Setting up a Raspberry Pi Kubernetes Cluster (with Blinkt! Strips that Show Number of Pods per Node!) Using k3sup

Posted by Graham Smith on November 19, 2019No Comments (click here to comment)

If, like me, you are interested in the worlds of both Raspberry Pi and Kubernetes you may have built or considered building a Raspberry Pi Kubernetes cluster (see here for just one of many examples). I built a three-unit cluster in early 2018 using Raspberry Pi 3 Model B+ boards and bootstrapped Kubernetes using an early version of Alex Ellis' guide and it was all pretty straightforward. By itself it's not a great thing to demo (in my case at my local Raspberry Jam for example) as there is no display so a nice improvement is to fit Pimoroni Blinkt! LED strips to the GPIO pins of each unit and then use the guide here to make the LEDs light up according to the number of pods that have been deployed. The Blinkt! improvement went fine and was working nicely—right up to the day when I decided to upgrade the Raspberry Pi OS from Raspbian Stretch to Raspbian Buster (which came out to support the new Raspberry Pi 4 model).

The first problem was that Docker wouldn't install on Buster but that was solved through a post by Alex Ellis. However I encountered other problems such as the swapfile not turning off between reboots and (critically) the Weave networking pods failing to start and I spent a lot of time messing around to no avail. The sensible option would have been to revert to Raspbian Stretch but by now I had the bit between my teeth and I wasn't giving up lightly. After even more messing about trying different configurations and getting nowhere I decided to follow Alex Ellis' advice and try k3s—a stripped-down version of Kubernetes from Rancher Labs (the k3s name is a twist on the often-used k8s abbreviation for Kubernetes). In fact, you can make things even easier by using Alex's k3sup tool to automate most of the process.

TL;DR: I had everything up-and-running on Raspbian Buster in next no time at all, including the Blinkt! LED strips displaying the number of pods on each node!

If you are looking to learn bare-metal Kubernetes installation then k3s/k3sup may not be for you. But if you just want to get a cluster configured with minimal fuss then it's just the ticket. As always there were a few twists and turns so here is a write-up of what worked for me, although I'm not documenting every single step because it's already well covered by Alex. I'm using a Windows 10 development machine so this write-up is from that perspective, however Alex's documentation is more Linux/macOS focused so everyone should be able to follow along.

Install k3s Using k3sup

I used three guides that together provided a complete picture for installing k3s on the Raspberry Pi platform using k3sup:

  1. Will it cluster? k3s on your Raspberry Pi
  2. Kubernetes Homelab with Raspberry Pi and k3sup
  3. k3sup

Use the first guide to (if necessary) build your cluster and then prepare each Pi. In addition to setting the GPU memory split, changing the hostname and changing the password for each Pi I also expanded the filesystem to use all of the SD card. You will need the IP addresses of your master and worker nodes so setting static IP addresses is a good way to go.

Follow the first guide up to and including Enable container features. I simply used sudo nano /boot/cmdline.txt to edit the file being careful not to add an extra line.

Use guides 2 and 3 to install k3s using k3sup. The key point to understand here is that you run k3sup from your development machine. As I'm running Windows 10 it was a case of grabbing the Windows binary from the k3sup releases page and copying it to my working folder for this project. On Windows bootstrapping the master node is simply a matter of opening a command prompt at your working folder and running this command, replacing $SERVER with the IP address of the master node:

Bootstrapping the worker nodes is similarly straightforward:

where $AGENT is the IP of the worker node and $SERVER the IP of the master node.

Communicating with the Cluster using kubectl

The k3s installation includes kubectl so from this point on it's just like working with any standard Kubernetes cluster. You'll obviously need kubectl installed on your development machine, and then you configure kubectl to talk to the cluster using whichever of the several techniques works best for you, using the kubeconfig file that is handily copied to the working folder on your workstation. In my case I chose simply to copy kubeconfig to ~\.kube\ (which had been previously created through working with Azure Kubernetes Service but you can create it yourself) and rename kubeconfig to config to end up with C:\Users\Graham\.kube\config. Running kubectl get nodes establishes that everything is (hopefully!) working correctly.

Configuring the Cluster so Pimoroni Blinkt! LED strips Indicate the Number of Pods per Node

The master guide to follow is here. With the Blinkt! LED strips installed you'll need to download the following files from the guide's repo to your working folder:

  • kubernetes/blinkt-k8s-controller-rbac.yaml
  • kubernetes/blinkt-k8s-controller-ds.yaml

You'll also need to create a manifest containing a deployment. Create a file in the working folder called deployment.yaml and copy the following:

Now run the following commands against the cluster, from a command prompt at the working folder:

  1. kubectl label node $NODE_NAME deviceType=blinkt (where $NODE_NAME is the name of each node with a Blinkt! strip)
  2. kubectl create -f blinkt-k8s-controller-rbac.yaml
  3. kubectl create -f blinkt-k8s-controller-ds.yaml
  4. kubectl apply -f deployment.yaml

At this point you should see five LEDs light up according to how Kubernetes has decided which nodes the five pods should run on. You can now open deployment.yaml in your favourite code editor and play around with the number of replicas, repeating the final command above after saving each change to the file. Watch in joy as pods are created with a green flash and destroyed with a red flash, and settle on a satisfying blue (which you can change in blinkt-k8s-controller-ds.yaml ) for running pods.

Final Thoughts

I've been thrilled with how easy k3sup makes installing k3s and even if you do want to experience the pain thrill of the kubeadm procedure on Raspberry Pi I would still recommend you check out Alex's posts mentioned here and others on his blog as they offer tremendous extra value and learning.

Cheers -- Graham

A Better Way of Deploying a Dockerized Application to Azure Kubernetes Service Using Azure Pipelines

Posted by Graham Smith on January 21, 2019No Comments (click here to comment)

Throughout 2018 I wrote a mini blog post series aimed at providing specific and detailed guidance on how to create a CI/CD pipeline using VSTS/Azure DevOps to deploy a dockerized ASP.NET Core application to Azure Kubernetes Service (AKS):

Whilst the resulting solution works I wasn't entirely happy with several aspects and I've spent a great deal of time thinking and tinkering to come up with something better. In this blog post I explain what I wasn't happy with and how my new solution addresses most of my concerns. You don't necessarily need to read the posts above as I'm going to provide some context, but it will probably make things much clearer if you are planning to implement any of my suggestions.

The sample application I've been using to deploy to Kubernetes consists of the following components:

  • ASP.NET Core web application, that sends messages to a
  • NATS message queue service, which pushes messages to a
  • .NET Core message queue handler application, which saves messages to an
  • Azure SQL database

Apart from the database all the components run as docker containers. The container images are built in in an Azure Pipelines build pipeline and images pushed to an Azure Container Registry (ACR). An Azure Pipelines release pipeline then deploys the necessary services and deployments to AKS which causes the images to be pulled from ACR and instantiated as containers inside pods. My release pipeline consists of two environments: dat (developer automated test where automated acceptance tests might take place) and prd (production). That's just arbitrary of course and in a live scenario the pipeline can have whatever environments are needed.

My sample application is called MegaStore and you can find the code on GitHub here. In the rest of this post I explain my areas of concern and how I addressed them.

Azure Pipelines Tasks

Whilst there is no doubt that Azure Pipelines Tasks are great for quickly building a pipeline and definitely make it easier for those less familiar with the technology behind a task to get started, I now see some tasks as more of a curse than a blessing. I've particularly taken issue with tasks that manipulate a command line application (such as docker or kubectl) and which results in the task becoming something of a Swiss Army Knife task. Why have I taken issue? There are several reasons, some specific to the Swiss Army Knife variety and some of tasks in general:

  • There is often a need to set mandatory fields in ‘Swiss Army Knife' tasks even though those parameters will not be used by the chosen sub-command. Where there are multiple instances of the same task in use this becomes very tedious and is a potential maintenance problem when something changes. (Yes, I know tasks can be cloned but this doesn't make me any happier.)
  • Tasks by their nature only allow you to do what they have been coded to do and you can sometimes find yourself in a blind alley. For example, at the time of writing the only way I know of updating an existing Kubernetes ConfigMap without deleting it first and re-creating it is with a piped command, for example:

    Running a command such as this isn't possible with the current Deploy to Kubernetes Azure DevOps task, which is very limiting.
  • Speaking of command lines, my next issue is that tasks abstract you from what is actually going on behind the scenes. For simple tasks such as copying files this might be fine, however I've become frustrated at the way tasks such as Docker or Deploy to Kubernetes ‘hide' what they are doing, and the way that makes fine-tuning that little bit harder. Additionally, for me it's also a lost learning opportunity—a missed chance to learn the full syntax of a command because the task is constructing it on your behalf.
  • Another big issue is that tasks such as Docker or Deploy to Kubernetes offer nothing in the way of code usability, and break the DRY principle in multiple dimensions (ie there is scope for repetition within an environment and also across environments). To illustrate, the release pipeline in my 2018 mini blog series consisted of no fewer than 30 Deploy to Kubernetes tasks across two environments, resulting in a great deal of repetition.
  • Finally, the use of tasks in the current version of Azure Pipelines releases means that you don't have your ‘code' under proper version control. I know there are changes coming that will help to address this, and whilst they will be welcome I think there is an opportunity to do better.

