Refactoring PowerShell – Switch Statements

Regardless of your experience within technology, the process of creating code usually starts out with a solution that is just enough to get the job done. The solution is then typically tweaked and improved continuously until it is either “production worthy” or “good enough for the requirements“. This process of improvement is called refactoring. Refactoring is a skill that all technical professionals should become proficient in, regardless if you are an IT Pro, a developer or just someone who needs to automate things.

There are many reasons to refactor code, including:

  • Add new features
  • Remove unused features
  • Improve performance
  • Increase readability, maintainability or test-ability
  • Improve design
  • Improve adherence to standards or best practices

Refactoring in Code Reviews

One of the jobs of a code reviewer is to suggest areas that could be refactored to improve some of the areas above. I’ve often found that I’ll suggest the same set of refactorings in my reviews. So rather than just putting the suggesting into the code review, I thought I’d start writing them down here in a series that I could refer contributors to as well as help anyone else who happens to come across these.

Unit Tests and Refactoring

Because refactoring requires changing code, how can we be sure that we’re not breaking functionality or introducing bugs? That is where unit testing comes in. With PowerShell, we’ll typically use the PowerShell Pester module to create unit tests that allow us to more safely refactor code. Unit testing is beyond the scope of this post.

Switch Statement Refactoring

One of the common patterns I’ve come across is PowerShell switch statements being used is to map from one values to another set of values. For example:

This converts a colour name (e.g. red, green, blue, white) to a colour value. If a colour name can not be matched then it returns 0x0 (black). Admittedly, this is a bit of an unlikely example, but it demonstrates the pattern.

This is by no means incorrect or “bad code”. It is completely valid and solves the problem perfectly well. But as you can see this requires a lot of code to perform a simple mapping.

Mapping using a Hash Table

An alternative to using a switch statement is to use a hash table:

This can simplify the code slightly by removing the break statement and braces.

Note: The break statement is not strictly required in this example from a functional perspective, but including them increases overall performance of the switch statement.

You may have noticed that the hash table above does not quite match the behavior of the switch statement: the default mapping to 0x0 is not handled. So, in this case, we’d need to include additional code to handle this:

To handle the default we’re using a quasi null coalescing operator. PowerShell doesn’t have a native null coalescing operator like many languages, but it does have a way of simulating it using the line:

You could definitely argue that using a hash table mapping with a null coalescing operator does not make the code easier to read or maintain. But the purpose here is not to define which approach is best, rather to offer alternative patterns.

One other benefit of using a hash table for mapping is that it can be separated out into a separate psd1 file. This allows editing of the mapping table elements without having to edit the code itself:

The psd1 file containing the mapping data (mapping.psd1):

Reversing the Mapping

How do we use a similar pattern to reverse the mapping? For example, mapping a colour value back to a colour name:

To implement the same functionality using a hash table also including the null coalescing operator you could use:

Using a Hash Table with Script Values

Switch blocks may contain more than just a single statement. For example:

If your switch blocks do more than just perform a mapping, you can assign script blocks to the hash table values instead:

Instead of containing a value in each hash table item, a script block is specified. The Invoke() method can then be called on the script block.

Enumerated Types

If you’re using PowerShell 5.0 or above (you hopefully are), you’re also able to use the enum keyword to define an enumerated type that can also be used to replace switch statements in some situations.

The enumerated type only needs to be declared once.

But what do we need to do if we want to have a default returned if the value is invalid in the mapping? In that case we need to use the TryParse method of the enumerated type to try and parse the value and return a default value if it is invalid:

However, we can’t assign scriptblocks to the values in an enumerated type – only constant values may be assigned. This means we can’t implement scenarios where we’d like to have the value contain more than one instruction. But this shouldn’t be too much of a problem, because if you do find yourself being limited by this, then you should probably be looking to use more advanced object oriented programming patterns such as polymorphism – which is well beyond the scope of this post. But if you’re interested to know more, review this article (not PowerShell specific).

