servers of different clients is different, the system platform is different, and the running services are different, it is very challenging to deploy, upgrade, or reinstall the services to the servers.
In the prior art, when the software deployment is performed, a manner of manually logging in a server, downloading and configuring related components and frames, and horizontally expanding the software scale is generally adopted. This deployment approach is applicable to a one-time deployment, or to a deployment of only a few servers.
However, when persistent deployment is required or a large-scale server cluster is required to be deployed, the same server needs to be repeatedly logged in according to the version update of the software, or each host in the cluster needs to be logged in one by one for manual deployment; moreover, each new customer served also needs to complete the same deployment operation, so that the deployment process adopted by the prior art has the disadvantages of excessive repetitive operation, time consumption and high misoperation rate; in addition, when the software needs to be repeatedly deployed after being updated, the target host needs to be logged in for operation for many times, and the private cloud client considering the safety is not friendly.
In order to solve the above problems, a technical solution capable of supporting software automation deployment needs to be provided.
Disclosure of Invention
The software automation deployment method comprises the following steps:
the method comprises the following steps that a first host automatically downloads software to be deployed from a cloud server;
the first host automatically sends the software to be deployed to the second host, the software to be deployed is automatically deployed on the second host,
the first host is a host in the server cluster or a host outside the server cluster, and the second host is a host in the server cluster.
The software automation deployment method further comprises the following steps:
receiving a configuration file specifying configuration information of the second host,
wherein the configuration information comprises at least one of: IP address or ID of the second host, operating system version information, installation path.
The software automation deployment method further comprises the following steps:
the method comprises the steps that a first host automatically detects whether the version of software to be deployed is updated or not, and when the version of the software to be deployed is detected to be updated, the updated version of the software to be deployed is automatically downloaded from a cloud server;
the first host automatically sends the updated version of the software to be deployed to the second host, and the updated version of the software to be deployed is automatically deployed on the second host.
The software automation deployment method further comprises the following steps:
installing the Ansible automatic operation and maintenance management software on the first host computer through remote downloading or local installation;
automatically downloading the software to be deployed by using the infrastructure, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
and automatically detecting whether the version of the software to be deployed is updated or not by using the infrastructure.
The software automation deployment method further comprises the following steps:
a repo tool is used to version manage the software to be deployed,
wherein the software to be deployed is in the form of an RPM package.
The software automation deployment method further comprises the following steps:
the method comprises the steps that a first host automatically acquires use information and data throughput information of software and hardware resources of each second host in a cluster server;
intelligently allocating the software and hardware resources of each second host and/or intelligently adjusting the configuration parameters of the software based on the use information of the software and hardware resources and the data throughput information,
wherein the software comprises at least one of: elasticissearch, Storm, Kafka, the hardware comprising at least one of: the configuration parameters of the software comprise at least one of the following parameters: the number of processes used, the size of the memory used.
The software automation deployment system comprises:
the cloud server is used for storing software to be deployed;
the first host is used for automatically downloading the software to be deployed from the cloud server, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
a second host for deploying software to be deployed,
the first host is a host in the server cluster or a host outside the server cluster, and the second host is a host in the server cluster.
According to the software automation deployment system, the first host is further used for:
receiving a configuration file specifying configuration information of the second host,
wherein the configuration information comprises at least one of: IP address or ID of the second host, operating system version information, installation path.
According to the software automation deployment system, the first host is further used for:
automatically detecting whether the version of the software to be deployed is updated or not, and automatically downloading the updated version of the software to be deployed from the cloud server when the version of the software to be deployed is detected to be updated;
automatically sending the updated version of the software to be deployed to the second host, automatically deploying the updated version of the software to be deployed on the second host,
its second host computer still is used for:
an updated version of the software to be deployed is deployed.
According to the software automation deployment system, the first host is further used for:
installing automatic operation and maintenance management software through remote downloading or local installation of the Ansine;
automatically downloading the software to be deployed by using the infrastructure, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
automatically detecting whether the version of the software to be deployed is updated by using the infrastructure;
automatically acquiring the use information and data throughput information of software and hardware resources of each second host in the cluster server;
intelligently allocating the software and hardware resources of each second host and/or intelligently adjusting the configuration parameters of the software based on the use information of the software and hardware resources and the data throughput information,
its cloud server is still used for:
a repo tool is used to version manage the software to be deployed,
wherein the software to be deployed is in the form of an RPM package, the software comprising at least one of: elasticissearch, Storm, Kafka, the hardware comprising at least one of: the configuration parameters of the software comprise at least one of the following parameters: the number of processes used, the size of the memory used.
