kubectl源码分析之cluster-info dump

kubectl滚动更新资源 rollout Manage the rollout of a resource. Valid resource types include: * deployments * daemonsets * statefulsets Examples: # Rollback to the previous deployment kubectl rollout undo deployment/abc # Check the rollout status of a da 阅读详情

 欢迎关注我的公众号:

 目前刚开始写一个月,一共写了18篇原创文章,文章目录如下:

istio多集群探秘,部署了50次多集群后我得出的结论

istio多集群链路追踪,附实操视频

istio防故障利器,你知道几个,istio新手不要读,太难!

istio业务权限控制,原来可以这么玩

istio实现非侵入压缩,微服务之间如何实现压缩

不懂envoyfilter也敢说精通istio系列-http-rbac-不要只会用AuthorizationPolicy配置权限

不懂envoyfilter也敢说精通istio系列-02-http-corsFilter-不要只会vs

不懂envoyfilter也敢说精通istio系列-03-http-csrf filter-再也不用再代码里写csrf逻辑了

不懂envoyfilter也敢说精通istio系列http-jwt_authn-不要只会RequestAuthorization

不懂envoyfilter也敢说精通istio系列-05-fault-filter-故障注入不止是vs

不懂envoyfilter也敢说精通istio系列-06-http-match-配置路由不只是vs

不懂envoyfilter也敢说精通istio系列-07-负载均衡配置不止是dr

不懂envoyfilter也敢说精通istio系列-08-连接池和断路器

不懂envoyfilter也敢说精通istio系列-09-http-route filter

不懂envoyfilter也敢说精通istio系列-network filter-redis proxy

不懂envoyfilter也敢说精通istio系列-network filter-HttpConnectionManager

不懂envoyfilter也敢说精通istio系列-ratelimit-istio ratelimit完全手册

 

加qq群,请联系:


————————————————

type ClusterInfoDumpOptions struct {//dump结构体
	PrintFlags *genericclioptions.PrintFlags
	PrintObj   printers.ResourcePrinterFunc

	OutputDir     string
	AllNamespaces bool
	Namespaces    []string

	Timeout          time.Duration
	AppsClient       appsv1client.AppsV1Interface
	CoreClient       corev1client.CoreV1Interface
	Namespace        string
	RESTClientGetter genericclioptions.RESTClientGetter
	LogsForObject    polymorphichelpers.LogsForObjectFunc

	genericclioptions.IOStreams
}
//创建dump命令
func NewCmdClusterInfoDump(f cmdutil.Factory, ioStreams genericclioptions.IOStreams) *cobra.Command {
	o := &ClusterInfoDumpOptions{//初始化dump结构体
		PrintFlags: genericclioptions.NewPrintFlags("").WithTypeSetter(scheme.Scheme).WithDefaultOutput("json"),

		IOStreams: ioStreams,
	}

	cmd := &cobra.Command{//创建cobra命令
		Use:     "dump",
		Short:   i18n.T("Dump lots of relevant info for debugging and diagnosis"),
		Long:    dumpLong,
		Example: dumpExample,
		Run: func(cmd *cobra.Command, args []string) {
			cmdutil.CheckErr(o.Complete(f, cmd))//准备
			cmdutil.CheckErr(o.Run())//运行
		},
	}

	o.PrintFlags.AddFlags(cmd)//打印选项

	cmd.Flags().StringVar(&o.OutputDir, "output-directory", o.OutputDir, i18n.T("Where to output the files.  If empty or '-' uses stdout, otherwise creates a directory hierarchy in that directory"))//output-directory选项
	cmd.Flags().StringSliceVar(&o.Namespaces, "namespaces", o.Namespaces, "A comma separated list of namespaces to dump.")//namespaces选项
	cmd.Flags().BoolVarP(&o.AllNamespaces, "all-namespaces", "A", o.AllNamespaces, "If true, dump all namespaces.  If true, --namespaces is ignored.")//all-namespace选项
	cmdutil.AddPodRunningTimeoutFlag(cmd, defaultPodLogsTimeout)//超时选项
	return cmd
}
//准备方法
func (o *ClusterInfoDumpOptions) Complete(f cmdutil.Factory, cmd *cobra.Command) error {
	printer, err := o.PrintFlags.ToPrinter()// print flag 转printer
	if err != nil {
		return err
	}