Wrapping Up

This post is all about looking at different ways of writing the same code. It isn’t trying to say which way is better or worse. If you have a preference and it works for you, by all means, keep on using it. This is simply to provide alternative methods that may or may not make code more readable and maintainable.

Feel free to comment with alternative methods of refactoring switch statements.

 

 

 

Deploy Sonarqube to Azure App Service Linux Containers using an Azure DevOps Pipeline

Sonarqube is a web application that development teams typically use during the application development process to continuous validate the quality of the code.

This post is not specifically about Sonarqube and how it works. It is intended to show Developers & IT Pros how to deploy a service to Azure using contemporary infrastructure as code and DevOps patterns.

The Implementation

A Sonarqube installation is made up of a web application front end backed by database.

ss_sonarqube_architecture

Sonarqube supports many different types of databases, but I chose to use Azure SQL Database. I decided to use Azure SQL Database for the following reasons:

    1. It is a managed service, so I don’t have to worry about patching, securing and looking after SQL servers.
    2. I can scale the database performance up and down easily with code. This allows me to balance my performance requirements with the cost to run the server or even dial performance right back at times when the service is not being used.
    3. I can make use of the new Azure SQL Database serverless (spoiler alert: there are still SQL servers). This allows the SQL Database to be paused when not being accessed by the Sonarqube front end. It can be used to further reduce costs running Sonarqube by allowing me to delete the front end every night and only pay for the storage costs when developers aren’t developing code.ss_sonarqube_sql_server_serverless

For the front end web application I decided to use the Azure Web App for Containers running a Linux container using the official Sonarqube Docker image. Because the Sonarqube web application is stateless it is a great target for being able to be delete and recreate from code. The benefits to using Azure Web App for Containers are:

  1. Azure Web App for Containers is a managed service, so again, no patching, securing or taking care of servers.
  2. I can scale the performance up and down and in and out from within my pipeline. This allows me to quickly and easily tune my performance/cost, even on a schedule.
  3. I can delete and rebuild my front end web application by running the pipeline in under 3 minutes. So I can completely delete my front end and save money when it is not in use (e.g. when teams aren’t developing in the middle of the night).

Architectural Considerations

The Sonarqube web application, as it has been architected, is accessible from the public internet. This might not meet your security requirements, so you might wish to change the architecture in the following ways:

  1. Putting an Azure Application Gateway (a layer 7 router) in front of the service.
  2. Isolate the service in a Azure Virtual Network from the internet and make it only accessible to your development services. This may also require Azure ExpressRoute or other VPN technologies to be used.
  3. We are using the SQL Server administrator account for the Sonarqube front end to connect to the backend. This is not advised for a production service – instead, a user account specifically for the use of Sonarqube should be created and the password stored in an Azure Key Vault.

These architectural changes are beyond the scope of this document though as I wanted to keep the services simple. But the pattern defined in this post will work equally well with these architectures.

Techniques

Before we get into the good stuff, it is important to understand why I chose to orchestrate the deployment of these services using an Azure Pipeline.

I could have quite easily built the infrastructure manually straight into the Azure Portal or using some Azure PowerShell automation or the Azure CLI, so why do it this way?

There are a number of reasons that I’ll list below, but this is the most mature way to deploy applications and services.

ss_sonarqube_journey_of_an_Azure_professional

  1. I wanted to define my services using infrastructure as code using an Azure Resource Manager template.
  2. I wanted the infrastructure as code under version control using Azure Repos. I could have easily used GitHub here or one of a number of other Git repositories, but I’m using Azure Repos for simplicity.
  3. I wanted to be able to orchestrate the deployment of the service using a CI/CD pipeline using Azure Pipelines so that the process was secure, repeatable and auditable. I also wanted to parameterize my pipeline so that I could configure the parameters of the service (such as size of the resources and web site name) outside of version control. This would also allow me to scale the services by tweaking the parameters and simply redeploying.
  4. I wanted to use a YAML multi-stage pipeline so that the pipeline definition was stored in version control (a.k.a. pipeline as code). This also enabled me to break the process of deployment into two stages:
    • Build – publish a copy of the Azure Resource Manager templates as an artifact.
    • Deploy to Dev – deploy the resources to Azure using the artifact produced in the build.