According to the technical scheme of the invention, the burden of operation and maintenance personnel can be greatly reduced, the repetitive work of software deployment is reduced, the efficiency is improved, and the misoperation rate is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention. In the drawings, like reference numerals are used to indicate like elements. The drawings in the following description are directed to some, but not all embodiments of the invention. For a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 shows a schematic flow diagram of a software automation deployment method according to the invention.
Fig. 2 schematically shows a schematic view of a software automation deployment system according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Fig. 1 shows a schematic flow diagram of a software automation deployment method according to the invention.
As shown in the solid line box of fig. 1, the software automation deployment method according to the present invention includes:
step S102: the method comprises the following steps that a first host automatically downloads software to be deployed from a cloud server;
step S104: the first host automatically sends the software to be deployed to the second host, the software to be deployed is automatically deployed on the second host,
the first host is a host in the server cluster or a host outside the server cluster, and the second host is a host in the server cluster.
Optionally, as shown in a dashed box of fig. 1, the software automation deployment method according to the present invention further includes:
step S106: receiving a configuration file specifying configuration information of the second host,
wherein the configuration information comprises at least one of: IP address or ID of the second host, operating system version information, installation path.
Optionally, as shown in a dashed box of fig. 1, the software automation deployment method according to the present invention further includes:
step S108: the method comprises the steps that a first host automatically detects whether the version of software to be deployed is updated or not, and when the version of the software to be deployed is detected to be updated, the updated version of the software to be deployed is automatically downloaded from a cloud server;
step S110: the first host automatically sends the updated version of the software to be deployed to the second host, and the updated version of the software to be deployed is automatically deployed on the second host.
Optionally, as shown in a dashed box of fig. 1, the software automation deployment method according to the present invention further includes:
step S112: installing the Ansible automatic operation and maintenance management software on the first host computer through remote downloading or local installation;
step S114: automatically downloading software to be deployed (including an installation configuration file of the software to be deployed) by using the infrastructure, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
step S116: and automatically detecting whether the version of the software to be deployed is updated or not by using the infrastructure.
That is, the above-described technical solution including steps S112 to S116 can implement a solution for continuous delivery of software based on the infrastructure open source software. The inconvenience that most of current software updating processes need to provide software versions which possibly need to be updated continuously for downloading by users and the updating is completed manually by the users is avoided, the whole operation does not need human intervention, and the automatic continuous delivery of the software is realized.
For example, corresponding to the step S116, the timing task may be set based on the cron timing task module in the infrastructure, and the version number of the software to be deployed is periodically checked (for example, by periodically executing the script generated by the script module in the infrastructure), so as to find that the update is automatically downloaded and deployed, thereby avoiding the trouble caused by frequent iteration of the software, and implementing continuous delivery and continuous deployment. Namely, when the service is deployed, a system cron task is newly added, whether the latest software version number on the cloud server is consistent with the local software version number is regularly checked, if the latest software version number is updated, a new version software package is downloaded, and the firmware is operated to be deployed again, so that automatic continuous deployment is realized.
For example, corresponding to the step S114, in order to prevent the software to be deployed (including the installation configuration file thereof) from being modified by mistake, when the service is deployed, a system cron task may be added newly, the infrastructure playlist is run periodically, and based on the exponentiation of the infrastructure operation, the software (including the installation configuration file thereof) is guaranteed to be correct all the time, so as to prevent the modification by mistake.
For example, corresponding to step S106 above, when a solution for continuous delivery of software is implemented based on the infrastructure open source software, the task to be executed may be configured in the configuration file in the yaml format according to the corresponding service, and the service deployment may be completed by running the infrastructure on the required deployment machine (i.e., the first host).
