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Installing and Maintaining #Azure Kubernetes Cluster #AKS #ContainerInsights #AzureDevOps

Start Creating Azure Kubernetes Cluster for your Containers.

Managed Azure Kubernetes Service (AKS) makes deploying and managing containerized applications easy. It offers serverless Kubernetes, an integrated continuous integration and continuous delivery (CI/CD) experience, and enterprise-grade security and governance. As a hosted Kubernetes service, Azure handles critical tasks like health monitoring and maintenance for you. The Kubernetes masters are managed by Azure. You only manage and maintain the agent nodes. As a managed Kubernetes service, AKS is free – you only pay for the agent nodes within your clusters, not for the masters. In the following steps you can see the different ways for creating Azure Kubernetes Cluster via the Azure Portal, or via Azure Cloud Shell, or via Azure Resource Template. When the Microsoft Azure Kubernetes Cluster is running, then I will explain the different ways for deploying container workloads on AKS. When your workload is running on Azure Kubernetes Services, you also have to monitor your Container workloads with Azure Monitor Container Insights to keep in Controle. Let’s start with installing Azure Kubernetes Services (AKS)

Installing Azure Kubernetes Cluster via the Portal.

To begin you need of course a Microsoft Azure Subscription and you can start for free here

Basics information of the Azure Kubernetes Cluster

To Create the Azure Kubernetes Cluster, you have to follow these steps and type the right information in the Portal:

  1. Basics
  2. Scale
  3. Authentication
  4. Networking
  5. Monitoring
  6. Tags
  7. Review + Create

At the basics screen you select the right Azure Subscription and the Resource Group. You can create a New Resource Group or one you already made.
At Cluster details, you give your Cluster a name and select the Kubernetes version.

Here you select the Kubernetes Node size for your Container workload and the number of nodes.
You can start a Cluster already with One node, but choose to start with the right size for your workloads.
When you click on Change size, you can choose your nodes to do the job. 😉

Select the right Size node

Then we go to step 2 and that is Scale.

2. Scale options in Azure Kubernetes Cluster

Here you have two options :

  1. Virtual Nodes
  2. VM Scale sets (Preview)

To quickly deploy workloads in an Azure Kubernetes Service (AKS) cluster, you can use virtual nodes. With virtual nodes, you have fast provisioning of pods, and only pay per second for their execution time. In a scaling scenario, you don’t need to wait for the Kubernetes cluster autoscaler to deploy VM compute nodes to run the additional pods. Virtual nodes are only supported with Linux pods and nodes. More information here about Virtual Nodes

To create an AKS cluster that can use multiple node pools, first enable two feature flags on your subscription. Multi-node pool clusters use a virtual machine scale set (VMSS) to manage the deployment and configuration of the Kubernetes nodes. With this Preview feature you can run Linux Containers and Windows Containers on the same Cluster. More information here about VM Scale sets (Preview)

3, Authentication

The service principal is needed to dynamically create and manage other Azure resources such as an Azure load balancer or container registry (ACR). To interact with Azure APIs, an AKS cluster requires an Azure Active Directory (AD) service principal. More information about the Service Principal can be found here

Azure Kubernetes Service (AKS) can be configured to use Azure Active Directory (Azure AD) for user authentication. In this configuration, you can sign in to an AKS cluster by using your Azure AD authentication token.
Cluster administrators can configure Kubernetes role-based access control (RBAC) based on a user’s identity or directory group membership. More information about RBAC for AKS

4. Networking

Configuring the virtual Networks for your Azure Kubernetes Cluster is important for the right IP range but later on also for the Network Security Groups (NSG).

Here you see an example of the Kubernetes NSG which is connected to the Internet by Default after installation, you can deep dive into security but be careful which settings you do here because Microsoft resources must have access to service the Azure Kubernetes Cluster.

NSG created after installation is finished

NSG Rule set Inbound and outbound

In a container-based microservices approach to application development, application components must work together to process their tasks. Kubernetes provides various resources that enable this application communication. You can connect to and expose applications internally or externally. To build highly available applications, you can load balance your applications. More complex applications may require configuration of ingress traffic for SSL/TLS termination or routing of multiple components. For security reasons, you may also need to restrict the flow of network traffic into or between pods and nodes.

Best practices for network connectivity and security in Azure Kubernetes Service (AKS):

Here is more information about networking and Security for AKS

5. Monitoring

Keep Azure Monitoring Enabled and Connect to your Log Analytics workspace or create a new workspace for Container monitoring of your Azure Kubernetes Cluster.