Note: I’ve made my version of all these components public, so you can see how everything is built. You can find my Azure DevOps repository here and the Azure Pipeline definition here.

Step 1 – Create a project in Azure DevOps

First up we need to have an Azure DevOps organization. You can sign up for a completely free one that will everything you need by going here and clicking start free. I’m going to assume you have your DevOps organization all set up.

  1. In your browser, log in to your Azure DevOps organization.
  2. Click + Create project to create a new project.
  3. Enter a Project Name and optionally a Description.
  4. Select Public if you want to allow anyone to view your project (they can’t contribute or change it). Otherwise leave it as Private to make it only visible to you.
  5. Click Create.

ss_sonarqube_createproject

You’ve now got an Azure Repo (version control) as well as a place to create Azure Pipelines as well as a whole lot of other tools, such as Azure Boards, that we’re not going to be using for this project.

Step 2 – Add ARM Template Files to the Repo

Next, we need to initialize our repository and then add the Azure Resource Manager (ARM) template files and the Azure Pipeline definition (YAML) file. We’re going to be adding all the files to the repository directly in the browser, but if you’re comfortable using Git, then I’d suggest using that.

  1. Select Repos > Files from the nav bar.
  2. Make sure Add a README is ticked and click Initialize.ss_sonarqube_initializerepo
  3. Click the ellipsis (…) next to the repo name and select Create a new folder.
  4. Set the Folder name to infrastructure. The name matters because the pipeline definition expects to find the ARM template files in that folder.
  5. Enter a checkin comment of “Added infrastructure folder”.
  6. Click Create.ss_sonarqube_createinfrastructurefolder
  7. Once the folder has been created, we need to add two files to it:
    • sonarqube.json – The ARM template representing the infrastructure to deploy.
    • sonarqube.parameters.json – The ARM template default parameters.
  8. Click here to download a copy of the sonarqube.json. You can see the content of this file here.
  9. Click here to download a copy of the sonarqube.parameters.json. You can see the content of this file here.
  10. Click the ellipsis (…) next to the infrastructure folder and select Upload file(s).
  11. Click the Browse button and select the sonarqube.json and sonarqube.parameters.json files you downloaded.
  12. Set the Comment to something like “Added ARM template”.
  13. Ensure Branch name is set to master (it should be if you’re following along).
  14. Click Commit.ss_sonarqube_uploadarmtemplate

We’ve now got the ARM Template in the repository and under version control. So we can track any changes to them.

Note: When we created the infrastructure folder through the Azure DevOps portal a file called _PlaceHolderFile.md was automatically created. This is created because Git doesn’t allow storing empty folders. You can safely delete this file from your repo if you want.

Step 3 – Create your Multi-stage Build Pipeline

Now that we’ve got a repository we can create our mulit-stage build pipeline. This build pipeline will package the infrastructure files and store them and then perform a deployment. The multi-stage build pipeline is defined in a file called azure-pipelines.yml that we’ll put into the root folde of our repository.

  1. Click here to download a copy of the azure-pipelines.yml. You can see the content of this file here.
  2. Click the ellipsis (…) button next to the repository name and select Upload file(s).
  3. Click Browse and select the azure-pipelines.yml file you dowloaded.
  4. Set the Comment to something like “Added Pipeline Defnition”.
  5. Click Commit.ss_sonarqube_uploadpipelinefile
  6. Click Set up build button.
  7. Azure Pipelines will automatically detect the azure-pipelines.yml file in the root of our repository and configure our pipeline.
  8. Click the Run button. The build will fail because we haven’t yet created the service connection called Sonarqube-Azure to allow our pipeline to deploy to Azure. We also still still need to configure the parameters for the pipeline.ss_sonarqube_createbuildpipeline

Note: I’ll break down the contents of the azure-pipelines.yml at the end of this post so you get a feel for how a multi-stage build pipeline can be defined.

