A method and system for visualizing continuous delivery

Abstract

The application discloses a visual continuous delivery method and system, and relates to the technical field of automatic development and operation and maintenance.The method comprises the following steps: according to user drag operation information on a visual streaming continuous delivery designer page, a continuous integration / continuous delivery pipeline task is generated, and the continuous integration / continuous delivery pipeline task is saved into a pipeline task definition file; a task scheduling tree model is formed by using the pipeline task definition file in which the continuous integration / continuous delivery pipeline task has been saved, and the task scheduling tree model is converted into a jenkins pipeline script; after the task scheduling tree model is converted into the jenkins pipeline script, an image package is constructed, and the image package is published to a resource pool instance on a hybrid cloud.

Description

Technical Field

[0001] This invention relates to the field of automated development and operation technology, and in particular to a visual continuous delivery method and system. Background Technology

[0002] From a technical perspective, cloud-native technologies, represented by containers, microservices, and dynamic orchestration, are booming and have become a significant driving force for business innovation, already being applied to core enterprise operations. From a market perspective, cloud-native technologies have been widely validated in multiple industries such as finance, manufacturing, and the internet, supporting increasingly diverse business scenarios, and fostering a thriving industry ecosystem.

[0003] In recent years, with the maturity of cloud-native technologies and the upgrading of market demands, cloud computing has entered a new stage of development. The arrival of the hybrid multi-cloud era has made multi-cloud deployment of IT infrastructure the new normal for enterprises. Traditional single-cloud platform construction methods are inefficient, have long maintenance cycles, low levels of application automation testing and deployment, involve a large amount of manual configuration work, and are technically complex, making them unable to meet enterprise needs. More and more enterprises are adopting multi-cloud strategies, gradually shifting from single-cloud to multi-cloud. This is to fully leverage the productivity of the cloud, achieve agile IT response to business needs, and address the challenges brought by the complexity of multi-cloud environments. Summary of the Invention

[0004] The technical problem solved by the solution provided in the embodiments of the present invention is the low delivery efficiency and poor delivery quality of the R&D and testing team under the hybrid architecture of enterprise cloud platform.

[0005] A visual continuous delivery method provided according to an embodiment of the present invention includes:

[0006] Based on the drag-and-drop operation information of the user on the visual streaming continuous delivery designer page, a continuous integration / continuous delivery pipeline task is generated, and the continuous integration / continuous delivery pipeline task is saved to the pipeline task definition file;

[0007] Using the pipeline task definition file that already contains the continuous integration / continuous delivery pipeline tasks, a task scheduling tree model is formed, and the task scheduling tree model is converted into a Jenkins pipeline script;

[0008] After converting the task scheduling tree model into a Jenkins pipeline script, an image package is built and published to a resource pool instance on a hybrid cloud.

[0009] A visual continuous delivery system provided according to an embodiment of the present invention includes:

[0010] A visual streaming continuous delivery designer is used to generate continuous integration / continuous delivery pipeline tasks based on the user's drag-and-drop operation information on the page, and save the continuous integration / continuous delivery pipeline tasks to the pipeline task definition file;

[0011] The streaming processing module is used to form a task scheduling tree model using the pipeline task definition file that has been saved with the continuous integration / continuous delivery pipeline tasks, and to convert the task scheduling tree model into Jenkins pipeline scripts.

[0012] The build and delivery module is used to build an image package after converting the task scheduling tree model into a Jenkins pipeline script, and to publish the image package to a resource pool instance on the hybrid cloud.

[0013] According to the solutions provided in the embodiments of the present invention, they can be applied to cloud-native application development, enabling flexible scheduling of resources between private clouds and dedicated clouds, and completing continuous integration and continuous delivery pipelines for software development under a hybrid cloud architecture in a visualized manner, thus meeting the common demands of businesses for stable operation and agile innovation. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to understand the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0015] Figure 1 This is a flowchart of a visual continuous delivery method provided in an embodiment of the present invention;

[0016] Figure 2 This is a schematic diagram of a visual continuous delivery system provided in an embodiment of the present invention;

[0017] Figure 3 This is a schematic diagram of data flow between modules in the visualized continuous delivery system provided in this embodiment of the invention;

[0018] Figure 4 This is a schematic diagram of the visual streaming continuous delivery module architecture provided in an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram illustrating the principle of visualized streaming continuous delivery provided in an embodiment of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments described below are only for illustration and explanation of the present invention and are not intended to limit the present invention.

