Low-code application platform supporting containerized deployment

Abstract

The invention discloses a low-code application platform supporting containerized deployment, and relates to the technical field of low-code application, and the platform comprises a historical renting log calling module which is used for calling a renting deployment record; the periodic processing module is used for performing periodic processing on the historical renting logs; the container deployment descending processing module is used for acquiring any container and performing descending processing on the container deployment list; the deployment frequency judgment module is used for judging whether the deployment frequency of the container is in a preset frequency threshold value or not; the container adding module is used for adding the container of which the deployment frequency is in a preset frequency threshold value to the target buffer; and the containerization deployment module is used for carrying out containerization deployment. The technical problems that in the prior art, containerized deployment depends on manual configuration and manual management, the deployment efficiency is low, and the request processing time is long are solved, and the technical effects that the request processing speed and the application deployment efficiency are improved through intelligent container resource scheduling and automatic deployment are achieved.

Description

Technical Field

[0001] The present invention relates to the field of low-code application technology, and specifically to a low-code application platform that supports containerized deployment. Background Art

[0002] With the rapid development of cloud computing and containerization technologies, application deployment faces increasingly complex challenges. Traditional containerized deployment often relies on manual configuration and manual management, which not only increases the complexity of resource management, but also leads to inefficient resource allocation and excessive manual intervention. Although low-code platforms can simplify the application development process, there are still bottlenecks in containerized deployment and resource scheduling that cannot achieve rapid response and efficient scheduling, resulting in slow system response, waste of resources, and low deployment efficiency. Summary of the invention

[0003] This application provides a low-code application platform that supports containerized deployment, which is used to solve the technical problems in the prior art that containerized deployment relies on manual configuration and manual management, has low deployment efficiency and long request processing time.

[0004] The present application provides a low-code application platform that supports containerized deployment, and the platform includes: a historical rental log calling module, which is used to call historical rental logs, and the historical rental logs include multiple rental deployment records with tenant identifiers; a periodic processing module, which is used to perform periodic processing on the historical rental logs to obtain a target rental log corresponding to a target segment in a target period, wherein the target rental log includes multiple container deployment lists with tenant identifiers; a container deployment descending processing module, which is used to obtain any container, and based on any deployment frequency of the any container, perform descending processing on the multiple container deployment lists with tenant identifiers to obtain a target container deployment descending list; a deployment frequency determination module, which is used to determine whether a first deployment frequency of a first container is within a predetermined frequency threshold, and the first container refers to any container in the target container deployment descending list; a container adding module, which is used to add the first container to the target cache corresponding to the target segment if the first deployment frequency is within the predetermined frequency threshold; and a containerized deployment module, which is used to activate the target cache for containerized deployment when the periodic process of the low-code application platform reaches the target segment.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: The low-code application platform supporting containerized deployment provided by the present application relates to the field of low-code application technology. By periodically processing historical rental logs, containers that meet the frequency threshold are screened out and added to the target cache. As the periodic process of the low-code platform progresses, the target cache is activated for containerized deployment, thereby optimizing the management of container resources and improving deployment efficiency. This solves the technical problems in the prior art that containerized deployment relies on manual configuration and manual management, has low deployment efficiency and long request processing time, and achieves the technical effect of improving request processing speed and application deployment efficiency through intelligent container resource scheduling and automated deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0007] Figure 1 A schematic diagram of the structure of a low-code application platform supporting containerized deployment provided in an embodiment of the present application; Figure 2 A schematic diagram of the parallel application service structure in a multi-tenant environment of a low-code application platform that supports containerized deployment provided in an embodiment of the present application.

[0008] Explanation of the accompanying drawings: historical rental log call module 11, periodic processing module 12, container deployment descending processing module 13, deployment frequency determination module 14, container adding module 15, containerized deployment module 16, low-code application instance component module 71, container configuration module 72, parallel application service module 73. DETAILED DESCRIPTION

[0009] This application provides a low-code application platform that supports containerized deployment, which is used to solve the technical problems in the prior art that containerized deployment relies on manual configuration and manual management, has low deployment efficiency and long request processing time.

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0012] Embodiment 1, as Figure 1 As shown, this application provides a low-code application platform that supports containerized deployment, which includes: The historical lease log calling module 11 is used to call the historical lease log, and the historical lease log includes a plurality of lease deployment records with tenant identifiers.

[0013] Specifically, the historical rental log calling module 11 of the present application is mainly used to retrieve the rental logs related to container deployment from historical data to provide necessary data support for subsequent containerized deployment. Specifically, this module is responsible for obtaining rental deployment records containing multiple tenant identifiers, which reflect the container deployment history of different tenants on the platform.

