CN113760448A - Big data management platform based on kubernets - Google Patents
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- CN113760448A CN113760448A CN202110484469.7A CN202110484469A CN113760448A CN 113760448 A CN113760448 A CN 113760448A CN 202110484469 A CN202110484469 A CN 202110484469A CN 113760448 A CN113760448 A CN 113760448A
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/60—Software deployment
- G06F8/61—Installation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45575—Starting, stopping, suspending or resuming virtual machine instances
Abstract
The invention discloses a big data management platform based on kubernets, which comprises big data component software management, big data component installation and starting, big data component suspension, big data component recovery and big data component deletion, big data component configuration synchronization, big data component log collection and big data component monitoring. Has the advantages that: the problem that a traditional open source big data management platform (hdp, cdh) cannot deploy big data components in multiple instances is solved. The invention aims to realize a big data platform management tool based on the characteristics of a kubernets management platform, and can effectively manage big data components, wherein the management functions comprise big data component management, big data component installation, starting, suspending, recovering and deleting, big data component configuration modification and synchronization, big data component log collection and big data component monitoring.
Description
Technical Field
The invention relates to the field of big data and container cloud platforms, in particular to a big data management platform based on kubernets.
Background
The hadoop is an open source software framework capable of performing distributed storage and calculation on mass data. Has the characteristics of expandability, high fault tolerance and high efficiency. With the advent of the big data age, hadoop technology is increasingly widely used. However, as the cluster becomes larger and larger, the cost of deploying and maintaining the cluster becomes higher and a low-cost solution that can deploy the hadoop cluster by one key is needed.
Docker is an open source application container engine, so that developers can package their applications and dependency packages into a portable container, and then distribute the container to any popular Linux machine, and also realize virtualization. The containers are fully sandboxed without any interface between each other. Docker is the most widely used container technology, and creates a service by packaging images and starting containers. However, as applications become more complex, the number of containers also becomes greater, thereby deriving a significant problem in managing operation and maintenance containers, and thus kubernets (k8s) container cloud management tools have come into force.
The big data system is generally constructed based on an open source community version, a common open source big data platform management tool is HDP or CDH, the tools manage big data assemblies capable of being installed and managed in a component library mode, the big data assemblies (such as hadoop) can be installed through an interactive user page, parameters of the big data assembly assemblies are configured on the page, and starting and stopping of the big data assemblies are controlled. And the method supports the collection of logs of the big data component and the monitoring of the performance of the big data component. However, by using the big data management platform, in a cluster managed by the big data management platform, only one instance is supported to be deployed in the cluster for one component, and multiple instances or different versions of big data component deployment cannot be supported.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
The invention aims to provide a big data management platform based on kubernets, aims to realize a big data platform management tool based on the characteristics of the kubernets management platform, and can effectively manage big data components so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a big data management platform based on kubernets comprises big data component software management, big data component installation and starting, big data component suspension, big data component recovery and big data component deletion, big data component configuration synchronization, big data component log collection and big data component monitoring;
wherein the big data component software manages: packaging the big data assembly into a docker form, managing by matching with a yaml file which can be analyzed based on kubernets, and managing charts packages by using a hardor or git;
the big data assembly management platform provides an application store to manage all big data assembly applications, a harbor or git address is added in an application store interface, an installation package is uploaded in a push mode, and the installation package is downloaded in a pull mode;
the big data assembly is installed and started: the big data component management platform is based on the characteristics of kubernets, the big data components are completely packaged into dockers, and different types of controllers in the kubernets are used for deployment;
big data component pause: when the big data component is suspended, the user-defined api is called to store the pod information into the kubernetes crd, after the storage state is finished, the stateful scale api in the kube-apiserver is called to reduce the pod installation serial number from large to small, until the pod number is 0, the operation is suspended;
and (3) large data component recovery: when the big data component is recovered, firstly calling the api to take out component information needing to be recovered from the kubernetes crd, then calling the stateful scale api in the kube-apiserver to expand the capacity of the pod until the pod quantity is recovered to the quantity before stopping, and completing the recovery operation;
big data component delete: the kubernets can be directly used for deleting the controller, so that the resource objects managed by the controller are deleted, and the purpose of deleting the components is achieved;
the big data component configuration synchronization: the components in the big data management platform are configured and synchronized through a configmap in the kubernets;
the big data component log collection: the components running on the big data management platform are all in a docker packaging form, and logs of all big data components can be collected based on kubernets and docker characteristics;
the big data component monitors: the big data component management platform provides unified monitoring on the deployed components based on a kubernets mechanism.