Step 4 – Create Service Connection to Azure

For Azure Pipelines to be able to deploy to Azure (or access other external services) it needs a service connection defined. In this step we’ll configure the service sonnection called Sonarqube-Azure that is referred to in the azure-pipelines.yml file. I won’t go into too much detail about what happens when we create a service connection as Azure Pipelines takes care of the details for you, but if you want to know more, read this page.

Important: This step assumes you have permissions to create service connections in the project and permissions to Azure to create a new Serivce Principal account with contributor permissions within the subscription. Many users won’t have this, so you might need to get a user with the enough permissions to the Azure subscription to do this step for you.

  1. Click the Project settings button in your project.
  2. Click Service connections under the Pipelines section.
  3. Click New service connection.
  4. Select Azure Resource Manager.
  5. Make sure Service Principal Authentication is selected.
  6. Enter Sonarqube-Azure for the Connection name. This must be exact, otherwise it won’t match the value in the azure-pipelines.yml file.
  7. Set Scope level to Subscription.
  8. From the Subscription box, select your Azure Subscription.
  9. Make sure the Resource group box is empty.
  10. Click OK.
  11. An authorization box will pop up requesting that you authenticate with the Azure subscription you want to deploy to.
  12. Enter the account details of a user who has permissions to create a Service Principal with contributor access to the subscription selected above.ss_sonarqube_createserviceconnection

You now have a service connection to Azure that any build pipeline (including the one we created earlier) in this project can use to deploy services to Azure.

Note: You can restrict the use of this Service connection by changing the Roles on the Service connection. See this page for more information.

Step 5 – Configure Pipeline Parameters

The ARM template contains a number of parameters which allow us to configure some of the things about the Azure resources we’re going to deploy, such as the Location (data center) to deploy to, the size of the resources and the site name our Sonarqube service will be exposed on.

In the azure-pipelines.yml file we configure the parameters that are passed to the ARM template from pipeline variables. Note: There are additional ARM template parameters that are exposed (such as sqlDatabaseSkuSizeGB and sonarqubeImageVersion), but we’ll leave the configuration of those parameters as a seprate exercise.

The parameters that are exposed as pipeline variables are:

  • siteName – The name of the web site. This will result in the Sonarqube web site being hosted at [siteName].azurewebsites.net.
  • sqlServerAdministratorUsername – The administrator username that will be used to administer this SQL database and for the Sonarqube front end to connct using. Note: for a Production service we should actually create another account for Sonarqube to use.
  • sqlServerAdministratorPassword – The password that will be used by Sonarqube to connect to the database.
  • servicePlanCapacity – The number of App Service plan nodes to use to run this Sonarqube service. Recommend leaving it at 1 unless you’ve got really heavy load.
  • servicePlanPricingTier – This is the App Service plan pricing tier to use for the service. Suggest S1 for testing, but for systems requiring greater performance then S2, S3, P1V2, P2V2 or P3V2.
  • sqlDatabaseSkuName – this is the performance of the SQL Server. There are a number of different performance options here and what you chose will need to depend on your load.
  • location – this is the code for the data center to deploy to. I use WestUS2, but chose whatever datacenter you wish.

The great thing is, you can change these variables at any time and then run your pipeline again and your infrastructure will be changed (scaled up/down/in/out) accordingly – without losing data. Note: You can’t change location or siteName after first deployment however.