[0014] During execution, the module first retrieves historical rental logs by accessing the database or cache system. Each historical rental log record contains a tenant ID, which is a unique identifier that distinguishes different tenants. In a multi-tenant platform, the tenant ID ensures the data independence and isolation of each tenant. These log information can be obtained from the storage system through the query interface. In the database, SQL query statements are used to filter log records for specific tenants.

[0015] In order to improve data access efficiency, the platform can use a cache mechanism. When the system requests a log, it first checks whether there is relevant data in the cache. If the cache hits, the data is read directly from the cache; if the cache misses, it falls back to the database for query. This mechanism can effectively reduce frequent access to the database and improve the system's response speed. Especially in high concurrency situations, the cache can significantly improve the efficiency of data reading.

[0016] After retrieving the historical rental logs, the log data is classified and processed according to the tenant ID. Each log record carries a tenant ID, which is used to isolate the log data of each tenant to ensure that there is no data confusion in subsequent processing. In a multi-tenant platform, this data isolation mechanism ensures the data independence and privacy protection of each tenant.

[0017] After processing, historical rental logs will be organized into a data set, which contains container deployment records for multiple tenant identifiers. Each record includes detailed information such as the container type, version, configuration, and deployment time and status. These historical records will provide data support for subsequent periodic processing modules, enabling the system to optimize containers and sort deployment frequencies based on these data, thereby improving the efficiency and flexibility of containerized deployment.

[0018] The periodic processing module 12 is used to perform periodic processing on the historical lease log to obtain a target lease log corresponding to a target segment in a target period, wherein the target lease log includes a plurality of container deployment lists with tenant identifiers.

[0019] It should be understood that the periodic processing module 12 of the present application is mainly used to perform periodic processing on the historical rental logs, so as to extract the target rental logs within a specific time period and provide targeted support for subsequent container deployment. Specifically, the task of this module is to perform time segment processing on the historical rental logs, determine the target segment within each target period, and obtain a container deployment list containing multiple tenant identifiers.

[0020] During the execution process, the periodic processing module will first divide the historical rental logs by time. Each log record contains a timestamp, which indicates the specific time of the deployment. By analyzing these timestamps, the module can divide the historical logs into different time periods, such as by day, week, month, etc. Each time period is a target period, and the system will determine the time range that needs to be processed according to the preset period division rules.

[0021] Next, the module will filter out the log records that meet the target period requirements from all historical rental logs. For example, if the system needs to process data from the "past week", all container deployment records that occurred in the past week will be extracted. These records will be classified according to the tenant ID to ensure that the log data of each tenant is not mixed up, so as to obtain a list of container deployments for each tenant in the target period.

[0022] The target lease log corresponding to the target segment of each target cycle will include multiple tenant identifiers and a list of container deployments related to these tenants. Through periodic processing, the platform can count and analyze information such as the frequency and type of container deployment for a specific time period, providing strong support for subsequent container optimization and deployment decisions. In this way, the system can flexibly adjust the container deployment strategy according to different target cycles to cope with the changing needs of different tenants in different time periods, and improve the intelligence and flexibility of containerized deployment.

[0023] The container deployment descending order processing module 13 is used to obtain any container, and perform descending order processing on the plurality of container deployment lists with tenant identifiers based on any deployment frequency of the any container, to obtain a target container deployment descending order list.

[0024] Optionally, the container deployment descending processing module 13 of the present application is mainly used to perform descending processing on multiple container deployment lists with tenant identifiers according to the deployment frequency of the container, thereby generating a target container deployment descending list. Specifically, the task of this module is to select any container and sort the container list based on its deployment frequency, so that containers with higher deployment frequencies are ranked in front, so that containers with frequent deployments can be given priority for optimization.

[0025] During the execution process, the container deployment descending processing module first needs to extract multiple container deployment lists from the target rental log provided by the periodization processing module 12. Each container deployment list corresponds to a container deployment record of a specific tenant, including relevant information of the container, such as container type, version, configuration, etc., as well as the frequency of deployment. This information provides a basis for sorting.

[0026] Next, select a container and analyze the deployment frequency of the container in the historical log. The deployment frequency refers to the number of times a container is deployed in a specific time period, reflecting the frequency of use of the container. By counting the deployment frequency of each container, the module can determine which containers are frequently deployed and which containers are less frequently deployed.

[0027] After determining the deployment frequency of each container, sort the container deployment list in descending order. The sorting is based on the deployment frequency of the container, that is, the container with a higher frequency is ranked higher. For example, if container A is deployed 10 times and container B is deployed 5 times, then in the sorted list, container A will be ranked before container B. This descending sorting ensures that the system prioritizes and optimizes those containers that are deployed frequently, which is critical to improving the efficiency of container deployment and system performance.

[0028] By processing the container deployment list in descending order, the module can provide valuable data support for subsequent container addition, deployment, and optimization. The system can make reasonable decisions based on this target container deployment descending list, such as prioritizing resource allocation or containerized deployment for containers with higher deployment frequency, thereby improving the overall deployment response speed and system agility.