So what's my solution to all this? Very simply, get rid of multiple Swiss Army Knife tasks and implement Bash scripts running from a single Bash task. I started off by using the Inline script feature of Bash tasks but this didn't help with getting code in to version control and I also quickly realised that there were big code reusability opportunities to be had across environments by using File Path scripts. By using Bash scripts stored in the repo I solved all the issues mentioned above and in the case of the release portion of the pipeline I reduced the number of tasks from 15 in each environment to two! What follows are the techniques I used to achieve this for the Docker builds and Kubernetes deployments.

Converting Docker builds to use a Bash script was reasonably straightforward so I'll start by discussing the first problem I encountered when converting Deploy to Kubernetes tasks to Bash scripts, which was how to authenticate to Kubernetes. Tasks rely on the creation of a Kubernetes service connection (Project Settings > Service connections) and I'd been using the Kubeconfig version which involves pasting in the contents of the Kubeconfig file that gets created (if you run the appropriate command) when you set up an AKS cluster:

By tracing the logging output of the Deploy to Kubernetes tasks I could see what was happening: a Kubeconfig file was being saved to disk and referenced in a kubectl command using the --kubeconfig parameter that points to the file on disk. I could successfully pass the file in from an Artifact as a proof of concept but how to store the Kubeconfig contents securely and create the file dynamically? The obvious choice was a secret variable however that didn't work because it destroyed the Kubeconfig formatting which is important in the re-hydrated file on disk. After a lot of fiddling I finally turned to LoECDA who are super-responsive via Twitter, and very quickly the suggestion came back to try using Secure files (Pipelines > Library > Secure files). This worked perfectly: a file is first uploaded to the Secure files area and this is then available for use using the Download Secure File task. The file is downloaded in to a temporary folder which can be referenced as the $AGENT_TEMPDIRECTORY variable in a Bash script. Great!

Next up was sorting out the practicalities of using Bash scripts in Bash tasks. I created a deployment (dep) folder in the repo to hold the scripts and then arranged for this folder to be available as an Artifact created directly from the GitHub repo:

I used VS Code to create the Bash files however in order for the file to be executed as a Bash script it needs its permissions setting to make it executable (chmod +x). This needs to be done from a Linux environment and there are several possibilities for achieving this including Windows Subsystem for Linux if you are on Windows 10. I chose to go with Azure Cloud Shell, which can be configured to run either a Bash or a PowerShell command line in the cloud! Once that was configured it was a case of cloning my repo, navigating to the dep folder and running chmod +x some-filename-sh. There's no GUI in Azure Cloud Shell so it does involve using git commands to push the changes back to GitHub. If this is new to you then git add *, git commit -m "Commit message" and git push origin master are what you need. To authenticate you'll likely need to use a personal access token unless you go to the bother of setting up SSH. It gets to be a bit of a pain having to enter credentials every time you want to push to GitHub however the git config credential.helper store command will save credentials across Azure Cloud Shell sessions to make life easier.

Finding out what commands needed to be executed in the Bash scripts required a bit of detective work, and involved a combination of understanding what the task was attempting to accomplish and then looking at the build or release logs to see the actual output. With the basic command figured out this exercise offered the opportunity to do a bit of fine tuning. For example, I'd been tagging my docker images with the latest tag but it turns out that this isn't a great idea for release pipelines. By writing the actual command myself I was able to get exactly what I wanted.

I describe how I organised the Bash scripts to move away from a monolithic pipeline below. In this section I want to describe the tips and tricks I used to actually write the Bash scripts. Generally, the scripts make heavy use of variables to make them applicable to all release environments, however there are some essential things to know:

  • Variables created as part of Azure DevOps pipelines can be used as variables (ie passed in to a script) however with the exception of secrets they are also created as environment variables which are available directly in scripts. This means that a variable created as MyVariable is available as $MYVARIABLE directly in a Bash script (in Bash scripts the variable is really a constant which convention dictates should be in upper case and any periods need converting to underscores to ensure valid syntax).
  • Variables created as part of Azure DevOps pipelines can have the same name as long as they are scoped to a different environment. So you can have two variables called MyVariable with different values for each environment and simply refer to $MYVARIABLE in the Bash script, ie no need to pass $MYVARIABLE in as a parameter to the script for different environments.
  • As mentioned above, secrets are not created as environment variables and must be passed in to a script via the Arguments field, and in the script a variable is declared to accept the incoming parameter. Important: as of the time of writing a secret needs to be passed in to the Argument field as $(MYSECRET) ie with parentheses around the actual parameter name. If you omit the parentheses the secret is not passed in. A non-secret parameter doesn't require parentheses and I have queried whether this is a a bug here.
  • Later in this post I explain how I break up a monolithic pipeline in to multiple pipelines, which results in the same variables being needed in different pipelines. By using Variable Groups I was able to avoid repeated variable declarations and manage many variables from just one location.
  • In addition to variables that are created manually, built-in variables are also available as environment variables in the script. The ones I've used are $AGENT_TEMPDIRECTORY to define the download location of the Kubeconfig file from the Secure files area, $RELEASE_ENVIRONMENTNAME to refer to the environment (ie dat or prd) and also $BUILD_BUILDNUMBER used to tag docker images with a unique build number in the build process and then to refer to them by their unique name in the release. However, there are many built-in variables available to use—see here for details but remember that for use in Bash scripts you should change text to uppercase and must replace periods with an underscore.

I'm not a Bash scripting expert and I'm sure my scripts would be considered very rudimentary. The great thing though is that you can do whatever you like now the code is a script. Possibilities might include adding error handling or refactoring further using functions. There's potential to really go to town here.

Monolithic Pipeline

At the time of writing this article in early 2019 there aren't that many blog post examples of implementing a CI/CD pipeline to deploy an application to Kubernetes. Furthermore, the posts that do exist tend, not unreasonably, to use a simplistic application scenario to illustrate the concepts. Typically, this involves deploying the whole application as part of a single pipeline, and indeed this is the route I took with my 2018 blog post mini series. However, it became quickly apparent to me that this is an unsatisfactory arrangement for two main reasons:

  • Just one change to one of the application components would cause all the components of the application to be redeployed (or more correctly the parts of the application that have their docker images built by the pipeline).
  • A change to the Kubernetes configuration would also trigger a redeployment of all of the application components. Sometimes this is necessary but often it's not.

These issues arise because the trigger for the build component of the pipeline is set as the root of the GitHub repo, so if anything changes in the repo a build is triggered. Clearly not an optimal situation.

My solution to this problem is to divide the monolithic pipeline in to multiple pipelines that correspond to the individual components of the overall application. Then with a bit of refactoring of the codebase it's possible to use a very nifty feature of Azure Pipelines that allows a build to be triggered from one or more specific folders (or files for that matter) in the repo, ie a much more granular solution.

One complication that I had to cater for is that the pipeline isn't just building docker images and marshalling them in to the Kubernetes cluster: additionally, the pipeline is configuring Kubernetes elements such as Namespaces, Secrets and ConfigMaps.

Through the use of Bash scripts as described above the number of tasks needed is drastically reduced: just one Bash task for the builds and two tasks for releases (a Download Secure File task to copy the kubeconfig file to disk and a Bash task to host the bash script). All scripts are Namespace/environment aware.

In terms of Azure Pipelines build and release pipelines my current CI/CD solution is as follows:

megastore.init.release

This is a release that is not associated with a build and its sole purpose is to configure a Kubernetes Namespace in preparation for the deployment of the application. As such, this component is only intended to be run to either initialise a new Kubernetes cluster or (rarely) if one of the configuration items needs to change (in which case elements of the application will likely have to be redeployed for the configuration to be built in to the appropriate pods).

The configuration handled by megastore.init.release is as follows:

  • Creation of a Namespace for a corresponding Azure Pipelines environment.
  • Creation (or update) of ACR credentials (as a specialised Secret) that allow Deployments to pull docker images from ACR.
  • Creation (or update) of the message queue URL as a ConfigMap.
  • Creation (or update) of the Application Insights instrumentation key as a ConfigMap.

