Azure Functions tip: working locally with F# Scripts

I have been working with Azure Functions quite a bit lately. As a result, with more and more functions to develop and maintain, figuring out what a sane workflow might look like has gained some urgency. This post is not particularly deep, and is intended mainly as notes on things I have been trying out to get a decent local development experience with Azure Functions using F# scripts.

First, what is the problem?

While the development experience of Azure Functions in the Azure portal is decent, given the constraints, this is clearly not acceptable for anything beyond work “in the small”. What works for a small script quickly becomes painful for larger function apps: the editor is slow, offers limited support (no Intellisense…), and the workflow ends up being essentially “try out code and hope it works”, with no source control.

What we really want is, not that: we want a decent editor, and the ability to run code locally before committing it to source control and shipping it.

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Creating an Azure Functions solution diagram

One aspect of Azure Functions I found intriguing is that each function contains both its code, and a description of the environment it is expecting to run in, contained in its function.json file. What’s interesting about this, is that as a result, scanning a function app (a collection of functions) provides a fairly complete self-description of the application and its environment, which we should be able to visualize.

In this post, we’ll explore that idea, and sketch out an approach to automatically generate diagrams, visualizing bindings and code dependencies for a code base. We will only support a subset of the full functionality available in Azure Functions, mainly because we are lazy, and create graphs such as the one below, using F# and GraphViz.

Azure Function App Diagram

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Creating an Azure Function in F# from the ground up (Part 2)

In our last post, we went through setting up a simple Azure Function App through the portal. Starting from a local script that retrieved exchange rates from Yahoo and posted an update on Slack, we converted part of the code, using a Timer function to automatically make web requests every 15 seconds.

Today, we will finish turning our script into a Function App, illustrating as many useful tips as we can in the process. Among others, we’ll see how to consume NuGet packages, use queues (and more generally bindings) to allow communication between functions, and upload and reference arbitrary files.

Creating a function triggered by a Queue

So far, we have ported the first half of our script, pulling exchange rate data from Yahoo every 15 seconds. What we have left to do is to port the second half, creating a nicely-formatted message and posting it to Slack. Let’s do that now.

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Creating an Azure Function in F# from the ground up (Part 1)

If you follow me on Twitter, you may have noticed a recurring topic lately: Azure Functions. I have found it both useful for many use cases, and simply fun to work with; and it fits pretty nicely with F#. I recently gave a talk at NDC London (the video should be online at some point), where I demoed a small example, trying to fit in as many features as I could, in as little time and code as possible. Someone took up my offer to write a tutorial from the ground up, so I figured, let’s take that example and turn it into a post. It is a demo, so what it does is not particularly useful by itself, but it illustrates many of the features and tricks I found useful, and should be a good starting point to write “real” code.

The app: sending exchange rate updates on Slack

What we will build is an app which will post, on a regular cadence, the latest available USD/GBP exchange rate on Slack. The reason I picked that example is two fold. First, the exchange rate changes often, which will help verify that things are indeed working. Then, we’ll be able to showcase how easy it is to integrate functions to put together a working application.

Before starting with the code itself, we will need two things: exchange rates, and Slack.

For the exchange rate, we will use Yahoo, while it’s still there. Yahoo has a free API for exchange rates, available at the following URL:

http://query.yahooapis.com/v1/public/yql?q=select * from yahoo.finance.xchange where pair in ("GBPUSD")&env=store://datatables.org/alltableswithkeys

This returns an xml document, which looks like this:

<query xmlns:yahoo="http://www.yahooapis.com/v1/base.rng" yahoo:count="1" yahoo:created="2017-02-11T19:56:24Z" yahoo:lang="en-US">
    <results>
        <rate id="GBPUSD">
            <Name>GBP/USD</Name>
            <Rate>1.2486</Rate>
            <Date>2/10/2017</Date>
            <Time>10:02pm</Time>
            <Ask>1.2489</Ask>
            <Bid>1.2486</Bid>
        </rate>
    </results>
</query>

So the first part of our job will be to regularly call that URL, and extract the Rate from the xml document.

Posting to Slack isn’t very difficult either. I created my own personal Slack at mathias-brandewinder, where I can talk to myself quietly, as well as test examples like this one. I then created a webhook, by going to https://mathias-brandewinder.slack.com/apps/manage, selecting Custom Integrations, Incoming WebHooks, and pick a channel to post to. I created a channel #exchange_rate for the occasion. Once the setup is done, you get a WebHook URL, which looks like https://hooks.slack.com/services/S0meL0ngCrypt1cK3y, where you can now POST JSON messages.

So the second part of our job will be to take that rate, create a JSON message and POST it.

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New relase of @fsibot, now on Azure Functions

About 2 years ago, I wrote a little application, @fsibot. @fsibot is a Twitter bot which, when it receives a Tweet that is a valid F# expression, will evaluate it and return the result to the sender. Got to code FizzBuzz in an interview? Impress your audience, and send a Tweet from your cell phone to @fsibot:

It was very fun to write, rather pointless, but turned out to be an interesting exercise, which taught me a lot. And, in spite of its simplicity, it’s a decent sample app, which touches on many aspects a real-world app might encounter.

After some hiccups early on, @fsibot has been running pretty smoothly, until I noticed issues recently. Rather than trying to figure out what the hell was going on, I decided to port it over Azure Functions, which sounded like a better fit for it. While at it, I also made a couple of changes to the bot. If you are interested, you can find the code on GitHub.

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