Further, the following method is mainly used for synchronizing the configuration file by using the configmap:
the environment variable is defined directly in the configmap, and the environment variable defined in the configmap is referenced by env when the pod is started.
Further, the use of configmap to synchronize the profile master may also use the following:
and directly encapsulating a complete configuration file in the configmap, and then mounting the complete configuration file in the pod in a shared volume mode to realize the transmission of the application.
Further, in the big data component log collection, the following modes are mainly used for collecting logs:
based on the characteristics of the kubernets daemon controller, a workload can be started on each kubernets node, so that the log collection module fluent is started in the mode of the kubernets daemon controller, logs generated by all large data component containers are collected uniformly, one fluent is started on each node of kubernets, logs of a docker are collected, and logs of all large data components are collected.
Further, in the big data component log collection, the following method can be used for collecting logs: and integrating a log collection container in the workload of the big data component, reading the log generated by the big data component by using an emptydir shared volume mode, and then sending the log to a specified target.
According to another aspect of the present invention, there is provided a big data component installation and startup method, the method comprising the steps of:
s11, selecting a host: binding a host based on kubernets nodeselectors or pv to realize host selection;
s12, downloading: packaging the big data assembly into a docker mirror image, and uploading the docker mirror image to a mirror image server;
s13, distribution configuration: the big data component management platform realizes the configuration mapping synchronous configuration of the big data component through the configmap in the kubernets;
s14, starting sequence control: the big data component instances have an incidence relation among each other, and have sequence and role correlation, when the big data component is started, the big data component instances need to be started in sequence according to the dependency relation among the modules, and the component sequential starting can be realized only by a state controller stateful mechanism based on kubernets;
s15, multi-instance management: after the big data assembly is started, the big data assembly can be used as the load of a controller of kubernets to be managed, and the kubernets supports independent deployment of a plurality of controllers as long as names are different in the same name space.
Further, the step S12 downloads: encapsulating the big data assembly into a docker mirror image, and uploading the docker mirror image to a mirror image server specifically comprises the following steps:
s121, writing a yaml file which can be analyzed by kubernets and used for installing a big data assembly;
s122, analyzing the yaml file by the big data management platform during installation by a user;
and S123, starting a controller defined in the yaml file, and downloading a big data component docker image configured in the file.
According to another aspect of the present invention, there is provided a big data component suspension method, comprising the steps of:
s21, recording the original state of the workload based on kubernets api-server, creating a crd resource, recording a load name space, a load name, the load quantity and the like, and storing the load name space, the load name, the load quantity and the like in kubernets;
s22, based on the mechanism of kubernets statefulset, the number of the workload is adjusted to 0, and the effect of pause is achieved.
Compared with the prior art, the invention has the following beneficial effects: the invention realizes the efficient construction of the big data management platform. The problem that a traditional open source big data management platform (hdp, cdh) cannot deploy big data components in multiple instances is solved. The invention aims to realize a big data platform management tool based on the characteristics of a kubernets management platform, and can effectively manage big data components, wherein the management functions comprise big data component management, big data component installation, starting, suspending, recovering and deleting, big data component configuration modification and synchronization, big data component log collection and big data component monitoring.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a diagram of the overall architecture of a big data component management platform of a big data management platform based on kubernets according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating installation and startup of big data components in a big data management platform based on kubernets according to an embodiment of the present invention;
FIG. 3 is a flow chart of big data component suspension resuming in a big data management platform based on kubernets according to an embodiment of the present invention;
fig. 4 is a flowchart of big data component log collection in a big data management platform based on kubernets according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following drawings and detailed description:
referring to fig. 1 to 4, a big data management platform based on kubernets according to an embodiment of the present invention includes big data component software management, big data component installation and start, big data component suspension, big data component recovery and deletion, big data component configuration synchronization, big data component log collection, and big data component monitoring.
As shown in fig. 1, for big data component software management, a conventional big data management platform generally takes the form of making a big data component into an rpm package or a deb package, and a background call yum or an apt-get command is called when a user installs software to perform installation.