This configuration is handled by init.sh.

megastore.message-queue.release

This is another release that is not associated with a build, and in this case the requirement is to deploy the NATS message queue service. The absence of a build is due to the docker image being pulled from Docker Hub. The downside of not having a build associated with the release is that if any of the NATS configuration changes the release needs to be triggered manually. I see this as an infrequent requirement though. The message queue service doesn't have any dependencies on any other part of the application and so is the first component to be deployed following the initial Kubernetes configuration.

The configuration handled by megastore.message-queue.release is as follows:

  • Deployment of the Kubernetes Service for the message queue.
  • Deployment of the Kubernetes Deployment for the message queue.

This configuration is handled by message-queue.sh.

megastore.savesalehandler.build and megastore.savesalehandler.release

This build and linked release are responsible for deploying a new version of the .NET Core message queue handler application which receives message from the message queue and saves them to an Azure SQL database. The docker image is built and uploaded to ACR using this generic Bash script. This in turn triggers the megastore.savesalehandler.release which deals with the following configuration:

  • Creation (or update) of the database connection string as a Secret.
  • Deployment of the Kubernetes Deployment for the message queue handler component.
  • Update the image for the Deployment to the latest version using the unique tag for the build that triggered the release.

This configuration is handled by megastore-savesalehandler.sh. The build is triggered through the Azure Pipelines Path filters feature:

Using the Path filters feature ensures that the build will only be triggered for continuous integration if a file in the specified folder is changed.

megastore.web.build and megastore.web.release

This build and linked release are responsible for deploying a new version of the ASP.NET Core web application which sends messages to the message queue service. As with the message queue handler, the docker image is built and uploaded to ACR using this generic Bash script. The build triggers the megastore.web.release which deals with the following configuration:

  • Creation (or update) of the ASPNETCORE_ENVIRONMENT environment variable as a ConfigMap.
  • Deployment of the Kubernetes Deployment for the web component.
  • Deployment of the Kubernetes Service for the web component.
  • Update the image for the Deployment to the latest version using the unique tag for the build that triggered the release.

This configuration is handled by megastore-web.sh and once again the build is triggered through the Azure Pipelines Path filters feature:

As before, using the Path filters feature ensures that the build will only be triggered for continuous integration if a file in the specified folder is changed.

And Finally...

In breaking down a monolithic pipeline in to multiple pipelines I exposed the problem of what to do with the shared helper library of functions that is use both by the megastore.web and megastore.savesalehandler components, because if this code changes one or sometimes both components will need redeploying. I think the answer is that helper libraries like these do not belong in the Visual Studio solution and instead should be developed separately and distributed and referenced as NuGet packages.

One of my aspirations is to get as much pipeline configuration in the GitHub repo as possible and you might well ask why I'm not using yaml files. Apart from the fact that I just haven't had time to look at this in detail yet, at the time of writing it's only a partial solution as it's only available for the build portion of the pipeline. This will change hopefully later this year when the release portion of the pipeline is supported, and at that point I'll make the switch.

That's it for now! Whether you are deploying to AKS or somewhere else I hope this post has provided you with ideas to supercharge your Azure DevOps pipelines.

Cheers -- Graham

Deploy a Dockerized ASP.NET Core Application to Azure Kubernetes Service Using a VSTS CI/CD Pipeline: Part 4

Posted by Graham Smith on September 11, 2018No Comments (click here to comment)

In this blog post series I'm working my way through the process of deploying and running an ASP.NET Core application on Microsoft's hosted Kubernetes environment. These are the links to the full series of posts to date:

In this post I take a look at application monitoring and health. There are several options for this however since I'm pretty much all-in with the Microsoft stack in this blog series I'll stick with the Microsoft offering which is Azure Application Insights. This posts builds on previous posts, particularly Part 3, so please do consider working through at least Part 3 before this one.

In this post, I continue to use my MegaStore sample application which has been upgraded to .NET Core 2.1, in particular with reference to the csproj file. This is important because it affects the way Application Insights is configured in the ASP.NET Core web component. See here and here for more details. All my code is in my GitHub repo and you can find the starting code here and the finished code here.

Understanding the Application Insights Big Picture

Whilst it's very easy to get started with Application Insights, configuring it for an application with multiple components which gets deployed to a continuous delivery pipeline consisting of multiple environments running under Kubernetes requires a little planning and a little effort to get everything configured in a satisfactory way. As of the time of writing this isn't helped by the presence of Application Insights documentation on both docs.microsoft.com and github.com (ASP.NET Core | Kubernetes) which sometimes feels like it's conflicting, although it's nothing that good old fashioned detective work can't sort out.

The high-level requirements to get everything working are as follows:

  1. A mechanism is needed to separate out telemetry data from the different environments of the continuous delivery pipeline. Application Insights sends telemetry to a ‘bucket' termed an Application Insights Resource which is identified by a unique instrumentation key. Separation of telemetry data is therefore achieved by creating an individual Application Insights Resource, each for the development environment and the different environments of the delivery pipeline.
  2. Each component of the application that will send telemetry to an Application Insights Resource needs configuring so that it can be supplied with the instrumentation key for the Application Insights Resource for the environment the application is running in. This is a coding issue and there are lots of ways to solve it, however in the MegaStore sample application this is achieved through a helper class in the MegaStore.Helper library that receives the instrumentation key as an environment variable.
  3. The MegaStore.Web and MegaStore.SaveSaleHandler components need configuring for both the core and Kubernetes elements of Application Insights and a mechanism to send the telemetry back to Azure with the actual name of the component rather than a name that Application Insights has chosen.
  4. Each environment needs configuring to create an instrumentation key environment variable for the Application Insights Resource that has been created for that environment. In development this is achieved through hard-coding the instrumentation key in docker-compose.override.yaml. In the deployment pipeline it's achieved through a VSTS task that creates a Kubernetes config map that is picked up by the Kubernetes deployment configuration.

That's the big picture—let's get stuck in to the details.

Creating Application Insights Resources for Different Environments

In the Azure portal follow these slightly outdated instructions (Application Insights is currently found in Developer Tools) to create three Application Insights Resources for the three environments: DEV, DAT and PRD. I chose to put them in one resource group and ended up with this:

For reference there is a dedicated Separating telemetry from Development, Test, and Production page in the official Application Insights documentation set.

Configure MegaStore to Receive an Instrumentation Key from an Environment Variable

As explained above this is a specific implementation detail of the MegaStore sample application, which contains an Env class in MegaStore.Helper to capture environment variables. The amended class is as follows:

Obviously this class relies on an external mechanism creating an environment variable named APP_INSIGHTS_INSTRUMENTATION_KEY. Consumers of this class can reference MegaStore.Helper and call Env.AppInsightsInstrumentationKey to return the key.

Configure MegaStore.Web for Application Insights

If you've upgraded an ASP.NET Core web application to 2.1 or later as detailed earlier then the core of Application Insights is already ‘installed' via the inclusion of the Microsoft.AspNetCore.All meta package so there is nothing to do. You will need to add Microsoft.ApplicationInsights.Kubernetes via NuGet—at the time of writing it was in beta (1.0.0-beta9) so you'll need to make sure you have told NuGet to include prereleases.

In order to enable Application Insights amend BuildWebHost in Program.cs as follows:

Note the way that the instrumentation key is passed in via Env.AppInsightsInstrumentationKey from MegaStore.Helper as mentioned above.

Telemetry relating to Kubernetes is enabled in ConfgureServices in Startup.cs as follows:

Note also that a CloudRoleTelemetryInitializer class is being initialised. This facilitates the setting of a custom RoleName for the component, and requires a class to be added as follows:

Note here that we are setting the RoleName to MegaStore.Web. Finally, we need to ensure that all web pages return telemetry. This is achieved by adding the following code to the end of _ViewImports.cshtml:

and then by adding the following code to the end of the <head> element in _Layout.cshtml:

Configure MegaStore.SaveSaleHandler for Application Insights

I'll start this section with a warning because at the time of writing the latest versions of Microsoft.ApplicationInsights and Microsoft.ApplicationInsights.Kubernetes didn't play nicely together and resulted in dependency errors. Additionally the latest version of Microsoft.ApplicationInsights.Kubernetes was missing the KubernetesModule.EnableKubernetes class described in the documentation for making Kubernetes work with Application Insights. The Kubernetes bits are still in beta though so it's only fair to expect turbulence. The good news is that with a bit of experimentation I got everything working by installing NuGet packages Microsoft.ApplicationInsights (2.4.0) and Microsoft.ApplicationInsights.Kubernetes (1.0.0-beta3). If you try this long after publication date things will have moved on but this combination works with this initialisation code in Program.cs:

Please do note that this a completely stripped down Program class to just show how Application Insights and the Kubennetes extension is configured. Note again that this component uses the CloudRoleTelemetryInitializer class shown above, this time with the RoleName set to MegaStore.SaveSaleHandler. What I don't show here in any detail is that you can add lots of client.Track* calls to generate rich telemetry to help you understand what your application is doing. The code on my GitHub repo has details.