[0029] In summary, the key role of the container deployment descending processing module 13 is to count and sort the deployment frequency of containers to ensure that frequently used containers are given priority. By descending the containers in the target rental log, more efficient and intelligent container management and deployment can be achieved.

[0030] The deployment frequency determination module 14 is used to determine whether a first deployment frequency of a first container is within a predetermined frequency threshold, wherein the first container refers to any container in the target container deployment descending list.

[0031] Specifically, the deployment frequency determination module 14 of the present application is mainly used to determine whether the deployment frequency of a container meets the predetermined frequency threshold. Specifically, the task of this module is to deploy any container in the descending list of target containers and check whether its deployment frequency reaches the preset frequency threshold to determine whether to continue to perform subsequent container deployment operations.

[0032] During the execution process, the module first receives the target container deployment descending list generated by the container deployment descending processing module 13. The list contains multiple containers and is arranged in descending order according to the deployment frequency, with the containers with higher frequency at the front. The module checks the containers in the list one by one and selects the first container for frequency determination.

[0033] The first container refers to the container that ranks first in the descending list of container deployments, that is, the container with the highest deployment frequency. The module analyzes the first deployment frequency of the container, that is, the total number of deployments of the container in the target period. This frequency reflects the frequency of use of the container in the past period of time, helping the system determine whether the container is worth prioritizing or deploying.

[0034] After obtaining the first deployment frequency of the container, it is compared with the predetermined frequency threshold. The predetermined frequency threshold is a standard value set by the system, which means that when the container deployment frequency reaches this threshold, the container will be considered a "high-frequency" container and worthy of further optimization and processing. If the first deployment frequency of the container is greater than or equal to the predetermined frequency threshold, the module will determine that the container is a qualified container, which means that the container has a high enough deployment frequency and should enter the subsequent containerized deployment process.

[0035] If the first deployment frequency of the first container does not reach the predetermined threshold, the module will not continue to execute the containerized deployment, but will skip the container and may mark it as a low-frequency container for further optimization and adjustment. Through this frequency determination mechanism, the system can effectively prioritize containers with high frequency deployment, while containers with lower frequency can be delayed or adjusted according to demand. This method not only improves the intelligence of containerized deployment, but also helps to reasonably allocate resources and avoid wasting too many resources on infrequently used containers.

[0036] The container adding module 15 is configured to add the first container to a target buffer corresponding to the target segment if the first deployment frequency is within a predetermined frequency threshold.

[0037] Exemplarily, the container adding module 15 of the present application is mainly used to add qualified containers to the target cache, especially when the deployment frequency of the container meets the predetermined threshold, the container is added to the target cache to prepare for the subsequent containerized deployment. Specifically, the task of the module is to check whether the deployment frequency of each container reaches the predetermined frequency threshold. If it meets the conditions, the container is added to the target cache of the target segment to ensure that the container can be quickly deployed within the target period.

[0038] During the execution process, the container adding module first receives the information provided by the deployment frequency determination module 14, especially the deployment frequency data of the first container. The module checks the first deployment frequency of the container to confirm whether it reaches the predetermined frequency threshold. If the deployment frequency of the container meets or exceeds the threshold, the module determines that the container is a qualified container and prepares to add it to the target cache.

[0039] The target cache is a cache area that stores temporary container deployment information. Its function is to provide fast access for subsequent containerized deployment. During the containerized deployment process, the cache can reduce the time to load container data from the database or other storage system, thereby improving deployment efficiency. Therefore, when the deployment frequency of the first container meets the predetermined conditions, it will be added to the corresponding target cache. The process of adding a container to the target cache can be achieved through a simple cache operation.

[0040] In this way, the container adding module can ensure that frequently deployed containers can enter the cache in advance, reduce delays in subsequent deployments, and speed up response. In this way, the system can dynamically adjust the cache content according to the frequency of use of different containers to ensure that high-frequency containers are processed first.

[0041] The containerized deployment module 16 is used to activate the target cache for containerized deployment when the periodic process of the low-code application platform reaches the target segment.

[0042] Specifically, the containerized deployment module 16 of the present application is mainly used to activate the target cache and perform containerized deployment when the periodic process of the low-code application platform reaches a specific target segment. Specifically, when the periodic process of the system enters the preset target segment, the module triggers the containerized deployment process based on the container data previously stored in the target cache, thereby achieving fast and effective container deployment.

[0043] During execution, the containerized deployment module will first track the periodic progress of the low-code application platform. Low-code platforms usually perform scheduled checks or operations through periodic tasks, and these cycles can be based on time dimensions such as days, weeks, and months. When the periodic process enters a target segment, the module automatically triggers the containerized deployment operation. The target segment refers to a specific time range or data segment set by the platform based on business needs and system configuration, which is usually closely related to the deployment requirements of the container.