To create your variables:

  1. Click Pipelines.
  2. Click the SonarqubeInAzure pipeline.
  3. Click the Edit button.
  4. Click the menu button (vertical ellipsis) and select Variables.
  5. Click the Add button and add the following parameters and values:
    • siteName – The globally unique name for your site. This will deploy the service to [siteName].azurewebsites.net. If this does not result in a globally unique name an error will occur during deployment.
    • sqlServerAdministratorUsername – Set to sonarqube.
    • sqlServerAdministratorPassword – Set to a strong password consisting of at least 8 characters including upper and lower case, numbers and symbols. Make sure you click the lock symbol to let Azure DevOps know this is a password and to treat it accordignly.
    • servicePlanCapacity – Set to 1 for now (you can always change and scale up later).
    • servicePlanPricingTier – Set to S1 for now (you can always change and scale up later).
    • sqlDatabaseSkuName – Set to GP_Gen5_2 for now (you can always change and scale up later). If you want to use the SQL Serverless database, use GP_S_Gen5_1, GP_S_Gen5_2 or GP_S_Gen5_4.
    • location – set to WestUS2 or whatever the code is for your preferred data center.
  6. You can also click the Settable at Queue time box against any of the parameters you want to be able to set when the job is manually queued.ss_sonarqube_createvariablesss_sonarqube_variables
  7. Click the Save and Queue button and select Save.

We are now ready to deploy our service by triggering the pipeline.

Step 6 – Run the Pipeline

The most common way an Azure Pipeline is going to get triggered is by committing a change to the repository the build pipeline is linked to. But in this case we are just going to trigger a manual build:

  1. Click Pipelines.
  2. Click the SonarqubeInAzure pipeline.
  3. Click the Run pipeline.
  4. Set any of the variables we want to change (for example if we wanted to scale up our services).
  5. Click Run.
  6. You can then watch the build and deploy stages complete.ss_sonarqube_runpipeline.gif

Your pipeline should have completed and your resources will be on thier way to being deployed to Azure. You can rerun this pipeline at any time with different variables to scale your services. You could even delete the front end app service completely and use this pipeline to redeploy the service again – saving lots of precious $$$.

Step 7 – Checkout your new Sonarqube Service

You can login to the Azure Portal to see the new resource group and resources that have been deployed.

  1. Open the Azure portal and log in.
  2. You will see a new resource group named [siteName]-rg.
  3. Open the [siteName]-rg.ss_sonarqube_resources
  4. Select the Web App with the name [siteName].ss_sonarqube_webapp
  5. Click the URL.
  6. Your Sonarqube application will open after a few seconds. Note: It may take a little while to load the first time depending on the performance you configured on your SQL database.ss_sonarqube_theapplication
  7. Login to Sonarqube using the username admin and the password admin. You’ll want to change this immediately.

You are now ready to use Sonarqube in your build pipelines.

Step 8 – Scaling your Sonarqube Services

One of the purposes of this process was to enable the resources to be scaled easily and non-desrtructively. All we need to do is:

  1. Click Pipelines.
  2. Click the SonarqubeInAzure pipeline.
  3. Click the Run pipeline.
  4. Set any of the variables we want to change to scale the service up/down/in/out.
  5. Click Run.
  6. You can then watch the build and deploy stages complete.

Of course you could do a lot of the scaling with Azure Automation, which is a better idea in the long term than using your build pipeline to scale the services because you’ll end up with hundreds of deployment records over time.

A Closer look at the Multi-stage Build Pipeline YAML

At the time of writing this post, the Multi-stage Build Pipeline YAML was relatively new and still in a preview state. This means that it is not fully documented. So, I’ll break down the file and highlight the interesting pieces:

Trigger

ss_sonarqube_yamltrigger

This section ensures the pipeline will only be triggered on changes to the master branch.

Stages

ss_sonarqube_yamlstages

This section contains the two stages: Build and Deploy. We could have as many stages as we like. For example: Build, Deploy Test, Deploy Prod.

Build Stage

ss_sonarqube_yamlbuildstage

This defines the steps to run in the build stage. It also requires the execution of the stage on an Azure DevOps agent in the vs2017-win2016 pool.

