Deployment method, device and equipment of sidecar container, storage medium and program product

Abstract

The invention provides a deployment method, device and equipment of a sidecar container, a storage medium and a program product. Relates to the technical field of cloud computing. The method comprises the following steps: acquiring a service attribute of a target business application, wherein the service attribute comprises a service type and an environment label; determining a target template corresponding to the target business application according to the service type and a preset template association relationship, wherein the target template comprises a configuration strategy, a deployment sequence and an environment adaptation strategy of a side vehicle container; generating configuration data of the sidecar container based on the target template, wherein the configuration data comprises a basic configuration field and a personalized configuration field; and adjusting the configuration data according to the environment label and the environment configuration strategy, and completing deployment of the side vehicle container based on the adjusted configuration data. According to the method, the sidecar container is automatically matched and deployed through the template, manual operation is reduced, configuration errors are avoided, and it is ensured that configuration of different teams to the sidecar container is consistent.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, and in particular to a method, apparatus, device, storage medium, and program product for deploying a sidecar container. Background Technology

[0002] With the mainstream trend of containerized deployment of cloud-native applications, business applications are mostly deployed and run in the form of Kubernetes (K8s) Pods. A Pod typically contains a core business container and multiple sidecar containers. As auxiliary containers that share network namespaces and storage volumes with business containers, sidecar containers undertake key non-business functions such as monitoring, log collection, security policies, and service governance. Typical examples include Fluentd log collection containers, Prometheus Exporter monitoring containers, and Open Policy Agent security containers. These containers are usually provided by third-party infrastructure or platform teams and have become an important part of cloud-native application deployment.

[0003] The current management of sidecar containers mainly relies on manual operation and documented specifications. The specific process is as follows: the business team first communicates with the infrastructure team through offline documents or meetings to clarify the type, configuration requirements and deployment specifications of the sidecar containers; then, they obtain the image address, configuration template and deployment examples from the documents; subsequently, they manually create Pods on the container platform, add sidecar containers one by one and fill in configuration parameters such as environment variables and storage volume mount paths; for different environments such as development, testing and production, it is also necessary to manually adjust the log collection path, security policy rules and other configurations to complete the environment adaptation.

[0004] However, manual operation is prone to errors, and manual configuration is prone to missing key parameters or causing configuration inconsistencies, leading to service deployment failures or abnormal operation. Configuration adjustments for different environments require repeated manual operations, which is inefficient and further increases the probability of errors, seriously affecting the efficiency and stability of business delivery. Summary of the Invention

[0005] The sidecar container deployment method, apparatus, equipment, storage medium, and program products provided in this application are used to achieve standardized and automated deployment of sidecar containers.

[0006] In a first aspect, embodiments of this application provide a method for deploying a sidecar container, the method comprising:

[0007] Obtain the service attributes of the target business application, including service type and environment tag;

[0008] The target template corresponding to the target business application is determined based on the service type and the association relationship between the preset template. The target template includes the configuration strategy, deployment order and environment adaptation strategy of the side vehicle container.

[0009] Configuration data for the sidecar container is generated based on the target template. The configuration data includes basic configuration fields and personalized configuration fields.

[0010] The configuration data is adjusted according to the environment label and environment configuration policy, and the sidecar container is deployed based on the adjusted configuration data.

[0011] In one possible implementation, generating configuration data for the sidecar container based on the target template includes:

[0012] A preset clustering analysis algorithm is used to generate personalized configuration recommendation information based on the service attributes of the target business application and the historical configuration data of the sidecar container of the deployed business application.

[0013] In one possible implementation, before deploying the sidecar container based on the adjusted configuration data, the process further includes:

[0014] The adjusted configuration data is checked according to the preset conflict detection strategy;

[0015] If a configuration conflict is detected, the deployment of the sidecar container is paused and the conflict type and specific location are output.

[0016] In one possible implementation, it also includes:

[0017] Use preset monitoring tools to acquire configuration change events;

[0018] In response to receiving a configuration change event, the corresponding template in the preset template library is updated based on the configuration change event, and a configuration change message is sent to the business application that deploys the sidecar container based on the template.

[0019] In one possible implementation, the environment adaptation strategy includes preset resource allocation rules, and the step of adjusting configuration data according to environment tags and environment configuration strategies includes:

[0020] Retrieve the environmental load information of the deployment environment corresponding to the environment tag;

[0021] Adjust the resource quota configuration of the sidecar container based on the environmental load information and preset resource allocation rules.

[0022] In one possible implementation, the configuration data includes communication optimization strategies between the sidecar container and the service container, and the method further includes:

[0023] Obtain network transmission characteristic data of the target business application;

[0024] The communication configuration parameters of the sidecar container are generated based on the network transmission characteristic data;

[0025] In response to the completion of the deployment of the sidecar container, the communication characteristic data between the sidecar container and the service container are periodically acquired, and the communication configuration parameters of the sidecar container are updated based on the communication optimization strategy.

[0026] In one possible implementation, it also includes:

[0027] If the target business application needs to be migrated, obtain the configuration adaptation characteristics of the sidecar container in the source environment and the infrastructure characteristics of the target environment.

[0028] Based on the infrastructure characteristics of the target environment, adjust the environment dependencies in the configuration adaptation features and generate a migration configuration compatible with the target environment.

[0029] Secondly, embodiments of this application provide a deployment apparatus for a sidecar container, comprising:

[0030] The acquisition module is used to acquire the service attributes of the target business application, including service type and environment tag;

[0031] The determination module is used to determine the target template corresponding to the target business application based on the service type and the preset template association relationship. The target template includes the configuration strategy, deployment order and environment adaptation strategy of the side vehicle container.

[0032] The generation module is used to generate configuration data for the sidecar container based on the target template. The configuration data includes basic configuration fields and personalized configuration fields.