[0021] This invention enables comprehensive management of virtual machines, containers, networks, and other resources in an enterprise hybrid cloud platform. It virtualizes and slices these resources and allocates them to virtual organizations. Through resource quotas and namespace isolation, it ensures that tenants do not interfere with each other. It provides a streaming continuous delivery designer to shield CI / CD orchestration details and the technical difficulties of hybrid cloud deployment. At runtime, it achieves automatic execution without human intervention, improves integrated R&D efficiency, and promotes the digital transformation of enterprises.

[0022] Figure 1 This is a visual continuous delivery method provided by an embodiment of the present invention, such as... Figure 1 As shown, it includes:

[0023] Step S101: Based on the drag-and-drop operation information of the user on the visual streaming continuous delivery designer page, generate a continuous integration / continuous delivery pipeline task and save the continuous integration / continuous delivery pipeline task to the pipeline task definition file.

[0024] Step S102: Using the pipeline task definition file that has been saved with the continuous integration / continuous delivery pipeline tasks, form a task scheduling tree model, and convert the task scheduling tree model into a Jenkins pipeline script;

[0025] Step S103: After converting the task scheduling tree model into a Jenkins pipeline script, build an image package and publish the image package to a resource pool instance on the hybrid cloud.

[0026] The drag-and-drop operation information includes: code repository toolbox, compilation and build toolbox, code scanning toolbox, automated testing toolbox, cloud resource pool toolbox, and delivery rule toolbox.

[0027] Before generating the continuous integration / continuous delivery pipeline task, the process also includes receiving the user's configuration information, which includes: code repository, repository credentials, compilation tools, scanning engine type, image name and version, service name, network port, and resource quota.

[0028] Specifically, before the image package is built, the process includes: calling Jenkins to execute the pipeline task definition file of the continuous integration / continuous delivery pipeline task, compiling and packaging it, and sending the task execution node information back to the front-end page via WebSocket.

[0029] Specifically, building the image package and publishing the image package to the resource pool instance on the hybrid cloud includes: starting the code scanning toolbox, scanning the code and packaging the code into an image package; and publishing the image package to the resource pool instance on the hybrid cloud based on the results of automated testing and custom test pass rules.

[0030] Figure 2 This is a schematic diagram of a visual continuous delivery system provided in an embodiment of the present invention, such as... Figure 2 As shown, it includes: a visual streaming continuous delivery designer 201, used to generate continuous integration / continuous delivery pipeline tasks based on user drag-and-drop operation information on the page, and save the continuous integration / continuous delivery pipeline tasks to a pipeline task definition file; a streaming processing module 202, used to form a task scheduling tree model using the pipeline task definition file containing the saved continuous integration / continuous delivery pipeline tasks, and convert the task scheduling tree model into Jenkins pipeline scripts; and a build and delivery module 203, used to build an image package after converting the task scheduling tree model into Jenkins pipeline scripts, and publish the image package to a resource pool instance on a hybrid cloud.

[0031] The drag-and-drop operation information includes: code repository toolbox, compilation and build toolbox, code scanning toolbox, automated testing toolbox, cloud resource pool toolbox, and delivery rule toolbox.

[0032] Specifically, before generating continuous integration / continuous delivery pipeline tasks, the process also includes: receiving user configuration information; the configuration information includes: code repository, repository credentials, compilation tools, scanning engine type, image name and version, service name, network port, and resource quota.

[0033] Specifically, the streaming processing module 202 is also used to call Jenkins to execute the pipeline task definition file of the continuous integration / continuous delivery pipeline task, compile and package it, and send the task execution node information back to the front-end page via WebSocket.

[0034] Specifically, the build and delivery module 203 is used to launch the code scanning toolbox, scan the code and package the code into an image package; and publish the image package to the resource pool instance on the hybrid cloud according to the results of automated testing and custom test pass rules.

[0035] This patented system addresses the business model of cloud-native application development and testing in enterprise hybrid cloud environments. It designs a visualized continuous delivery system for hybrid cloud resource pools, balancing visualization and automation of the software delivery process while improving software delivery quality. The system comprises three main parts: hybrid cloud resource management, resource pool slicing, and a streaming continuous delivery engine. Each part and its corresponding function are described in Table 1.

[0036] Table 1: Modules and Functions of a Visualized Continuous Delivery System for Resource Pools in Hybrid Cloud Environments

[0037]

[0038]

[0039] The following is combined Figures 3-5 Explanation of information interaction between various modules within the system:

[0040] The first step involves the hybrid cloud resource management module acquiring managed resource data based on cloud environment configuration and various platform adapters. Combined with resource slicing information from the resource pool slicing module, this data displays the total cluster resources and their occupancy status to the user. It also provides available cluster resource data for the resource pool slicing module to access.

