>In a recent post, we shared about how we do bare metal clustering for Kubernetes on-the-fly at the Edge in our restaurants. One of the most common (and best) questions we got was “but why?”. In the post we will answer that question. [emphasis mine]
Luckily there's a whole article on that topic. I try to avoid "Did you read the article?" comments, but the first three sentences in the article tell you that this is the question they're answering.
This is the relevant pair of paragraphs from the article:
>As a simple example, image a forecasting model that attempts to predict how many Waffle Fries (or replace with your favorite Chick-fil-A product) should be cooked over every minute of the day. The forecast is created by an analytics process running in the cloud that uses transaction-level sales data from many restaurants. This forecast can most certainly be produced with a little work. Unfortunately, it is not accurate enough to actually drive food production. Sales in Chick-fil-A restaurants are prone to many traffic spikes and are significantly affected by local events (traffic, sports, weather, etc.).
>However, if we were to collect data from our point-of-sale system’s keystrokes in real-time to understand current demand, add data from the fryers about work in progress inventory, and then micro-adjust the initial forecast in-restaurant, we would be able to get a much more accurate picture of what we should cook at any given moment. This data can then be used to give a much more intelligent display to a restaurant team member that is responsible for cooking fries (for example), or perhaps to drive cooking automation in the future.
I've enjoyed reading (all) your comments here. Just wanted to chime in and say I have a great appreciation for what CFA does and have had great experiences with the app and in general the entire ordering process at CFA. I've been, in fact, quite impressed. Nice to read about the stuff in the background that helps make that happen.
Doesn't hurt that every employee I've interacted with at CFA has been courteous, kind, and respectful.
Why pretend to be making a disinterested comment about how "kind and courteous" employees are (which is totally irrelevant) when you're actually motivated by sympathy to the company on the basis of their regressive, hate-fueled ideology? This kind of manipulation doesn't speak well of your fellow extremists.
I'd heard a few years ago about a random company called Chic-fil-a and that their founders were Christians and one of them said some things defending marriage that stirred up controversy. Then promptly forgot about them.
Then a couple years ago, one showed up near my work, and I heard they had good chicken. The first thing I noticed about CFA was how their employees acted. I was impressed! Then, I downloaded and installed the app and had a fantastic experience. I was impressed! And their food is great!
Certainly, I agree with the ideology of their founders.
However, that is independent of the fact that that I've been impressed! When I go to CFA, I wonder HOW they get me through the line so fast. I appreciate how they've trained their employees in good-old-fashioned southern hospitality. As far as I've seen, at this location, they hire friendly people who care about taking care of the customer.
So, back on topic... how DO they get me through the line so fast?
Hey this is Brian and I wrote the article. Great question.
1) MQTT -- we have a lot of cases where we want to share data between physical or "software" devices in-restaurant, without internet connections. MQTT is our primary channel for sharing messages between things, and for collecting data in general to be exfiltrated to the cloud.
2) "Brain" apps -- these are "smart" applications that collect data from MQTT topics and make decisions about what should be done for a given restaurant process. Today, the reality is we do not automatically cook anything. We do, however, have some restaurants that have more intelligent screens to help them know what to cook at any given moment based off of forecasting models that use the data we collect from many different things. These generally run at the edge to preserve their function in cases where there is high latency or bad WAN connections.
3) In the past we had a POS Server in the restaurant. It was a single point of failure for things like drive thru order taking with iPads and mobile orders. The move to K8s lets us run a microservice that can intake mobile orders on a more resilient infrastructure. To be clear, we have not made this shift just yet, but its something we are working on that will ultimately be transparent to customers but that will make their ordering experience more reliable. We have many cases like this.
Overall, our workloads aren't "heavy" which is why we did an array of three moderately sized commodity compute devices. We wanted a smart, sensible ROI on the hardware. We do have enough business-important apps that need to run in a low-latency high-availability environment to push us out of just cloud (internet dependent) and to the Edge. In the future, maybe this can change and we can use cloud only. It seems Edge is an industry trend though, with many workloads moving towards their consumers (a lot of gaming, but a lot of retail as well).
Great question and I hope this helped -- if not just let me know.
Good answer, seems we finally got to the meat of the product (no pun intended).
So the current workload doesn't do any of the fancy stuff but is just typical POS stuff with a 'smart' display for orders driven by a process monitoring a few MQTT sources.
You've just put in enough beef to allow for future expansion.
I can honestly say that answer provides more information than the whole original article.
Iot device control, such as timers for food, and cameras tracking food (to name a few). These also output streams of data that is interesting to us, such that we want to exfiltrate them up to the cloud.
I’m not a fan of hype... k8s is one of the few hyped technologies that has delivered on its promises.
Luckily there's a whole article on that topic. I try to avoid "Did you read the article?" comments, but the first three sentences in the article tell you that this is the question they're answering.