[0033] An adjustment module is used to adjust the configuration data according to the environment label and environment configuration strategy;

[0034] The deployment module is used to deploy the sidecar container based on the adjusted configuration data.

[0035] Thirdly, embodiments of this application provide a deployment device for a sidecar container, including: a memory and a processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0039] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0040] The sidecar container deployment method, apparatus, device, storage medium, and program product provided in this application clearly define the core functional characteristics of the business application through service types, and identify the deployment environment through environment tags. Obtaining the service type allows understanding of the business's basic requirements for resources, network, etc., providing a basis for selecting sidecar container configuration strategies. Environment tags enable sidecar container deployment to be adjusted according to the actual environment, avoiding deployment failures due to environmental differences and improving flexibility and adaptability. Preset templates are a collection of configuration schemes optimized through practice, containing best practices and general configurations for different service types. Quickly determining the target template based on the service type eliminates the tedious process of configuring from scratch, shortening deployment time and improving efficiency. The target template clearly defines the sidecar container deployment order, which is crucial for complex business systems; a reasonable order ensures correct component dependencies and collaboration, avoiding startup or operational anomalies. Basic configuration fields contain parameters necessary for the normal operation of the sidecar container, forming the basis for starting and providing basic services, ensuring basic functional performance under various conditions. Personalized configuration fields are customized according to specific business needs, allowing for fine-tuning of the sidecar container. Combining environment tags and environment configuration strategies to adjust configuration data ensures a perfect match with the current environment, leveraging advantages and improving system performance and stability. Environment configuration strategies provide adjustment solutions for different environments, avoiding deployment failures. The combination of these two approaches enables automated deployment. Scripts or tools can be used to select strategies based on environment tags, adjust data, and deploy, improving efficiency, reducing errors, and supporting continuous delivery and rapid iteration. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] Figure 1 An application scenario diagram for the deployment method of the sidecar container provided in this application;

[0043] Figure 2 A schematic flowchart illustrating a method for deploying a sidecar container according to an embodiment of this application;

[0044] Figure 3 A schematic flowchart illustrating a method for deploying a sidecar container according to another embodiment of this application;

[0045] Figure 4 A schematic diagram of the structure of a deployment device for a sidecar container provided in an embodiment of this application;

[0046] Figure 5This is a schematic diagram of the deployment device for a sidecar container provided in an embodiment of this application.

[0047] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0049] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0050] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0051] To clearly understand the technical solution of this application, the solutions of the prior art will be described in detail first.

[0052] With containerized deployment of cloud-native applications becoming mainstream, most business applications run in the form of Kubernetes (K8s) Pods. A Pod typically contains a core business container and multiple sidecar containers. Sidecar containers share network namespaces and storage volumes with the business containers and are responsible for non-critical functions such as monitoring, log collection, security policies, and service governance. Containers for Fluentd log collection, Prometheus Exporter monitoring, and Open Policy Agent security are often provided by third-party infrastructure or platform teams and are a key part of cloud-native deployments. Currently, sidecar container management mainly relies on manual processes and documentation. Business teams first determine the type, configuration requirements, and deployment specifications of sidecar containers with the infrastructure team through offline documents or meetings, and then obtain image addresses, configuration templates, and deployment examples from the documents. Next, Pods are manually created on the container platform, and sidecar containers are added one by one, with environment variables, storage volume mount paths, and other parameters filled in. For different environments, log collection paths, security policy rules, etc., must also be manually adjusted to adapt. However, manual operation is prone to errors; manual configuration may miss key parameters or produce inconsistencies, leading to service deployment failures or operational anomalies. Adjusting configurations for different environments requires repetitive manual operations, which is inefficient and prone to errors, seriously affecting business delivery efficiency and stability.

[0053] Therefore, to address the technical challenges of existing technologies and resolve issues such as error-prone manual operations and lack of standardized configuration, the sidecar container configuration specifications, deployment best practices, and environment adaptation rules are integrated into preset templates. An automatic matching mechanism between service attributes and preset templates is built. By acquiring core attributes such as the service type of the target business application, precise matching and automatic invocation of target templates are achieved, avoiding subjective errors from manual selection and configuration. Simultaneously, the templates clearly define unified configuration strategies and deployment sequences, reducing configuration discrepancies in cross-team collaboration and ensuring configuration standardization and traceability. To address the issues of complex environment adaptation and low efficiency from repetitive operations, environment tags are added to service attributes to accurately identify the deployment environment. Environment adaptation strategies are embedded in the templates, and upon receiving a deployment request, configuration data is automatically adjusted based on the environment tags, eliminating the need for repeated manual modifications, reducing the additional costs associated with environment switching, and achieving seamless integration of multi-environment deployments. To balance standardized configurations with personalized business needs, a strategy of separating basic and personalized configurations is adopted. In the configuration data generated by the template, the basic configuration fields follow standardized specifications to ensure deployment compliance, while the personalized configuration fields allow business teams to adjust them as needed. This avoids the chaos of completely manual configuration and the rigidity of completely fixed configurations, while maintaining flexible adaptation space while standardizing the process.

[0054] Figure 1 This is an application scenario diagram illustrating the sidecar container deployment method provided in this application, such as... Figure 1As shown in the diagram, the scenario corresponding to the sidecar container deployment method provided in this application includes: a terminal device 101, a server 102, and a preset template library 103. The sidecar container deployment device can be integrated into the server 102.

[0055] Specifically, users can deploy target business applications on terminal device 101. When server 102 detects a new business application being deployed, it initiates the sidecar container deployment process, obtains the service attributes of the target business application, including service type and environment tag; determines the target template corresponding to the target business application from the preset template library 103 based on the association between service type and preset template, the target template includes the sidecar container's configuration strategy, deployment order, and environment adaptation strategy; server 102 generates configuration data for the sidecar container based on the target template, the configuration data including basic configuration fields and personalized configuration fields; finally, it adjusts the configuration data according to the environment tag and environment configuration strategy, and completes the deployment of the sidecar container based on the adjusted configuration data.

