CN111274002A

CN111274002A - Construction method and device for supporting PAAS platform, computer equipment and storage medium

Info

Publication number: CN111274002A
Application number: CN202010128551.1A
Authority: CN
Inventors: 周起如; 林必毅; 耿伟; 王英明; 胡进贤
Original assignee: Industrial & Commercial College Anhui University Of Technology; Shenzhen Sunwin Intelligent Co Ltd
Current assignee: Industrial & Commercial College Anhui University Of Technology; Shenzhen Sunwin Intelligent Co Ltd
Priority date: 2020-02-28
Filing date: 2020-02-28
Publication date: 2020-06-12

Abstract

The invention relates to a method and a device for constructing a supporting PAAS platform, computer equipment and a storage medium, wherein the method comprises the steps of constructing a research and development environment of the supporting PAAS platform by using Docker and a virtual machine management technology to obtain a research and development environment; automatically sensing and upgrading the virtual environment of the Docker; constructing a service discovery architecture based on Docker to obtain a large-scale container cluster; managing the large-scale container cluster by adopting k8s to obtain a supporting PAAS platform. According to the invention, a research and development environment is built through Docker and a vagrant virtual machine management technology, a service discovery framework is built based on a container service discovery technology, and management is performed by adopting k8s, so that the complexity of platform environment building and deployment is effectively reduced, the platform migration and upgrading cost is reduced, and the research, development, deployment and implementation efficiency of a large data support platform is greatly improved.

Description

Construction method and device for supporting PAAS platform, computer equipment and storage medium

Technical Field

The invention relates to the technical field of big data, in particular to a method and a device for constructing a support Platform As A Service (PAAS) platform, computer equipment and a storage medium.

Background

With the rapid development of technologies such as internet of things, cloud computing, mobile internet and the like, the informatization of smart cities is rapidly implemented, mass data are generated in the smart city data in an unprecedented way and at an unprecedented speed, large data support platforms become increasingly large and complex, and various difficulties are faced from platform system design, development and testing to deployment and implementation. The big data support Platform is a typical PAAS (Platform as a Service) Platform application scene, and in the face of the increase of mass big data, the traditional smart city cloud Platform architecture and processing mode cannot effectively cope with a big data processing environment.

The existing traditional smart city cloud platform has two main processing modes, namely, a development mode is improved to process massive big data, but in the face of a complex system, an agile development mode is adopted, and the platform design is continuously corrected to meet more and more business construction requirements; and secondly, an enterprise-level development framework is utilized to adapt to the construction of a huge business system. However, from the aspect of a mode and a method or from the aspect of an enterprise-level architecture, practical problems in a series of developments still exist, such as difficulty in environment building and upgrading, difficulty in team cooperation, difficulty in quality guarantee, high cost of platform migration and upgrading and the like.

Therefore, a new method is needed to be designed, so that the complexity of platform environment construction and deployment is effectively reduced, the platform migration and upgrade costs are reduced, and the efficiency of research, development, deployment and implementation of the big data support platform is greatly improved.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a method and a device for constructing a supporting PAAS platform, computer equipment and a storage medium.

In order to achieve the purpose, the invention adopts the following technical scheme: the construction method of the supporting PAAS platform comprises the following steps:

establishing a PAAS platform research and development environment by using Docker and a virtual machine management technology to obtain a research and development environment;

automatically sensing and upgrading the virtual environment of the Docker;

constructing a service discovery architecture based on Docker to obtain a large-scale container cluster;

managing the large-scale container cluster by adopting k8s to obtain a supporting PAAS platform.

The further technical scheme is as follows: the method for establishing and supporting PAAS platform research and development environment by using Docker and through a vagrant virtual machine management technology to obtain research and development environment comprises the following steps:

configuring a development environment by using a Docker;

manufacturing a mirror image of the Docker through a Docker build command;

virtualizing the mirror image of the Docker into a system by using a vagrant virtual machine management technology to obtain a virtual container;

developing a Docker port through a configuration file of the vagrant;

and simultaneously operating a plurality of virtual containers to obtain a research and development environment.

The further technical scheme is as follows: the virtual container comprises an application server, a data storage server and a development server.