Configure the Development Environment to Create an Instrumentation Key Environment Variable

This is a simple matter of editing docker-compose.override.yaml with the new APP_INSIGHTS_INSTRUMENTATION_KEY environment variable and the instrumentation key for the corresponding Application Insights Resource:

Make sure you don't just copy the code above as the actual key needs to come from the Application Insights Resource you created for the DEV environment, which you can find as follows:

Configure the VSTS Deployment Pipeline to Create Instrumentation Key Environment Variables

The first step is to amend the two Kubernetes deployment files (megastore-web-deployment.yaml and megastore-savesalehandler-deployment.yaml) with details of the new environment variable in the respective env sections:

Now in VSTS:

  1. Create variables called DatAppInsightsInstrumentationKey and PrdAppInsightsInstrumentationKey scoped to their respective environments and populate the variables with the respective instrumentation keys.
  2. In the task lists for the DAT and PRD environments clone the Delete ASPNETCORE_ENVIRONMENT config map and Create ASPNETCORE_ENVIRONMENT config map tasks and amend them to work with the new APP_INSIGHTS_INSTRUMENTATION_KEY environment variable configured in the *.deployment.yaml files.

Generate Traffic to the MegaStore Web Frontend

Now the real fun can begin! Commit all the code changes to trigger a build and deploy. The clumsy way I'm having to delete an environment variable and then recreate it (to cater for a changed variable name) will mean that the release will be amber in each environment for a couple of releases but will hopefully eventually go green. In order to generate some interesting telemetry we need to put one of the environments under load as follows:

  1. Find the public IP address of MegaStore.Web in the PRD environment by running kubectl get services --namespace=prd:
  2. Create a PowerShell (ps1) file with the following code (using your own IP address of course):
  3. Run the script (in Windows PowerShell ISE for example) and as long as the output is white you know that traffic is getting to the website.

Now head over to the Azure portal and navigate to the Application Insights Resource that was created for the PRD environment earlier and under Investigate click on Search and then Click here (to see all data in the last 24 hours):

You should see something like this:

Hurrah! We have telemetry! However the icing on the cake comes when you click on an individual entry (a trace for example) and see the Kubernetes details that are being returned with the basic trace properties:

Until Next Time

It's taken my quite a lot of research and experimentation to get to this point so that's it for now! In this post I showed you how to get started monitoring your Dockerized .NET Core 2.1 applications running in AKS using Application Insights. The emphasis has been very much on getting started though as Application Insights is a big beast and I've only scratched the surface in this post. Do bear in mind that some of the NuGets used in this post are in beta and some pain is to be expected.

As I publish this blog post VSTS has had a name change to Azure DevOps so that's the title of this series having to change again!

Cheers—Graham

Deploy a Dockerized ASP.NET Core Application to Azure Kubernetes Service Using a VSTS CI/CD Pipeline: Part 3

Posted by Graham Smith on May 24, 2018No Comments (click here to comment)

In this blog post series I'm working my way through the process of deploying and running an ASP.NET Core application on Microsoft's hosted Kubernetes environment. Formerly known as Azure Container Service (AKS), it has recently been renamed Azure Kubernetes Service, which is why the title of my blog series has changed slightly. In previous posts in this series I covered the key configuration elements both on a developer workstation and in Azure and VSTS and then how to actually deploy a simple ASP.NET Core application to AKS using VSTS. This is the full series of posts to date:

In this post I introduce MegaStore (just a fictional name), a more complicated ASP.NET Core application (in the sense that it has more moving parts), and I show how to deploy MegaStore to an AKS cluster using VSTS. Future posts will use MegaStore as I work through more advanced Kubernetes concepts. To follow along with this post you will need to have completed the following, variously from parts 1 and 2:

Introducing MegaStore

MegaStore was inspired by Elton Stoneman's evolution of NerdDinner for his excellent book Docker on Windows, which I have read and can thoroughly recommend. The concept is a sales application that rather than saving a ‘sale' directly to a database, instead adds it to a message queue. A handler monitors the queue and pulls new messages for saving to an Azure SQL Database. The main components are as follows:

  • MegaStore.Web—an ASP.NET Core MVC application with a CreateSale method in the HomeController that gets called every time there is a hit on the home page.
  • NATS message queue—to which a new sale is published.
  • MegaStore.SaveSalehandler—a .NET Core console application that monitors the NATS message queue and saves new messages.
  • Azure SQL Database—I recently heard Brendan Burns comment in a podcast that hardly anybody designing a new cloud application should be managing storage themselves. I agree and for simplicity I have chosen to use Azure SQL Database for all my environments including development.

You can clone MegaStore from my GitHub repository here.

In order to run the complete application you will first need to create an Azure SQL Database. The easiest way is probably to create a new database (also creates a server at the same time) via the portal and manage with SQL Server Management Studio. The high-level procedure is as follows:

  1. In the portal create a new database called MegaStoreDev and at the same time create a new server (name needs to be unique). To keep costs low I start with the Basic configuration knowing I can scale up and down as required.
  2. Still in the portal add a client IP to the firewall so you can connect from your development machine.
  3. Connect to the server/database in SSMS and create a new table called dbo.Sale:
  4. In Security > Logins create a New Login called sales_user_dev, noting the password.
  5. In Databases > MegaStoreDev > Security > Users create a New User called sales_user mapped to the sales_user_dev login and with the db_owner role.

In order to avoid exposing secrets via GitHub the credentials to access the database are stored in a file called db-credentials.env which I've not committed to the repo. You'll need to create this file in the docker-compose project in your VS solution and add the following, modified for your server name and database credentials:

If you are using version control make sure you exclude db-credentials.env from being committed.

With docker-compose set as the startup project and Docker for Windows running set to Linux containers you should now be able to run the application. If everything is working you should be able to see sales being created in the database.

To understand how the components are configured you need to look at docker-compose.yml and docker-compose-override.yml. Image building is handled by docker-compose.yml, which can't have anything else in it otherwise VSTS complains if you want to use the compose file to build the images. The configuration of the components is specified in docker-compose-override.yml which gets merged with docker-compose.yml at run time. Notice the k8s folder. This contains the configuration files needed to deploy the application to AKS.

By now you may be wondering if MegaStore should be running locally under Kubernetes rather than in Docker via docker-compose. It's a good question and the answer is probably yes. However at the time of writing there isn't a great story to tell about how Visual Studio integrates with Kubernetes on a developer workstation (ie to allow debugging as is possible with Docker) so I'm purposely ignoring this for the time being. This will change over time though, and I will cover this when I think there is more to tell.

Create Azure SQL Databases for Different Release Pipeline Environments

I'll be creating a release pipeline consisting of DAT and PRD environments. I explain more about these below but to support these environments you'll need to create two new databases—MegaStoreDat and MegaStorePrd. You can do this either through the Azure portal or through SQL Server Management Studio, however be aware that if you use SSMS you'll end up on the standard pricing tier rather than the cheaper basic tier. Either way, you then use SQL Server Management Studio to create dbo.Sale and set up security as described above, ensuring that you create different logins for the different environments.

Create a Build in VSTS

Once everything is working locally the next step is to switch over to VSTS and create a build. I'm assuming that you've cloned my GitHub repo to your own GitHub account however if you are doing it another way (your repo is in VSTS for example) you'll need to amend accordingly.

  1. Create a new Build definition in VSTS. The first thing you get asked is to select a repository—link to your GitHub account and select the MegaStore repo:
  2. When you get asked to Choose a template go for the Empty process option.
  3. Rename the build to something like MegaStore and under Agent queue select your private build agent.
  4. In the Triggers tab check Enable continuous integration.
  5. In the Options tab set Build number format to $(Date:yyyyMMdd)$(Rev:.rr), or something meaningful to you based on the available options described here.
  6. In the Tasks tab use the + icon to add two Docker Compose tasks and a Publish Build Artifacts task. Note that when configuring the tasks below only the required entries and changes to defaults are listed.
  7. Configure the first Docker Compose task as follows:
    1. Display name = Build service images
    2. Action = Build service images
    3. Azure subscription = [name of existing Azure Resource Manager endpoint]
    4. Azure Container Registry = [name of existing Azure Container Registry]
    5. Additional Image Tags = $(Build.BuildNumber)
  8. Configure the first Docker Compose task as follows:
    1. Display name = Push service images
    2. Azure subscription = [name of existing Azure Resource Manager endpoint]
    3. Azure Container Registry = [name of existing Azure Container Registry]
    4. Action = Push service images
    5. Additional Image Tags = $(Build.BuildId)
  9. Configure the Publish Build Artifacts task as follows:
    1. Display name = Publish k8s config
    2. Path to publish = k8s
    3. Artifact name = k8s-config
    4. Artifact publish location = Visual Studio Team Services/TFS

You should now be able to test the build by committing a minor change to the source code. The build should pass and if you look in the Repositories section of your Container Registry you should see megastoreweb and megastoresavesalehandler repositories with newly created images.