[0056] Optionally, users can modify personalized configuration data in terminal device 101 to achieve a balance between template-based and flexible configuration.

[0057] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0058] Figure 2 This is a flowchart illustrating a method for deploying a sidecar container according to an embodiment of this application, as shown below. Figure 2 As shown, the execution entity in this embodiment is a deployment device for a sidecar container. This device can be implemented through a computer program, or through a medium storing the relevant computer program, such as a USB flash drive and / or optical disc, or through a physical device integrating or installing the relevant computer program, such as a chip or a deployment device for the sidecar container. The deployment device for the sidecar container can be a server, a server cluster, etc. The deployment method for the sidecar container provided in this embodiment includes the following steps:

[0059] S201. Obtain the service attributes of the target business application. The service attributes include service type and environment tag.

[0060] The target business application refers to the cloud-native business application that currently needs to deploy the sidecar container.

[0061] Among them, service attributes refer to a set of key information used to uniquely identify the core characteristics of the target business application, including service type, such as Web service, data computing service, etc.; environment tags are tags used to identify the deployment environment of the target business application, corresponding to typical cloud-native deployment environments, such as development environment, testing environment, etc.

[0062] Specifically, when business personnel initiate a sidecar container deployment request on a cloud-native application deployment platform, such as the Kubernetes management console, a service attribute collection process is automatically triggered. This process involves, for example, calling the Kubernetes API to obtain metadata information of the target business application Pod, performing matching analysis on the Pod metadata, and outputting identification results. For instance, if the image name contains 'nginx' or 'tomcat', it is determined to be a web service. Then, the namespace information of the target business application to be deployed is read and used as an environment label.

[0063] Optionally, if there are special scenarios, such as custom business types or cross-environment deployment requirements, business personnel can manually modify the service type and adjust the environment label.

[0064] S202. Determine the target template corresponding to the target business application based on the service type and the preset template association. The target template includes the configuration strategy, deployment order and environment adaptation strategy of the side vehicle container.

[0065] The preset template association refers to a predefined set of mapping rules between service types and templates. These mapping rules support binding a single template to a single service type or allowing multiple service types to share a common template, and they are dynamically updated.

[0066] The target template refers to a set of sidecar container configurations specifically designed for the target business application, which are matched based on the service type and the pre-defined template association.

[0067] The configuration strategy refers to the standardized configuration parameters of the sidecar container, such as the recommended image version, default values ​​of environment variables, storage volume mount path, resource quota (CPU / memory), network access rules, etc.

[0068] The deployment order refers to the startup logic of the side vehicle container and the business container, such as the security policy container, log collection container, monitoring container, and business container.

[0069] Among them, the environment adaptation strategy refers to the differentiated configuration rules for different environments, such as enabling data encryption configuration in the production environment and disabling redundant log output in the development environment.

[0070] Specifically, based on the obtained service type, a query is performed in the mapping table corresponding to the preset template association relationship to extract the target template that matches the service type.

[0071] Optionally, if no matching template is found, a general template will be matched; if there are multiple template matching conflicts, the template with the highest deployment success rate in the last 30 days will be selected.

[0072] Furthermore, from the matched templates, the environment adaptation strategies corresponding to the environment tags are filtered out; if the target business application contains multiple environment tags, the adaptation strategies for multiple environments are loaded at the same time, and the corresponding strategies are dynamically enabled according to the actual deployment environment in subsequent steps.

[0073] S203. Generate configuration data for the sidecar container based on the target template. The configuration data includes basic configuration fields and personalized configuration fields.

[0074] The configuration data refers to a set of structured parameters generated based on the target template that can be directly used for the deployment of the side vehicle container.

[0075] Among them, the basic configuration fields refer to the standardized and unmodifiable core configuration items in the configuration data that are directly inherited from the target template, such as the recommended image address and version of the sidecar container, the default storage volume mount path, etc.

[0076] Personalized configuration fields refer to adjustable configuration items reserved in the configuration data to meet the special needs of the target business application. These can be flexibly modified by the business team according to their own scenarios, such as custom paths for log collection and personalized whitelists for security policies.

[0077] Specifically, the deployment device of the sidecar container reads the locked target template, extracts the configuration strategy module, and parses it according to the structured format of configuration item type, parameter name, parameter value, whether it can be modified, and description to generate a template configuration list. Configuration items marked as "no" for "whether it can be modified" in the template configuration list are directly included in the basic configuration field set. Configuration items marked as "yes" for "whether it can be modified" are included in the personalized configuration field set.

[0078] Furthermore, the standardized parameter values ​​defined in the target template are populated into the basic configuration fields, such as populating fluentd:v1.16.0 into the log container image. The basic configuration fields and personalized configuration fields are then integrated according to deployment specifications, such as JSON / YAML format, to generate complete sidecar container configuration data.

[0079] Optionally, after generating complete configuration data, a visual preview function is provided to display the configuration hierarchy in a tree structure, making it easy for business personnel to view intuitively. After the business personnel confirm that the configuration is correct, they can click the submit button, and the configuration data will be stored in the deployment database to provide a basis for subsequent environment adaptation and deployment.

[0080] Optionally, based on the set of personalized configuration fields, a visual configuration interface can be automatically generated on the deployment platform. For fields with value range restrictions, such as CPU quota, a slider or drop-down selection box can be provided to avoid entering illegal values. For personalized fields that are required, such as business system number, a required field can be marked, and configuration submission can be prevented if the field is not filled in. For complex configuration items, such as access whitelist, batch addition and import / export functions can be provided to support business personnel in efficient configuration.

[0081] Optionally, if there are personalized configuration records for similar business applications, similar configurations can be automatically recommended to help business personnel make quick decisions.