Build Stage Checkout Step

ss_sonarqube_yamlbuildcheckout

This step causes the repository to be checked out onto the Azure DevOps agent.

Build Stage Publish Artifacts Step

ss_sonarqube_yamlbuildpublish

This step takes the infrastructure folder from the checked out repository and stores it as an artifact that will always be accessible as long as the build record is stored. The artifact will also be made available to the next stage (the Deploy Stage). The purpose of this step is to ensure we have an immutable artifact available that we could always use to redeploy this exact build.

Deploy Stage

ss_sonarqube_yamldeploystage

The deploy stage takes the artifact produced in the build stage and deploys it. It runs on an Azure DevOps agent in the vs2017-win2016 pool.

It also specifies that this is a deployment to an environment called “dev“. This will cause the environment to show up in the environments section under pipelines in Azure DevOps.

ss_sonarqube_environments.png

The strategy and runOnce define that this deployment should only execute once each time the pipeline is triggered.

Deploy Stage Azure Resource Group Deployment Step

ss_sonarqube_yamldeploystep

This deploy step takes the ARM template from the infrastructure artifact and deploys it to the Sonarqube-Azure Service connection. It overrides the parameters (using the overrideParameters) property using build variables (e.g. $(siteName), $(servicePlanCapacity)).

But what about Azure Blueprints?

One final thing to consider: this deployment could be a great use case for implementing with Azure Blueprints. I would strongly suggest taking a look at using your build pipeline to deploy an Azure Blueprint containing the ARM template above.

Thank you very much for reading this and I hope you found it interesting.

 

Azure Resource Manager Templates Hands-on Lab and #GlobalAzure 2019

Recently I helped organize and present at the 2019 Global Azure Bootcamp in Auckland. The Global Azure Bootcamp is an huge event run by Azure communities throughout the world all on the same day every year. It is an opportunity for anyone with an interest in Azure to come and learn from experts and presenters and share their knowledge. If you’re new to Azure or even if you’re an expert it is well worth your time to attend these free events.

AucklandGAB2019-1

The Global Azure Bootcamp is also an awful lot of fun to be a part of and I got to meet some fantastic people!

We also got to contribute to the Global Azure Bootcamp Science lab, which was a really great way to learn Azure as well as contribute to the goal of finding potential exosolar planets (how cool is that?). A global dashboard was made available where all locations could compare their contributions. The Auckland Team did fantastically well, given the size of Auckland comparatively: We managed to get 8th on the team leaderboard:

AucklandGAB2019-TeamLeaderboard

Hands-On Workshop Material

As part of my session this year, I produced a Hands-on workshop and presentation showing attendees the basics of using Azure Resource Manager templates as well as some of the more advanced topics such as linked/nested templates and security.

The topics covered are:

AucklandGAB2019-ARMTemplatesWorkshop

I’ve made all of this material open and free for the community to use to run your own sessions or modify and improve.

You can find the material in GitHub here:

https://github.com/PlagueHO/Workshop-ARM-Templates

Thanks for reading and hope to see some of you at a future Global Azure Bootcamp!

Allow Integer Parameter to Accept Null in a PowerShell Function

One of the great things about PowerShell being based on .NET is that we get to the huge number of types built into the framework.

A problem I came across today was that I needed to have a function that took a mandatory integer parameter, but that parameter needed to allow Null. In .NET there is a generic type System.Nullable<T> that allows other types to take on a null value.

This allows me to call the function above with the following:

Set-AdapterVlan -Adapter $adapter -Vlan $null

Which will clear the Vlan ID from the virtual network adapter.

The magic is in the parameter definition:

[Parameter(Mandatory = $true)]
[AllowNull()]
[Nullable[System.Int32]]
$VlanId

The [AllowNull()] attribute allows the $VlanId parameter to accept a Null even though it is mandatory, and the [Nullable[System.Int32]] allows $VlanId to be assigned a null value.

