CN115827008A - Cloud native big data component management system based on cloud native platform Kubernets - Google Patents
Cloud native big data component management system based on cloud native platform Kubernets Download PDFInfo
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Abstract
The invention provides a cloud native big data component management system based on a cloud native platform Kubernetes, which is characterized by comprising the following steps: the mirror image management module is used for pulling a needed large data component mirror image from the public mirror image warehouse and storing the constructed mirror image into the private mirror image warehouse; the container deployment module is used for generating a temporary mirror image and an automatic deployment script according to user configuration and completing containerized deployment of the big data assembly through the temporary mirror image and the automatic deployment script; the cluster monitoring module is used for managing resources in a Kubernetes cluster and monitoring the state of the deployed containerized big data assembly; and the network management module is used for realizing network configuration management of components in the Kubernetes cluster by using Service resources and realizing external exposure of services by using Ingress resources.
Description
Technical Field
The invention belongs to the technical field of big data.
Background
Currently, a mainstream big data warehouse architecture is a big data architecture integrating flow batches, and in a technical scheme for implementing the architecture, the architecture mainly includes a lambda architecture and a kappa architecture, but the lambda architecture and the kappa architecture have various problems in a production environment, such as poor real-time performance, low update efficiency, and incapability of ensuring data consistency, so that a suitable technical scheme is often selected for a specific service scenario when a real-time warehouse architecture is designed. However, in the process of constructing the real-time big data warehouse, due to the complex architecture and the dependence on numerous big data components, system operation and maintenance personnel need to deploy various distributed big data computing components on multiple hosts and perform complex configuration on each component, so that the components can be communicated with each other. The existing operation and maintenance mode lacks uniform component management, and the version and configuration information of a big data component cannot be managed on the same platform; in addition, because a large number of environment dependencies need to be configured for large data components to be deployed, the deployment difficulty is high, and the flexibility is insufficient; moreover, for different big data components, unified user authority management cannot be carried out, and a multi-tenant scene cannot be dealt with; and the cluster crash caused by the failure of a single job can influence the execution of other jobs, and the resource isolation capability is lacked.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to provide a cloud native big data component management system based on a cloud native platform Kubernetes, which is used for realizing the quick online of a big data cluster by configuring component deployment information and monitoring the state of each cluster on the platform.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a cloud native big data component management system based on a cloud native platform kubernets, including:
the mirror image management module is used for pulling a needed large data component mirror image from the public mirror image warehouse and storing the constructed mirror image into the private mirror image warehouse;
the container deployment module is used for generating a temporary mirror image and an automatic deployment script according to user configuration and completing containerized deployment of the big data assembly through the temporary mirror image and the automatic deployment script;
the cluster monitoring module is used for managing resources in a Kubernetes cluster and monitoring the state of the deployed containerized big data assembly;
and the network management module is used for realizing network configuration management of components in the Kubernetes cluster by using Service resources and realizing external exposure of services by using Ingress resources.
In addition, the cloud native big data component management system based on the cloud native platform kubernets according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the image management module is further configured to:
deploying a private mirror image warehouse Harbor in a Kubernets cluster, and creating and maintaining a Pod resource through the cloud native big data assembly management system, wherein the Pod resource comprises a container with a Docker environment, a micro-service for pulling and pushing a mirror image is operated in the container, and an API is exposed to the outside;
and submitting a request to the API through the cloud native big data component management system, executing a script for pulling the mirror image to a local warehouse by the micro-service in the running environment, then executing a script for pushing the mirror image to the private warehouse, responding to the success of pulling after the pushing is finished, and cleaning the local warehouse to save the disk space.
Further, in an embodiment of the present invention, the container deployment module is further configured to:
acquiring user configuration parameters, wherein the user configuration parameters comprise required mirror images, the number of deployed copies, the number of system resources required by Pod, core configuration file information and information of a dependent Jar package;
generating a Docker file according to the mirror image configuration information of the user configuration parameters and constructing a temporary Docker mirror image in a specified environment;
and analyzing deployment environment configuration information, generating a deployment script in a Yaml format by combining with a Yaml template, submitting the deployment script to a Kubernets API, and deploying on the Kubernets cluster by using the temporary Docker mirror image.
Further, in an embodiment of the present invention, the cluster monitoring module is further configured to:
and monitoring the containers completing the deployment task, including checking the container state and the resource occupancy rate.