[0082] S204. Adjust the configuration data according to the environment label and environment configuration policy, and complete the deployment of the side vehicle container based on the adjusted configuration data.

[0083] Specifically, the system reads the environment configuration policy corresponding to the environment tag from the target template, iterates through the environment configuration policies, matches the corresponding fields in the original configuration data one by one, and adjusts the original configuration data accordingly. For example, if the policy rule requires modification of the basic configuration, the parameter values ​​in the original basic configuration are directly overwritten; if the personalized configuration conflicts with the environment policy, the conflict point is displayed on the visualization interface, and two options, automatic adaptation and manual adjustment, are provided, which business personnel can select to complete the adjustment.

[0084] Furthermore, the adjusted configuration data is sent to the target cluster's interface to initiate a Pod creation request, while simultaneously recording the deployment task ID and submission time. Node resources are scheduled according to the deployment order defined in the target template, sequentially pulling the image, creating container instances, mounting storage volumes and network namespaces, and completing the deployment of the sidecar container.

[0085] Optionally, after deployment, check the running status of all sidecar containers and business containers, and verify the network connectivity between containers, such as whether business containers can push logs to the log sidecar container and the validity of storage volume mounts. Perform automated verification of the core functions of the sidecar containers to ensure that they function normally. After all verifications pass, push a deployment success notification to business personnel, and archive the adjusted configuration data, deployment logs, and verification results to the database for subsequent operation and maintenance and auditing.

[0086] The sidecar container deployment method provided in this application embodiment clarifies the core functional characteristics of the business application through service types and identifies its deployment environment through environment tags. Obtaining the service type allows understanding the business's basic requirements for resources, network, etc., providing a basis for selecting sidecar container configuration strategies. Environment tags enable sidecar container deployment to be adjusted according to the actual environment, avoiding deployment failures due to environmental differences and improving flexibility and adaptability. Preset templates are a collection of configuration schemes optimized through practice, containing best practices and general configurations for different service types. Quickly determining the target template based on the service type eliminates the tedious process of configuring from scratch, shortening deployment time and improving efficiency. The target template clarifies the deployment order of sidecar containers, which is crucial for complex business systems; a reasonable order ensures correct component dependencies and collaboration, avoiding startup or operational anomalies. Basic configuration fields contain parameters necessary for the normal operation of the sidecar container, forming the basis for starting and providing basic services, ensuring basic functional performance under various conditions. Personalized configuration fields are customized according to specific business needs, allowing for fine-tuning of the sidecar container. Combining environment tags and environment configuration strategies to adjust configuration data ensures a perfect match with the current environment, leveraging advantages and improving system performance and stability. Environment configuration strategies provide adjustment solutions for different environments, avoiding deployment failures. The combination of these two approaches enables automated deployment. Scripts or tools can be used to select strategies based on environment tags, adjust data, and deploy, improving efficiency, reducing errors, and supporting continuous delivery and rapid iteration.

[0087] As an optional implementation, based on the above embodiments, configuration data for the sidecar container is generated based on the target template, including:

[0088] A pre-defined clustering analysis algorithm is used to generate personalized configuration recommendations based on the service attributes of the target business application and the historical configuration data of the sidecar containers of the deployed business applications.

[0089] Among them, the pre-configured clustering analysis algorithm refers to the pre-configured unsupervised learning algorithm used to discover the configuration patterns of similar business applications, such as the K-means clustering algorithm.

[0090] Among them, the historical configuration data of the sidecar container of the deployed business applications refers to the collection of configuration files of all business applications that have completed the deployment of sidecar containers, which includes two types of core data: one is the service attributes of the deployed applications; the other is the corresponding personalized configuration data of the sidecar containers, such as custom log paths, resource quota adjustment values, security policy whitelists, etc.

[0091] Personalized configuration recommendations refer to personalized configuration references generated based on clustering analysis results and adapted to the target business application, such as recommending a CPU quota of 0.5 cores and recommending the log collection path as / var / log / web-app.

[0092] Understandably, the preset clustering analysis algorithm is trained on historical data. It extracts data pairs of service attributes and personalized configurations for all deployed business applications from the configuration history database and performs data cleaning. The pre-processed training dataset is then input into the algorithm, which calculates the feature similarity between different applications based on service attribute feature vectors. Applications with feature similarity exceeding a preset threshold are grouped into the same cluster. After clustering, each cluster is matched with corresponding core service attribute features, high-frequency personalized configuration parameters, and their value ranges.

[0093] Specifically, the service attributes of the target business application are input into a preset clustering analysis algorithm to calculate the similarity between the application and each cluster. The cluster with the highest similarity is selected as the target cluster, and the high-frequency personalized configuration parameters and value patterns of the target cluster are extracted as the core basis for generating recommendation information.

[0094] For example, numerical configuration recommendations suggest the midpoint of the range with the highest percentage of values ​​in the target cluster. For instance, if 60% of applications in the cluster have a CPU quota of 0.5-0.7 cores, then 0.6 cores would be recommended, and it would be noted that 80% of applications in the same cluster use this configuration to adapt to high-concurrency scenarios. Personalized configuration recommendations are embedded in a visual configuration interface, displaying each personalized configuration field, its recommended value, the basis for the recommendation, and similar cases. This allows business teams to directly adopt recommended values ​​or make minor adjustments based on them.

[0095] Optionally, whenever a new business application completes its sidecar container deployment, its service attributes and personalized configuration data are added to the configuration history database.

[0096] The sidecar container deployment method provided in this application addresses the unique service attributes of each business application, such as service type, business scale, and performance requirements. Through clustering analysis algorithms, the similarity between the target business application's service attributes and successfully deployed cases can be deeply analyzed. This allows for the recommendation of precisely matched personalized configurations, avoiding performance bottlenecks or functional deficiencies caused by improper configuration. After generating personalized configuration recommendations using clustering analysis algorithms, developers can directly refer to the recommended solutions for configuration, eliminating the need for trial and error and significantly shortening configuration time, thus accelerating the deployment speed of the sidecar container.