Create a DAT Release Environment in VSTS

With the build working it's now time to create the release pipeline, starting with an environment I call DAT which is where automated acceptance testing might take place. At this point there is a style choice to be made for creating Kubernetes Secrets and ConfigMaps. They can be configured from files or from literal values. I've gone down the literal values route since the files route needs to specify the namespace and this would require either a separate file for each namespace creating a DRY problem or editing the config files as part of the release pipeline. To me the literal values technique seems cleaner. Either way, as far as I can tell there is no way to update a Secret or ConfigMap via a VSTS Deploy to Kubernetes task as it's a two step process and the task can't handle this. The workaround is a task to delete the Secret or ConfigMap and then a task to create it. You'll see that I've also chosen to explicitly create the image pull secret. This is partly because of a bug in the Deploy to Kubernetes task however it also avoids having to repeat a lot of the Secrets configuration in Deploy to Kubernetes tasks that deploy service or deployment configurations.

  1. Create a new release definition in VSTS, electing to start with an empty process and rename it MegaStore.
  2. In the Pipeline tab click on Add artifact and link the build that was just created which in turn makes the k8s-config artifact from step 9 above available in the release.
  3. Click on the lightning bolt to enable the Continuous deployment trigger.
  4. Still in the Pipeline tab rename Environment 1 to DAT, with the overall changes resulting in something like this:
  5. In the Tasks tab click on Agent phase and under Agent queue select your private build agent.
  6. In the Variables tab create the following variables with Release Scope:
    1. AcrAuthenticationSecretName = prmcrauth (or the name you are using for imagePullSecrets in the Kubernetes config files)
    2. AcrName = [unique name of your Azure Container Registry, eg mine is prmcr]
    3. AcrPassword = [password of your Azure Container Registry from Settings > Access keys], use the padlock to make it a secret
  7.  In the Variables tab create the following variables with DAT Scope:
    1. DatDbConn = Server=tcp:megastore.database.windows.net,1433;Initial Catalog=MegaStoreDat;Persist Security Info=False;User ID=sales_user;Password=mystrongpwd;MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30; (you will need to alter this connection string for your own Azure SQL server and database)
    2. DatEnvironment = dat (ie in lower case)
  8. In the Tasks tab add 15 Deploy to Kubernetes tasks and disable all but the first one so the release can be tested after each task is configured. Note that when configuring the tasks below only the required entries and changes to defaults are listed.
  9. Configure the first Deploy to Kubernetes task as follows:
    1. Display name = Delete image pull secret
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = delete
    5. Arguments = secret $(AcrAuthenticationSecret)
    6. Control Options > Continue on error = checked
  10. Configure the second Deploy to Kubernetes task as follows:
    1. Display name = Create image pull secret
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = create
    5. Arguments = secret docker-registry $(AcrAuthenticationSecretName) --namespace=$(DatEnvironment) --docker-server=$(AcrName).azurecr.io --docker-username=$(AcrName) --docker-password=$(AcrPassword) --docker-email=fred@bloggs.com (note that the email address can be anything you like)
  11. Configure the third Deploy to Kubernetes task as follows:
    1. Display name = Delete ASPNETCORE_ENVIRONMENT config map
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = delete
    5. Arguments = configmap aspnetcore.env
    6. Control Options > Continue on error = checked
  12. Configure the fourth Deploy to Kubernetes task as follows:
    1. Display name = Create ASPNETCORE_ENVIRONMENT config map
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = create
    5. Arguments = configmap aspnetcore.env --from-literal=ASPNETCORE_ENVIRONMENT=$(DatEnvironment)
  13. Configure the fifth Deploy to Kubernetes task as follows:
    1. Display name = Delete DB_CONNECTION_STRING secret
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = delete
    5. Arguments = secret db.connection
    6. Control Options > Continue on error = checked
  14. Configure the sixth Deploy to Kubernetes task as follows:
    1. Display name = Create DB_CONNECTION_STRING secret
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = create
    5. Arguments = secret generic db.connection --from-literal=DB_CONNECTION_STRING="$(DatDbConn)"
  15. Configure the seventh Deploy to Kubernetes task as follows:
    1. Display name = Delete MESSAGE_QUEUE_URL config map
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = delete
    5. Arguments = configmap message.queue
    6. Control Options > Continue on error = checked
  16. Configure the eighth Deploy to Kubernetes task as follows:
    1. Display name = Create MESSAGE_QUEUE_URL config map
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = create
    5. Arguments = configmap message.queue --from-literal=MESSAGE_QUEUE_URL=nats://message-queue-service.$(DatEnvironment):4222
  17. Configure the ninth Deploy to Kubernetes task as follows:
    1. Display name = Create message-queue service
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = apply
    5. Use Configuration files = checked
    6. Configuration File = $(System.DefaultWorkingDirectory)/_MegaStore/k8s-config/message-queue-service.yaml
  18. Configure the tenth Deploy to Kubernetes task as follows:
    1. Display name = Create megastore-web service
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = apply
    5. Use Configuration files = checked
    6. Configuration File = $(System.DefaultWorkingDirectory)/_MegaStore/k8s-config/megastore-web-service.yaml
  19. Configure the eleventh Deploy to Kubernetes task as follows:
    1. Display name = Create message-queue deployment
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = apply
    5. Use Configuration files = checked
    6. Configuration File = $(System.DefaultWorkingDirectory)/_MegaStore/k8s-config/message-queue-deployment.yaml
  20. Configure the twelfth Deploy to Kubernetes task as follows:
    1. Display name = Create megastore-web deployment
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = apply
    5. Use Configuration files = checked
    6. Configuration File = $(System.DefaultWorkingDirectory)/_MegaStore/k8s-config/message-queue-deployment.yaml
  21. Configure the thirteenth Deploy to Kubernetes task as follows:
    1. Display name = Update megastore-web with latest image
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = set
    5. Arguments = image deployment/megastore-web-deployment megastoreweb=$(AcrName).azurecr.io/megastoreweb:$(Build.BuildNumber)
  22. Configure the fourteenth Deploy to Kubernetes task as follows:
    1. Display name = Create megastore-savesalehandler deployment
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = apply
    5. Use Configuration files = checked
    6. Configuration File = $(System.DefaultWorkingDirectory)/_MegaStore/k8s-config/megastore-savesalehandler-deployment.yaml
  23. Configure the fifthteenth Deploy to Kubernetes task as follows:
    1. Display name = Update megastore-savesalehandler with latest image
    2. Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint]
    3. Namespace = $(DatEnvironment)
    4. Command = set
    5. Arguments = image deployment/megastore-savesalehandler-deployment megastoresavesalehandler=$(AcrName).azurecr.io/megastoresavesalehandler:$(Build.BuildNumber)

That's a heck of a lot of configuration, so what exactly have we built?

The first eight tasks deal with the configuration that support the services and deployments:

  • The image pull secret stores the credentials to the Azure Container Registry so that deployments that need to pull images from the ACR can authenticate.
  • The ASPNETCORE_ENVIRONMENT config map sets the environment for ASP.NET Core. I don't do anything with this but it could be handy for troubleshooting purposes.
  • The DB_CONNECTION_STRING secret stores the connection string to the Azure SQL database and is used by the megastore-savesalehandler-deployment.yaml configuration.
  • The MESSAGE_QUEUE_URL config map stores the URL to the NATS message queue and is used by the megastore-web-deployment.yaml and megastore-savesalehandler-deployment.yaml configurations.

As mentioned above, a limitation of the VSTS Deploy to Kubernetes task means that in order to be able to update Secrets and ConfigMaps they need to be deleted first and then created again. This does mean that an exception is thrown the first time a delete task is run however the Continue on error option ensures that the release doesn't fail.

The remaining seven tasks deal with the deployment and configuration of the components (other than the Azure SQL database) that make up the MegaStore application:

  • The NATS message queue requires a service so other components can talk to it and the deployment that specifies the specification for the image.
  • The MegaStore.Web front end requires a service so that it is exposed to the outside world and the deployment that specifies the specification for the image.
  • MegaStore.SaveSalehandler monitoring component only needs the deployment that specifies the specification for the image as nothing connects to it directly.

If everything has been configured correctly then triggering a release should result in a megastore-web-service being created. You can check the deployment was successful by executing kubectl get services --namespace=dat to get the external IP address of the LoadBalancer which you can paste in to a browser to confirm that the ASP.NET Core website is running. On the backend, you can use SQL Server Management Studio to connect to the database and confirm that records are being created in dbo.Sale.