	o.PrintObj = printer.PrintObj//设置printObj函数

	config, err := f.ToRESTConfig()//获取restConfig
	if err != nil {
		return err
	}

	o.CoreClient, err = corev1client.NewForConfig(config)//设置coreClient
	if err != nil {
		return err
	}

	o.AppsClient, err = appsv1client.NewForConfig(config)//设置appsClient
	if err != nil {
		return err
	}

	o.Timeout, err = cmdutil.GetPodRunningTimeoutFlag(cmd)//设置超时
	if err != nil {
		return err
	}

	o.Namespace, _, err = f.ToRawKubeConfigLoader().Namespace()//设置namespace
	if err != nil {
		return err
	}
	// TODO this should eventually just be the completed kubeconfigflag struct
	o.RESTClientGetter = f//设置restclientgetter
	o.LogsForObject = polymorphichelpers.LogsForObjectFn//设置LogsForObject函数

	return nil
}
//运行
func (o *ClusterInfoDumpOptions) Run() error {
	nodes, err := o.CoreClient.Nodes().List(metav1.ListOptions{})//获取所有节点
	if err != nil {
		return err
	}

	if err := o.PrintObj(nodes, setupOutputWriter(o.OutputDir, o.Out, "nodes.json")); err != nil {//打印节点
		return err
	}

	var namespaces []string
	if o.AllNamespaces {//如果指定了all-namespaces
		namespaceList, err := o.CoreClient.Namespaces().List(metav1.ListOptions{})//获取所有namespace
		if err != nil {
			return err
		}
		for ix := range namespaceList.Items {//append namespaces
			namespaces = append(namespaces, namespaceList.Items[ix].Name)
		}
	} else {
		if len(o.Namespaces) == 0 {//如果namespaces为空
			namespaces = []string{//则用系统namespace和当前namespace
				metav1.NamespaceSystem,
				o.Namespace,
			}
		}
	}
	for _, namespace := range namespaces {//遍历namespaces
		// TODO: this is repetitive in the extreme.  Use reflection or
		// something to make this a for loop.
		events, err := o.CoreClient.Events(namespace).List(metav1.ListOptions{})//获取所有事件
		if err != nil {
			return err
		}
		if err := o.PrintObj(events, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "events.json"))); err != nil {//打印事件
			return err
		}

		rcs, err := o.CoreClient.ReplicationControllers(namespace).List(metav1.ListOptions{})//获取rc
		if err != nil {
			return err
		}
		if err := o.PrintObj(rcs, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "replication-controllers.json"))); err != nil {//打印rc
			return err
		}

		svcs, err := o.CoreClient.Services(namespace).List(metav1.ListOptions{})//获取service
		if err != nil {
			return err
		}
		if err := o.PrintObj(svcs, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "services.json"))); err != nil {//打印service
			return err
		}

		sets, err := o.AppsClient.DaemonSets(namespace).List(metav1.ListOptions{})//获取daemonset
		if err != nil {
			return err
		}
		if err := o.PrintObj(sets, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "daemonsets.json"))); err != nil {//打印daemonset
			return err
		}

		deps, err := o.AppsClient.Deployments(namespace).List(metav1.ListOptions{})//获取deployment
		if err != nil {
			return err
		}
		if err := o.PrintObj(deps, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "deployments.json"))); err != nil {//打印deployment
			return err
		}

		rps, err := o.AppsClient.ReplicaSets(namespace).List(metav1.ListOptions{})//获取rs
		if err != nil {
			return err
		}
		if err := o.PrintObj(rps, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "replicasets.json"))); err != nil {//打印rs
			return err
		}

		pods, err := o.CoreClient.Pods(namespace).List(metav1.ListOptions{})//获取pod
		if err != nil {
			return err
		}

		if err := o.PrintObj(pods, setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, "pods.json"))); err != nil {//打印pod
			return err
		}