This isn’t something I use often, but thought it was worth sharing.

Enable CORS Support in Cosmos DB using PowerShell

Support for Cross-Origin Resource Sharing (CORS) was recently added to Cosmos DB. If you want to enable CORS on an existing Cosmos DB account or create a new Cosmos DB account with CORS enabled it is very easy to do with Azure Resource Manager (ARM) templates or the Azure Portal.

But what if you’re wanting to find out the state of the CORS setting on an account or set it using PowerShell? Well, look no further.

The Cosmos DB PowerShell module (version 3.0.0 and above) supports creating Cosmos DB accounts with CORS enabled as well as updating and removing the CORS headers setting on an existing account. You can also retrieve the CORS setting for an existing Cosmos DB account.

Installing the CosmosDB Module

The first thing you need to do is install the CosmosDB PowerShell module from the PowerShell Gallery by running this in a PowerShell console:

ss_cosmosdbcors_installmodule

This will also install the Az PowerShell modules Az.Accounts and Az.Resources modules if they are not installed on your machine. The *-CosmosDbAccount functions in the CosmosDB module are dependent on these modules.

Note: The CosmosDB PowerShell module and the Az PowerShell modules are completely cross-platform and support Linux, MacOS and Windows. Running in either Windows PowerShell (Windows) or PowerShell Core (cross-platform) is supported.

Versions of the CosmosDB PowerShell module earlier than 3.0.0.0 use the older AzureRm/AzureRm.NetCore modules and do not support the CORS setting.

Authenticating to Azure with ‘Az’

Before using the CosmosDB PowerShell module accounts functions to work with CORS settings you’ll first need to authenticate to Azure using the Az PowerShell Modules. If you’re planning on automating this process you’ll want to authenticate to Azure using a Service Principal identity.

Side note: if you’re using this module in an Azure DevOps build/release pipeline the Azure PowerShell task will take care of the Service Principal authentication process for you:

ss_cosmosdbcors_azuredevopspowershelltask

But if you’re just doing a little bit of experimentation then you can just use an interactive authentication process.

To use the interactive authentication process just enter into your PowerShell console:

then follow the instructions.

ss_cosmosdbcors_authenticateaz.png

Create a Cosmos DB Account with CORS enabled

Once you have authenticated to Azure, you can use the New-CosmosDbAccount function to create a new account:

ss_cosmosdbcors_newcosmosdbaccountThis will create a new Cosmos DB account with the name dsrcosmosdbtest in the resource group dsrcosmosdbtest-rgp in the West US location and with CORS allowed origins of https://www.fabrikam.com and https://www.contoso.com.

Important: the New-CosmosDbAccount command assumes the resource group that is specified in the ResourceGroup parameter already exists and you have contributor access to it. If the resource group doesn’t exist then you can create it using the New-AzResourceGroup function or some other method.

It will take Azure a few minutes to create the new Cosmos DB account for you.

Side note: But if you want your PowerShell automation or script to be able to get on and do other tasks in the meantime, then add the -AsJob parameter to the New-CosmosDbAccountcall. This will cause the function to immediately return and provide you a Job object that you can use to periodically query the state of the Job. More information on using PowerShell Jobs can be found here.

Be aware, you won’t be able to use the Cosmos DB account until the Job is completed.

If you look in the Azure Portal, you will find the new Cosmos DB account with the CORS allowed origin values set as per your command:

ss_cosmosdbcors_cosmosdbinportalwithcors

Get the CORS Allowed Origins on a Cosmos DB Account

Getting the current CORS Allowed Origins value on an account is easy too. Just run the following PowerShell command:

ss_cosmosdbcors_getcosmosdbcors

This will return a string containing all the CORS Allowed Origins for the Cosmos DB account dsrcosmosdbtest.