Optionally, as shown in a dashed box of fig. 1, the software automation deployment method according to the present invention further includes:
step S118: a repo tool is used to version manage the software to be deployed,
wherein the software to be deployed is in the form of an RPM package.
For example, a specific embodiment of the above technical solution comprising step S118 is as follows:
making an RPM (rotating speed limit) packet in advance by software, and establishing a cloud server repo source; running a preset script on the first host, automatically pulling a cloud repo source by the script, and downloading an alarm and a configuration script (and installing, corresponding to the step S112); and executing the infrastructure playlist to complete service deployment.
For different clients, the adaptation is completed only by setting corresponding variables (e.g., corresponding to specific configuration information in the configuration file in step S106) according to the status of the user server (i.e., the second host) before deployment.
For example, each software version corresponds to a unique MD5 value, a timing task for checking whether the version is updated may be set based on the cron timing task module in the infrastructure, and whether the version is consistent may be determined by comparing the MD5 value of the RPM packet in the remote cloud server with the MD5 value in the local RPM packet (e.g., periodically executing scripts generated by the script module in the infrastructure).
Optionally, as shown in a dashed box of fig. 1, the software automation deployment method according to the present invention further includes:
step S120: the method comprises the steps that a first host automatically acquires use information and data throughput information of software and hardware resources of each second host in a cluster server;
step S122: intelligently allocating the software and hardware resources of each second host and/or intelligently adjusting the configuration parameters of the software based on the use information of the software and hardware resources and the data throughput information,
wherein the software comprises at least one of: elasticissearch, Storm, Kafka, the hardware comprising at least one of: the configuration parameters of the software comprise at least one of the following parameters: the number of processes used, the size of the memory used.
For example, the above technical solution including step S120 and step S122 may be implemented by the following specific steps:
1. for operating systems of different release versions (e.g., red hat), the judged version of the system file (i.e., corresponding to the operating system version information in step S106 above) is read during the deployment process, and the deployment of the operating system is automatically adapted.
2. During and/or after deployment, cluster information is obtained and intelligently allocated to service framework resources (i.e., the above-mentioned software, such as Elasticsearch, Storm, Kafka, etc. frameworks) according to existing resources and cluster expected throughput. The specific implementation method comprises the following steps:
1) information is collected about the total resources (e.g., memory, I/O, CPU, etc.) of the entire cluster during and/or after deployment is complete.
2) Alternatively, the various services may be assigned to different servers in the cluster in a default configuration. The default configuration may not optimally utilize all machine resources in the cluster.
3) Historical data over a specified time period, such as historical data for each time period every day and every week, can be collected (e.g., through a self-developed engine tool) so that cluster data throughput (e.g., log data volume) for each time period in the future can be intelligently predicted based on the historical data, thereby achieving flexible scalability.
For example, regression analysis may be performed on data throughput of a period of time (day, week), data throughput at a future time may be predicted according to the tide of the traffic, and elastic expansion and contraction (vertical and horizontal expansion) may be performed according to the prediction result, thereby avoiding waste of machine resources. For example, for a service with small fluctuation, elastic expansion and contraction capacity can be directly performed according to the result of regression analysis, so that waste of machine resources is avoided; for services with large fluctuation and no rule, a large resource demand trend graph can be described, and elastic expansion and contraction are carried out only after the fluctuation amplitude reaches or exceeds a preset threshold value, so that the cluster operation health is ensured, risks are resisted, and unnecessary resource adjustment operation is avoided.
4) According to the cluster resources given by prediction, allocating the cluster resources to each frame according to a certain proportion: for example, if the amount of log data is increased, the Storm worker number is increased, and the Storm memory is enlarged.
5) In order to ensure normal operation of the master service with higher priority, for example, other services with lower priority may be subjected to service degradation (e.g., reclaiming part of the cluster resources allocated to the other services) in the case that the cluster resources are completely allocated.
The technical scheme can acquire and predict the future use information of the software and hardware resources and the data throughput information, elastically expand the capacity based on the information, and automatically adjust the use condition of the software and hardware resources of the cluster.
FIG. 2 schematically illustrates a diagram of a software automation deployment system 200 according to the present invention.