		printContainer := func(writer io.Writer, container corev1.Container, pod *corev1.Pod) {//打印容器日志
			writer.Write([]byte(fmt.Sprintf("==== START logs for container %s of pod %s/%s ====\n", container.Name, pod.Namespace, pod.Name)))
			defer writer.Write([]byte(fmt.Sprintf("==== END logs for container %s of pod %s/%s ====\n", container.Name, pod.Namespace, pod.Name)))

			requests, err := o.LogsForObject(o.RESTClientGetter, pod, &corev1.PodLogOptions{Container: container.Name}, timeout, false)//获取日志请求
			if err != nil {
				// Print error and return.
				writer.Write([]byte(fmt.Sprintf("Create log request error: %s\n", err.Error())))
				return
			}

			for _, request := range requests {//遍历日志请求
				data, err := request.DoRaw()
				if err != nil {
					// Print error and return.
					writer.Write([]byte(fmt.Sprintf("Request log error: %s\n", err.Error())))
					return
				}
				writer.Write(data)//打印日
			}
		}

		for ix := range pods.Items {//遍历所有pod
			pod := &pods.Items[ix]
			containers := pod.Spec.Containers
			writer := setupOutputWriter(o.OutputDir, o.Out, path.Join(namespace, pod.Name, "logs.txt"))

			for i := range containers {//打印容器日志
				printContainer(writer, containers[i], pod)
			}
		}
	}

	dest := o.OutputDir
	if len(dest) == 0 {
		dest = "standard output"
	}
	if dest != "-" {//打印结束
		fmt.Fprintf(o.Out, "Cluster info dumped to %s\n", dest)
	}
	return nil
}

yolov8训练模型的一些心得-总结篇 先放在这,等过几天我都弄完了详细给大家说说(哈哈其实是给我自己说说,记录一下怕忘记) 阅读详情

相关推荐

K8S系列:Pod的扩缩容方法

手动扩缩容机制 scale 自动扩缩容机制 scaleTargetRef 也可以自定义一些指标 当到达指标设置阈值时,进行扩容

NIO4444 647

k8s实战之kubectl

本课程将详细介绍k8s所有命令,以及命令的go源码分析,学习知其然,知其所以然

IAR调试CC1310的错误:Fatal error:Failed to connect to the XDS emlulator (Error -241 @ 0x0:)

从simplelink_cc13x0_sdk_2_10_00_36导入collector工程后,rebuild后进行debug,出现了如图错误:Fatal error:Failed to connect to the XDS emlulator (connect ICEPick)...(Error -241 @ 0x0)A router subpath could not be accessed....

qq_40417648的博客 6228

kubectl源码分析之apply

发布一个k8s部署视频:https://edu.youkuaiyun.com/course/detail/26967 课程内容:各种k8s部署方式。包括minikube部署,kubeadm部署,kubeasz部署,rancher部署,k3s部署。包括开发测试环境部署k8s,和生产环境部署k8s。 腾讯课堂连接地址https://ke.qq.com/course/478827?taid=4373109931462251&tuin=ba64518 第二个视频发布https://edu.youkuaiyun.com/c..

mark's technic world 1809

AMD锐龙笔记本隐藏玩法:分核降压+ PBO2超频全攻略(以5900HX/6900HX为例)

本文详细解析了AMD锐龙笔记本(以5900HX/6900HX为例)的分核降压与PBO2超频技术,帮助用户释放处理器隐藏性能。通过BIOS设置、Curve Optimizer参数调整及稳定性测试,实现性能提升与温度控制的完美平衡,适合硬件爱好者进阶调校。

weixin_30617737的博客 352

无需Root!安卓模拟器+BurpSuite轻松搞定微信小程序抓包

本文详细介绍了如何在不root安卓设备的情况下,使用安卓模拟器和BurpSuite实现微信小程序抓包。通过夜神模拟器降级安卓版本、配置BurpSuite代理及安装证书等步骤,开发者可以轻松绕过微信的证书绑定,高效抓取小程序数据包。文章还提供了常见问题排查和高阶技巧,帮助开发者提升抓包效率。

weixin_29213655的博客 365

kubectl源码分析之cluster-info

发布一个k8s部署视频:https://edu.youkuaiyun.com/course/detail/26967 课程内容:各种k8s部署方式。包括minikube部署,kubeadm部署,kubeasz部署,rancher部署,k3s部署。包括开发测试环境部署k8s,和生产环境部署k8s。 腾讯课堂连接地址https://ke.qq.com/course/478827?taid=4373109931462251&tuin=ba64518 第二个视频发布https://edu.youkuaiyun.com/c..