The further technical scheme is as follows: the automatic sensing and upgrading of the virtual environment of the Docker comprises the following steps:

organizing a task execution plan through an Azkaban task manager, and executing corresponding version checking and upgrading footsteps so as to automatically sense and upgrade the virtual environment of Docker.

The further technical scheme is as follows: the constructing of the service discovery architecture based on the Docker to obtain the large-scale container cluster comprises the following steps:

constructing a service discovery architecture based on Docker by adopting the technical combination of a Docker set, an etcd set and a haproxy set;

the service is registered in an etcd cluster of a service discovery architecture based on the Docker, and the drive terminal performs service forwarding through the service discovery architecture based on the Docker and then displays the service on the terminal in a proxy mode to obtain a large-scale container cluster.

The further technical scheme is as follows: the managing the large-scale container cluster by using k8s to obtain a supporting PAAS platform comprises:

automated deployment, automated scale-up and maintenance management of an environment with large-scale container clusters using k8s to obtain a supporting PAAS platform.

The invention also provides a device for constructing the supporting PAAS platform, which comprises:

the environment building unit is used for building a PAAS platform research and development environment by using Docker and a virtual machine management technology to obtain a research and development environment;

the upgrading unit is used for automatically sensing and upgrading the virtual environment of the Docker;

the service construction unit is used for constructing a Docker-based service discovery architecture to obtain a large-scale container cluster;

and the management unit is used for managing the large-scale container cluster by adopting k8s so as to obtain a supporting PAAS platform.

The further technical scheme is as follows: the environment construction unit includes:

the development environment configuration subunit is used for configuring a development environment by using Docker;

the mirror image making subunit is used for making a Docker mirror image through a Docker build command;

the virtual subunit is used for virtualizing the mirror image of the Docker into a system through a vagrant virtual machine management technology to obtain a virtual container;

the port configuration subunit is used for developing a Docker port through a configuration file of the vagrant;

and the operation subunit is used for simultaneously operating a plurality of virtual containers so as to obtain a research and development environment.

The invention also provides computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes the method when executing the computer program.

The invention also provides a storage medium storing a computer program which, when executed by a processor, is operable to carry out the method as described above.

Compared with the prior art, the invention has the beneficial effects that: according to the invention, a research and development environment is built through Docker and a vagrant virtual machine management technology, a service discovery framework is built based on a container service discovery technology, and management is carried out by adopting k8s so as to build a smart city big data support PAAS platform, so that the complexity of platform environment building and deployment is effectively reduced, the platform migration and upgrading cost is reduced, and the research, development, deployment and implementation efficiency of the big data support platform is greatly improved.

The invention is further described below with reference to the accompanying drawings and specific embodiments.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of a method for supporting a PAAS platform according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a method for constructing a PAAS platform according to an embodiment of the present invention;

FIG. 3 is a schematic view of a sub-flow of a method for constructing a PAAS platform according to an embodiment of the present invention;

FIG. 4 is a schematic view of a sub-flow of a method for constructing a PAAS platform according to an embodiment of the present invention;

FIG. 5 is a schematic block diagram of a PAAS platform construction device according to an embodiment of the present invention;

fig. 6 is a schematic block diagram of an environment building unit supporting a PAAS platform building apparatus according to an embodiment of the present invention;

FIG. 7 is a schematic block diagram of a service building unit supporting a PAAS platform building apparatus according to an embodiment of the present invention;

FIG. 8 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

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, 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 will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of a method for supporting a PAAS platform according to an embodiment of the present invention. Fig. 2 is a schematic flowchart of a method for constructing a supporting PAAS platform according to an embodiment of the present invention. The construction method of the supporting PAAS platform is applied to a server. The server performs data interaction with the terminal, and the server is released to the terminal for use after a good supporting PAAS platform is built.

Fig. 2 is a schematic flow chart of a method for constructing a supporting PAAS platform according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S140.

S110, building a PAAS platform research and development environment by using Docker and a vagrant virtual machine management technology to obtain a research and development environment.

In this embodiment, the research and development environment refers to an environment for research and development of a supported PAAS platform, and specifically includes a production environment, a test environment, a research and development environment based on a Docker engine virtualization container pool, a production environment, a test environment, and a research and development environment based on a Docker supported service discovery engine, and a PAAS platform research and development environment based on a Docker.