Further, in an embodiment of the present invention, the network management module is further configured to:
and performing network management by adopting Service resources and Ingress resources in the Kubernetes cluster.
Further, in an embodiment of the present invention, the network management module is further configured to:
defining a network type, a port name, and a port number for a big data component container deployed on the Kubernets cluster;
generating a Yaml configuration file according to the network type, the port name, the port number and the container name, and submitting the Yaml configuration file to a Kubernets API (application program interface) to create Service information;
defining a network dependency relationship between two containers, and acquiring Service information of a depended and Service information of a depended;
and injecting the network of the depended person and the related configuration information into the container of the depended person in a mode of defining environment variables according to the network dependency relationship, the Service information of the depended person and the created ConfigMap resource.
Further, in an embodiment of the present invention, the network management module is further configured to:
for a component that needs to expose an API outside of the cloud native platform kubernets, create Ingress resources from the component's Service, including,
generating a TLS certificate and a private key for encryption;
creating a Secret resource under the Namespace of the component, and configuring the TLS certificate and the private key into the Secret;
and according to user configuration, combining the Service information of the component and calling the Secret to realize encryption and create the Ingress resource.
In order to achieve the above object, a second embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements a cloud native big data component management system based on a cloud native platform kubernets as described above.
To achieve the above object, a third embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a cloud native big data component management system based on a cloud native platform kubernets as described above.
The cloud native big data component management system based on the cloud native platform Kubernetes provided by the embodiment of the invention has the beneficial effects that: 1) The large data assembly is arranged in a container mode, so that the resource utilization rate is improved, resource isolation is facilitated, the starting speed is improved, and the problem of environment dependence is solved; 2) The required big data components can be respectively deployed under different name spaces aiming at different warehouse architectures, the flexibility is strong, and the resource isolation is realized; 3) Depending on the scheduling capability of Kubernetes, the failed pod is automatically restarted, and the disaster tolerance capability is achieved; 4) A cloud native big data component management platform is developed, so that operation and maintenance personnel can realize quick online of a big data cluster on the platform through configuration component deployment information, meanwhile, the state of each cluster can be monitored on the platform, and the deployment operation and maintenance difficulty is reduced.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a system architecture diagram of a cloud native big data component management system based on a cloud native platform Kubernetes according to an embodiment of the present invention.
Fig. 2 is a flowchart of cloud-based deployment of big data components according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The cloud native big data component management system based on the cloud native platform kubernets of the embodiment of the invention is described below with reference to the attached drawings.
Fig. 1 is a schematic flow diagram of a cloud native big data component management system based on a cloud native platform Kubernetes according to an embodiment of the present invention.
As shown in fig. 1, the cloud native big data component management system based on the cloud native platform kubernets includes:
the mirror image management module is used for pulling a needed large data component mirror image from the public mirror image warehouse and storing the constructed mirror image into the private mirror image warehouse;
further, in an embodiment of the present invention, the image management module is further configured to:
deploying a private mirror image warehouse Harbor in a Kubernets cluster, creating and maintaining a Pod resource through a cloud native big data assembly management system, wherein the Pod resource comprises a container with a Docker environment, a micro-service for pulling and pushing a mirror image is operated in the container, and an API is exposed to the outside;
the cloud native big data component management system submits a request to the API, the micro-service executes a script for pulling the mirror image to the local warehouse in the running environment, then executes a script for pushing the mirror image to the private warehouse, responds to the success of pulling after pushing is completed, and cleans the local warehouse to save disk space.
In the flow-batch integrated big data architecture, a plurality of big data components of various types are required, such as big data computing engines Spark, flink and the like, databases Hbase, clickHouse and the like, data lakes Hudi, iceberg and the like, query engines Hive, presto and the like, workflow arrangement tools Airflow, argo and the like, a search engine elastic search, an online notewood development tool Zeppelin, various big data visualization tools and the like. When a big data development environment and a production environment are deployed, a large number of big data components of different types are often deployed at the same time, and the problems that complex environment dependence needs to be managed and multiple versions of the big data components need to be managed exist. Therefore, the invention provides a big data component mirror image construction method, which is used for constructing a Docker mirror image for a big data component to be deployed and uniformly managing the big data component in a mirror image mode.