The big data component management platform is used for packaging big data components into docker and managing the big data components in cooperation with yaml files which can be analyzed based on kubernets. Charbor or git is used to manage charts packets.
The big data component management platform provides an application store to manage all big data component applications. Adding a hardor or git address in the application store interface. The installation package is uploaded in a push mode, and the installation package is downloaded in a pull mode.
Taking the management hadoop component as an example: first, a hadoop mirror image is made and uploaded to the hardor. Then, a yaml file is written, and made into charts packages, and uploaded to a harbor in a push mode. When a user selects a specified hadoop component from an application store for installation, a helm client side can pull charts to a tiller server in kubernets, the tiller server receives a request to analyze the dependency relationship of the charts, calls helm install for installation, and generates a release file in the kubernets. When the designated big data component is deleted, the helm client sends a request to the tiller server, and the tiller server calls the helm delete to delete the designated release.
Referring to fig. 2-3, for the big data component installation start, pause, resume and delete, when the big data component is started by the conventional big data management platform, the following processes are generally performed:
step A, selecting a host: selecting a host to be installed;
and step B, downloading: downloading a big data assembly installation package;
step C, distribution configuration: uniformly distributing big data configuration files;
step D, starting sequence control: and calling a starting script and starting the components in sequence.
The method can only install a single-version big data instance in one cluster, and cannot meet the requirement of multiple versions and multiple instances of a user.
The big data component management platform is based on the characteristics of kubernets, the big data components are completely packaged into dockers, different types of controllers in the kubernets are used for deployment, the kubernets can deploy a plurality of different types of controllers simultaneously, and therefore the big data components with multiple instances and multiple versions can be deployed. The specific process is as follows:
step S11, select host: and the host selection is realized based on the kubernets nodeselector or the pv binding host.
Step S12, download: and encapsulating the big data assembly into a docker mirror image, and uploading the docker mirror image to a mirror image server. Defining big data components needing to be installed as a controller of kubernets, namely writing yaml files which can be parsed by the kubernets for installing the big data components. When the user installs the big data management platform, the big data management platform can analyze the yaml file, starts a controller defined in the yaml file, and downloads the big data component docker mirror image configured in the file.
Step S13, distribution configuration: and the big data component management platform realizes the configuration mapping synchronous configuration of the big data component through the configmap in the kubernets. The specific implementation modes include the following modes: 1. the environment variable is defined directly in the configmap, and the kubernets controller refers to the environment variable defined in the configmap by env at startup. 2. And directly encapsulating a complete configuration file in the configmap, and then mounting the complete configuration file to a pod started by the kubernets controller in a shared volume mode to realize the transmission of the application to the application.
Step S14, start sequence control: the big data component instances have an incidence relation with each other, and have a relevance in terms of sequence and role. The big data component is started in sequence according to the dependency relationship among the modules. Only based on kubernets, a state controller stateful mechanism can realize sequential starting of components, and the specific principle is as follows: the stateful controller provides a stable and unique network identifier for each instance of the big data component, stable and persistent storage, ordered and graceful deployment and expansion, ordered and graceful deletion and termination, and ordered and automatic rolling updates. The stable meaning is that after the big data component instance is restarted, the container host name generated by the controller will not change, and the bound back-end storage thereof will not be lost. Sequential launching of big data components on the management platform can be achieved with this feature.
Step S15, multiple instance management: the big data component, when activated, manages as the load of a controller in kubernets. Kubernets supports the separate deployment of multiple controllers, as long as the names are different within the same namespace. Based on the above capabilities, multi-instance management of big data components can be achieved.
The big data component management platform supports the suspension of the big data component, when the component is suspended, the process stops running, but the running state needs to be saved, and the previous configuration information of the host and the component needs to be recorded so as to be recovered when the component is restarted. The concrete implementation mode based on kubernets is as follows:
step 21, kubernets automatically adjusts according to the number of the current workload. All the workloads stop running, but the running state and the configuration information before stopping are reserved, and the specific implementation mode is that the original state of the workloads is recorded based on kubernets api-server, an crd resource is newly built, and a load name space, a load name, a load quantity and the like are recorded and stored in the kubernets.
In step 22, when the component is suspended, the workload needs to be stopped, but the running state needs to be kept, and kubernets cannot be directly called to carry out deletion operation. The invention adjusts the quantity of the working load to 0 based on the mechanism of kubernets statefulset, thereby realizing the effect of pause.