Azure Monitor for containers is a feature designed to monitor the performance of container workloads deployed to either Azure Container Instances or managed Kubernetes clusters hosted on Azure Kubernetes Service (AKS). Monitoring your containers is critical, especially when you’re running a production cluster, at scale, with multiple applications.

Azure Monitor for containers gives you performance visibility by collecting memory and processor metrics from controllers, nodes, and containers that are available in Kubernetes through the Metrics API. Container logs are also collected. After you enable monitoring from Kubernetes clusters, metrics and logs are automatically collected for you through a containerized version of the Log Analytics agent for Linux. Metrics are written to the metrics store and log data is written to the logs store associated with your Log Analytics workspace.

6. Tags

When you build more Azure Kubernetes Clusters for different departments or teams you can TAG your Clusters for organizing your billing and security for example. Here you find more information about tagging.

After this you click on the last step Review and Create
The Azure portal will do a validation of your Azure Kubernetes Cluster settings, and when it’s validated you hit Create. But when you want more Automation, you can download the JSON ARM template first and use that.

Installing Azure Kubernetes Cluster via Cloud Shell

Azure Cloud Shell AKS CLI

Azure hosts Azure Cloud Shell, an interactive shell environment that you can use through your browser. Cloud Shell lets you use either bash or PowerShell to work with Azure services. You can use the Cloud Shell pre-installed commands to run the code in this article without having to install anything on your local environment.

Here you see an Example of AKS CLI with Auto Scaler with max count of nodes 😉

Installing Azure Kubernetes Cluster via Template

Create Azure Kubernetes Cluster via Template in the Portal

Here you find an Example at GitHub for a Template deployment

Now you have your Microsoft Azure Kubernetes Cluster (AKS) running in the Cloud, you want to deploy your Container workloads on the Cluster. In the following steps you see different deployments.

Deploy Container workload with Azure DevOps Project

Deployment Center

First you select your repository where your source code is of your workload.

Set the information right and click Next.

Simple example Click Next

Create a Container Registry.

Building Pipeline with Azure DevOps.

Here you see the Building in Microsoft Azure DevOps.

Build, test, and deploy in any language, to any cloud—or on-premises. Run in parallel on Linux, macOS, and Windows, and deploy containers to individual hosts or Kubernetes.

Here you find all the information about Microsoft Azure DevOps for your workloads, code and Deployments.

Deploying Container workload completed with Azure DevOps.

 

Deploy Container Workloads via Visual Studio Code

When you download and install Visual Studio Code on your computer, you can install the Azure Kubernetes extension for VSCode.

Install Kubernetes extension for VSCode

VSCode with Kubernetes Extension

Here you see Microsoft Visual Studio Code connected with my Azure subscription where my Azure Kubernetes Cluster is running. With the standard Helm Repository packages for deployment to your AKS Cluster. Here you see a WordPress yaml file which I deployed to the Kubernetes Cluster on Azure.

Just Select your Package and Install on Azure Kubernetes.

From here you can into the Container and read the logs.

I’m using Visual Studio Code a lot for Azure Kubernetes but also for Docker Containers and images.
Making Azure ARM JSON templates and this great for Infrastructure as Code.

 

Azure Monitoring with Container Insights

In One Dashboard you can see the Status of all your Clusters

 

Azure Monitor Container Insights Live View

Because we installed Azure Monitor for Containers on the Microsoft Azure Kubernetes Cluster, we can live see what is happening inside the Kubernetes Cluster with the containers. This is a great feature when you have a issue with a Container for troubleshooting fast and see what is happening.

Conclusion

Microsoft Azure Kubernetes Cluster is fast and easy to manage. You can upgrade your Cluster without downtime of your Container workload. With Azure Monitor for Containers you can see what’s happening inside the container and you can set alerts when something went wrong. This keeps you in Controle of the solution. With Deployment center alias Azure DevOps Projects you can deploy your workload via Azure DevOps Pipeline and work on versioning, testplans, Azure DevOps repo and work together with a Team on the following releases. Working with Azure Kubernetes Multi node pools with Linux and Windows on the same Cluster is possible. Try it yourself and start with a Proof of Concept for your Business.