[0044] When the system's periodic process reaches the target segment, the containerized deployment module reads the pre-stored container data from the target cache. Since these container data have been screened and cached by the container adding module 15, only containers that meet the frequency threshold will be stored in the cache, so these containers can be considered as priority deployment targets. The module extracts the specific configuration information of the container from the cache, including container type, version, configuration parameters, etc., and prepares to deploy these containers in a containerized manner.

[0045] Specifically, the process of containerized deployment may include: container startup, that is, starting the container instance and allocating necessary resources to it according to the container data in the cache. Environment configuration, that is, automatically configuring the operating environment according to the configuration information of the container, such as setting network parameters, storage volumes, environment variables, etc. Container pull and deployment, that is, if the container needs to be pulled from a remote image repository, the image pull operation is automatically performed, and then the container is started and deployed to the specified environment. This process can be performed by a container management platform (such as Kubernetes). The specific operation can be called through the API or command line tool to call the service of the container management platform to start the required container.

[0046] In this way, the containerized deployment module 16 can ensure that when the periodic process reaches the target segment, the system can automatically execute the container deployment work, thereby improving deployment efficiency and system response speed, reducing manual intervention, and ensuring the consistency and repeatability of the containerized deployment process.

[0047] Further, such as Figure 2 As shown, the embodiment of the present application also includes: The low-code application instance component module 71 is used to form multiple low-code application instances; the container configuration module 72 is used to read the round-robin allocation mechanism, and configure containers for the multiple low-code application instances in combination with the target container deployment descending list to obtain multiple instance configuration results; the parallel application service module 73 is used to provide parallel application services for multiple tenants through the multiple instance configuration results.

[0048] In a possible embodiment of the present application, the embodiment of the present application further includes several key modules, which are intended to improve the flexibility and efficiency of the low-code platform through horizontal expansion, especially in containerized deployment and multi-tenant services. These modules include a low-code application instance component module 71, a container configuration module 72, and a parallel application service module 73, which are responsible for the creation of low-code application instances, the allocation and configuration of containers, and the provision of parallel application services for multiple tenants.

[0049] Among them, the main function of the low-code application instance component module 71 is to form multiple low-code application instances. In the low-code platform, an application instance usually refers to a specific application or service instance, which is built based on the low-code tools provided by the platform. The function of this module is to create multiple independent application instances according to demand. Each instance can have its own data and resource configuration, and support application separation and resource independence in multi-tenant scenarios. Through this module, the platform can quickly generate customized application instances according to the different needs of tenants, thereby providing personalized services for each tenant.

[0050] The container allocation module 72 is responsible for allocating container resources to each low-code application instance according to a round-robin allocation mechanism. Specifically, assuming that the platform creates instance A, instance B, and instance C, these three instances will allocate containers in turn. Container 1 is allocated to instance A, container 2 is allocated to instance B, container 3 is allocated to instance C, then container 4 is allocated to instance A, container 5 is allocated to instance B, container 6 is allocated to instance C, container 7 is allocated to instance A, and so on. This round-robin allocation mechanism ensures that each application instance can obtain the appropriate number of container resources, and the resource allocation between different instances is relatively balanced, avoiding performance bottlenecks caused by uneven resource allocation in some instances.

[0051] In addition, the container allocation module also optimizes container allocation in combination with the target container deployment descending list. By analyzing the deployment frequency of containers, the platform can prioritize resource allocation for high-frequency deployment application instances based on which containers are frequently deployed, ensuring that these high-demand instances can quickly obtain the required container resources, thereby improving deployment efficiency and system response speed.

[0052] Finally, the role of the parallel application service module 73 is to provide parallel application services for multiple tenants based on the container configuration results. By processing multiple application instances in parallel, the system can meet the needs of multiple tenants at the same time and achieve efficient resource sharing and load balancing. This module reasonably schedules and manages the resources of each low-code application instance to ensure that each tenant can run its application instance in parallel without interfering with each other, thereby improving the processing capacity and service quality of the overall system.

[0053] In summary, these modules improve the flexibility and scalability of the low-code platform through horizontal expansion. The low-code application instance component module is responsible for quickly creating application instances, the container configuration module ensures the reasonable allocation of container resources through round-robin allocation and optimization strategies, and the parallel application service module improves the overall performance and response speed of the system through multi-tenant parallel processing. Through the collaborative work of these modules, the platform can efficiently provide services to different tenants and support large-scale, efficient containerized deployment and multi-tenant management.