[0097] As an optional implementation, based on the above embodiments, before deploying the sidecar container based on the adjusted configuration data, the following steps are also included:

[0098] The adjusted configuration data is checked according to the preset conflict detection strategy;

[0099] If a configuration conflict is detected, the deployment of the sidecar container is paused and the conflict type and specific location are output.

[0100] Among them, the preset conflict detection strategy refers to a predefined set of rules used to identify contradictory or incompatible items in the sidecar container configuration data, such as port occupancy, CPU / memory quota conflicts, inconsistent storage volume paths, and environment variable naming conflicts.

[0101] Specifically, the adjusted configuration data is converted into a structured configuration object model, broken down by Pod configuration, container configuration, and configuration item hierarchy. Conflict detection is performed layer by layer according to a preset conflict detection strategy, such as verifying the syntax correctness of the configuration data based on Kubernetes deployment specifications, checking for basic errors such as empty container image addresses; checking whether the ports of containers within the same Pod are duplicated, and whether the startup order of dependent containers is reasonable; obtaining information such as target node resources, network policies, and storage configurations, and comparing the configuration data for compatibility.

[0102] Furthermore, during the verification process, if a configuration item that violates the policy is detected, a conflict is recorded, such as storing the conflict information in the format of conflict type, specific location, configuration value, conflict basis, and error message.

[0103] Optionally, if no conflict is detected, a configuration verification pass report is generated, including the number of verification policies, details of verification items, and verification results, and the process automatically proceeds to the subsequent sidecar container deployment process; if a conflict is detected, the deployment process is paused, and a complete conflict report is displayed to business personnel on the deployment platform, including the type and specific location of the conflict.

[0104] The sidecar container deployment method provided in this application involves numerous complex configuration parameters, such as network configuration, resource allocation, and permission settings. Conflicts in these configurations can cause containers to fail to start, become unstable, or malfunction. Conflict detection allows for the early detection and resolution of these issues, ensuring that the sidecar container can be deployed smoothly and run stably as expected, reducing deployment failures and subsequent maintenance challenges caused by configuration errors.

[0105] As an optional implementation, based on the above embodiments, it further includes:

[0106] Use preset monitoring tools to acquire configuration change events;

[0107] In response to a configuration change event, the corresponding template in the preset template library is updated based on the configuration change event, and a configuration change message is sent to the business application that deploys the sidecar container based on the template.

[0108] Among them, the preset monitoring tools refer to a set of dedicated tools that are pre-configured and used to monitor changes in the configuration sources of sidecar containers in real time, such as the configuration API of Kubernetes clusters and the webhook of image repositories.

[0109] Among them, configuration change events refer to standardized event data that are captured by preset monitoring tools and trigger the update of the side vehicle container template. They contain complete change context information, such as change type and change object.

[0110] The preset template library refers to a centralized database that stores all sidecar container templates.

[0111] Among them, configuration change messages refer to notification messages that inform users of template updates.

[0112] Specifically, the pre-configured monitoring tool receives raw change signals from various change sources. For example, by configuring a webhook for the image repository, when a new version of the sidecar container image is released or its tag is updated, a webhook callback is triggered to obtain the image change event. The configuration change event is read, and the template to be updated is determined based on the template ID field in the event. The new values ​​of the changed configuration items in the event are then used to replace the corresponding configuration items in the template, such as updating the log container image field from fluentd:v1.16.0 to fluentd:v1.17.0.

[0113] Optionally, if the configuration item associated with the change event is a core field of the template, a new version of the template is created, and the historical version is retained for rollback; if it is a non-core field, the current version is updated directly without creating a new version; if the change event involves an environment adaptation strategy, the adaptation rules for the corresponding environment in the template are updated synchronously.

[0114] Furthermore, the template dependency table is queried, and all related business applications are retrieved using the template ID field. Personalized message content is generated for different change types, such as image update messages emphasizing vulnerability fixes and performance optimizations, and then pushed to the relevant business personnel for each application.

[0115] Optionally, if business personnel report that the updated template causes deployment failure or service anomalies, a quick rollback can be performed based on the historical version of the template, retrieving the old version template from the preset template library and replacing the current version with one click.

[0116] The sidecar container deployment method provided in this application embodiment has a preset monitoring tool that can capture configuration change events in real time. Once a configuration change is detected, the corresponding template in the preset template library is immediately updated based on the change event. This ensures that the template always remains consistent with the actual configuration state, avoiding configuration errors caused by outdated or inaccurate templates, and providing a reliable foundation for subsequent sidecar container deployment or other operations based on the template. Configuration change messages are sent to business applications that deploy sidecar containers based on this template, eliminating the need for manual notification and operation, further improving the automation level of operation and maintenance and reducing operation and maintenance costs.

[0117] As an optional implementation, based on the above embodiments, the environment adaptation strategy includes preset resource allocation rules and adjusting configuration data according to environment tags and environment configuration strategies, including:

[0118] Retrieve the environmental load information of the deployment environment corresponding to the environment tag;

[0119] Adjust the resource quota configuration of the sidecar container based on environmental load information and preset resource allocation rules.

[0120] Among them, the preset resource allocation rules refer to the set of dynamic resource allocation rules built into the target template and associated with the environment tag, such as reducing the resource quota by 10% when the environment load rate is >70%.

[0121] Among them, environmental load information refers to the resource usage status data of the deployment environment at the current moment, including node CPU utilization, memory utilization, etc.

[0122] Resource quota configuration refers to the resource usage restrictions and request configuration of the sidecar container, including the minimum amount of resources requested when the container starts and the maximum amount of resources allowed to be used when the container is running.