In an embodiment, referring to fig. 3, the step S110 may include steps S111 to S115.

And S111, configuring a development environment by using Docker.

In this embodiment, the development environment includes a development environment including developers, testers, version managers, and the like for different users.

And configuring a development environment by using Docker, and configuring the development environment aiming at different users by using the characteristics of container increment. Docker is a lightweight virtualization container technology, and a virtualization technology based on a shared linux kernel can achieve the resource utilization rate close to that of a physical host, and compared with a virtual machine, the Docker container has the most obvious characteristics of being fast in starting and small in resource occupation. Therefore, the method is more suitable for constructing an isolated standardized operation environment and a lightweight PAAS platform capable of being transversely expanded, and is structurally different from a virtualization technology not based on a container. Secondly, the virtualization based on Docker is based on containers, and independent application systems can be more conveniently virtualized by sharing the containers virtualized at the bottom layer, on one hand, each virtual mirror image only keeps a changed part in terms of the occupation of physical resources, and the space consumed by the virtual mirror images is greatly reduced. On the other hand, aiming at the virtualization of the container, the capacity of incremental release can be fully utilized, the mirror image is manufactured in an inheritance mode, and the flexibility is higher. A comparison of the container technology and the virtual machine technology is shown in table 1.

TABLE 1 comparison of Container technology and virtual machine technology

The smart city big data support platform is a cloud computing platform which needs a large amount of read-write data, so that the smart city big data support platform has better performance than a traditional virtual machine when deployed under container virtualization, and has a resource utilization rate close to a physical host. Since the Docker separates the data from the operating environment, the constructed big data support PAAS platform can be released as a mirror image, the nodes of the big data support platform can be conveniently added or replaced at any time, and the efficiency of deployment and implementation of the big data support platform can be greatly improved.

S112, manufacturing a Docker mirror image through a Docker build command.

In the embodiment, the problem that the development environment is difficult to build and maintain can be solved by means of a Docker virtualization development environment and mirror image generation.

In the Development Environment structure based on Docker, the Development Environment is virtualized into a plurality of images for management, such as a virtual application server, a data storage server, other middleware servers, and the like, and meanwhile, basic container images having JDK (Java Development Kit) and other dependent system environments, IDE (integrated Development Environment) having respective Development personnel requirements or advanced container images of the Development platform, and Development Environment container images personalized by the Development personnel can be virtualized in an incremental manner. The structural design fully ensures the independence of the development environment, fully utilizes the increment of the container, customizes the general basic container for the project, fully considers the individual requirements of research and development personnel, and provides the self-set environment container.

On the aspect of environment upgrading requirements, the effect that all environments can be modified by changing a container can be achieved by upgrading the container, moreover, Docker can be upgraded in a mode of manually or automatically sensing container changes, and great convenience is brought to environment upgrading. The problem that the development environment is difficult to build in the research and development of the big data support platform system can be effectively solved, and only a unified and standard environment mirror image is formulated, research and development personnel only need to execute a simple script to quickly build the own development environment.

S113, virtualizing the mirror image of the Docker into a system through a vagrant virtual machine management technology to obtain a virtual container.

In this embodiment, the virtual container refers to a system in which a mirror structure of Docker is virtually formed.

The images of the Docker are virtualized into a system through a vagrant virtual machine management technology, and the Docker container can be virtualized and operated through the vagrant based on different operating systems. By such a feature, the limitation on the development environment is reduced and is not limited to the 64-bit linux operating system on which Docker depends.

The virtualization technology based on Docker can virtualize a test environment by using limited resources, and greatly reduces the consumption cost of hardware resources. In the process of building the test environment, the environment mirror images of the application server, the data storage server and the middleware server corresponding to the development environment can be utilized to achieve the purpose of synchronizing the development environment and the test environment. For the service application server, a continuous construction technology can be adopted to construct an independent service system. Under a service-oriented distribution architecture, testing may be performed for functionality testing, performance testing, and the like for a single service. The tester can also establish an automatic testing environment of the tester, and the independence of the environment is kept, which is similar to the environment maintenance of the research and development personnel.

