Category: how-to

As I’ve been playing around with Azure Functions I’ve slowly outgrown the web-based editor. It’s not that it’s not useful, it’s just that I miss intellisense (I’ll come back to this in a later post), and I accidentally deployed a change which broke one of my functions. I’d made dozens of tiny changes, but I simply could not figure out which one it was. Not having a version history, I was kinda screwed.

I had seen the “Configure Continuous Integration” option before, but never really looked at it. I keep my source code in private GitHub repos, so it was relatively trivial to set up a new repo tied to this function app. After reading the setup instructions, however, I was a little confused by what exactly to do to put my existing functions in to repo, but it was actually much simpler than I thought. It turns out one of the best features is the ability to roll back to a previous commit with a single click:

First, I created a new private GitHub repo and cloned it to my local machine. I chose not to use branching – but I guess you could map different function apps to different branches to support a separation between “dev”, “test”, “production” etc. In the root of my repo, I created a folder for each of the functions I wished to deploy, named exactly the same as the existing functions (I assume they’re not case sensitive but I kept to the same case).

Then, I needed to put the actual code in there. Under the visual editor for each of the functions is a “view files” link: . Clicking this, I was able to see the function.json and run.csx files within each function. I simply cut and pasted the code from there to a file of the same name in the relevant folder.

Next, I needed to find the host.json file. That’s a bit more tricky. In the end, I figured the easiest way was to use the Dev Console. Navigate to Function App Settings, and select “Open dev console”. After a few seconds, the dev console appears:

This appears to be a Linux shell. You should start in the d:\home\site\wwwroot folder – that’s where host.json lives. Just type cat host.json to see the contents. It turns out mine was empty (just an open and close curly brace):

D:\home\site\wwwroot

> ls
D:\home\site\wwwroot
QueueTriggerCSharp1
fetchDepartureBoardOrchestrator
host.json
postToTwitter
> cat host.json
D:\home\site\wwwroot
{}
>

I created this in the root of my repo, then committed the changes and pushed them back to GitHub. Within a few seconds, I was able to see the change by clicking “Configure continuous integrations” in Function App Settings. My changes deployed immediately. And when I next screw up, because I’m forced to push changes via GIT, I know I’ll be able to roll back to a known-good configuration.

I started writing EnfieldTownBot using Azure Logic Apps. It’s pretty easy, but i soon hit a challenge – it’s so expensive! My app was pretty simple – a trigger, a “for…each”, a condition and a http callout to my Twitter Poster Function App:

So – if there are no delays, that’s (recurrence + httprequest + foreach + 3 x (condition)) = 6 actions. There could be up to 9 if the postToTwitter action also triggers. I want to run this function 2x (once for trains FROM Enfield Town, once for trains TO Enfield Town), so that’s 12-18 actions per request. And I want to run it every 15-30 seconds to get the latest information published ASAP. So that’s 48-72 actions per minute. Over a day, that’s 69,120 to 103,680 actions. Over a (31 day) month, that’s 2,142,720 to 3,214,080 actions. Taking a mean of these (2,678,400), and looking at the current pricing, it would cost me £450 a month to run this app. Wow. I don’t care about late trains THAT much…

So – I rewrote the whole lot as a function app (actually 2 or 3 function apps, as some of the items, such as parsing the XML from National Rail to JSON are reusable elsewhere). Each execution takes about 300ms, and it executes (24 hours x 60 minutes per hour x 4 times per minute) = 5,760 per day. Over a month that’s 178,560 executions. That’s well within the “permanent free grant” provided for Functions. In fact, I’d have to run it something like 30,000,000 times per month, or about 600 times per second to even hit a cost of £1.