[0123] Specifically, the target deployment environment is determined based on the environment tag. If the environment tag is "prod", then the system locates the namespace and associated cluster node corresponding to the production environment. Monitoring interfaces are invoked to collect environmental load information of the target node, such as CPU load, memory load, and resource usage distribution. The environmental load is matched one by one with preset resource allocation rules to determine the adjustment rules triggered by the current load scenario. For example, if the environment tag is "dev", and the node's average CPU utilization is 62% and average memory utilization is 71%, then the corresponding preset resource allocation rule is the "dev" environment load association rule.

[0124] Furthermore, based on the matched adjustment rules, the adjusted resource quota value is calculated. For example, if the original limits.cpu of the sidecar container in the dev environment was 0.3 cores, and a rule to reduce it by 15% is triggered, then the adjusted limits.cpu = 0.3 × (1 - 15%) = 0.255 cores. The calculated resource quota value replaces the resource quota field in the original configuration data to form the complete configuration data after environment load adaptation.

[0125] The sidecar container deployment method provided in this application addresses the different load characteristics of different deployment environments. By acquiring environmental load information and combining it with preset resource allocation rules, it can accurately allocate resource quotas to sidecar containers that match their actual needs, avoiding resource idleness and waste, and achieving precise resource utilization. Real-time adjustment of the resource quota configuration of sidecar containers based on environmental load information ensures that resource allocation always remains consistent with the actual load, improving overall resource utilization efficiency.

[0126] As an optional implementation, based on the above embodiments, the configuration data includes communication optimization strategies between the sidecar container and the service container, and the method further includes:

[0127] Obtain network transmission characteristic data of the target business application;

[0128] The communication configuration parameters of the sidecar container are generated based on the network transmission characteristic data;

[0129] In response to the completion of the deployment of the sidecar container, the communication characteristic data between the sidecar container and the business container is periodically obtained, and the communication configuration parameters of the sidecar container are updated based on the communication optimization strategy.

[0130] Among them, communication optimization strategies refer to a set of rules embedded in configuration data to improve the efficiency and stability of network communication between the side vehicle container and the business container, such as data compression strategies and traffic priority scheduling rules.

[0131] Among them, network transmission characteristic data refers to the core characteristic data of the target business application when the business container interacts with the outside network during the operation of the application. It is used to reflect the network communication requirements of the business, such as transmission protocol type, data packet size distribution, etc.

[0132] Among them, communication configuration parameters refer to specific parameters generated based on communication optimization strategies and network transmission characteristic data, used to configure the network communication behavior of the side vehicle container, such as connection pool size and data compression threshold.

[0133] Among them, communication characteristic data refers to the real-time operational data generated during the actual communication process between the side vehicle container and the business container after their deployment, such as actual communication latency and data packet loss rate.

[0134] For example, the network monitoring component of the cluster where the target business application is located is invoked to collect network interaction data of the business container over the past 30 minutes, extract network transmission characteristics, such as the usage ratio of protocols such as TCP / UDP / HTTP, and analyze data packet size distribution and average transmission rate.

[0135] Furthermore, communication optimization strategies are loaded from the configuration data. Based on the strategy type, applicable characteristics, configuration parameter templates, and other logical parameters, network transmission characteristic data is matched to generate targeted communication configuration parameters. For example, if network transmission characteristic data shows TCP protocol usage at 95%, small packet usage at 80%, and a peak concurrent connection count of 500, then a TCP connection optimization strategy is matched, and parameters are generated. The generated communication configuration parameters are encapsulated as environment variables or configuration file fragments according to specifications and integrated into the complete configuration data of the sidecar container.

[0136] Furthermore, after the sidecar container is deployed and establishes communication with the business container, a periodic data collection task is initiated, such as every 5 minutes. Using the network probe built into the sidecar container, core communication metrics between the two are collected in real time, generating communication characteristic data. This periodically collected communication characteristic data is then compared with the expected goals of the communication optimization strategy to determine if configuration parameters need adjustment.

[0137] For example, if the communication characteristic data meets the expected targets, such as an average latency of 30ms < 50ms and a packet loss rate of 0.1% < 0.5%, the current communication configuration parameters remain unchanged, and the verification result is recorded. If the communication characteristic data does not meet expectations or there is room for optimization, the parameters are dynamically updated according to the adjustment rules of the communication optimization strategy. If the communication status report shows that the P99 latency is 95ms, close to the expected threshold of 100ms, and the connection reuse rate is only 60%, then the latency optimization adjustment rules are triggered. The original connection pool size is adjusted from 600 to 800, etc.

[0138] Optionally, if the communication characteristic data still does not meet expectations after three consecutive collection cycles, an alarm will be pushed to the operation and maintenance personnel, indicating that the optimization strategy may not be suitable for the current business scenario and manual intervention is required to adjust the strategy rules.

[0139] The sidecar container deployment method provided in this application leverages the unique network transmission characteristics of different target business applications. By acquiring this characteristic data, a deeper understanding of the specific communication needs of the business applications can be gained, thereby generating precisely matched sidecar container communication configuration parameters. This ensures fast and stable data transmission, reduces communication latency and packet loss rate, and improves overall communication performance. Periodically acquiring communication characteristic data between the sidecar container and the business container enables real-time monitoring of communication status, allowing for timely detection of potential communication bottlenecks and faults. Based on communication optimization strategies, the communication configuration parameters of the sidecar container can be adjusted promptly, such as adjusting bandwidth allocation and switching communication paths, to avoid communication failures and ensure stable system operation.

[0140] As an optional implementation, based on the above embodiments, it further includes:

[0141] If the target business application needs to be migrated, obtain the configuration adaptation characteristics of the sidecar container in the source environment and the infrastructure characteristics of the target environment.

[0142] Based on the infrastructure characteristics of the target environment, adjust the environment dependencies in the configuration adaptation features and generate a migration configuration compatible with the target environment.

[0143] Environment migration refers to the process of migrating a target business application from its current source environment, such as a development environment, to another target environment, such as a verification environment.

