Building Firewatch Australia, Part 1 - Data Processing

Firewatch Australia is a free app released over the 2019-2020 Australian Bushfire season to help track bushfires. This is part 1 in a series of 5 posts on building the app. Check out the introduction or take a look at the next post on Scaling on the Cheap to learn more.

When I first started building Firewatch Australia I was very much building the plane whilst flying it. I had a few goals in mind but didn’t know exactly how they would manifest or how I would implement them. One early feature I knew I wanted was a visual history of each fire showing its progression. This seemed important to me so that the speed at which a fire is spreading or if it has slowed down could be better understood.

The RFS feed only offers the current size and shape of the fires so in order to achieve this I needed to start scraping and saving the data before deciding how I was going to manage and serve it long term. I also needed to start gathering the data ASAP so that history would be available when the app launched.

Video of the app showing a timelapse of the Clyde Mountain Fire progression over 5 days.

Gathering Data

I wanted to go serverless for the whole architecture for various reasons but mostly for time to market - I needed to get this app out ASAP for it to be useful to people. No servers to setup is a real time saver and it means I can spend all my time writing code and adding features. I also wanted to use GCP as that’s where I’m most comfortable.

Google Clouds serverless compute product is simply called Cloud Functions and pretty much does what it says on the tin - you deploy individual functions to run and they can be triggered through a variety of sources including HTTP endpoints and events within GCP. No servers to manage and you pay per 100ms of execution with a generous perpetual free tier.

I created a simple Cloud Function to make a request to the RFS feed and fetch the data. The response is GeoJSON and contains a list of all the currently active fires. I wanted to make sure that each fire was captured separately and only make a new record when it changed - I didn’t want storage costs to blow out by storing the same data every time it was fetched.

To do this the Cloud Function splits the data up into each fire and generates an md5 hash of the GeoJSON for that fire. The GeoJSON for each individual fire and its md5 hash are then written directly to Cloud Storage using the python client. The next time the GeoJSON is fetched the hash of the latest data is compared with that in storage to determine if the fire has changed, if it has the the new data is written and the hash is updated.

Image the contents of a Cloud Storage bucket showing a fires history of changes and the md5 hash file
Listing of a Cloud Storage bucket for a single fire showing one file for each change in its history and the md5 hash.

Next I needed a way to trigger this periodically. Google have a product called Cloud Scheduler which they describe as a “Fully managed cron job service”. This is a really great service that often seems overlooked and little talked about. It allows you to specify a crontab syntax for when the job should run and provide something to trigger such as an HTTP endpoint which is perfect for Cloud Functions. As an added bonus you can even specify a time zone along with your crontab so, if you’re lazy like me, you don’t have to do the time zone conversions yourself.

It doesn’t hurt that you get 3 free Scheduler jobs and only 10 cents per job after that - that’s 10 cents per job, not per execution, so if you run it once a month or thousands of times it still only costs 10 cents. Note that you do pay for any Cloud Function resources you consume if a Cloud Function is your target.

I wanted to refresh the data every 5 minutes so that it was never too far out of date. A simple crontab of */5 * * * * does this and I made the target the HTTPS endpoint of the Cloud Function.

Screenshot of the configuration of the Google Cloud Scheduler job
Screenshot of the configuration of the Google Cloud Scheduler job. Here you can see the crontab and Cloud Function being used as a trigger.

At this point I’ve deployed a single Cloud Function, scheduled it periodically and am starting to gather history into Cloud Storage. This was really quick in a serverless world on GCP and I quite literally did it in a couple of hours whilst enjoying a few Christmas beers!

Having every distinct change for each fire being written to Cloud Storage periodically forms a really solid base for what comes next.

Processing Data

Cloud Functions support more than just HTTP triggers, another super useful trigger is “Cloud Storage”. This allows you to trigger a function based on an object event in a Cloud Storage Bucket - you can trigger when an object is created, deleted or archived.

Screenshot a configuration for a Cloud Storage trigger for a Cloud Function
Screenshot of a Cloud Function trigger configuration that fires when an object is created in a Cloud Storage Bucket.

Using this trigger type coupled with having each distinct change in Cloud Storage means it can process each change as it arrives. A Cloud Function receives the metadata about the newly created file (i.e. a brand new fire or a change to an existing fire), fetches that file from Cloud Storage processes it into a format suitable for the app and stores it to Cloud Firestore.

Firestore is Google’s serverless NoSQL document store product. It organises data in “documents” which is a set of fields with values (think JSON) and “collections” which is a group of similar documents. One nice feature is that collections can be nested in documents as “subcollections”.

Processing the data from Cloud Storage and writing it to Firestore boils down the to the following steps:

  1. Check if the fire already exists in Firestore, if it doesn’t then insert a new document in a root-level collection called “incidents”. Populate this document with some basic information.
  2. Each fire document has a “history” subcollection to contain all the changes over time. Insert the lastest data into this subcollection indexed by the timestamp that the update occurred at.

Using subcollections for the history has a couple of great advantages. Firstly, the parent document can be kept small so when loading the main screen of the app doesn’t have to fetch the full history for every fire. Secondly, subcollections can be inserted into without having to fetch the parent document or the rest of the history subcollection. Some of these fires lasted for more than 2 months with regular updates of GeoJSON meaning that the documents were pretty big, not having to fetch them all the time was a huge help.

Screenshot the Firestore UI
Screenshot the Firestore UI showing some root-level collections on the left, a list of incidents/fires in the middle, and fields contained in an incident on the right. The right panel also shows the history subcollection at the top.

Why not go Straight to Firestore?

This is a good question - why did I write to Cloud Storage first only to immediately fetch it and write to FireStore? Couldn’t I have gone direct to Firestore?

That would have been an option, but as mentioned at the beginning I was very much figuring out what I was doing as I was doing it. I didn’t know the final schema for Firestore when I started. I was also not familiar with the RFS data feed nor with GeoJSON which meant if I did too much processing upfront I was nervous that something would change and it would all break. Storing the raw data first meant I could change my mind about processing and the Firestore schema and simply run the functions again on top of the raw data. This turned out to be a very robust solution.

Wrapping Up

Whilst I’ve spoken only about the fires here, this is the exact same process I’ve applied to the active hotspots and traffic data.

I really liked the simplicity of this solution - there are two functions with two distinct roles - one fetches data, the other processes it into something more useful. The triggers on Cloud Functions as well as Cloud Scheduler make this super simple and the whole thing runs reliably in severless environment. It’s been running reliably now for about 4 months entirely untouched.

In the next post I’ll be talking about how this data was served via APIs (also Cloud Functions) and how I managed scale on the cheap.

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