If you are running in to problems, you can run the Kubernetes Dashboard to find out what is failing. Typically it's deployments that fail, and navigating to Workloads > Deployments can highlight the failing deployment. You can find out what the error is from the New Replica Set panel by clicking on the Logs icon which brings up a new browser tab with a command line style output of the error. If there is no error it displays any Console.WiteLine output. Very neat:

Create a PRD Release Environment in VSTS

With a DAT environment created we can now create other environments on the route to production. This could be whatever else is needed to test the application, however here I'm just going to create a production environment I'll call PRD. I described this process in my previous post so here I'll just list the high level process:

  1. Clone the DAT environment and rename it PRD.
  2. In the Variables tab rename the cloned DatDbConn and DatEnvironment variables (the ones with PRD scope) to PrdDbConn and PrdEnvironment and change their values accordingly.
  3. In the Tasks tab visit each task and change all references of $(DatDbConn) and $(DatEnvironment) to $(PrdDbConn) and $(PrdEnvironment). All Namespace fields will need changing and many of the tasks with use the Arguments fields will need attention.
  4. Trigger a build and check the deployment was successful by executing kubectl get services --namespace=prd to get the external IP address of the LoadBalancer which you can paste in to a browser to confirm that the ASP.NET Core website is running.

Wrapping Up

Although the final result is a CI/CD pipeline that certainly works there are more tasks than I'm happy with due to the need to delete and then recreate Secrets and ConfigMaps and this also adds quite a bit of overhead to the time it takes to deploy to an environment. There's bound to be a more elegant way of doing this that either exists now and I just don't know about it or that will exist in the future. Do post in the comments if you have thoughts.

Although I'm three posts in I've barely scratched the surface of the different topics that I could cover, so plenty more to come in this series. Next time it will probably be around health and / or monitoring.

Cheers—Graham

Deploy a Dockerized ASP.NET Core Application to Kubernetes on Azure Using a VSTS CI/CD Pipeline: Part 2

Posted by Graham Smith on March 21, 2018No Comments (click here to comment)

If you need to provision a new environment for your deployment pipeline, what's your process and how long does it take? For many of us the process probably starts with a request to an infrastructure team for new virtual machines. If the new VMs are in Azure the request might be completed quite quickly; if they are on premises it might take much longer. In both scenarios you might have to justify your request: there will be actual cost in Azure and on premises it's another chunk of the datacentre ‘gone'.

With the help of containers and container orchestrators I predict (and sincerely hope) that this sort of pain will become a distant memory for much of the software development community for whom it is currently an issue. The reason is that container orchestration technologies abstract away the virtual (or physical) server layer and allow you to focus on configuring services and how they communicate with each other—all through configuration files. The only time you'd need to think of virtual (or physical) servers is if the cluster running your orchestrator needed more capacity, in which case someone will need to add more nodes. A whole new environment for your pipeline just by doing some work with a configuration file? What's not to like?

In this blog post I hope to make my prediction come alive by showing you how new environments can be quickly created using Kubernetes running in Microsoft's Azure Container Service (AKS), crucially using declarative configuration files that get deployed as part of a VSTS release pipeline. This post follows directly on from a previous post, both in terms of understanding and also the components that were built in that first post, so if you haven't already done so I recommend working your way through that post before going further.

Housekeeping

In the previous post we deployed to the default namespace so it probably makes sense to clean all this up. This can all be done by the command line of course but to mix it up a bit I'll illustrate using the Kubernetes Dashboard. You can start the dashboard using the following command, substituting in the name of your resource group and the name of the cluster:

This should open the dashboard in a browser displaying the default namespace. Navigate to Workloads > Deployments and using the hamburger menu delete the deployment:

Navigate to Discovery and Load Balancing > Services and delete the service:

Navigate to Config and Storage > Secret and delete the secret:

Environments and Namespaces

The Kubernetes feature that we'll use to create environments that together form part of our pipeline is Namespaces. You can think of namespaces as a way to divide the Kubernetes cluster in to virtual clusters. Within a namespace resource names need to be unique but they don't have to be across namespaces. This is great because effectively we have network isolation so that across each environment resource names stay the same. Say goodbye to having to append the environment name to all the resources in your environment to make them unique.

In this post I'll make a pipeline consisting of two environments. I'm sticking with a convention I established several years ago so I'll be creating DAT (developer automated test) and PRD (production) environments. In a complete pipeline I might also create a DQC (developer quality control) environment to sit between DAT and PRD but that won't really add anything extra to this exercise.

First up is to create the namespaces. There is an argument for saying that namespace creation should be part of the release pipeline however in this post I'm going to create everything manually as I think it helps to understand what's going on. Create a file called namespaces.yaml and add the following contents:

Note that namespace name needs to be in lower case as it needs to be DNS compatible. Open a command prompt at the same location as namespaces.yaml and execute the the following command: kubectl create -f namespaces.yaml. You should get a message back advising the namespaces have been created and at one level that's all there is to it. However there's a couple of extra bits worth knowing.

When you first start working with kubectl at the command line you are working in the default namespace. To work with other namespaces needs some configuration.

To return details of the configuration stored in C:\Users\<username>\.kube\config use:

My cluster returned the following output:

From this output you need to determine your cluster name (which you probably already know) as well as the name of the user. These details are fed in to the following command for creating a new context for an environment (in this case the DAT environment):

To switch to working to this context (and hence the dat namespace) use:

To confirm (or check) the current context use:

To get back to the default namespace use:

Normally that would be most of what you need to know to work with namespaces, however as of the time of writing there is a bug in the VSTS Deploy to Kubernetes task which requires some extra work. The bug may be fixed by the time you read this however it's handy to examine the issue to further understand what is going on behind the scenes.

Each namespace needs to access the Azure Container Registry (ACR) we created in the previous post to pull down images. This is a private registry so we don't want open access and so some form of authentication is required. This is provided by the creation of a Kubernetes secret that holds the authentication details to the ACR. The VSTS Deploy to Kubernetes task can create this secret for us however the bug is that it only creates the secret for the default namespace and fails to create the secret when a different namespace is specified. The workaround is to create the secret manually in each namespace using the following command:

In the above command secret-name is any arbitrary name you choose for the secret, namespace is the namespace in which to create the secret, acr-name is the name of your ACR, acr-admin-password is the password from the Access keys panel of your ACR and any-valid-email-address is just that. You'll need to run this command for each namespace of course. One final thing: you'll need to make sure that in the codebase the imagePullSecrets name in deployment.yaml matches the name of the secret you just created.

Amend the VSTS Pipeline to Support Multiple Environments

In this section we amend the release pipeline that was built in the previous post to support multiple environments.

  1. In the Pipeline tab rename Environment 1 to DAT:
  2. In the Variables tab create a variable to hold the name of the secret created above to authenticate with ACR. Create a second variable for the DAT environment namespace and change its scope to DAT. Remember that the value needs to be lower case:
  3. In the Tasks tab amend all three Deploy to Kubernetes tasks so that the Namespace field contains the $(DatEnvironment) variable. At the same time ensure that Secret name field matches the name of the secret variable created above:
  4. In order to test that deploying to DAT works, either trigger a build or, if you updated deployment.yaml above on your workstation commit your code. If the deployment was successful find the external IP address of the LoadBalancer by executing kubectl get services --namespace=dat and paste in to a browser to confirm that the ASP.NET Core website is running.

Amend the VSTS Pipeline to Support a New Environment

Now for the fun bit where we see just how easy it is to configure a new, network-isolated environment.

  1. In the Pipeline tab use the arrow next to Environments > Add to show and then select Clone environment:
  2. Rename the cloned environment to PRD. Create a new variable (ie PrdEnvironment) scoped to PRD to hold the prd namespace and amend each of the three Deploy to Kubernetes tasks so that the Namespace field contains the $(PrdEnvironment) variable.
  3. Trigger a build and check the deployment was successful by executing kubectl get services --namespace=prd to get the external IP address of the LoadBalancer which you can paste in to a browser to confirm that the ASP.NET Core website is running.

And That's It!

Yep—that really is all there is to it! Okay, this is just a trivial example, however even with more services the procedure would be the same. Granted, in a more complex application there might be some environment variables or secrets that might change but even so, it's just configuration.

I'm thrilled by the power that Kubernetes gives to developers—no more thinking about VMs or tin, no more having to append resources with environment names, and the ability to create a new environment in the blink of an eye—wow!

There's lots more I'm planning to cover in the deployment pipeline space however next time I'll be looking at the development inner loop and the options for running Kubernetes whilst developing code.