When the component is recovered, the original information of the component is taken out from the kubernets crd resource according to the name of the component needing to be recovered, and the load quantity is adjusted to the original quantity based on a kubernets stateful controller mechanism. The recovery operation is complete.
The big data component management platform supports deletion operation on the installed components: the operation of all the component workloads needs to be stopped during the deleting operation, and the operation state does not need to be reserved, so that the controller can be directly deleted by using kubernets, the resource objects managed by the controller are further deleted, and the purpose of deleting the components is achieved.
For the above-mentioned technology, in one embodiment, hadoop is taken as an example: the modules of the high-availability hadoop cluster are controlled to start by using kubernets stateful, so that the order of starting the pods is ensured, and a unique and fixed identifier is maintained for each pod. When the Hadoop component starts, the Kubectl is called to send a request to the Kube-apiserver, and the Kube-apiserver calls a statefulets interface in etcd v3 to store starting information into the etcd and return the starting information to the Kubelelet. And the Kubelet receives the information and calls the kube-apiserver to take the pod information needing to be created out of the etcd, pull the mirror image and start the container. And after each module pod is started and the state is active, finishing the starting.
When the big data component is suspended, the user-defined api is called to store the pod information into the kubernetes crd. And after the storage state is finished, calling a stateful scale api in the kube-api to reduce the capacity of the pod installation serial numbers in a descending order until the pod number is 0, and finishing the pause operation.
When the big data component is recovered, firstly calling the api to take out the component information needing to be recovered from the kubernetes crd, then calling the stateful scale api in the kube-apiserver to expand the capacity of the pod until the pod number is recovered to the number before stopping, and completing the recovery operation.
For big data component configuration synchronization, the components in the big data management platform perform configuration synchronization through the configmap in the kubernets. The configmap is used to synchronize the configuration file in two ways: 1. the environment variable is defined directly in the configmap, and the environment variable defined in the configmap is referenced by env when the pod is started. 2. And directly encapsulating a complete configuration file in the configmap, and then mounting the complete configuration file in the pod in a shared volume mode to realize the transmission of the application.
The configmap of the big data component can be modified through a big data management platform interface, and the latest configmap synchronous modified configuration file can be automatically mounted after a container controlled by the kubernets controller is restarted.
As shown in FIG. 4, for big data component log collection, log collection of a conventional big data management platform typically requires assigning to various big data component log catalogs and then collecting logs.
The components running on the big data management platform are all in a docker packaging mode, and logs of all big data components can be collected without specifying a specific log path of the big data components based on kubernets and docker characteristics. There are several ways to collect logs: 1. based on the characteristics of the kubernets daemon controller, a workload can be started on each kubernets node, so that the log collection module fluent is started in the mode of the kubernets daemon controller, logs generated by all large data component containers are collected uniformly, one fluent is started on each node of kubernets, logs of a docker are collected, and logs of all large data components are collected. 2. And integrating a log collection container in the workload of the big data component, and enabling the log collection container to read the log generated by the big data component by using an emptydir shared volume mode. The log is then sent to the specified destination.