mark's technic world 1098

Pyrosim进阶指南:从隧道火灾模拟到复杂通风策略分析

本文深入探讨Pyrosim在隧道火灾模拟与通风策略分析中的高级应用。从精确几何建模到火源参数设置,再到通风系统的量化分析,详细介绍了如何利用Pyrosim优化隧道安全设计。特别强调了临界风速验证、横向通风配置及结果可视化技巧,为工程师提供实用的进阶指南。

weixin_30644369的博客 140

kubernetes基础命令

一、名称空间 资源隔离、但是网络不隔离 # 创建 kubectl create ns test #删除 kubectl delete ns test # yaml文件创建 [root@k8s-master ~]# vi test.yaml [root@k8s-master ~]# cat test.yaml apiVersion: v1 kind: Namespace metadata: name: test [root@k8s-master ~]# kubectl ap...

cbzhunian 8031

acados安装与调试指南:版本兼容性问题

在计算机编程领域,软件工具和库的安装与调试是每个开发者必经的挑战。最近,在安装和使用acados库时,我遇到了一个有趣的问题,这里我将详细分享我的经验和解决方案。

m0_62153576的博客 741

SAP 权限配置(复合角色)

复合角色创建步骤:业务场景:通常单一角色权限颗粒度划分比较细,当需要分配单一角色权限A、B(多个单一角色)给某账号用户Y时,可以先建立一个复合角色C,将A、B绑定到复合角色C中,再将角色C分配给账号Y即完成权限配置。 业务模拟: 1、PFCG创建复合角色: 2、在角色页面添加子角色 在角色分页内添加子角色,点击“菜单”分页中的“读菜单”即可导入子角色中的菜单: 3、将用户分配给...

willieyuan的博客 7089

k8s存储卷(empyDir;hostPath;nfs)

xxxxx

qq_44135433的博客 2133

RK3588 平台imx334 驱动调试记录

rk3588调试imx334

xiaoning132的专栏 1791

五、Kubernetes(K8S):Kubectl常用命令详解

Kubectl是Kubernetes命令行工具,用于管理Kubernetes集群资源和应用程序的部署、升级和扩展等。其作用包括但不限于: 1.创建、更新和删除应用程序、服务和负载均衡器等资源 2.管理集群节点的状态和部署 3.管理Kubernetes集群的配置和存储 4.监控集群和应用程序的状态 5.诊断和调试应用程序和集群故障 6.扩展和调整集群规模和资源使用 7.与Kubernetes API服务器通信 Kubectl是使用Kubernetes进行部署和管理的核心工具之一。

SteveRocket's-Blog 7273

Floyd (弗洛伊德)算法简述

Floyd在1962年由Robert Floyd以其当前公认的形式出版。算法作为三个嵌套for循环的现代公式首先由Peter Ingerman在1962年描述。Floyd 算法是解决图论问题的比较经典的算法,是解决给定的加权图中顶点间的最短路径的一种算法,可以正确处理有向图的最短路径问题。

慕羽★的博客 2万+

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

k8s之vpa浅析(附带案例)

VPA 参考链接:https://www.kubecost.com/kubernetes-autoscaling/kubernetes-vpa/ 官网链接:https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler VPA简介 kubernetes(VPA Vertical Pod Autoscaler)垂直 Pod 自动扩缩容, VPA 会基于 Pod 的资源使用情况自动为集群设置资源占用的限制,从而让集群将

jiayu的博客 4974

数学建模模型总结

这是本人在两年内准备参加数学建模竞赛搜集到的模型资料,已经经过了本人的筛选和整理,希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助!

