Defore I get into the code I should call out the bindings and triggers that Azure provides for interacting with Functions. There is a function.json file that defines the configuration for a Function. The bindings sesiont of this json file tells Azure how the Function should be triggered and how it’s output should be treated. Theres a simple example here:

Depending on SDK you are using (C# here but the Java SDK is very similar) the bindings into a Function can be autogenerated from attributes on the parameters. A future version of the runtime will remove the need for having the bindings section and the just the attributes will be used.

The bindings list here gives a good view as to what is supported for interacting with other Azure products. The HTTP and Timer are probably the two simplest bindings.

In my examples below I’m mostly working with HTTP, Service Bus, CosmosDB and Blob inputs/outputs.

Disclaimer, all these examples are trivial and contrived, none even resemble a prototype.

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The first Azure Function is triggered by a HTTP call and sends an event to Service Bus when the function is triggered.

Input

The HTTPTrigger attribute on the req param tells Azure that this function should be exposed as a HTTP endpoint, the HttpRequest is available in the req object, allowing you full access to headers, form data, etc.

Output

The ServiceBus attribute allows you to add messages to the Service Bus queue defined by the name and connection string in the attribute properties.

To get the service bus binding you need the following nuget package

I take a name from the query string and populate a StartMovieProcess object that I add to the queue.

The method returns a Http response to sattisfy the http request.

The second Function in the pipeline is triggered by any message in the TriggerMovieProcessQueue.

The ServiceBusTrigger Attribute tells Azure to invoke this function whenever messages are sent to the queue configured by the properties. The triggerEventJson will contain the data from the message. I could probably have used a strongly typed domain object here instead of flattened json but I haven’t tried it.

The output here is going to be a blob that I upload a file to.

In the snippet above I grab the data from a CSV hosted on github and upload it to the blob on Azure Storage.

I added a couple of Metadata values to the blob form the message just to show how easy it is to push these attributes with the blob.

Once the file is uploaded this second function ends.

If you are using Visual Studio you benefit from the tight developer experience that MS offer, in this article I’ll be using VS Code on Ubuntu.

The Azure Function extension for VSCode is definitely worth looking into.

To run the functions locally you will need the sdk nuget package and the Azure Functions Core Tools which offers a local runtime for Functions.

Running the functions on the local Function host is the same as running any other application once you have the prerequisites above setup.

The runtime gives some good logs around which Functions have been detected:

URLs HTTP triggered Functions can be invoked from:

That’s all I’m going to cover in this part, in the next article I’ll look at Functions that process the blob and Write to CosmosDB

The first step is to create a Lambda function that will host the endpoint for the Skill.

Once the skill is set up and you have an Amazon Resource Name (ARN) to reference your Lambda function, you can set up your Skill to point at it.

You create a Skill from the developer.amazon.com portal

The interaction model is made up of Intents, Slots and Utterances.
I defined 2 intents, one to allow a user to query their own activities and the other to query their friends activities.

Slots are like variables that Amazon extracts from the users request based on how the user invokes the intent. There are built in slots like AMAZON.DATE and you can create custom slots too, LIST_OF_ACTIVITIES is a custom slot I defined.

The Sample Utterances are where you list the ways you expect users to interact with your skill. You associate utterances with Intents in this section aswell as adding your slots as tokens. For example, if a user spoke Who Ran or Who Cycyled they would both match the utterance WhoWasActive Who {ActivityType}. The Intent that would be requested form your endpoint would be WhoWasActive and the Slot would contain an ActivityType of Ran or Cycled.

For more info see the docs

Making the skill work with the Strava OAuth took a while to configure correctly but didn’t require any code which was nice. Once this config is setup users get prompted to authorise the app from their Strava account

Once the user authorizes the skill, Amazon takes care of requesting and refreshing the auth token. The token is supplied to your request handler with every invocation of the skill and you pass the token through to the API call. Painless.

There is an AWS Plugin for Eclipse available here.

I used the alexa-skills-kit-java library from Amazon for taking care of a lot of the boilerplate.

The entry point to the code is the RequestStreamHandler, this is the handler defined in the Skill config:

The StriderSpeechlet implements Speechlet class is where you handle events like onLaunch, onIntent, onSessionEnded, etc.

The full instructions are here
First, install openssl using Homebrew

Install the .NET Core SDK from the page above.

I had an issue after the install where the dotnet command wasn’t exposed on my path. Found the following fix on one of the github issue comments:

Some comments suggest it may be because I use oh-my-zsh.

Once the sdk is setup successfully you can init a project:

That should produce a simple Hello World output:

Now that we have the basic .NET Core app let’s install VS Code to use as our IDE. The OSX download is available at code.visualstudio.com

We need to add some nuget packages to the project so the first step is adding the Nuget package manager.

Go to View → Extensions and search for Nuget, the Docker extension is also useful for adding docker commands to VS Code.

Once you have Nuget installed you can access Nuget commands in the command bar with F1:

I added Kestrel and a couple of MVC nuget packages, you can see the list in the project.json file further down.

Once you add a nuget package through the UI you will get prompted to restore (dotnet restore is the terminal command):

Now that we can run the service locally, lets add it to a docker container. The first step is to create a dockerfile. The Docker extension for VS Code has some commands built in:

This was the first version of my dockerfile, I’ll walk through some of the issues I had to troubleshoot further down.

Building my docker file from VS Code:

Running the docker container:

My first issue was the incorrect version of my app compared to the aspnetcore version I pulled down in my base image.

I updated the base image in the dockerfile and rebuilt: FROM microsoft/aspnetcore:1.1.0

The issue this time turned out to be the COPY command wasn’t copying my build output to the working dir of the image.

COPY $source/bin/Debug/netcoreapp1.1/ .

This time I realized I was copying the build output but I needed to be running a publish so that dependencies are included. I needed to run dotnet publish and change the COPY to pick up the published output.

COPY $source/bin/Debug/netcoreapp1.1/publish .

The publish did the trick, now I was able to run the docker container…​ but not access the endpoint.

I ran the docker container specifying * as the host to bind to and forwarding port 5000 on the container to post 5001 on my machine.

Curl confirms that there is nothing running on port 5000 and the container is exposed on port 5001.

The docker ps command lists the running containers

That’s it, a simple microservice running on the .NET Core Kestrel server and packaged up in a docker image. It’s a basic structure for somethign that can be built upon in future.

I’ve included the terminal commands/output just in case others hit similar issues and hopefully the solutions above will help them.