[0041] The second step involves the user using the streaming continuous delivery engine to determine the service resource limit based on the resource slicing suggestions. Then, the user enters the visual configuration function to define the streaming continuous delivery task. The continuous delivery job runs and displays the visual process. At the same time, code issues are automatically scanned and sent to the visual configuration function for display to support continuous improvement and enhance quality and efficiency.

[0042] Third, during service operation, the continuous delivery engine retrieves monitoring data of the deployed applications from the resource management module, including node-level monitoring, container-level monitoring, and service-level monitoring data. Simultaneously, application deployment information is synchronized in real-time to the hybrid cloud resource management module for unified display and analysis.

[0043] Through the organic interaction of three core modules, the system achieves unified resource management capabilities for hybrid clouds. After the resource pool is flexibly sliced ​​as needed, the streaming continuous delivery engine manages the configuration in a visual manner. It also integrates and centrally displays the service monitoring data running on the hybrid cloud in real time, thus forming a visual continuous delivery system for resource pools in hybrid cloud environments.

[0044] The following provides a detailed explanation of each module.

[0045] Hybrid Cloud Resource Management Module

[0046] The authentication client program communicates with the cloud platform cluster to obtain tokens or certificates, which are then encrypted using the national cryptographic algorithm SM4 and stored securely. When connecting to the cluster, the tokens or certificates are dynamically matched to achieve secure authentication with the cloud platform or cluster. This transforms physically dispersed resources into a logically unified cloud resource pool, enabling unified management and monitoring of computing, storage, and network resources.

[0047] It performs health and load monitoring of cloud virtual machines and containers, supports heartbeat monitoring of continuously delivered business applications in the form of probes, supports custom load rate warning thresholds and proactively notifies the operation and maintenance team, and provides real-time control over the operation status of infrastructure and business applications in hybrid cloud.

[0048] The process includes the following steps:

[0049] Step 102: The system queries and retrieves the authentication token, and establishes communication with the cloud platform or cluster via API interface.

[0050] Step 102: The system calls the interface to query the number of nodes on the cloud platform and the resource usage of each node, and writes the data to the cache database.

[0051] Step 103: Run the scheduled program to send a heartbeat to the agent program of the cloud platform or cluster every minute to detect the liveness status of nodes and containers and write it to the cache.

[0052] Step 104: If the cloud platform or cluster is healthy, no action is taken; otherwise, the message service is invoked to proactively send an alarm to the operations and maintenance personnel.

[0053] Resource pool slicing module

[0054] By leveraging the namespace isolation principle of cloud platforms or clusters, tenant spaces are divided, and virtualized resources such as resource pools and resource quotas are allocated to end users.

[0055] The process includes the following steps:

[0056] Step 201: The system queries and retrieves the authentication token, and establishes communication with the cloud platform or cluster via API interface.

[0057] Step 202: Compare the amount of resources requested by the user (number of CPUs, memory capacity, etc.) with the total amount of resources remaining on the cloud. If the upper limit of the total amount of resources is reached, return a prompt message; otherwise, call the resource slicing program to divide the resources.

[0058] Step 203: The resource slicing program calls the resource allocation interface, calls the corresponding adaptation module program to make service calls according to the input cloud platform type, creates a virtual resource pool and allocates resource space and quotas to it.

[0059] Step 204: After the resource creation is completed, allocate the resource pool to the user.

[0060] Streaming Continuous Delivery Engine

[0061] The process includes the following steps:

[0062] Step 301: Implement drag-and-drop task orchestration using a visual, streaming continuous delivery designer. This includes multiple drag-and-drop subtasks, categorized into the Code Repository Toolbox, Compilation and Build Toolbox, Code Scanning Toolbox, Automated Testing Toolbox, Cloud Resource Pool Toolbox, and Delivery Rules Toolbox. This module receives user-configured code repository, repository credentials, compilation tools, scanning engine type, image name and version, service name, network port, and resource quotas. It then generates highly flexible continuous integration / continuous delivery pipeline tasks and saves them as XML structures to the pipeline task definition file.

[0063] Step 302: The pipeline task definition file is parsed and processed by the streaming CI / CD processing module to form a task scheduling tree model. The task tree is then converted into a Jenkins pipeline script and saved.

[0064] Step 303: Call Jenkins to execute pipeline tasks and synchronously send the task execution node information back to the front-end page via WebSocket, causing the corresponding node in the designer to flash.