As shown in the solid line box of fig. 2, the software automation deployment system 200 according to the present invention includes:
the cloud server 201 is used for storing software to be deployed;
the first host is used for automatically downloading the software to be deployed from the cloud server 201, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
a second host for deploying software to be deployed,
the first host is the host 205 in the server cluster 207 or the host 203 outside the server cluster 207, and the second host is the host 205 in the server cluster 207.
For example, the cloud server 201 includes a cloud repo repository, and the cloud repo repository includes:
different client files, software used by the client, and configuration files corresponding to the different client files (e.g., including the version of the software package used by the client, machine information, corresponding software configuration information, etc.).
Optionally, the first host is further configured to:
receive a configuration file (e.g., submitted by user 209) specifying configuration information for the second host,
wherein the configuration information comprises at least one of: IP address or ID of the second host, operating system version information, installation path.
Optionally, the first host is further configured to:
automatically detecting whether the version of the software to be deployed is updated, and automatically downloading the updated version of the software to be deployed from the cloud server 201 when detecting that the version of the software to be deployed is updated;
automatically sending the updated version of the software to be deployed to the second host, automatically deploying the updated version of the software to be deployed on the second host,
the second host is further configured to:
an updated version of the software to be deployed is deployed.
Optionally, the first host is further configured to:
installing automatic operation and maintenance management software through remote downloading or local installation of the Ansine;
automatically downloading the software to be deployed by using the infrastructure, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
automatically detecting whether the version of the software to be deployed is updated by using the infrastructure;
automatically acquiring the use information and data throughput information of software and hardware resources of each second host in the cluster server;
intelligently allocating the software and hardware resources of each second host and/or intelligently adjusting the configuration parameters of the software based on the use information of the software and hardware resources and the data throughput information,
the cloud server 201 is further configured to:
a repo tool is used to version manage the software to be deployed,
wherein the software to be deployed is in the form of an RPM package, the software comprising at least one of: elasticissearch, Storm, Kafka, the hardware comprising at least one of: the configuration parameters of the software comprise at least one of the following parameters: the number of processes used, the size of the memory used.
For example, the first host computer installing the ansable automation operation and maintenance management software through remote downloading comprises the following steps:
and downloading tools such as infrastructure and the corresponding configuration files from the cloud repa warehouse and installing the tools.
For example, when the first host is also one host 205 in the server cluster 207, while it runs the anstile, the anchor communicates with other hosts 205 through the SSH protocol, completing all the deployment between the machines (the deployment flow task, and the deployment process is not dependent on the deployment order between the hosts).
Optionally, when the first host is also one host 205 in the server cluster 207, the first host is further configured to:
automatically detecting whether the version of the software to be deployed is updated, sending information through an SSH protocol to inform a second host computer of updating the software when detecting that the version of the software to be deployed is updated,
the second host is further configured to:
and automatically downloading the updated version of the software to be deployed from the cloud server 201, and deploying the updated version of the software to be deployed.
According to the technical scheme of the invention, the method has the following advantages:
1. the method is based on the concept of unified automatic management, does not completely depend on a custom script, is suitable for most of current mainstream network application software, and can meet the personalized requirements of different companies. The method has the advantages of greatly reducing the burden of operation and maintenance personnel, reducing the repeated work of deployment, improving the efficiency, reducing the misoperation rate or the error rate, simplifying the operation steps of the deployment flow, avoiding the trouble of manually operating the server for each update, and improving the safety and the software iteration rate.
2. And the automatic delivery and continuous deployment of the network software can be realized by combining with the infrastructure aiming at specific service logic, so that the automatic operation and maintenance are realized, the manual operation is reduced, and the deployment and operation and maintenance efficiency is improved.
3. And the future use information of the software and hardware resources and data throughput information can be obtained and predicted, elastic expansion is carried out based on the information, and the use condition of the software and hardware resources of the cluster is automatically adjusted.
The above-described aspects may be implemented individually or in various combinations, and such variations are within the scope of the present invention.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Finally, it should be noted that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A software automation deployment method is characterized by comprising the following steps:
the method comprises the following steps that a first host automatically downloads software to be deployed from a cloud server;
the first host automatically sends the software to be deployed to a second host, the software to be deployed is automatically deployed on the second host,
wherein the first host is a host in a server cluster or a host outside the server cluster, and the second host is a host in the server cluster.