[0054] Furthermore, the parallel application service module 73 is also used to perform the following steps: P73-1: Extract any tenant from the multi-tenants; P73-2: Match any container deployment list corresponding to the any tenant in the multiple container deployment lists with tenant identifiers; P73-3: Obtain any functional module, and match a second arbitrary container corresponding to the arbitrary functional module in the arbitrary container deployment list; P73-4: Calculate a first matching degree between the second arbitrary container and the first instance, wherein the first instance refers to any one of the multiple instance configuration results; P73-5: When the first matching degree reaches a predetermined matching threshold, assign the arbitrary tenant to the first instance for application service.

[0055] Optionally, in an embodiment of the present application, the parallel application service module 73 further includes some specific steps to optimize the container resource allocation and the provision of application services in a multi-tenant environment, and can more accurately allocate appropriate container instances to each tenant, ensuring efficient use of resources and quality of services.

[0056] First, the module selects a tenant from a multi-tenant environment for processing. Each tenant represents an independent user group whose needs and resource allocations may be different. The goal of this step is to extract a target tenant from multiple tenants in order to provide it with personalized application services.

[0057] After determining the target tenant, the module will then search and match the container deployment list corresponding to the tenant. Each container deployment list records the container resources and configurations related to the tenant. Through the tenant identifier, the module can extract the container corresponding to the tenant from multiple container deployment lists to ensure that the container deployment meets the tenant's needs and resource configuration.

[0058] After obtaining the container deployment list corresponding to the tenant, the module will further analyze and extract the functional modules. Each functional module represents a specific application or service function supported by a container. The module will find the functional modules that match the tenant's requirements and select containers based on the functional requirements to ensure that the container can support the required functions. For example, if a container is responsible for database services, the module will match the database functional module required by the tenant.

[0059] After matching the container with the functional module, the module will further evaluate the matching degree between the container and the low-code application instance. The matching degree is usually calculated based on the resource requirements, performance requirements and adaptability of the instance configuration of the container. For example, if a container consumes a lot of resources and the application instance has limited resources, the module will calculate the matching degree between the container and the instance to evaluate whether the container is suitable for allocation to the instance.

[0060] If the matching degree between the container and the application instance reaches the predetermined threshold, that is, the container can meet the resource and function requirements of the instance, then the module will assign the tenant to the instance and start providing the corresponding application services to the tenant. In this way, the tenant can obtain services in the appropriate application instance without worrying about insufficient container resources or mismatched functions.

[0061] Through these steps, the module can dynamically and intelligently allocate the most appropriate container resources and application instances to each tenant, ensuring service quality and resource utilization in a multi-tenant environment. The core idea of ​​the steps is to achieve efficient and accurate resource allocation through a comprehensive evaluation of tenant requirements, container functions, instance configuration and other factors, thereby improving the flexibility and scalability of the platform.

[0062] Furthermore, the parallel application service module 73 is configured to perform the following steps when calculating the first matching degree between the second arbitrary container and the first instance: P73-41: Determine whether the second arbitrary container is equipped on the first instance; P73-42: If the second arbitrary container is equipped on the first instance, add the arbitrary functional module to the matching set; if the second arbitrary container is not equipped on the first instance, add the arbitrary functional module to the unmatched set; P73-43: Take the ratio of the matching set to the unmatched set, recorded as the first matching degree, wherein the first matching degree is used to characterize the matching degree between the arbitrary tenant and the first instance.

[0063] Specifically, in further implementation, the parallel application service module 73 can calculate the matching degree between the container and the instance by executing a series of refinement steps to ensure that each tenant can be allocated to the most suitable application instance. Through detailed matching analysis, it is evaluated whether each container is suitable for allocation to a certain instance, and the optimal allocation decision is made based on this matching degree.

[0064] First, the module checks whether the container has been allocated and configured to the target application instance. Each container may have a specific set of configurations and functions, and the module needs to determine whether the container has been associated with the target instance. The goal of this step is to understand the resource adaptation of the container and the instance. If the container has been deployed in the instance, the matching calculation can continue; if it has not been deployed, it means that the adaptability of the container needs to be re-evaluated.

[0065] Next, the module will add the functional modules corresponding to the container to different sets according to the deployment of the container. If the container is already equipped in the target instance, the module will add the functional modules of the container to the matching set, indicating that the container and the instance have a high degree of match. On the contrary, if the container is not equipped in the instance, the functional modules will be added to the unmatched set, indicating that the container and the instance have a low degree of match or no match. Through such classification, the module can clearly identify which functional modules have been successfully matched with the container and the instance, and which modules still need further matching or adjustment.

[0066] Finally, the module calculates the first degree of match, which is an indicator used to characterize the degree of match between the container and the instance. The specific calculation method is to measure it based on the ratio of the number of functional modules in the matching set and the number of modules in the unmatched set. For example, if the number of modules in the matching set is much larger than the number in the unmatched set, it means that the tenant's needs are more compatible with the instance, and the first degree of match will be larger, and vice versa. If the first degree of match reaches a predetermined threshold, the module can decide to assign the tenant to the instance for application services.