In order to realize the uniform management of the mirror images, a private mirror image warehouse Harbor is deployed in the system, the mirror image warehouse is deployed in a Kubernets cluster, a PV volume of the Kubernets is used as persistent storage, then a deployment tool Helm is used for deploying the Harbor mirror image warehouse, then an Ingressnginx controller is deployed, and Ingress resources of the Kubernets are used for exposing services. In the private image repository, the storage format of the image is "hostname/repository name/image name: version number". The system creates and maintains a Pod resource, wherein the Pod comprises a container with a Docker environment, and a micro-service for pulling and pushing images is operated in the container, and the API is exposed to the outside. When the mirror image needs to be pulled to the private warehouse, the system submits a request to the API, then the micro-service executes the script for pulling the mirror image to the local warehouse in the operating environment, executes the script for pushing the mirror image to the private warehouse after the pulling is finished, responds to the success of the pulling after the pushing is finished, and cleans the local warehouse to save the disk space.
The container deployment module is used for generating a temporary mirror image and an automatic deployment script according to user configuration and completing containerized deployment of the big data assembly through the temporary mirror image and the automatic deployment script;
further, in an embodiment of the invention, the container deployment module is further configured to:
acquiring user configuration parameters, wherein the user configuration parameters comprise required images, the number of deployed copies, the number of system resources required by Pod, core configuration file information and Jar package dependent information;
generating a Dockerfile according to the mirror image configuration information of the user configuration parameters and constructing a temporary Docker mirror image in a specified environment;
and analyzing the deployment environment configuration information and combining with a Yaml template to generate a deployment script in a Yaml format, submitting the deployment script to a Kubernets API, and deploying on the Kubernets cluster by using a temporary Docker mirror image.
FIG. 2 shows a big data component cloud native deployment flow. For each platform user, the system creates a ServiceAccount resource of kubernets and provides authority for creating namespaces. The user needs to create a name space first, after the name space is created, the system automatically creates account binding, sets administrator authority of the name space for the account, and then deployment operation of the user is performed in the name space. When a user performs the operation of deploying the big data assembly, some configuration parameters, such as the number of required images, the number of deployed copies, the number of system resources required by Pod, core configuration file information, information of dependent Jar packages, and the like, need to be specified first, and then a deployment task is submitted. The system generates a Docker file according to mirror image configuration information submitted by a user and constructs a temporary Docker mirror image in a specified environment. The method comprises the steps that dependent Jar packages required by an image are stored in an object storage, the Jar packages need to be uploaded to the object storage of a platform before use, the required Jar packages are downloaded to a construction environment when the image is constructed, and the system automatically cleans the construction environment after the image is constructed. And then the system analyzes the deployment environment configuration information and combines with a Yaml template provided by the system to generate a deployment script in a Yaml format, the script is submitted to a Kubernets API, and the temporary Docker mirror image constructed in the previous step is used for deployment on a Kubernets cluster. After deployment is completed, the system will retain the temporary image for a period of time for the user to re-deploy or deploy multiple times, and if the image is not used for a long time, the system will automatically clean the image.
For complex deployment tasks requiring a large number of components, such as the need to deploy a whole set of large data warehouse architecture from a computing engine to a query tool, the system provides a custom deployment flow function. The user creates a deployment flow or uses a default deployment flow provided by the system, the deployment flow comprises a plurality of components and configuration information of each component, and container services in the cluster, and the deployment flow is stored in a metadata storage in a Json format. When the user deploys by using the deployment process, the system analyzes the Json configuration information, and deploys each component and service in the Json configuration information by using the component deployment method.
The system also provides the capacity of container monitoring, and after the deployment task is completed, the indexes such as the container state, the resource occupancy rate and the like can be checked on the platform.
The cluster monitoring module is used for managing resources in a Kubernetes cluster and monitoring the state of the deployed containerized big data assembly;
further, in an embodiment of the present invention, the cluster monitoring module is further configured to:
and monitoring the containers completing the deployment task, including checking the container state and the resource occupancy rate.
And the network management module is used for realizing network configuration management of components in the Kubernetes cluster by using Service resources and realizing external exposure of services by using Ingress resources.
Further, in an embodiment of the present invention, the network management module is further configured to:
and performing network management by adopting Service resources and Ingress resources in the Kubernetes cluster.