2. The software automation deployment method of claim 1 further comprising:
receiving a configuration file specifying configuration information of the second host,
wherein the configuration information comprises at least one of: IP address or ID of the second host, operating system version information, installation path.
3. The software automation deployment method of claim 1 or 2 further comprising:
the first host automatically detects whether the version of the software to be deployed is updated or not, and automatically downloads the updated version of the software to be deployed from the cloud server when the version of the software to be deployed is detected to be updated;
and the first host automatically sends the updated version of the software to be deployed to the second host, and the updated version of the software to be deployed is automatically deployed on the second host.
4. The software automation deployment method of claim 1 or 2 further comprising:
installing the Ansible automatic operation and maintenance management software on the first host computer through remote downloading or local installation;
automatically downloading the software to be deployed by using an infrastructure, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
and automatically detecting whether the version of the software to be deployed is updated or not by using the infrastructure.
5. The software automation deployment method of claim 1 or 2 further comprising:
a repo tool is used to version manage the software to be deployed,
wherein the software to be deployed is in the form of an RPM package.
6. The software automation deployment method of claim 1 or 2 further comprising:
the first host automatically acquires the use information and data throughput information of software and hardware resources of each second host in the cluster server;
intelligently allocating the software and hardware resources of each second host and/or intelligently adjusting the configuration parameters of the software based on the usage information of the software and hardware resources and the data throughput information,
wherein the software comprises at least one of: elasticissearch, Storm, Kafka, the hardware comprising at least one of: memory, I/O port, CPU, the configuration parameter of the said software includes at least one of the following: the number of processes used, the size of the memory used.
7. A software automation deployment system, comprising:
the cloud server is used for storing software to be deployed;
the first host is used for automatically downloading the software to be deployed from the cloud server, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
the second host is used for deploying the software to be deployed,
wherein the first host is a host in a server cluster or a host outside the server cluster, and the second host is a host in the server cluster.
8. The software automation deployment system of claim 7 wherein:
the first host is further configured to:
receiving a configuration file specifying configuration information of the second host,
wherein the configuration information comprises at least one of: IP address or ID of the second host, operating system version information, installation path.
9. The software automation deployment system of claim 7 or 8 characterized in that:
the first host is further configured to:
automatically detecting whether the version of the software to be deployed is updated or not, and automatically downloading the updated version of the software to be deployed from the cloud server when the version of the software to be deployed is detected to be updated;
automatically sending the updated version of the software to be deployed to the second host, automatically deploying the updated version of the software to be deployed on the second host,
the second host is further configured to:
and deploying the updated version of the software to be deployed.
10. The software automation deployment system of claim 7 or 8 characterized in that:
the first host is further configured to:
installing automatic operation and maintenance management software through remote downloading or local installation of the Ansine;
automatically downloading the software to be deployed by using an infrastructure, automatically sending the software to be deployed to the second host, and automatically deploying the software to be deployed on the second host;
automatically detecting whether the version of the software to be deployed is updated by using an firmware;
automatically acquiring the use information and data throughput information of software and hardware resources of each second host in the cluster server;
intelligently allocating the software and hardware resources of each second host and/or intelligently adjusting the configuration parameters of the software based on the usage information of the software and hardware resources and the data throughput information,
the cloud server is further configured to:
a repo tool is used to version manage the software to be deployed,
wherein the software to be deployed is in the form of an RPM package, the software comprising at least one of: elasticissearch, Storm, Kafka, the hardware comprising at least one of: memory, I/O port, CPU, the configuration parameter of the said software includes at least one of the following: the number of processes used, the size of the memory used.
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| CN111966379A (en) * | 2020-08-19 | 2020-11-20 | 中国人民解放军战略支援部队信息工程大学 | Simulation web cloud service system software cluster updating method and system |
| CN112463725A (en) * | 2020-11-19 | 2021-03-09 | 北京思特奇信息技术股份有限公司 | Cloud architecture log file batch processing method and device and storage medium |
| CN112685503A (en) * | 2021-01-04 | 2021-04-20 | 上海圣剑网络科技股份有限公司 | Data processing method, device and system based on automatic operation and maintenance tool |
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