And S114, developing a Docker port through a configuration file of the vagrant.

Developing a container port through a configuration file of a vagrant aiming at port mapping of the container and a native host: network "forward _ port", Guest:8080, host: 8080; in this way, the problem of integration between virtual systems is solved.

And S115, simultaneously operating a plurality of virtual containers to obtain a research and development environment.

In this embodiment, the virtual container includes an application server, a data storage server, and a development server.

In this embodiment, the virtual structure is completed according to the actual application, that is, a plurality of virtual containers are operated simultaneously. In the configuration of the vagrant, three virtual machines, namely virtual containers, are configured, one is an application server, one is a data storage server, and one is a development server.

In the implementation process of a production environment, a chaos situation that a plurality of applications or services are deployed in a single environment is often encountered, and a situation that system upgrading is difficult is also often encountered. The independence of the deployment environment based on the virtualization technology of the Docker and the convenience of system upgrade brought by the service discovery mechanism based on the Docker can effectively solve the problems in the implementation management of the production environment and the operation and maintenance management.

And S120, automatically sensing and upgrading the virtual environment of the Docker.

In this embodiment, a task execution plan is organized by an Azkaban task manager, and corresponding version checking and upgrading steps are executed, so as to perform automatic sensing and upgrading of the virtual environment of Docker.

The basic environment change sensing function provided by Docker can be realized by the following commands:

LATEST＝‘docker inspect--format“{{.Id}}”$IMAGE’；

RUNNING＝’docker inspect--format“{{.Image}}”$im’。

by comparing the execution results of the two statements, it can be obtained whether the current mirror is the latest result. Docker also provides an upgrade order for the image:

Docker run itech/docker-unattented-upgrade。

by utilizing the core instructions, steps for automatic checking and upgrading can be compiled. In practical application, the big data support platform organizes a task execution plan through the Azkaban task manager, executes corresponding version checking and upgrading footsteps, and realizes automatic perception and upgrading of a container virtual environment.

Based on the service discovery technology, distributed collaborative research and development can be realized. In the process of project research and development, the research and development unit is divided into enough independent units through system modularization and service element design. Different services run independently in virtual containers, so that research and development personnel can concentrate on research and development work on single service. In the initial stage of research and development, pseudo-realized services can be deployed under the condition that specific services are not realized and service dependency relationships exist, so that system integration is improved. After the research and development task is completed, the completed functional service can be deployed by heat through a service discovery technology without restarting. Therefore, the business system module can carry out system integration and upgrade under the condition of no perception, reduces the dependence in the research and development process and realizes the effect of distributed collaborative research and development. The problems that a system is difficult to upgrade and distributed collaborative research and development is difficult in the research and development process can be effectively solved through the service discovery technology.

The development environment also comprises a service-oriented distributed architecture, as distributed technologies develop, more and more service-oriented distributed system architectures appear, the technical maturity of the prices is very high, the stability and the performance of the system are very outstanding, and as the hardware level is improved, the network overhead, the memory overhead and the calculation overhead brought by various distributed technologies can be almost ignored. Long-term system research and development practices prove that an excellent distributed design can effectively complete the work of system decoupling and improve the maintainability and flexibility of the system. Research and development teams or individuals can put more effort to concern the business itself, thereby improving the quality of the system and reducing the maintenance cost.

S130, constructing a service discovery framework based on Docker to obtain a large-scale container cluster.

In the present embodiment, a large-scale container cluster is a set of multiple virtual containers and provided with services.

In an embodiment, referring to fig. 4, the step S130 may include steps S131 to S132.

S131, constructing a service discovery architecture based on Docker by adopting technical combination of a Docker, etcd and haproxy set;

s132, registering the service in the etcd cluster of the Docker-based service discovery architecture, and displaying the service on the terminal in a proxy mode after the drive terminal forwards the service through the Docker-based service discovery architecture so as to obtain the large-scale container cluster.

The service discovery based on Docker is realized by adopting the technical combination of Docker + etcd + haproxy, the service is registered in the etcd cluster of the service discovery, the service is forwarded through the service discovery at the terminal, and the final service is displayed at the terminal in a proxy mode, so that the purpose of service calling is achieved. The service discovery architecture based on Docker includes a service discovery engine etcd, which is a highly available key value storage system and is mainly used for shared configuration and service discovery.