Further, in an embodiment of the present invention, the network management module is further configured to:
defining a network type, a port name, and a port number for a big data component container deployed on the Kubernetes cluster;
generating a Yaml configuration file according to the network type, the port name, the port number and the container name, and submitting the Yaml configuration file to a Kubernets API to create Service information;
defining a network dependency relationship between two containers, and acquiring Service information of a relying party and Service information of a depended party;
and injecting the network of the depended person and the related configuration information into the container of the depended person in a mode of defining environment variables according to the network dependency relationship, the Service information of the depended person and the created ConfigMap resource.
Further, in an embodiment of the present invention, the network management module is further configured to:
for a component that needs to expose an API outside of the cloud native platform Kubernets, creating Ingress resources from the Service of the component, including,
generating a TLS certificate and a private key for encryption;
creating a Secret resource under the Namespace of the component, and configuring the TLS certificate and the private key into the Secret;
and according to the user configuration, combining the Service information of the component and calling the Secret to realize encryption and creating an Ingress resource.
The invention adopts Service resources and Ingress resources of Kubernetes to carry out network management. For internal networks of large data components, network relationships within the same namespace use a ClusterIP type of Service, and network relationships between different namespaces use an ExternalName type of Service. For the service needing to be exposed outside the system, the network management module deploys an Nginxcontroller as an Ingress controller, configures a corresponding service name and port by using an Ingress resource, and encrypts by using a TLS protocol.
When configuring the deployment information of the big data assembly, a user designates a Service name and a port number which need to be exposed for the assembly, the system generates a script file according to the configuration information and submits the script file to be deployed as a corresponding Service resource, and meanwhile, the information is stored in a metadata base. For the intercommunication relation between the components needing to be configured, for example, a data source component needs to be configured for the computing component, if the two components are in the same name space, no additional configuration is needed, and if the two components are not in the same name space, a network relation needs to be newly established, an ExternalName type Service is newly established in the name space of the computing component according to the information of the name space, the Service name, the port number and the like of the data source component, and the computing component can access the data source component according to the information of the Service name and the port number. In particular, for two large data component containers in a cluster, which need to configure network interworking, there is often a dependency relationship, for example, the storage framework Hive needs to rely on the computing engine Spark to perform SQL computing tasks, however, each large data component is separately deployed on the kubernets cluster, and thus the network interworking relationship between the components needs to be configured. The invention uses the ConfigMap resource of Kubernetes for configuration, and the specific flow is as follows: 1) For a big data component container deployed on a Kubernetes cluster, defining a network type, a port name and a port number for the big data component container; 2) Generating a Yaml configuration file according to the container network definition and the container name and submitting the Yaml configuration file to a Kubernetes API to create Service resources; 3) Defining a network dependency relationship between two containers, including dependency information and depended information; 4) According to the dependency relationship, the Service information of the depended and the created ConfigMap resource, the network of the depended and the related configuration information are injected into the container of the depended in a mode of defining the environment variable. For example, configuring a computing engine Spark for a storage frame Hive, firstly respectively creating Service resources corresponding to Hive and Spark containers, and designating port numbers of services; defining the dependency relationship of the two, namely, hive runs depending on Spark, and writing a configuration file depending on which needs to be modified; then the system generates ConfigMap resources by using a template according to the dependency relationship definition and configuration files of the user, wherein the ConfigMap resources comprise the name space of Spark, the service path of Spark and related configuration information; and finally, the Hive container generates an environment variable according to the configuration in the ConfigMap resource, and the Spark related configuration is loaded in the environment variable mode, so that the Hive on Spark configuration is completed.
For some components needing to expose the API to the outside of the platform, the system creates Ingress resources according to the Service of the component, and the specific flow is as follows: 1) Generating a TLS certificate and a private key for encryption; 2) Creating a Secret resource under the Namespace of the component, and configuring the generated certificate and the private key into the Secret; 3) And (4) using user configuration, combining the Service information of the component and calling Secret to realize encryption and creating Ingress resources. The encryption information is only disclosed to the user of the component, so the encryption information of the user needs to be carried when the API is used.
The cloud native big data component management system based on the cloud native platform Kubernetes provided by the embodiment of the invention has the beneficial effects that: 1) The large data assembly is arranged in a container mode, so that the resource utilization rate is improved, resource isolation is facilitated, the starting speed is improved, and the problem of environment dependence is solved; 2) The required big data components can be respectively deployed under different name spaces aiming at different warehouse architectures, the flexibility is strong, and the resource isolation is realized; 3) Depending on the scheduling capability of Kubernetes, the failed pod is automatically restarted, and the disaster tolerance capability is achieved; 4) A cloud native big data component management platform is developed, so that operation and maintenance personnel can realize quick online of a big data cluster on the platform through configuration component deployment information, meanwhile, the state of each cluster can be monitored on the platform, and the deployment operation and maintenance difficulty is reduced.