You could easily split this string into an array variable by using:

ss_cosmosdbcors_getcosmosdbcorssplit

Update the CORS Allowed Origins on an existing Cosmos DB Account

To set the CORS Allowed Origins on an existing account use the Set-CosmosDbAccount function:

ss_cosmosdbcors_setcosmosdbcors

This will take a few minutes to update. So you can use the -AsJob parameter to run this as a Job.

Remove the CORS Allowed Origins from an existing Cosmos DB Account

You can remove the CORS Allowed Origins setting by setting using the Set-CosmosDbAccount function but passing in an empty string to the AllowedOrigin parameter:

ss_cosmosdbcors_removecosmosdbcors

This will take a few minutes to update as well. As always, you can use the -AsJob parameter to run this as a Job.

 

Final Words

Hopefully, you can see it is fairly simple to automate and work with the Cosmos DB CORS Allowed Origins setting using the PowerShell Cosmos DB module.

If you have any issues or queries or would like to contribute to the PowerShell Cosmos DB module, please head over to the GitHub repository.

 

Use Pester to Test Azure Resource Manager Templates for Best Practices

Recently I came across the amazing Secure DevOps Kit for Azure (AzSK). This contains a really useful AzSK PowerShell Module that contains cmdlets for performing different types of security scanning on Azure Resources, Subscriptions and Resource Manager Templates.

The feature of this module that I was most interested in for my current project was being able to scan ARM templates for best practice violations. The module contains several

To install the module, open a PowerShell Window and run:

Important: At the time of writing this post, the AzSK module has dependencies on the AzureRM.Profile and other AzureRM.* PowerShell modules. As of December 2018, the AzureRM.* PowerShell Modules are going to be renamed to Az.* (see this post). The AzureRM and Az modules can not be installed side-by-side, so if you’ve installed the Az PowerShell modules on your system then the installation of AzSK will fail because the AzureRM modules will also be installed and a conflict will occur.

The cmdlet we’re most interested in is the Get-AzSKARMTemplateSecurityStatus. It can be used to scan one or more ARM templates or entire folders of ARM templates for best practice violations:

ss_azsk_scanning

This will scan the ARM templates and produce a CSV report in a folder Microsoft\AzSKLogs\ARMChecker within your $ENV:LOCALAPPDATA folder and open the folder in Explorer. This isn’t ideal for automation scenarios or using during Continuous Integration or Continuous Delivery pipelines. I’ve raised an issue with the AzSK team on GitHub to see if this can be improved.

In my case, I wanted to be able to use the PowerShell Pester Module, a PowerShell testing framework, to execute tests on the output and then use the nUnit output Pester generates to publish into a Continuous Integration pipeline. To do that I needed to create a custom test script that would take the CSV report, count the failures of each level (High, Medium or Low) and fail if any are counted in the specific level.

This is what the script looks like:

You can download the script from GitHub Gist directly or get it from the PowerShell Gallery by running:

Install-Script -Name AzSKARMTemplateSecurityStatus.Test

To use it you will need to install Pester 4.3.0 and AzSK 3.6.1 modules:

Once that is done, you can use Invoke-Pester and pass in the TemplatePath and Severity parameters to the test script:

This will execute the Pester tests in the file above on the specified ARM template. The tests will fail when there are any best practice violations with the specified Severity or above. If you didn’t pass in a Severity then it will default to failing on Medium and High.

ss_azsk_invokepester

If you use the OutputFile and OutputFormat parameters to cause Pester to output an NUnit format file that most Continuous Integration tools will happily accept and use to display the output of the tests.

If you installed the script from the PowerShell Gallery, you can also run the tests like this:

AzSKARMTemplateSecurityStatus.Test -TemplatePath D:\101-webapp-basic-windows\azuredeploy.json

Finally, if you’re using Azure DevOps, you can also get this function as part Secure DevOps Kit (AzSK) CICD Extensions for Azure in the Azure DevOps Marketplace.

Which ever way you choose to consume AzSK, it is a great module and well worth including in your CI/CD pipelines to ensure your ARM templates meet best practices.