Cheers—Graham

Deploy a Dockerized ASP.NET Core Application to Kubernetes on Azure Using a VSTS CI/CD Pipeline: Part 1

Posted by Graham Smith on February 20, 2018No Comments (click here to comment)

Over the past 18 months or so I've written a handful of blog posts about deploying Docker containers using Visual Studio Team Services (VSTS). The first post covered deploying a container to a Linux VM running Docker and other posts covered deploying containers to a cluster running DC/OS—all running in Microsoft Azure. Fast forward to today and everything looks completely different from when I wrote that first post: Docker is much more mature with features such as multi-stage builds dramatically streamlining the process of building source code and packaging it in to containers, and Kubernetes has emerged as a clear leader in the container orchestration battle and looks set to be a game-changing technology. (If you are new to Kubernetes I have a Getting Started blog post here with plenty of useful learning resources and tips for getting started.)

One of the key questions that's been on my mind recently is how to use Kubernetes as part of a CI/CD pipeline, specifically using VSTS to deploy to Microsoft's Azure Container Service (AKS), which is now specifically targeted at managing hosted Kubernetes environments. So in a new series of posts I'm going to be examining that very question, with each post building on previous posts as I drill deeper in to the details. In this post I'm starting as simply as I possibly can whilst still answering the key question of how to use VSTS to deploy to Kubernetes. Consequently I'm ignoring the Kubernetes experience on the development workstation, I only deploy a very simple application to one environment and I'm not looking at scaling or rolling updates. All this will come later, but meantime I hope you'll find that this walkthrough will whet your appetite for learning more about CI/CD and Kubernetes.

Development Workstation Configuration

These are the main tools you'll need on a Windows 10 Pro development workstation (I've documented the versions of certain tools at the time of writing but in general I'm always on the latest version):

  • Visual Studio 2017—version 15.5.6 with the ASP.NET and web development workload.
  • Docker for Windows—stable channel 17.12.0-ce.
  • Windows Subsystem for Linux (WSL)—see here for installation details. I'm still using Bash on Ubuntu on Windows that I installed before WSL moved to the Microsoft Store and in this post I assume you are using Ubuntu. The aim of installing WSL is to run Azure CLI, although technically you don't need WSL as Azure CLI will run happily under a Windows command prompt. However using WSL facilitates running Azure CLI commands from a Bash script.
  • Azure CLI on Windows Subsystem for Linux—see here for installation (and subsequent upgrade) instructions. There are several ways to login to Azure from the CLI however I've found that the interactive log-in works well since once you're logged-in you remain so for quite a long time (many days for me so far). Use az -v to check which version you are on (2.0.27 was latest at time of writing).
  • kubectl on Azure CLI—the kubectl CLI is used to interact with a Kubernetes cluster. Install using sudo az aks install-cli.

Create Services in Microsoft Azure

There are several services you will need to set up in Microsoft Azure:

  • Azure Container Registry—see here for an overview and links to the various methods for creating an ACR. I use the Standard SKU for the better performance and increased storage.
  • Azure Container Service (AKS) cluster—see here for more details about AKS and how to create a cluster, however you may find it easier to use my script below. I started off by creating a cluster and then destroying it after each use until I did some tests and found that a one-node cluster was costing pennies per day rather than the pounds per day I had assumed it would cost and now I just keep the cluster running.
    • From a WSL Bash prompt run nano create_k8s_cluster.sh to bring up the nano editor with a new empty file. Copy and paste (by pressing right mouse key) the following script:
    • Change the variables to your suit your requirements. If you only have one Azure subscription you can delete the lines that set a particular subscription as the default, otherwise use az account list to list your subscriptions to find the ID.
    • Exit out of nano making sure you save the changes (Ctrl +X, Y) and then apply permissions to make it executable by running chmod 700 create_k8s_cluster.sh.
    • Next run the script using ./create_k8s_cluster.sh.
    • One the cluster is fully up-and-running you can show the Kubernetes dashboard using az aks browse --resource-group $resourceGroup --name $clusterName.
    • You can also start to use the kubectl CLI to explore the cluster. Start with kubectl get nodes and then have a look at this cheat sheet for more commands to run.
    • The cluster will probably be running an older version of Kubernetes—you can check and find the procedure for upgrading here.
  • Private VSTS Agent on Linux—you can use the hosted agent (called Hosted Linux Preview at time of writing) but I find it runs very slowly and additionally because a new agent is used every time you perform a build it has to pull docker images down each time which adds to the slowness. In a future post I'll cover running a VSTS agent from a Docker image running on the Kubernetes cluster but for now you can create a private Linux agent running on a VM using these instructions. Although they date back to October 2016 they still work fine (I've checked them and tweaked them slightly).
    • Since we will only need this agent to build using Docker you can skip steps 5b, 5c and 5d.
    • Install a newer version of Git—I used these instructions.
    • Install docker-compose using these instructions and choosing the Linux tab.
    • Make the docker-user a member of the docker group by executing usermod -aG docker ${USER}.

Create VSTS Endpoints

In order to talk to the various Azure services you will need to create the following endpoints in VSTS (from the cog icon on the toolbar choose Services > New Service Endpoint):

  • Azure Resource Manager—to point to your MSDN subscription. You'll need to authenticate as part of the process.
  • Kubernetes Service Connection—to point to your Kubernetes cluster. You'll need the FQDN to the cluster (prepended with https://) which you can get from the Azure CLI by executing az aks show --resource-group $resourceGroup --name $clusterName, passing in your own resource group and cluster names. You'll also need the contents of the kubeconfig file. If you used the script above to create the cluster then the script copied the config file to C:\Users\Public and you can use Notepad to copy the contents.

Configure a CI Build

The first step to deploying containers to a Kubernetes cluster is to configure a CI build that creates a container and then pushes the container to a Docker registry—Azure Container Registry in this case.

Create a Sample App
  • Within an existing Team Project create a new Git repository (Code > $current repository$ > New repository) called k8s-aspnetcore. Feel free to select the options to add a README and a VisualStudio .gitignore.
  • Clone this repo on your development workstation:
    • Open PowerShell at the desired root folder.
    • Copy the URL from the VSTS code view of the new repository.
    • At the PowerShell prompt execute git clone along with the pasted URL.
  • Make sure Docker for Windows is running.
  • In Visual Studio create an ASP.NET Core Web Application in the folder the git clone command created.
  • Choose an MVC app and enable Docker support for Linux.
  • You should now be able to run your application using the green Docker run button on the Standard toolbar. What is interesting here is that the build process is using a multi-stage Dockerfile, ie the tooling to build the application is running from a Docker container. See Steve Lasker's post here for more details.
  • In the root of the repository folder create a folder named k8s-config, which we'll use later to store Kubernetes configuration files. In Visual Studio create a New Solution Folder with the same name and back in the file system folder create empty files named service.yaml and deployment.yaml. In Visual Studio add these files as existing items to the newly created solution folder.
  • The final step here is to commit the code and sync it with VSTS.
Create a VSTS Build
  • In VSTS create a new build based on the repository created above and start with an empty process.
  • After the wizard stage of the setup supply an appropriate name for the build and select the Agent queue created above if you are using the recommended private agent or Hosted Linux Preview if not.
  • Go ahead and perform a Save & queue to make sure this initial configuration succeeds.
  • In the Phase 1 panel use + to add two Docker Compose tasks and one Publish Build Artifacts task.
  • If you want to be able to perform a Save & queue after configuring each task (recommended) then right-click the second and third tasks and disable them.
  • Configure the first Docker Compose task as follows:
    • Display name = Build service images
    • Container Registry Type = Azure Container Registry
    • Azure subscription = [name of Azure Resource Manager endpoint created above]
    • Azure Container Registry = [name of Azure Container Registry created above]
    • Docker Compose File = **/docker-compose.yml
    • Project Name = $(Build.Repository.Name)
    • Qualify Image Names = checked
    • Action = Build service images
    • Additional Image Tags = $(Build.BuildId)
    • Include Latest Tag = checked
  • Configure the second Docker Compose task as follows:
    • Display name = Push service images
    • Container Registry Type = Azure Container Registry
    • Azure subscription = [name of Azure Resource Manager endpoint created above]
    • Azure Container Registry = [name of Azure Container Registry created above]
    • Docker Compose File = **/docker-compose.yml
    • Project Name = $(Build.Repository.Name)
    • Qualify Image Names = checked
    • Action = Push service images
    • Additional Image Tags = $(Build.BuildId)
    • Include Latest Tag = checked
  • Configure the Publish Build Artifacts task as follows:
    • Display name = Publish k8s config
    • Path to publish = k8s-config (this is the folder we created earlier in the repository root folder)
    • Artifact name = k8s-config
    • Artifact publish location = Visual Studio Team Services/TFS
  • Finally, in the Triggers section of the build editor check Enable continuous integration so that the build will trigger on a commit from Visual Studio.