In order to achieve the above object, a second embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the cloud native big data component management system based on the cloud native platform kubernets as described above.
To achieve the above object, a third embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a cloud native big data component management system based on a cloud native platform kubernets as described above.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. A cloud native big data component management system based on a cloud native platform Kubernets is characterized by comprising:
the mirror image management module is used for pulling a needed large data component mirror image from the public mirror image warehouse and storing the constructed mirror image into the private mirror image warehouse;
the container deployment module is used for generating a temporary mirror image and an automatic deployment script according to user configuration and completing containerized deployment of the big data assembly through the temporary mirror image and the automatic deployment script;
the cluster monitoring module is used for managing resources in a Kubernetes cluster and monitoring the state of the deployed containerized big data assembly;
and the network management module is used for realizing network configuration management of components in the Kubernetes cluster by using Service resources and realizing external exposure of services by using Ingress resources.
2. The system of claim 1, wherein the image management module is further configured to:
deploying a private mirror image warehouse Harbor in a Kubernets cluster, and creating and maintaining a Pod resource through the cloud native big data assembly management system, wherein the Pod resource comprises a container with a Docker environment, a micro-service for pulling and pushing a mirror image is operated in the container, and an API is exposed to the outside;
and submitting a request to the API through the cloud native big data component management system, executing a script for pulling the mirror image to a local warehouse by the micro-service in an operating environment, executing a script for pushing the mirror image to a private warehouse, responding to successful pulling after pushing is completed, and cleaning the local warehouse to save disk space.
3. The system of claim 1, wherein the vessel deployment module is further configured to:
acquiring user configuration parameters, wherein the user configuration parameters comprise required mirror images, the number of deployed copies, the number of system resources required by Pod, core configuration file information and Jar package dependent information;
generating a Docker file according to the mirror image configuration information of the user configuration parameters and constructing a temporary Docker mirror image in a specified environment;
and analyzing deployment environment configuration information, generating a deployment script in a Yaml format by combining with a Yaml template, submitting the deployment script to a Kubernets API, and deploying on the Kubernets cluster by using the temporary Docker mirror image.
4. The system of claim 1, wherein the cluster monitoring module is further configured to:
and monitoring the containers completing the deployment task, including checking the container state and the resource occupancy rate.
5. The system of claim 1, wherein the network management module is further configured to:
and performing network management by adopting Service resources and Ingress resources in the Kubernetes cluster.
6. The system of claim 1 or 5, wherein the network management module is further configured to:
defining a network type, a port name, and a port number for a big data component container deployed on the Kubernetes cluster;
generating a Yaml configuration file according to the network type, the port name, the port number and the container name, and submitting the Yaml configuration file to a Kubernets API to create Service information;
defining a network dependency relationship between two containers, and acquiring Service information of a relying party and Service information of a depended party;
and injecting the network of the depended person and the related configuration information into the container of the depended person in a mode of defining environment variables according to the network dependency relationship, the Service information of the depended person and the created ConfigMap resource.
7. The system of claim 1 or 5, wherein the network management module is further configured to:
for a component that needs to expose an API outside of the cloud native platform kubernets, create Ingress resources from the component's Service, including,
generating a TLS certificate and a private key for encryption;
creating a Secret resource under the Namespace of the component, and configuring the TLS certificate and the private key into the Secret;
and according to the user configuration, combining the Service information of the component and calling the Secret to realize encryption and creating an Ingress resource.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the cloud native big data component management system based on the cloud native platform kubernets of any of claims 1-7 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the cloud native big data component management system based on the cloud native platform kubernets of any one of claims 1 to 7.
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| CN116107564A (en) * | 2023-04-12 | 2023-05-12 | 中国人民解放军国防科技大学 | Data-oriented cloud native software architecture and software platform |
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| CN117519989B (en) * | 2024-01-03 | 2024-04-02 | 上海燧原智能科技有限公司 | Distributed system hosting method and device, electronic equipment and storage medium |
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