[0065] Step 304: Perform code scanning and synchronously send the execution progress back to the front end. If the execution fails, terminate; otherwise, continue execution.

[0066] Step 305: Execute image building, synchronously transmit the execution progress back to the front end, call the continuous deployment standard interface program, and deploy to the public cloud or private cloud environment after adaptation through ECS adapter or K8S adapter.

[0067] Step 306: After the automated testing phase script is completed, the phase script defined by the automated delivery tool processes the delivery procedure according to the custom decision rules.

[0068] Step 307: If delivery is successful, update the application version.

[0069] This enables the visual orchestration of the entire streaming continuous delivery process and its automated execution without human intervention.

[0070] The solution provided by the embodiments of the present invention has the following effects:

[0071] 1. This patented hybrid cloud management module provides unified operation, deployment, and monitoring for public and private clouds, significantly improving the maintenance and operation efficiency of multi-cloud environments.

[0072] 2. Through the hybrid cloud monitoring of the present invention, the resource usage of public cloud and private cloud can be displayed in real time on a unified interface, realizing host monitoring, container monitoring, real-time alarm, and automatic reduction of resource capacity according to resource usage.

[0073] 3. The visual streaming continuous delivery module of this invention enables rapid pipeline orchestration via drag-and-drop interface design, one-click deployment to a hybrid cloud environment, real-time feedback on build progress, and improved deployment efficiency.

[0074] 4. The automated testing module of this invention can process delivery procedures according to customized judgment rules. This enables visualized orchestration and automated execution of the entire streaming continuous delivery process without manual intervention, thereby improving testing efficiency.

[0075] Although the present invention has been described in detail above, it is not limited thereto, and those skilled in the art can make various modifications based on the principles of the present invention. Therefore, all modifications made in accordance with the principles of the present invention should be understood to fall within the protection scope of the present invention.

Claims

1. A visual continuous delivery method, characterized in that, include: Based on the drag-and-drop operation information of the user on the visual streaming continuous delivery designer page, including the code repository toolbox, build toolbox, code scanning toolbox, automated testing toolbox, cloud resource pool toolbox, and delivery rule toolbox, and receiving the user's configured code repository, repository credentials, build tools, scanning engine type, image name and version, service name, network port, and resource quota, a continuous integration / continuous delivery pipeline task is generated and the continuous integration / continuous delivery pipeline task is saved to the pipeline task definition file; Using the pipeline task definition file that already contains the continuous integration / continuous delivery pipeline tasks, a task scheduling tree model is formed, and the task scheduling tree model is converted into a Jenkins pipeline script; The Jenkins pipeline script is invoked to execute the continuous integration / continuous delivery pipeline task definition file, compile and package it, and send the task execution node information back to the front-end page via WebSocket, causing the corresponding node in the designer to flash. Execute the code scanning toolkit and synchronously send the execution progress back to the front end; Execute image building, synchronously transmit the execution progress back to the front end, call the continuous deployment standard interface program, and deploy to the public cloud or private cloud environment after adaptation through ECS adapter or K8S adapter. After the automated testing phase script is completed, the phase script defined by the automated delivery tool processes the delivery procedure according to the custom judgment rules, and updates the application version after successful delivery.

2. A visual continuous delivery system, characterized in that, include: The visual streaming continuous delivery designer is used to generate continuous integration / continuous delivery pipeline tasks based on the drag-and-drop operation information of the user on the page, including the code repository toolbox, build toolbox, code scanning toolbox, automated testing toolbox, cloud resource pool toolbox, and delivery rule toolbox, as well as the code repository, repository credentials, build tools, scanning engine type, image name and version, service name, network port, and resource quota configured by the user, and save the continuous integration / continuous delivery pipeline tasks to the pipeline task definition file; The streaming processing module is used to form a task scheduling tree model using the pipeline task definition file that has been saved with the continuous integration / continuous delivery pipeline tasks, and to convert the task scheduling tree model into Jenkins pipeline scripts. The build and delivery module is used to call the Jenkins pipeline script to execute the continuous integration / continuous delivery pipeline task definition file, compile and package it, and send the task execution node information back to the front-end page via WebSocket, causing the corresponding node in the designer to flash; it also executes the code scanning toolbox and synchronously sends the execution progress back to the front-end. Execute the image build, synchronously transmit the execution progress back to the front end, call the continuous deployment standard interface program, and deploy to the public cloud or private cloud environment after adaptation through the ECS adapter or K8S adapter; after the automatic testing phase script is completed, the phase script defined by the automatic delivery tool processes the delivery program according to the custom judgment rules, and updates the application version after successful delivery.

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