[0067] Through these steps, the module can more accurately evaluate the compatibility between containers and application instances, and intelligently allocate resources based on this. The matching of each container and functional module is strictly judged and classified to ensure that highly matched containers can be quickly allocated to appropriate instances, thereby improving system performance and tenants' user experience, and optimizing service efficiency in a multi-tenant environment.

[0068] Furthermore, before allocating the arbitrary tenant to the first instance for application service, the parallel application service module 73 is further configured to perform the following steps: P73-51a: Dynamically obtain the first load index of the first instance; P73-52a: Determine whether the first load index meets the predetermined load threshold; P73-53a: If so, assign the arbitrary tenant to the first instance for application service; if not, calculate the second matching degree between the second arbitrary container and the second instance, wherein the second instance refers to any one of the multiple instance configuration results that is different from the first instance; P73-54a: When the second matching degree reaches the predetermined matching threshold, assign the arbitrary tenant to the second instance for application service.

[0069] In the further implementation process, the parallel application service module 73 adds a load balancing step before assigning the tenant to the target application instance to ensure that the load of each instance is within a reasonable range, thereby improving the overall performance and response speed of the platform. By dynamically monitoring the load situation and making corresponding instance adjustments according to the load situation, it is ensured that the tenant can always be assigned to an instance with appropriate performance.

[0070] First, the module dynamically obtains the load index of the target instance (i.e., the first instance). The load index is usually an indicator that measures the current resource usage of an instance. It may include data in multiple dimensions such as CPU usage, memory usage, and network bandwidth. This index is used to indicate the current resource load status of the instance. By obtaining the load index in real time, the platform can understand the resource usage of the instance and determine whether it is already in a high-load state.

[0071] Next, the module will judge the load index of the instance according to the preset load threshold. If the load index is lower than or equal to the preset load threshold, it means that the resource load of the instance is within the normal range and can continue to process more tenant requests; if the load index exceeds the load threshold, it means that the resources of the instance are already tight and it is necessary to consider allocating new tenants to other instances with lower loads.

[0072] If the result of the judgment is that the instance load is within the normal range, the module will assign the tenant to the first instance to continue the application service. At this time, the tenant can run its application smoothly on the instance without affecting the performance of the instance because the instance load has not reached saturation.

[0073] If the load of the first instance exceeds a predetermined threshold, the module will avoid assigning tenants to that instance. Instead, the module will select another instance (i.e., the second instance) for evaluation. In this step, the module will calculate the matching degree of the second instance, similar to the previous matching degree calculation process for the first instance. The matching degree measures whether the second instance is suitable for handling the needs of the tenant, and considers factors such as the availability of container resources and the matching degree of functional modules.

[0074] Finally, when the matching degree of the second instance reaches the predetermined threshold, the module will assign the tenant to the instance for service, ensuring that the tenant can obtain high-quality service under reasonable load and avoiding any instance from affecting service quality due to excessive load.

[0075] Through this load balancing mechanism, the platform can flexibly respond to fluctuations in resource demand in multi-tenant scenarios, ensuring that each tenant can run on a suitable instance and avoiding performance bottlenecks in certain instances due to excessive load. Load balancing can not only improve the resource utilization efficiency of the system, but also ensure the high availability and response speed of the platform under large-scale concurrent access.

[0076] Furthermore, when allocating the arbitrary tenant to the second instance for application service, the parallel application service module 73 is used to perform the following steps: P73-541a: Activate the low-code design plan, and under the second instance, provide application services for any tenant through the low-code design plan; P73-542a: Wherein, the low-code design plan at least includes a visual design plan, an API development plan and a business logic development plan, P73-543a: Wherein, the visual design plan supports drag-and-drop component layout and form design, the API development plan supports online API definition, testing and debugging, and the business logic development plan supports the implementation of complex business logic through scripting language.

[0077] Optionally, in a further implementation process, when the parallel application service module 73 decides to allocate the tenant to the second instance, it can ensure that the tenant can obtain the application service that meets its needs through further detailed steps. These steps specifically involve activating the low-code design plan, providing customized services for the tenant, and ensuring that different business needs can be flexibly responded to.

[0078] First, the module activates the low-code design plan, which is a mechanism to provide tenants with flexible and fast application services. The goal of the low-code design plan is to provide an efficient development environment that allows application services to be quickly built and customized through visualization, drag and drop, etc. The module ensures that in the second instance, tenants can use this design plan to quickly launch and customize their applications to meet their specific needs.

[0079] The low-code design plan includes several important components, namely the visual design plan, API development plan and business logic development plan. These plans can cover multiple development links from the front end to the back end, providing tenants with a complete development solution. Each plan is optimized for a specific function, so that different types of developers (even business personnel) can use these tools to quickly meet their needs.