When the service changes, the service discovery engine etcd manages the registered service of the manager, performs response operations such as registration, logout, management and the like of the service, and does not need to change a service terminal, so that the proxy and isolation mechanism realizes service discovery.

In a specific implementation process, a service discovery process is firstly operated, then services are registered to a service discovery engine etcd, and finally service discovery is operated at a terminal, so that a service discovery architecture based on docker can be designed.

And S140, managing the large-scale container cluster by adopting k8S to obtain a supporting PAAS platform.

In this embodiment, the PAAS supporting platform refers to a smart city big data supporting service platform.

The big data support platform can be successfully released, Docker simplifies the work, such as front-end application, background application, big data application, hadoop cluster, message queue and the like can be packaged into a mirror image and then deployed quickly, so that the problems related to software configuration and management are solved, the resource allocation requirements of users according to needs can be effectively met, and the availability and the isolation are ensured.

The k8s mainly realizes the functions of automatic deployment, automatic expansion and contraction, maintenance and the like of a large-scale container cluster environment, platform modules and components are numerous, the structure is complex, and two core authority roles exist in the large-scale container cluster: the master node is responsible for controlling the k8s cluster, and the node is a workload node, wherein the most core components of the master node are apiserver and Scheduler, the apiserver service Interface component provides an add/delete change API (Application Programming Interface) service Interface for all resources in k8s, and all resource changes need to be interfaced with the service Interface. The Scheduler scheduling component is a scheduling header pipe of k8s, is in direct communication with the apiserver, is responsible for scheduling the resource pod, can also have a plurality of callers for different scenes, the node nodes can be scheduled with task load by the master, and when a node is unavailable, the master can automatically schedule the load task to other available node nodes. In addition, key components which any node must contain include that the apuserver of the kubel docking master node is responsible for carrying out specific operation on the container, the node is a node work core, and the proxy is a component which is used for enabling k8s clusters to communicate with each other, exposing services to external access and carrying out load balancing. A Runtime Runtime environment, commonly referred to as a Docker container engine, also supports other container engines, responsible for operating containers upon receipt of a kubelet command. And finally, a database module of the k8s, namely a service discovery engine etcd, is a distributed database realized by using a raft consistency algorithm, is a center of resource states during data storage operation and operation of the whole k8s cluster, provides functions of data TTL invalidation, data change monitoring, multi-value, directory monitoring, distributed lock atom operation and the like, and can conveniently track and manage the states of cluster nodes.

The master node in the k8s is a core hub of the whole k8s cluster, and the implementation of the high-availability architecture is mainly based on a scheme of halopropy + keepalived. The Haproxy is high-performance load balancing software, mainly acts on four-layer tcp and seven-layer http, and is used for realizing load balancing on core components of a plurality of master nodes. Keepallved is software which ensures that a cluster or other services can find single-point fault in time through a virtual IP, heartbeats are sent at a plurality of monitored master nodes, when the nodes are found to be down, the nodes are timely replaced to realize high availability of the cluster services, and through combination with haproxy, the master node load balance and fault redundancy can be realized.

In the k8s cluster, the number of Pod running on each node is uncertain, each application or service has many components running simultaneously, a large number of logs are generated, and if centralized collection management is not performed, great difficulty is brought to maintenance and elimination of errors and problems, and the FEK, namely, the fluntd + elastic search + kibana scheme is adopted to perform unified log management on a platform system.

The fluent needs to run on each node separately, and collects log files under two directories of the current node/var/log and/var/lib/docker/contacts. The elastic search is used for collecting, classifying and arranging the logs collected by the fluentd and operating in a cluster mode. Kibana is used for indexing and inquiring the analysis result of the Elasticissearch cluster, and finally performing front-end interaction with operation and maintenance personnel. After cluster log management of the automatic integration FEK scheme is realized, if an error occurs in a container or a node of an operation platform system, query analysis can be rapidly carried out on a log management page.

The feasibility of the PAAS platform based on k8s + docker is verified by establishing the PAAS platform based on the big data support platform. The PAAS platform based on the big data support platform comprises a service integrated development tool, a development environment and a test environment, and the improvement of the performance ratio is shown in Table 2.