k8s学习--kubernetes服务自动伸缩之垂直伸缩(资源伸缩)VPA详细解释与安装

有任何疑问或不懂的地方均可评论或私信,欢迎交流官方链接。## 简单理解 与HPA类似,区别在于HPA自动控制的pod副本数量 而VPA则自动控制的是CPU 和 内存 的requests,从而允许在节点上进行适当的调度,以便为每个 Pod 提供适当的资源。 注: 不能与HPA(Horizontal Pod Autoscaler )一起使用 这个是博主写的有关HPA的博客,有兴趣的可以看看 链接: [HPA详细解释与应用](https://blog.youkuaiyun.com/lwxvgdv/article/deta

lwxvgdv的博客 3573

TMS_WebGMaps_1.9.8.0_Full_Source

TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder

arm架构安装Rancher并导入k8s集群解决Error: no objects passed to apply

Rancher介绍 Rancher 2.0-2.4版本 是一个开源的企业级容器管理平台。通过Rancher,企业再也不必自己使用一系列的开源软件去从头搭建容器服务平台。Rancher提供了在生产环境中使用的管理Docker和Kubernetes的全栈化容器部署与管理平台。 Rancher 2.5版本 是为使用容器的公司打造的容器管理平台。Rancher 简化了使用 Kubernetes 的流程,开发者可以随处运行 Kubernetes(Run Kubernetes Everywhere),满足 IT 需求规

yinjl123456的博客 4161

【Kubernetes】kubectl rollout 回滚操作

kubectl rollout 怎么用搞不清?一遍文章搞定~

http://DBAtao.cn/ 1万+

kubectl rollout restart重启pod

学亮编程手记 5163

Kubernetes 1.5 实践 如何给pod中的容器设置环境变量

Kubernetes 1.5 给POD设置变量When you create a Pod, you can set environment variables for the containers that run in the Pod. To set environment variables, include the env field in the configuration file. 在

wenwst的专栏 1万+

【kubernetes/k8s概念】k8s 坑问题汇总

1.Pod始终处于Pending状态 如果Pod保持在Pending的状态,意味着无法被正常的调度到节点上。由于某种系统资源无法满足Pod运行的需求 系统没有足够的资源:已经用尽了集群中所有的CPU或内存资源。需要清理一些不在需要的Pod,调整它们所需的资源量,或者向集群中增加新的节点。 用户指定了hostPort:通过hostPort用户能够将服务暴露到指定的主机端口上,会限......

zhonglinzhang 3万+

kubectl 命令详解(三十四):rollout status

kubectl rollout status 命令详解。

youzhouliu的博客 1918

Kubernetes运维必掌握的12个Kubectl命令

Kubectl命令行工具是与Kubernetes交互的主要工具,你可以使用Kubectl在Kubernetes集群中创建、更新和管理资源,例如Pod,Service和Deployment等。接下来我们将了解Kubernetes运维必须掌握的12个Kubectl命令。

weixin_42257277的博客 1821

Kubernetes实战(十五)-Pod垂直自动伸缩VPA实战

VPA 全称 Vertical Pod Autoscaler,即垂直 Pod 自动扩缩容,它根据容器资源使用率自动设置 CPU 和 内存 的requests,从而允许在节点上进行适当的调度,以便为每个 Pod 提供适当的资源。它既可以缩小过度请求资源的容器,也可以根据其使用情况随时提升资源不足的容量。VPA不会改变Pod的资源limits值,只调整pod的request 值。VPA目前还没有生产就绪,在使用之前需要了解资源调节对应用的影响。

sre救赎之路 2998

must specify at least one container source

"必须指定至少一个容器来源" 的意思是,您需要指定一个容器(例如 Docker 容器)作为您的应用程序的来源。这意味着,您需要指定一个包含您的应用程序代码和依赖项的容器,以便在运行应用程序时使用。 ...

weixin_42581003的博客 3342
上一篇: kubectl源码分析之cluster-info
下一篇: kubectl源码分析之top node
hxpjava1
博客等级 码龄19年 1365粉丝 511原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

hxpjava1

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值