[0080] Among them, the visual design plan mainly supports users to drag and drop layout components and design forms through visual tools, allowing users to intuitively build application interfaces and interactive logic. It does not require in-depth coding knowledge, greatly lowers the threshold for development, and is especially suitable for non-technical personnel. The API development plan provides an integrated development environment that supports online API definition, testing, and debugging. Tenants can use this plan to easily create interfaces for interacting with external systems to ensure compatibility and data flow between their applications and other systems. The business logic development plan uses scripting languages, and tenants can design and implement complex business logic in this part. This is crucial for application scenarios that require processing advanced data operations, rule engines, and other functions, especially when custom processes and special business rules are required.

[0081] In addition, the visual design plan supports drag-and-drop component layout and form design, allowing users to build user interfaces by dragging components and define input and output data through form design. Through this function, users can create interfaces, edit fields, layouts, and interactions without writing a lot of code, thereby improving development efficiency.

[0082] The API development plan provides a simple interface for online definition of API interfaces. Users can not only define the input and output of the interface, but also test and debug the API in real time to ensure its stability and availability in different scenarios. The creation of API can be seamlessly integrated into the tenant's application, improving the data interaction capabilities between systems.

[0083] Business logic development plans use scripting languages, allowing users to implement some complex business logic, such as conditional judgment, data processing, process control, etc. With this tool, tenants can customize business processes according to their own needs, achieving high flexibility and customization, and further improving the business processing capabilities of the application.

[0084] Through this series of steps, the parallel application service module 73 ensures that tenants can efficiently and flexibly obtain application services through low-code design plans. Each plan is designed as an easy-to-use and easy-to-configure tool that provides tenants with a comprehensive development environment that enables them to quickly start, customize and run their applications.

[0085] Furthermore, when allocating the arbitrary tenant to the second instance for application service, the parallel application service module 73 is further configured to perform the following steps: P73-541b: Activate the integrated monitor, and dynamically monitor and obtain real-time application service information through the integrated monitor; P73-542b: When the real-time application service information does not meet the security application constraints pre-stored in the integrated monitor, issue a security protection instruction; P73-543b: Perform security protection on the second instance based on the security protection instruction. The security protection of the second instance includes expansion processing and reduction processing.

[0086] Specifically, in the further implementation process, the parallel application service module 73 not only focuses on how to efficiently allocate tenants to instances, but also adds integrated monitoring and security protection mechanisms to ensure the security and reliability of the service. The running status of the application service can be monitored in real time, and a quick response can be made when a problem occurs, thereby ensuring the data security and application stability of the platform and tenants.

[0087] First, the module activates the integrated monitor, which is a real-time monitoring tool designed to continuously track the operation of application services on the assigned instance (in this case, the second instance). Through this monitor, the platform can obtain various data of real-time application services, such as performance indicators, resource usage, response time, exception logs, and other information. This monitoring tool is designed to ensure that problems can be discovered and adjusted in a timely manner during the service process to ensure the stability of tenant applications.

[0088] The integrated monitor will compare with the security application constraints to ensure that the application service meets the security standards of the platform. These security constraints may include performance requirements, data security, resource usage restrictions, etc. If the real-time monitoring information does not meet these pre-stored constraints, such as excessive resource usage, abnormal response time or potential security threats, the integrated monitor will automatically issue a security protection instruction. The purpose of this instruction is to remind the system to intervene and take appropriate measures to prevent potential security incidents or service interruptions.

[0089] Then, the module will perform necessary security protection on the second instance according to the security protection instruction. The security protection measures include but are not limited to expansion processing and reduction processing.

[0090] Capacity expansion means that when monitoring finds that the instance load is high and resource usage is close to or exceeds the limit, the system will automatically start capacity expansion measures. Capacity expansion usually includes adding more computing resources, storage resources, or distributing tasks to more instances. Through capacity expansion, the platform can ensure that application services are not affected by resource limitations and ensure that the system remains stable and available under high load.

[0091] Scaling means that when monitoring finds that the resource usage of an instance is lower than expected, or the tenant load is reduced, the system can trigger scaling to release unnecessary resources to save costs and optimize resource allocation. Scaling can help the platform avoid wasting resources while ensuring that performance requirements are met.

[0092] Through this integrated monitoring and dynamic protection mechanism, the parallel application service module 73 can respond to any performance or security issues in real time during the tenant application service process, ensuring the stability, reliability and efficient use of resources of the system. This adaptive mechanism enhances the flexibility of the platform, can quickly adjust resources under different load conditions, and ensure the efficient and secure operation of applications in a multi-tenant environment.