JOIN Containers in the Cloud Community Group on LinkedIn


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Installing and Maintaining #Azure Kubernetes Cluster with Multi Pool Nodes (Preview) for #Linux #Winserv Containers

Install AKS-Preview extension via Azure Cloudshell

NOTE ! This is a Preview blogpost, do not use in production! (only for test environments)

To create an AKS cluster that can use multiple node pools and run Windows Server containers, first enable the WindowsPreview feature flags on your subscription. The WindowsPreview feature also uses multi-node pool clusters and virtual machine scale set to manage the deployment and configuration of the Kubernetes nodes. Register the WindowsPreview feature flag using the az feature register command as shown in the following example:

I Have registered the following Preview Features from the Azure CloudShell :

  • az feature register –name WindowsPreview –namespace Microsoft.ContainerService
  • az feature register –name MultiAgentpoolPreview –namespace Microsoft.ContainerService
  • az feature register –name VMSSPreview –namespace Microsoft.ContainerService

This will take a few minutes and you can check the registration with the following command :

az feature list -o table –query “[?contains(name, ‘Microsoft.ContainerService/WindowsPreview’)].{Name:name,State:properties.state}”

When ready, refresh the registration of the Microsoft.ContainerService resource provider using the az provider register command:

 

Creating Azure Kubernetes Cluster

First you create a Resource Group in the right Azure Region for your AKS Cluster to run:

az group create –name myResourceGroup –location eastus

I created Resource Group KubeCon in location West-Europe.

Creating KubeCluster

With the following CLI command in Azure Cloudshell, I created the Kubernetes Cluster with a single node:

$PASSWORD_WIN=”P@ssw0rd1234″

az aks create –resource-group KubeCon –name KubeCluster –node-count 1 –enable-addons monitoring –kubernetes-version 1.14.0 –generate-ssh-keys –windows-admin-password $PASSWORD_WIN –windows-admin-username azureuser –enable-vmss –network-plugin azure

The Azure Kubernetes Cluster “KubeCluster” is created in the resource group “KubeCon” in a view minutes.

Adding a Windows Pool

Adding a Windows Server Node Pool

By default, an AKS cluster is created with a node pool that can run Linux containers. Use az aks nodepool add command to add an additional node pool that can run Windows Server containers.

az aks nodepool add –resource-group KubeCon –cluster-name KubeCluster –os-type Windows –name pool02 –node-count 1 –kubernetes-version 1.14.0

I added the Windows Server Pool via the Azure Portal.

When this has finished, we have an Azure Kubernetes Cluster with Multi node Pools for Linux and Windows Server Containers :

Pools for Linux and Windows Server Containers

The following will be created in Microsoft Azure too :

VNET, NSG and Virtual Machine Scale Set (VMSS)

Azure Monitor for containers is a feature designed to monitor the performance of container workloads deployed to either Azure Container Instances or managed Kubernetes clusters hosted on Azure Kubernetes Service (AKS). Monitoring your containers is critical, especially when you’re running a production cluster, at scale, with multiple applications.
Azure Monitor for containers gives you performance visibility by collecting memory and processor metrics from controllers, nodes, and containers that are available in Kubernetes through the Metrics API. Container logs are also collected. After you enable monitoring from Kubernetes clusters, these metrics and logs are automatically collected for you through a containerized version of the Log Analytics agent for Linux and stored in your Log Analytics workspace.

Container Insights Monitoring of the Linux Node

Container Insights Monitoring of the Windows Server Node

Here you can read all about Azure Monitoring with Container Insights

Scaling Multi Pool Node AKS Cluster

To Scale your Multi Pool Node AKS Cluster, you need to do this via the Azure Cloudshell CLI.

Here you see the two pools ( Linux and Windows Server)

Scaling up the Windows Server Pool

You can do this with the following command :

az aks nodepool scale –resource-group KubeCon –cluster-name KubeCluster –name pool02 –node-count 2 –no-wait

Scaling

Scaling Succesful after a few minutes

Upgrading Windows Server Pool Instance

When I scaled the Cluster there was a update released by Microsoft.

Windows Server Pool Instances

Just Click on Upgrade

Upgrade is Done 😉


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How to monitor your #Kubernetes clusters – Best Practices Series #AKS #AzureMonitor

Get best practices on how to monitor your Kubernetes clusters from field experts in this episode of the Kubernetes Best Practices Series. In this intermediate level deep dive, you will learn about monitoring and logging in Kubernetes from Dennis Zielke, Technology Solutions Professional in the Global Black Belts Cloud Native Applications team at Microsoft.

Multi-cluster view from Azure Monitor

Azure Monitor provides a multi-cluster view showing the health status of all monitored AKS clusters deployed across resource groups in your subscriptions. It shows AKS clusters discovered that are not monitored by the solution. Immediately you can understand cluster health, and from here you can drill down to the node and controller performance page, or navigate to see performance charts for the cluster. For AKS clusters discovered and identified as unmonitored, you can enable monitoring for that cluster at any time.