So what does this build do? The first Docker Compose task uses the docker-compose.yml file to work out what images need building as specified by Dockerfile file(s) for different services. We only have one service (k8s-aspnetcore) but there could (and usually would) be more. With the image built on the VSTS agent the second Docker Compose task pushes the image to the Azure Container Registry. If you navigate to this ACR in the Azure portal and drill in to the Repositories section you should see your image. The build also publishes the yaml configuration files needed to deploy to the cluster.

Configure a Release Pipeline

We are now ready to configure a release to deploy the image that's hosted in ACR to our Kubernetes cluster. Note that you'll need to complete all of this section before you can perform a release.

Create a VSTS Release Definition
  • In VSTS create a new release definition, starting with an empty process and changing the name to k8s-aspnetcore.
  • In the Artifacts panel click on Add artifact and wire-up the build we created above.
  • With the build now added as an artifact click on the lightning bolt to enable the Continuous deployment trigger.
  • In the default Environment 1 click on 1phase, 0 task and in the Agent phase click on + to create three Deploy to Kubernetes tasks.
  • Configure the first Deploy to Kubernetes task as follows:
    • Display name = Create Service
    • Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint created above]
    • Command = apply
    • Use Configuration files = checked
    • Configuration File = $(System.DefaultWorkingDirectory)/k8s-aspnetcore/k8s-config/service.yaml
    • Container Registry Type = Azure Container Registry
    • Azure subscription = [name of Azure Resource Manager endpoint created above]
    • Azure Container Registry = [name of Azure Container Registry created above]
    • Secret name [any secret word of your choosing, to be used consistently across all tasks]
  • Configure the second Deploy to Kubernetes task as follows:
    • Display name = Create Deployment
    • Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint created above]
    • Command = apply
    • Use Configuration files = checked
    • Configuration File = $(System.DefaultWorkingDirectory)/k8s-aspnetcore/k8s-config/deployment.yaml
    • Container Registry Type = Azure Container Registry
    • Azure subscription = [name of Azure Resource Manager endpoint created above]
    • Azure Container Registry = [name of Azure Container Registry created above]
    • Secret name [any secret word of your choosing, to be used consistently across all tasks]
  • Configure the third Deploy to Kubernetes task as follows:
    • Display name = Update with Latest Image
    • Kubernetes Service Connection = [name of Kubernetes Service Connection endpoint created above]
    • Command = set
    • Arguments = image deployment/k8s-aspnetcore-deployment k8s-aspnetcore=$yourAcrNameHere$.azurecr.io/k8s-aspnetcore:$(Build.BuildId)
    • Container Registry Type = Azure Container Registry
    • Azure subscription = [name of Azure Resource Manager endpoint created above]
    • Azure Container Registry = [name of Azure Container Registry created above]
    • Secret name [any secret word of your choosing, to be used consistently across all tasks]
  • Make sure you save the release but don't bother testing it out just yet as it won't work.
Create the Kubernetes configuration
  • In Visual Studio paste the following code in to the service.yaml file created above.
  • Paste the following code in to the deployment.yaml file created above. The code is for my ACR so you will need to amend accordingly.
  • You can now commit these changes and then head over to VSTS to check that the release was successful.
  • If the release was successful you should be able to see the ASP.NET Core website in your browser. You can find the IP address by executing kubectl get services from wherever you installed kubectl.
  • Another command you might try running is kubectl describe deployment $nameOfYourDeployment, where $nameOfYourDeployment is the metadata > name in deployment.yaml. A quick tip here is that if you only have one deployment you only need to type the first letter of it.
  • It's worth noting that splitting the service and deployment configurations in to separate files isn't necessarily a best practice however I'm doing it here to try and help clarify what's going on.

In terms of a very high level explanation of what we've just configured in the release pipeline, for a simple application such as an ASP.NET Core website we need to deploy two key objects:

  1. A Kubernetes Service which (in our case) is configured with an external IP address and acts as an abstraction layer for Pods which are killed off and recreated every time a new release is triggered. This is handled by the first Deploy to Kubernetes task.
  2. A Kubernetes Deployment which describes the nature of the deployment—number of Pods (via Replica Sets), how they will be upgraded and so on. This is handled by the second Deploy to Kubernetes task.

On first deployment these two objects are all that is needed to perform a release. However, because of the declarative nature of these objects they do nothing on subsequent release if they haven't changed. This is where the third Deploy to Kubernetes task comes in to play—ensuring that after the first release subsequent releases do cause the container to be updated.

Wrapping Up

That concludes our initial look at CI/CD with VSTS and Azure Container Service (AKS)! As I mentioned at the beginning of the post I've purposely tried to keep this walkthrough as simple as possible, so watch out for the next installment where I'll build on what I've covered here.

Cheers—Graham

Getting Started with Kubernetes

Posted by Graham Smith on February 1, 2018No Comments (click here to comment)

If you've been following the containers story you'll probably know that 2017 was a big year for Docker. You may also know that 2018 looks set to be a big year for Kubernetes, "an open-source system for automating deployment, scaling, and management of containerized applications". There are several systems competing in the same space as Kubernetes but for many the jury has voted and Kubernetes is the winner.

For many of us ‘containerized applications' means applications that have been containerised using Docker, and if you've been learning and working with Docker then learning Kubernetes is an obvious next step. I've been learning Kubernetes for a couple of months now and in this post I share some of the resource links that I've found most useful and provide pointers to the different ways I've created Kubernetes environments to provide practical hands-on experience.

Learning Resources

Run Kubernetes on your Development Machine Using Minikube

A quick and easy way to get started with Kubernetes is to install Minikube on your development machine. Minikube is a tool that runs a single-node Kubernetes cluster on a virtual machine running on your laptop or workstation. You can find the installation guide here and the getting started guide here. I installed Minikube on my Windows 10 workstation running Hyper-V and the minikube start command just worked. Don't forget you'll need to install kubectl as well as Minikube. As part of the installation process a kubeconfig file is created at %userprofile%\.kube\config (config is the actual file) which ‘connects' kubectl to Minikube. If you are connecting to different Kubernetes installations from your development machine you'll need to manage kubeconfig files—see later for more details.

If you've got Minikube installed and working you might be wondering what next, especially if you are a Windows user as the documentation isn't hugely Windows-friendly. If you are in this situation head over to this Getting Started with Kubernetes on your Windows Laptop with Minikube tutorial. Skip past the installation instructions to Starting our Cluster and follow on from there.

Run Kubernetes in Microsoft Azure

If you have a Microsoft Azure subscription or are prepared to sign up for a free trial it's ridiculously easy to start working with Kubernetes in Azure. There's actually a couple of ways to do it but the easiest is to create an Azure Container Service (AKS) cluster as this service abstracts away much of the complicated cluster stuff leaving you to focus on Kubernetes itself.

The Deploy an Azure Container Service (AKS) cluster walkthrough gets you up-an-running in no time with an actual app that you can run in your browser. Using Azure Cloud Shell is the easier way to use the Azure CLI although it does have an annoying habit of timing out on you. If you do switch to using the local version of the CLI watch out for the az aks get-credentials --resource-group myResourceGroup --name myK8sCluster comand which will merge connection information about the cluster you are creating with any previously created %userprofile%\.kube\config file that might be present (after installing Minikube for example) which may not be what you want.

Run Kubernetes on a Raspberry Pi Cluster

If you want to take your knowledge a step further and learn how to perform a bare-metal installation of Kubernetes then one option is to create a Raspberry Pi cluster and install and run Kubernetes on it. I've gone down this route and have had great fun doing so. There are a couple of key resources that will help you get this project off the ground:

My cluster ended up looking like this:

Some familiarity with Raspberry Pi obviously helps with this sort of project however I wouldn't say it's a definite prerequisite as there is plenty of help out there for anyone getting started with Raspberry Pi. You do really need to start off with at least three Pis so there is a modest cost involved but if you don't want your cluster to be portable then you don't need to hook it up to a switch or a router and WiFi works fine for me. A tip worth mentioning is that the 6-port RAVpower USB charger is slightly smaller than the 6-port Anker USB charger and fitted my enclosure much better.

Dealing with Multiple Kubernetes Instances

If you end up managing more than one instance of Kubernetes with the same instance of kubectl you'll somehow need to manage the issue of multiple kubeconfig files. There is detailed guidance about this here. My needs are very modest and at the moment I simply save different kubeconfig files with different extensions and then remove the extension of the one I want to work with. Not very elegant but it serves my simple needs for the moment.

And There's More...

The three techniques I've described for working with Kubernetes are only the tip of the iceberg. Google has a similar offering to Microsoft Azure with its Google Kubernetes Engine for example as does CodeFresh. I haven't tried these services but the point is that there are lots of options if none of the ones I've covered takes your fancy.

One particularly exciting development that I haven't tried yet but will soon is Kubernetes running in Docker for Windows. Scott Hanselman has a nice walkthrough here as does Stefan Stranger here. Enjoy!

Cheers—Graham