TABLE 2 promotion of Performance ratio for supporting PAAS platforms

Practical application shows that the PAAS platform is supported by big data based on k8s + docker, great convenience is brought to research, development, testing and implementation, and the efficiency of research, development, testing and deployment implementation can be effectively improved.

The development, testing, deployment and maintenance stages of a coverage platform are constructed based on the k8s + Docker private cloud PAAS technology, the use of hardware resources is saved, the problem of insufficient resources in the existing hardware environment is solved, the deployment environment of each application is more independent, and on the system architecture, the service-oriented architecture design is adopted and matched with a service discovery mechanism, so that the system is more flexible, the maintenance is more convenient and faster, and the team cooperation is smoother. In the implementation of a production environment, the container-based virtualization technology enables the compatibility and portability of the system to be higher; based on the service-oriented design, the upgrading of the platform system is smoother; based on the technology of service discovery, the system robustness is better, and the platform system is more stable. The platform is more convenient and rapid to update and transplant, and the traditional 'well digging and water taking' type service is improved into a cloud computing 'centralized water supply' service.

By technical means such as service-oriented distributed architecture, container virtualization, container service discovery, environment configuration management of private cloud and the like, an isolated standardized operation environment and a lightweight large data support PAAS platform capable of being expanded transversely are constructed.

According to the construction method of the support PAAS platform, a research and development environment is built through Docker and a vagrant virtual machine management technology, a service discovery framework is built based on a container service discovery technology, management is performed by adopting k8s, so that the smart city big data support PAAS platform is built, the complexity of platform environment building and deployment is effectively reduced, the platform migration and upgrading cost is reduced, and the research, development, deployment and implementation efficiency of the big data support platform is greatly improved.

Fig. 5 is a schematic block diagram of a supporting PAAS platform building apparatus 300 according to an embodiment of the present invention. As shown in fig. 5, the present invention further provides a supporting PAAS platform constructing apparatus 300 corresponding to the above supporting PAAS platform constructing method. The supporting PAAS platform building apparatus 300 includes a unit for performing the above supporting PAAS platform building method, and the apparatus may be configured in a server. Specifically, referring to fig. 5, the supporting PAAS platform building apparatus 300 includes an environment building unit 301, an upgrading unit 302, a service building unit 303, and a management unit 304.

The environment building unit 301 is configured to build a supporting PAAS platform research and development environment by using Docker and a virtual machine management technology to obtain a research and development environment; an upgrade unit 302, configured to automatically sense and upgrade a virtual environment of a Docker; a service construction unit 303, configured to construct a service discovery architecture based on Docker to obtain a large-scale container cluster; a management unit 304, configured to manage the large-scale container cluster by using k8s, so as to obtain a supporting PAAS platform.

In an embodiment, as shown in fig. 6, the environment building unit 301 includes a development environment configuration subunit 3011, a mirroring subunit 3012, a virtual subunit 3013, a port configuration subunit 3014, and a running subunit 3015.

A development environment configuration subunit 3011, configured to configure a development environment by using Docker; a mirror image making subunit 3012, configured to make a mirror image of Docker through a Docker build command; the virtual subunit 3013 is configured to virtualize the mirror image of the Docker into a system by using a vagrant virtual machine management technology to obtain a virtual container; a port configuration subunit 3014, configured to develop a port of Docker through a configuration file of the vagrant; and the running subunit 3015 is configured to run multiple virtual containers simultaneously to obtain a development environment.

In one embodiment, as shown in fig. 7, the service building unit 303 includes a framework building subunit 3031 and a calling subunit 3032.

An architecture construction subunit 3031, configured to construct a service discovery architecture based on Docker by using a combination of technologies of Docker, etcd, and haproxy sets; the calling subunit 3032 is configured to register the service in an etcd cluster of the service discovery architecture based on the Docker, and drive the terminal to perform service forwarding through the service discovery architecture based on the Docker and then display the service in the terminal in an agent manner, so as to obtain a large-scale container cluster.

It should be noted that, as will be clearly understood by those skilled in the art, the specific implementation processes of the supporting PAAS platform construction apparatus 300 and each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.