Understand AKS cluster performance with Azure Monitor for containers

Container Live Logs provides a real-time view into your Azure Kubernetes Service (AKS) container logs (stdout/stderr) without having to run kubectl commands. When you select this option, new pane appears below the containers performance data table on the Containers view, and it shows live logging generated by the container engine to further assist in troubleshooting issues in real time.
Live logs supports three different methods to control access to the logs:

AKS without Kubernetes RBAC authorization enabled
AKS enabled with Kubernetes RBAC authorization
AKS enabled with Azure Active Directory (AD) SAML based single-sign on

You even can search in the Container Live Logs for Troubleshooting and history.

View Container Live logs with Azure Monitoring for AKS | Kubernetes | Containers 


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View Container Live logs with #Azure Monitoring #AKS #Kubernetes #Containers #AzureDevOps

Monitoring Azure Kubernetes Cluster

Azure Monitor for containers is a feature designed to monitor the performance of container workloads deployed to either Azure Container Instances or managed Kubernetes clusters hosted on Azure Kubernetes Service (AKS). Monitoring your containers is critical, especially when you’re running a production cluster, at scale, with multiple applications.
Azure Monitor for containers gives you performance visibility by collecting memory and processor metrics from controllers, nodes, and containers that are available in Kubernetes through the Metrics API. Container logs are also collected. After you enable monitoring from Kubernetes clusters, these metrics and logs are automatically collected for you through a containerized version of the Log Analytics agent for Linux and stored in your Log Analytics workspace.

Here you find awesome documentation about Understanding AKS cluster performance with Azure Monitor for containers

What I really like is that you now can see the Container Live logs from the Azure portal and see what is going on in the background of a Container 🙂

Activate Azure Kubernetes Container Live Logs

Here you see the Container Live logs

This feature provides a real-time view into your Azure Kubernetes Service (AKS) container logs (stdout/stderr) without having to run kubectl commands. When you select this option, new pane appears below the containers performance data table on the Containers view, and it shows live logging generated by the container engine to further assist in troubleshooting issues in real time.
Live logs supports three different methods to control access to the logs:

  1. AKS without Kubernetes RBAC authorization enabled
  2. AKS enabled with Kubernetes RBAC authorization
  3. AKS enabled with Azure Active Directory (AD) SAML based single-sign on

You even can search in the Container Live Logs for Troubleshooting and history :

Search on ssh

Azure Monitor for containers uses a containerized version of the Log Analytics agent for Linux. After initial deployment, there are routine or optional tasks you may need to perform during its lifecycle.
Because of this agent you can work with Log Analytics in Azure Monitor :

Log Analytics on Containers.

Here you find more on Log Analytics query language

Conclusion :

When you have your production workload running on Azure Kubernetes Clusters, It’s important to monitor to keep you in Control of the solution in Microsoft Azure and watch for improvements like performance for the business. With Container Live logs you can see what is going on in the Containers when you have issues and that’s great for troubleshooting to get your problem solved fast. Get your workload into Azure Containers and make your Azure DevOps CI/CD Pipelines in the Cloud.

Join the LinkedIn Community Groups for :

Containers in the Cloud

Azure DevOps Community

Microsoft Azure Monitor & Security for Hybrid IT


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Getting started with #Microsoft Azure Cognitive Services in #Containers #Azure #AI #AKS #Docker

Microsoft Visual Studio Code Tools for AI

With container support, customers can use Azure’s intelligent Cognitive Services capabilities, wherever the data resides. This means customers can perform facial recognition, OCR, or text analytics operations without sending their content to the cloud. Their intelligent apps are portable and scale with greater consistency whether they run on the edge or in Azure.

Bringing AI to the Edge via  Corporate Vice President, Azure AI Eric Boyd

Get started with these Azure Cognitive Services Containers

Building solutions with machine learning often requires a data scientist. Azure Cognitive Services enable organizations to take advantage of AI with developers, without requiring a data scientist. We do this by taking the machine learning models and the pipelines and the infrastructure needed to build a model and packaging it up into a Cognitive Service for vision, speech, search, text processing, language understanding, and more. This makes it possible for anyone who can write a program, to now use machine learning to improve an application. However, many enterprises still face challenges building large-scale AI systems. Today Microsoft announced container support for Cognitive Services, making it significantly easier for developers to build ML-driven solutions.

