Skip to main content

Azure Functions integration with Visual Studio and CI

In one of my latest posts we took a look on Azure Functions and how we can write an Azure Function that extracts the GPS coordinates from a picture and draw them on the picture itself.

In this post we will look on:

  • How we can create and edit an Azure Function directly from Visual Studio
  • Run Azure Function on you development machine
  • Deploy Azure Function from Visual Studio
  • Setup continuous integration

How we can create and edit an Azure Function directly from Visual Studio
Until a few weeks before, we were able to create and manage Azure Functions only from the portal or using the API (e.g. using Power Shell library). Starting from now, there is a new tool for Visual Studio that allow us to create and write functions directly from Visual Studio. 
The tool allow us to run locally our functions and tests them. Pretty cool, especially when you need to write and maintenance more than a dozen of them. The current version is extremely stable. To tell you the truth, is expected to encounter issues, but works great.
The Azure Function tool comes with a Azure Function project template. Under this projects, each function will be added in a separate folder. The structure of the project and each function folder is the same to the one that we can find on Azure Portal, including function.json, project.json and run.csx.

There are multiple templates for functions, for different languages and triggers. I was able to find almost all the actions and binding that you can do from the portal.
For the case that we want to cover to use as trigger creation of a new file in OneDrive, I would recommend to create the function initially in the portal or at least the API Connection. If you already had the API Connection created, than you only need to specify in the binding the write connection - 'onedrive_ONEDRIVE'. You might encounter similar problems when triggers are SaaS or complex sources, but I expect that this will be solved in the future - we are still in beta.

Run Azure Function on you development machine
Running Azure Function on your local machine is like running any other web app locally. The trick is how you trigger different actions. For simple triggers, inputs and outputs things will go very smooth, but for more complex scenarios you might encounter small problems (tools are still in beta now). 
My experience was pretty good with time trigger functions, but for more complex one I had some issues running locally and I preferred to deploy them directly to Azure for now.

Deploy Azure Function from Visual Studio
The deployment is done in the same way as you would do a deployment of a web application. Write click on the project and click Publish. You'll need to specify an existing function app that you already created in the portal or create a new one. 
There is nothing special or different from deploying a web app.

As for an web app, you can attach a debugger to you function and do remote debugging. I'm so exited to see that nowadays remote debugging is simple and you don't need to lose 1 days to configure and to special setup.

Setup continuous integration
As any other services for Azure, Azure Functions is fully integrated with continuous integration system. Different types of repositories are supported, from GitHub to TFS. 
When you want to configure CI, you should remember that you'll configure it for the the Function App instance and not for each Azure Function. The configuration can be found in 'Function app settings > Deploy > Continuous Integration'.

Once you Setup the CI, any change done in your repository will trigger a build and a new deployment. You can have different Function Apps that are register to different branches, allowing us each sub-team has his own deployment and also have different environment in different branches, fully configured with CI.

If you configure as source for CI a repository where you have multiple projects, you might encounter during the CI process the following error:

Unable to determine which solution file to build

In this case don't panic. This happens because the CI is not able to identify what project you want to deploy. To resolve this problem you need to create a filed named '.deployment' in the root of the repository and specify the path to the project. In my case, I have on my GitHub repository the Azure Function project in the following path 'Azure/azure-function-extractgpslocation-onedrive-drawwatermark-git-ci-integration/'. This means that the .deployment' shall have the following content
[config]
project = Azure/azure-function-extractgpslocation-onedrive-drawwatermark-git-ci-integration

More about how you can customize the deployment using .deployment and Kudu can be found on this page.

Conclusion
In this post we saw the current integration of Azure Function with Visual Studio and Continuous Integration. Things are looking very good. Even if we are only in beta, the VS Tools are stable and full with features.
In the next post we will take an look on Azure Functions from functionality, features and price perspective. 

Comments

Popular posts from this blog

Windows Docker Containers can make WIN32 API calls, use COM and ASP.NET WebForms

After the last post , I received two interesting questions related to Docker and Windows. People were interested if we do Win32 API calls from a Docker container and if there is support for COM. WIN32 Support To test calls to WIN32 API, let’s try to populate SYSTEM_INFO class. [StructLayout(LayoutKind.Sequential)] public struct SYSTEM_INFO { public uint dwOemId; public uint dwPageSize; public uint lpMinimumApplicationAddress; public uint lpMaximumApplicationAddress; public uint dwActiveProcessorMask; public uint dwNumberOfProcessors; public uint dwProcessorType; public uint dwAllocationGranularity; public uint dwProcessorLevel; public uint dwProcessorRevision; } ... [DllImport("kernel32")] static extern void GetSystemInfo(ref SYSTEM_INFO pSI); ... SYSTEM_INFO pSI = new SYSTEM_INFO(

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

What to do when you hit the throughput limits of Azure Storage (Blobs)

In this post we will talk about how we can detect when we hit a throughput limit of Azure Storage and what we can do in that moment. Context If we take a look on Scalability Targets of Azure Storage ( https://azure.microsoft.com/en-us/documentation/articles/storage-scalability-targets/ ) we will observe that the limits are prety high. But, based on our business logic we can end up at this limits. If you create a system that is hitted by a high number of device, you can hit easily the total number of requests rate that can be done on a Storage Account. This limits on Azure is 20.000 IOPS (entities or messages per second) where (and this is very important) the size of the request is 1KB. Normally, if you make a load tests where 20.000 clients will hit different blobs storages from the same Azure Storage Account, this limits can be reached. How we can detect this problem? From client, we can detect that this limits was reached based on the HTTP error code that is returned by HTTP