[0144] Among them, configuration adaptation features refer to the core adaptation attribute set of the side vehicle container configuration data in the source environment, including the service address of the environment dependency, resource quota adaptation rules, etc.

[0145] Among them, infrastructure characteristics refer to the core attributes of the target environment, such as the underlying technical architecture, resource supply, and policy restrictions, including node resource specifications and network architecture.

[0146] Among them, environment dependencies refer to configuration items in the configuration adaptation features that are strongly bound to the source environment. These are parameters that are only valid in the source environment and need to be adjusted after migration to the target environment, such as the log collection server address of the source environment and the access whitelist specific to the development environment.

[0147] Migration configuration refers to the generation of sidecar container configuration data that is fully compatible with the target environment after adjusting the configuration adaptation characteristics of the source environment's sidecar containers based on the infrastructure characteristics of the target environment.

[0148] Specifically, when business personnel initiate a migration request for a target business application environment on the deployment platform, the sidecar container configuration migration and adaptation process is triggered. Simultaneously, the sidecar container configuration data corresponding to the business application in the source environment is locked to prevent modification of the source configuration during the migration process. Then, the complete sidecar container configuration data for the target business application in the source environment is extracted from the configuration database, and configuration adaptation features are filtered and extracted. Infrastructure features such as the target environment cluster version, target node resource specifications, and resource load baseline are obtained through a preset interface. By comparing the configuration adaptation features with the infrastructure features, the environment dependencies that need adjustment are identified.

[0149] For example, the log collection server address differs between the source environment (dev-specific address) and the target environment (prod) log center address; therefore, it should be replaced with the prod-specific address recorded in the target environment's registry center. Regarding resource quota dependencies, the source environment (test) has a CPU quota of 0.5 cores, while the target environment (prod) node has a higher load, and the preset resource allocation rules require the prod environment's sidecar container quota to be no less than 0.8 cores; therefore, the resource quota should be increased based on the target environment's resource load baseline and the preset rules.

[0150] Furthermore, all adjusted configuration items are integrated to generate a complete sidecar container migration configuration, while supplementing target environment-specific configuration items. The migration configuration is then synchronized to the target environment's deployment configuration, linking with the business application's migration and deployment process to complete the deployment of the sidecar container in the target environment.

[0151] The sidecar container deployment method provided in this application embodiment is crucial to ensuring that the sidecar container can quickly resume normal operation in the new environment during the business application migration process. By obtaining the configuration adaptation characteristics of the sidecar container in the source environment, understanding the various configuration information required for its normal operation, and then adjusting it in combination with the infrastructure characteristics of the target environment, a migration configuration compatible with the target environment can be generated. This enables the sidecar container to quickly adapt to the new environment after migration, achieve seamless business connection, minimize business interruption time, and ensure business continuity.

[0152] Figure 3 A flowchart illustrating a method for deploying a sidecar container according to another embodiment of this application is shown below. Figure 3 As shown, the deployment method of the sidecar container provided in this embodiment includes the following steps:

[0153] S301. Obtain the service attributes of the target business application. The service attributes include service type and environment tag.

[0154] S302. Obtain the environmental load information of the deployment environment corresponding to the environment tag.

[0155] S303. Determine the target template corresponding to the target business application based on the service type and the preset template association. The target template includes the configuration strategy, deployment order and environment adaptation strategy of the side vehicle container.

[0156] S304. Generate configuration data for the sidecar container based on the target template.

[0157] S305. Using a preset clustering analysis algorithm, personalized configuration recommendation information is generated based on the service attributes of the target business application and the historical configuration data of the sidecar container of the deployed business application.

[0158] S306. Adjust configuration data according to environment labels and environment configuration policies.

[0159] S307. Adjust the resource quota configuration of the sidecar container based on environmental load information and preset resource allocation rules.

[0160] S308. Detect the adjusted configuration data according to the preset conflict detection strategy.

[0161] S309. If a configuration conflict is detected, pause the deployment of the sidecar container and output the conflict type and specific location.

[0162] S310. If no configuration conflict is detected, the deployment of the sidecar container is completed based on the adjusted configuration data.

[0163] It should be noted that the execution order of S309-S310 is not important.

[0164] In this embodiment, the implementation method and technical effect of S301-S310 are similar to those of the corresponding solutions in the above embodiments, and will not be repeated here.

[0165] Figure 4 A schematic diagram of the deployment device for the sidecar container provided in this application is shown below. Figure 4 As shown, the sidecar container deployment device 40 provided in this embodiment includes: an acquisition module 41, a determination module 42, a generation module 43, an adjustment module 44, and a deployment module 45.

[0166] The system comprises the following modules: Acquisition module 41, which acquires the service attributes of the target business application, including service type and environment tag; Determination module 42, which determines the target template corresponding to the target business application based on the service type and the association with the preset template, including the configuration strategy, deployment order, and environment adaptation strategy of the sidecar container; Generation module 43, which generates configuration data for the sidecar container based on the target template, including basic configuration fields and personalized configuration fields; Adjustment module 44, which adjusts the configuration data according to the environment tag and environment configuration strategy; and Deployment module 45, which completes the deployment of the sidecar container based on the adjusted configuration data.

[0167] The sidecar container deployment device provided in this embodiment can perform... Figure 2 The implementation principles and technical effects of the methods shown are similar, and will not be repeated here.

[0168] Optionally, when generating configuration data for the sidecar container based on the target template, the generation module 43 is specifically used to: generate personalized configuration recommendation information based on the service attributes of the target business application and the historical configuration data of the sidecar container of the deployed business application using a preset clustering analysis algorithm.

[0169] Optionally, the deployment device for the sidecar container provided in this embodiment further includes a detection module and an output module.

[0170] Accordingly, the detection module is used to detect the adjusted configuration data according to the preset conflict detection strategy; the output module is used to pause the deployment of the side vehicle container and output the conflict type and specific location if a configuration conflict is detected.