Microsoft got the following Containers :

  • Text Analytics Containers
  • Face Container
  • Recognize Text Container

More information from Director of Program Management Applied AI Lance Olson here

Start with Installing and running Containers

Request access to the private container registry

You must first complete and submit the Cognitive Services Vision Containers Request form to request access to the Face container. The form requests information about you, your company, and the user scenario for which you’ll use the container. Once submitted, the Azure Cognitive Services team reviews the form to ensure that you meet the criteria for access to the private container registry.

Important !

You must use an email address associated with either a Microsoft Account (MSA) or Azure Active Directory (Azure AD) account in the form. If your request is approved, you then receive an email with instructions describing how to obtain your credentials and access the private container registry.

Read more about installing the Containers here

The Face container uses a common configuration framework, so that you can easily configure and manage storage, logging and telemetry, and security settings for your containers.
Configuration settings
Configuration settings in the Face container are hierarchical, and all containers use a shared hierarchy, based on the following top-level structure:

  • ApiKey
  • ApplicationInsights
  • Authentication
  • Billing
  • CloudAI
  • Eula
  • Fluentd
  • Logging
  • Mounts

Read more here about Configuring the Containers

Follow Containers in the Cloud Community Group

 


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#Microsoft SQL Server 2019 Preview Overview #SQL #SQL2019 #Linux #Containers #MSIgnite

Microsoft SQL Server 2019 Preview

What’s New in Microsoft SQL Server 2019 Preview

• Big Data Clusters
o Deploy a Big Data cluster with SQL and Spark Linux containers on Kubernetes
o Access your big data from HDFS
o Run Advanced analytics and machine learning with Spark
o Use Spark streaming to data to SQL data pools
o Use Azure Data Studio to run Query books that provide a notebook experience

• Database engine
o UTF-8 support
o Resumable online index create allows index create to resume after interruption
o Clustered columnstore online index build and rebuild
o Always Encrypted with secure enclaves
o Intelligent query processing
o Java language programmability extension
o SQL Graph features
o Database scoped configuration setting for online and resumable DDL operations
o Always On Availability Groups – secondary replica connection redirection
o Data discovery and classification – natively built into SQL Server
o Expanded support for persistent memory devices
o Support for columnstore statistics in DBCC CLONEDATABASE
o New options added to sp_estimate_data_compression_savings
o SQL Server Machine Learning Services failover clusters
o Lightweight query profiling infrastructure enabled by default
o New Polybase connectors
o New sys.dm_db_page_info system function returns page information

• SQL Server on Linux
o Replication support
o Support for the Microsoft Distributed Transaction Coordinator (MSDTC)
o Always On Availability Group on Docker containers with Kubernetes
o OpenLDAP support for third-party AD providers
o Machine Learning on Linux
o New container registry
o New RHEL-based container images
o Memory pressure notification

• Master Data Services
o Silverlight controls replaced

• Security
o Certificate management in SQL Server Configuration Manager

• Tools
o SQL Server Management Studio (SSMS) 18.0 (preview)
o Azure Data Studio

Introducing Microsoft SQL Server 2019 Big Data Clusters

SQL Server 2019 big data clusters make it easier for big data sets to be joined to the dimensional data typically stored in the enterprise relational database, enabling people and apps that use SQL Server to query big data more easily. The value of the big data greatly increases when it is not just in the hands of the data scientists and big data engineers but is also included in reports, dashboards, and applications. At the same time, the data scientists can continue to use big data ecosystem tools while also utilizing easy, real-time access to the high-value data in SQL Server because it is all part of one integrated, complete system.

Read the complete Awesome blogpost from Travis Wright about SQL Server 2019 Big Data Cluster here

Starting in SQL Server 2017 with support for Linux and containers, Microsoft has been on a journey of platform and operating system choice. With SQL Server 2019 preview, we are making it easier to adopt SQL Server in containers by enabling new HA scenarios and adding supported Red Hat Enterprise Linux container images. Today we are happy to announce the availability of SQL Server 2019 preview Linux-based container images on Microsoft Container Registry, Red Hat-Certified Container Images, and the SQL Server operator for Kubernetes, which makes it easy to deploy an Availability Group.

SQL Server 2019 preview containers now available

Microsoft Azure Data Studio

Azure Data Studio is a new cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. Previously released under the preview name SQL Operations Studio, Azure Data Studio offers a modern editor experience with lightning fast IntelliSense, code snippets, source control integration, and an integrated terminal. It is engineered with the data platform user in mind, with built-in charting of query resultsets and customizable dashboards.