[0093] In summary, the embodiments of the present application have at least the following technical effects: This application retrieves the historical rental log containing the tenant identifier and processes it periodically to obtain the container deployment log of the target period. By obtaining the containers and sorting them in descending order by deployment frequency, the containers whose frequency meets the predetermined threshold are screened out and added to the target cache. When the periodic process of the low-code platform reaches the target segment, the target cache is activated for containerized deployment.

[0094] The technical effect of improving request processing speed and application deployment efficiency has been achieved through intelligent container resource scheduling and automated deployment.

[0095] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0097] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A low-code application platform that supports containerized deployment, characterized by: include: A historical lease log calling module, used to call a historical lease log, wherein the historical lease log includes a plurality of lease deployment records with tenant identifiers; A periodic processing module, used for periodic processing the historical lease log to obtain a target lease log corresponding to a target segment in a target period, wherein the target lease log includes a plurality of container deployment lists with tenant identifiers; A container deployment descending order processing module is used to obtain any container, and perform descending order processing on the plurality of container deployment lists with tenant identifiers based on any deployment frequency of the any container, to obtain a target container deployment descending order list; A deployment frequency determination module, used to determine whether a first deployment frequency of a first container is within a predetermined frequency threshold, wherein the first container refers to any container in the target container deployment descending list; a container adding module, configured to add the first container to a target buffer corresponding to the target segment if the first deployment frequency is within a predetermined frequency threshold; The containerized deployment module is used to activate the target cache for containerized deployment when the periodic process of the low-code application platform reaches the target segment.

2. The low-code application platform supporting containerized deployment according to claim 1, characterized in that: The platform also includes: Low-code application instance component module, used to build multiple low-code application instances; A container configuration module is used to read the round-robin allocation mechanism and configure containers for the multiple low-code application instances in combination with the target container deployment descending list to obtain multiple instance configuration results; The parallel application service module is used to provide parallel application services for multiple tenants through the multiple instance configuration results.

3. The low-code application platform supporting containerized deployment according to claim 2, characterized in that: The parallel application service module is used for: Extract any tenant from the multi-tenants; Matching any container deployment list corresponding to any tenant in the multiple container deployment lists having tenant identifiers; Obtain an arbitrary function module, and match a second arbitrary container corresponding to the arbitrary function module in the arbitrary container deployment list; Calculating a first matching degree between the second arbitrary container and a first instance, wherein the first instance refers to any one instance in the multiple instance configuration results; When the first matching degree reaches a predetermined matching threshold, the arbitrary tenant is allocated to the first instance for application service.

4. The low-code application platform supporting containerized deployment according to claim 3, characterized in that: When calculating the first matching degree between the second arbitrary container and the first instance, the parallel application service module is further used to: determining whether the second arbitrary container is equipped on the first instance; If the second arbitrary container is equipped in the first instance, the arbitrary function module is added to a matching set; if the second arbitrary container is not equipped in the first instance, the arbitrary function module is added to an unmatched set; A ratio of the matching set to the unmatched set is calculated and recorded as the first matching degree, wherein the first matching degree is used to characterize the matching degree between the arbitrary tenant and the first instance.

5. The low-code application platform supporting containerized deployment according to claim 3 is characterized in that: Before allocating the arbitrary tenant to the first instance for application service, the parallel application service module is further used to: Dynamically obtain a first load index of the first instance; determining whether the first load index meets a predetermined load threshold; If satisfied, assigning the arbitrary tenant to the first instance for application service; if not satisfied, calculating a second matching degree between the second arbitrary container and the second instance, wherein the second instance refers to any one of the multiple instance configuration results that is different from the first instance; When the second matching degree reaches the predetermined matching threshold, the arbitrary tenant is allocated to the second instance for application service.

6. The low-code application platform supporting containerized deployment according to claim 5, characterized in that: When allocating the arbitrary tenant to the second instance for application service, the parallel application service module is used to: Activate the low-code design plan, and in the second instance, provide application services for any tenant through the low-code design plan; The low-code design plan includes at least a visual design plan, an API development plan, and a business logic development plan. Among them, the visual design plan supports drag-and-drop component layout and form design, the API development plan supports online API definition, testing and debugging, and the business logic development plan supports the implementation of complex business logic through scripting language.

7. The low-code application platform supporting containerized deployment according to claim 6, characterized in that: When allocating the arbitrary tenant to the second instance for application service, the parallel application service module is further used to: activating an integrated monitor, and dynamically monitoring and obtaining real-time application service information through the integrated monitor; When the real-time application service information does not conform to the security application constraints pre-stored in the integrated monitor, issuing a security protection instruction; The second instance is security protected based on the security protection instruction.

8. The low-code application platform supporting containerized deployment according to claim 7, characterized in that: The parallel application service module performs security protection on the second instance including capacity expansion processing and capacity reduction processing.

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