The supporting PAAS platform building apparatus 300 described above may be implemented in the form of a computer program which can be run on a computer device as shown in fig. 8.

Referring to fig. 8, fig. 8 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, which may be an independent server or a server cluster composed of a plurality of servers.

Referring to fig. 8, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer programs 5032 comprise program instructions that, when executed, cause the processor 502 to perform a method of supporting PAAS platform construction.

The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be enabled to perform a method for supporting a PAAS platform construction.

The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 8 is a block diagram of only a portion of the configuration relevant to the present teachings and does not constitute a limitation on the computer device 500 to which the present teachings may be applied, and that a particular computer device 500 may include more or less components than those shown, or combine certain components, or have a different arrangement of components.

Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps:

establishing a PAAS platform research and development environment by using Docker and a virtual machine management technology to obtain a research and development environment; automatically sensing and upgrading the virtual environment of the Docker; constructing a service discovery architecture based on Docker to obtain a large-scale container cluster; managing the large-scale container cluster by adopting k8s to obtain a supporting PAAS platform.

In an embodiment, when implementing the steps of establishing and supporting a PAAS platform development environment by using Docker and by using a vagrant virtual machine management technology to obtain a development environment, the processor 502 specifically implements the following steps:

configuring a development environment by using a Docker; manufacturing a mirror image of the Docker through a Docker build command; virtualizing the mirror image of the Docker into a system by using a vagrant virtual machine management technology to obtain a virtual container; developing a Docker port through a configuration file of the vagrant; and simultaneously operating a plurality of virtual containers to obtain a research and development environment.

The virtual container comprises an application server, a data storage server and a development server.

In an embodiment, when implementing the step of automatically sensing and upgrading the virtual environment of Docker, the processor 502 specifically implements the following steps:

In an embodiment, when implementing the step of constructing a service discovery architecture based on a Docker to obtain a large-scale container cluster, the processor 502 specifically implements the following steps:

constructing a service discovery architecture based on Docker by adopting the technical combination of a Docker set, an etcd set and a haproxy set; the service is registered in an etcd cluster of a service discovery architecture based on the Docker, and the drive terminal performs service forwarding through the service discovery architecture based on the Docker and then displays the service on the terminal in a proxy mode to obtain a large-scale container cluster.

In an embodiment, when implementing the step of managing the large-scale container cluster by using k8s to obtain the supporting PAAS platform, the processor 502 specifically implements the following steps:

It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.

Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the steps of:

In an embodiment, when the processor executes the computer program to implement the step of establishing a supporting PAAS platform research and development environment by using Docker and by using a vagrant virtual machine management technology to obtain a research and development environment, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step of automatically sensing and upgrading the virtual environment of the Docker, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step of constructing a service discovery architecture based on a Docker to obtain a large-scale container cluster, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step of managing the large-scale container cluster by using k8s to obtain a supporting PAAS platform, the following steps are specifically implemented:

The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.

The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. The construction method of the support PAAS platform is characterized by comprising the following steps:

automatically sensing and upgrading the virtual environment of the Docker;

2. The method for constructing the supporting PAAS platform according to claim 1, wherein the constructing a research and development environment of the supporting PAAS platform by using Docker and a virtual machine management technology to obtain a research and development environment comprises:

configuring a development environment by using a Docker;

manufacturing a mirror image of the Docker through a Docker build command;

developing a Docker port through a configuration file of the vagrant;

3. The method of claim 2 in which the virtual container comprises an application server, a data storage server, and a development server.

4. The method for constructing a supported PAAS platform according to claim 1, wherein the automatically sensing and upgrading the virtual environment of Docker comprises:

5. The method for constructing a supported PAAS platform according to claim 1, wherein the constructing a Docker-based service discovery architecture to obtain a large-scale container cluster comprises:

6. The method of claim 1, wherein said managing said large-scale container cluster with k8s to obtain a supporting PAAS platform comprises:

7. Support PAAS platform and construct device, its characterized in that includes:

8. The apparatus of claim 7, wherein the environment building unit comprises:

9. A computer device, characterized in that the computer device comprises a memory, on which a computer program is stored, and a processor, which when executing the computer program implements the method according to any of claims 1 to 6.

10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.