[0171] Optionally, the deployment device for the sidecar container provided in this embodiment further includes an update module and a sending module.

[0172] Accordingly, the acquisition module 41 is also used to acquire configuration change events using a preset monitoring tool; the update module is used to update the corresponding template in the preset template library based on the acquired configuration change event in response to the acquisition of the configuration change event; and the sending module is used to send configuration change messages to business applications that deploy sidecar containers based on the template.

[0173] Optionally, the environment adaptation strategy includes preset resource allocation rules. The adjustment module 44, when adjusting configuration data according to environment tags and environment configuration strategies, is specifically used to: obtain the environment load information of the deployment environment corresponding to the environment tag; and adjust the resource quota configuration of the side vehicle container based on the environment load information and preset resource allocation rules.

[0174] Optionally, the configuration data includes communication optimization strategies between the sidecar container and the service container. The acquisition module 41 is also used to acquire network transmission characteristic data of the target service application. The generation module 43 is also used to generate communication configuration parameters of the sidecar container based on the network transmission characteristic data. The acquisition module 41 is also used to periodically acquire communication characteristic data between the sidecar container and the service container after the deployment of the sidecar container is completed. The update module is also used to update the communication configuration parameters of the sidecar container based on the communication optimization strategy.

[0175] Optionally, the acquisition module 41 is further configured to acquire the configuration adaptation features of the sidecar container in the source environment and the infrastructure features of the target environment if the target business application needs to be migrated; the adjustment module 44 is further configured to adjust the environment dependencies in the configuration adaptation features based on the infrastructure features of the target environment; and the generation module 43 is further configured to generate a migration configuration compatible with the target environment.

[0176] Figure 5 This is a structural diagram of the deployment equipment for the sidecar container provided in this application. Figure 5 As shown, the sidecar container deployment device 50 provided in this embodiment includes a processor 51 and a memory 52. ​​The processor 51 and the memory 52 are connected via a bus and communicate with each other.

[0177] In the specific implementation process, the processor 51 executes the computer execution instructions stored in the memory 52, causing the processor 51 to perform the above-described method.

[0178] The specific implementation process of processor 51 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0179] In the above embodiments, it should be understood that the processor 51 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0180] The memory 52 may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0181] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0182] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0183] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0184] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0185] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0186] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0189] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0190] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0191] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of sidecar container deployment, characterized by, The method comprises: obtaining service attributes of a target service application, the service attributes comprising a service type and an environment label; determining a target template corresponding to the target service application according to the service type and a preset template association relationship, the target template comprising configuration strategies of a sidecar container, a deployment sequence and an environment adaptation strategy; generating configuration data of the sidecar container based on the target template, the configuration data comprising basic configuration fields and personalized configuration fields; adjusting the configuration data according to the environment label and an environment configuration strategy, and completing deployment of the sidecar container based on the adjusted configuration data.

2. The method of claim 1, wherein, The generating of the configuration data of the sidecar container based on the target template comprises: generating personalized configuration recommendation information according to the service attributes of the target service application and sidecar container configuration historical data of deployed service applications by using a preset clustering analysis algorithm.

3. The method of claim 1, wherein, Before the completing of the deployment of the sidecar container based on the adjusted configuration data, the method further comprises: detecting the adjusted configuration data according to a preset conflict detection strategy; if a configuration conflict is detected, suspending the deployment of the sidecar container and outputting a conflict type and a specific position.

4. The method of claim 1, wherein, The method further comprises: obtaining a configuration change event by using a preset monitoring tool; in response to obtaining the configuration change event, updating a corresponding template in a preset template library based on the configuration change event, and sending a configuration change message to a service application deploying the sidecar container based on the template.

5. The method of claim 1, wherein, The environment adaptation strategy comprises a preset resource allocation rule, and the adjusting of the configuration data according to the environment label and the environment configuration strategy comprises: obtaining environment load information of a deployment environment corresponding to the environment label; adjusting resource quota configuration of the sidecar container based on the environment load information and the preset resource allocation rule.

6. The method of claim 1, wherein, The configuration data comprises a communication optimization strategy of the sidecar container and a service container, and the method further comprises: obtaining network transmission feature data of the target service application; generating communication configuration parameters of the sidecar container according to the network transmission feature data; in response to completing the deployment of the sidecar container, periodically obtaining communication feature data of the sidecar container and the service container, and updating the communication configuration parameters of the sidecar container based on the communication optimization strategy.

7. The method of claim 1, wherein, The method further comprises: if the target service application is to be migrated, obtaining configuration adaptation features of the sidecar container in a source environment and infrastructure features of a target environment; adjusting environment dependent items in the configuration adaptation features based on the infrastructure features of the target environment, and generating a migration configuration compatible with the target environment.

8. An apparatus for sidecar container deployment, the apparatus comprising: The method comprises: an obtaining module, configured to obtain service attributes of a target service application, the service attributes comprising a service type and an environment label; a determining module, configured to determine a target template corresponding to the target service application according to the service type and a preset template association relationship, the target template comprising configuration strategies of a sidecar container, a deployment sequence and an environment adaptation strategy; a generating module, configured to generate configuration data of the sidecar container based on the target template, the configuration data comprising basic configuration fields and personalized configuration fields; an adjusting module, configured to adjust the configuration data according to the environment label and an environment configuration strategy; a deployment module, configured to complete deployment of the sidecar container based on the adjusted configuration data.

9. A sidecar container deployment device, characterized by, The method comprises: a memory, a processor; the memory stores computer-executed instructions; the processor executes the computer-executed instructions stored in the memory, so that the processor executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executed instructions, and the computer-executed instructions are executed by the processor to implement the method according to any one of claims 1-7.

11. A computer program product, characterised in that, The computer program is executed by the processor to implement the method according to any one of claims 1-7.

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