Read the Complete Blogpost About Microsoft Azure Data Studio for SQL Server here

SQL Server 2019: Celebrating 25 years of SQL Server Database Engine and the path forward

Awesome work Microsoft SQL Team and Congrats on your 25th Anniversary !


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Upgrading Azure #Kubernetes Cluster and Set #Azure monitor Alerts on #AKS


Current version of Kubernetes on Microsoft Azure.

Upgrading Microsoft Azure Kubernetes Services

Azure Kubernetes Service (AKS) makes it simple to deploy a managed Kubernetes cluster in Azure. AKS reduces the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Azure. As a hosted Kubernetes service, Azure handles critical tasks like health monitoring and maintenance for you. In addition, the service is free, you only pay for the agent nodes within your clusters, not for the masters.

AKS clusters support Role-Based Access Control (RBAC). An AKS cluster can also be configured to integrate with Azure Active Directory. In this configuration, Kubernetes access can be configured based on Azure Active Directory identity and group membership.
For more information, see, Integrate Azure Active Directory with AKS.

From here I will do a step-by-step Upgrade of a Microsoft Azure Kubernetes Cluster to a newer version and set Azure Monitor alert rule active for the future to get an Alert notification when a colleague is upgrading the AKS Services.

Here you see all the newer versions of Kubernetes.

Upgrading to version 1.11.1 of Kubernetes.

IMPORTANT NOTE :

When upgrading an AKS cluster, Kubernetes minor versions cannot be skipped. For example, upgrades between 1.8.x -> 1.9.x or 1.9.x -> 1.10.x are allowed, however 1.8 -> 1.10 is not. To upgrade, from 1.8 -> 1.10, you need to upgrade first from 1.8 -> 1.9 and then another do another upgrade from 1.9 -> 1.10

KubeCluster Activity Log

At the green arrow on this picture you can download the activities into CSV file. At the Red arrow you see the User ID who initiated the Upgrade of the Kubernetes Cluster. This is important information for Azure Alert monitoring.

10 minutes later Kubernetes Cluster is Upgraded to version 1.11.1

Upgrade is done.

We now do a minor Upgrade of Kubernetes from version 1.11.1 to 1.11.2 to get the newest version on Azure.
Click on 1.11.2 version and hit Save.

 

Microsoft Azure Monitoring Alerts

When you click on the second activity of the Upgrade you see at arrow 2 that you can add an Activity Log Alert by Azure monitoring.

Creating Rule Alerts.

  1. Define Alert condition is already set. We want an Alert notification on Upgrading KubeCluster.
  2. Define Alert details, must be set.
  3. Define Action Group, must be set to create the Alert Rule.

2. Define the Alert Details.

3. Define Action Group : Click on + New Action Group

Click on OK

Created Action Group name AKSAdmins

An action group is a collection of notification preferences defined by the user. Azure Monitor and Service Health alerts are configured to use a specific action group when the alert is triggered. Various alerts may use the same action group or different action groups depending on the user’s requirements.

More information on Creating and managing action groups in the Azure portal can be found here

For information on how to use Azure Resource Manager templates to configure action groups, see Action group Resource Manager templates.

 

From here you can Create the Alert Rule and make it Active.

Azure Monitor Alerts with one rule Enabled.

Here is our Active KubeCluster Alert Rule.

Now we will get a notification when a Colleague is Upgrading our KubeCluster in the Future 😉

KubeCluster is now running the latest available version of Kubernetes.

Kubernetes Cluster nodes are Healthy and running version 1.11.2

Here you see in the Kubernetes Dashboard the Node version of Kubernetes.

For Developers and DevOps it’s Great to work with Microsoft Visual Studio Code and the Azure Kubernetes Services (AKS) to work in a CI/CD Pipeline, to create continuous business applications in the Cloud.

Here is my Azure KubeCluster running in Visual Studio Code 🙂

And at last, most important thing is that my Application is running on my Azure Kubernetes Cluster for the Business My Test Site.

Hope this blogpost is useful for you and your business to manage your AKS Cluster in the Microsoft Cloud.

More information About Azure Kubernetes Service (AKS) :

 Upgrade an Azure Kubernetes Service (AKS) cluster via Azure CLI

Azure Kubernetes Service (AKS) Docs

Monitor Azure Kubernetes Service (AKS) container health (preview)

Microsoft Azure Kubernetes Services website Start Free here

Follow Containers in the Cloud Community Group on LinkedIn