For big data component monitoring, the big data component management platform provides unified monitoring on deployed components based on a kubernets mechanism. The big data component management platform realizes information collection of current resource use conditions of all kubernets nodes and big data component pots deployed on the kubernets nodes through deployment node _ exporter, custom exporter of big data components and metric-server, such as index data of multiple dimensions of kubernets nodes, CPU, memory, disk, network and the like, and information of memory and CPU used by pots. And the big data component management platform pulls the monitoring data from the exporter by deploying prometheus and stores the monitoring data. Data pulled by prometheus is converted into a data type recognizable by kubernets using kube-state-metrics. The big data component management platform integrates the grafana to visually display monitoring information, integrates the alert manager to realize monitoring alarm, and can timely inform operation and maintenance personnel through mails, short messages and the like so as to know and process the running conditions of all components on the big data component management platform in real time.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A big data management platform based on kubernets is characterized by comprising big data component software management, big data component installation and starting, big data component suspension, big data component recovery and big data component deletion, big data component configuration synchronization, big data component log collection and big data component monitoring;
wherein the big data component software manages: packaging the big data assembly into a docker form, managing by matching with a yaml file which can be analyzed based on kubernets, and managing charts packages by using a hardor or git;
the big data assembly management platform provides an application store to manage all big data assembly applications, a harbor or git address is added in an application store interface, an installation package is uploaded in a push mode, and the installation package is downloaded in a pull mode;
the big data assembly is installed and started: the big data component management platform is based on the characteristics of kubernets, the big data components are completely packaged into dockers, and different types of controllers in the kubernets are used for deployment;
big data component pause: when the big data component is suspended, the user-defined api is called to store the pod information into the kubernetes crd, after the storage state is finished, the stateful scale api in the kube-apiserver is called to reduce the pod installation serial number from large to small, until the pod number is 0, the operation is suspended;
and (3) large data component recovery: when the big data component is recovered, firstly calling the api to take out component information needing to be recovered from the kubernetes crd, then calling the stateful scale api in the kube-apiserver to expand the capacity of the pod until the pod quantity is recovered to the quantity before stopping, and completing the recovery operation;
big data component delete: the kubernets can be directly used for deleting the controller, so that the resource objects managed by the controller are deleted, and the purpose of deleting the components is achieved;
the big data component configuration synchronization: the components in the big data management platform are configured and synchronized through a configmap in the kubernets;
the big data component log collection: the components running on the big data management platform are all in a docker packaging form, and logs of all big data components can be collected based on kubernets and docker characteristics;
the big data component monitors: the big data component management platform provides unified monitoring on the deployed components based on a kubernets mechanism.
2. The big data management platform based on kubernets according to claim 1, wherein the configmap is used to synchronize the configuration file mainly in the following way:
the environment variable is defined directly in the configmap, and the environment variable defined in the configmap is referenced by env when the pod is started.
3. The big data management platform based on kubernets according to claim 1, wherein the syncing of the configuration files by using the configmap is further performed by:
and directly encapsulating a complete configuration file in the configmap, and then mounting the complete configuration file in the pod in a shared volume mode to realize the transmission of the application.
4. The big data management platform based on kubernets according to claim 1, wherein in the big data component log collection, the following ways are mainly used for collecting logs:
based on the characteristics of the kubernets daemon controller, a workload can be started on each kubernets node, so that the log collection module fluent is started in the mode of the kubernets daemon controller, logs generated by all large data component containers are collected uniformly, one fluent is started on each node of kubernets, logs of a docker are collected, and logs of all large data components are collected.
5. The big data management platform based on kubernets according to claim 1, wherein in the big data component log collection, the following method can be used for collecting logs: and integrating a log collection container in the workload of the big data component, reading the log generated by the big data component by using an emptydir shared volume mode, and then sending the log to a specified target.
6. A big data component installation and startup method for installing and starting a big data component in the kubernets-based big data management platform claimed in claim 1, comprising the following steps:
s11, selecting a host: binding a host based on kubernets nodeselectors or pv to realize host selection;
s12, downloading: packaging the big data assembly into a docker mirror image, and uploading the docker mirror image to a mirror image server;
s13, distribution configuration: the big data component management platform realizes the configuration mapping synchronous configuration of the big data component through the configmap in the kubernets;
s14, starting sequence control: the big data component instances have an incidence relation among each other, and have sequence and role correlation, when the big data component is started, the big data component instances need to be started in sequence according to the dependency relation among the modules, and the component sequential starting can be realized only by a state controller stateful mechanism based on kubernets;
s15, multi-instance management: after the big data assembly is started, the big data assembly can be used as the load of a controller of kubernets to be managed, and the kubernets supports independent deployment of a plurality of controllers as long as names are different in the same name space.
7. The big data component installation and startup method according to claim 6, wherein the step S12 is to download: encapsulating the big data assembly into a docker mirror image, and uploading the docker mirror image to a mirror image server specifically comprises the following steps:
s121, writing a yaml file which can be analyzed by kubernets and used for installing a big data assembly;
s122, analyzing the yaml file by the big data management platform during installation by a user;
and S123, starting a controller defined in the yaml file, and downloading a big data component docker image configured in the file.
8. A big data component suspension method for suspending big data components in the kubernets-based big data management platform claimed in claim 1, comprising the steps of:
s21, recording the original state of the workload based on kubernets api-server, creating a crd resource, recording a load name space, a load name, the load quantity and the like, and storing the load name space, the load name, the load quantity and the like in kubernets;
s22, based on the mechanism of kubernets statefulset, the number of workloads is adjusted to 0.
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