Vagrant bitcoin miner

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So together with the coin and the signature, we now have the ability to verify the coin. So let's put all this together now. So what we'll do is we're going to create a batch script, nothing too fancy. And we'll take the command that we've just run to sign the coin and pop that inside a batch script. Rob Barnes: Okay. The differences here, we're going to use JQ because we want to actually isolate the Vault signature that comes back and we want to store that. So hopefully this works, let's run that and see. And here we have it.

We have a signed HashiCorp Coin. As you can see, we have the Vault signature there at the bottom, just like we had hoped. Nic Jackson: We need to ensure that we have enough of these coins for everyone out there who wants to vote. And that means that we're going to take the batch script that we built and run it on Nomad as a batch job.

Jacquie Grindrod: Let's take a look at the file for it. We start out by defining the name of our job, which is miner in this case. Then Nomad gives us the option to choose what location we're going to run it in. So we specify that using the data center variable.

We set the type of the job to batch and then we give a name to the group that is going to run in. So our task group consists of more than just our miner in this case, but we're going to focus on specifically just generating our coins. So let's scroll down to the next part.

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Here, we have our miner task, which you can see we specify that Docker's the driver, because again, we're using a Docker container. And we're using this template stanza to pass in the data for our coin. And we're using Nomad allocation IDs to be able to assign the coin serial number. The reason we're doing that is that they're unique user IDs.

So that should ensure that none of our coins have overlapping serialization. Next up, we have the short and sweet Vault policy declaration. Which it's specifying the Vault policies that our job requires. So our Nomad client will automatically retrieve a Vault token, which is limited to the policies that we've specified. In this case, that's our transit side. We're declaring our environment variables. We now tell the job which image we're going to pull and what volume to mount.

The entry point scripts that I'm about to show you is where a lot of the magic happens in less than five lines. So the first three lines probably look familiar to you. We're doing the same thing here that Rob showed us in his demo. So we're generating a coin, we're getting the payload and then we're making a call to Vault to get our signature.

Then we put all the coins together. And finally, in our last line, we're pushing that coin to Redis. Now that we've generated our coins and we're even storing them somewhere, we're ready to make a bunch of them. Nic Jackson: So I mentioned earlier on that we're running two very different types of workloads and that we need to understand how these workloads behave under pressure.

And this all starts with observability.

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Unfortunately, our application isn't emitting any metrics, but we can use Consul's service mesh to fill that gap. This is going to give us the opposite of ability into the system that we need. Jono Sosulska: Surface mesh isn't just a buzzword, it provides you benefits straight out of the box, like network observability, security and reliability.

In a conventional deployment of services, one service talks directly to another. In a service mesh, each service talks to a proxy locally.

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All traffic flows between the proxies and this is how you're able to provide detailed information and control over your network traffic. This allows the proxy to provide a high level of information on the state of your traffic. Jono Sosulska: You can see things like number of requests per second, histograms regarding the duration of request, bytes transferred, number of successful requests, errors, and much, much more. These metrics can be emitted in a variety of different formats, such as StatsD, Datadog StatsD or Prometheus, like what we're using here.

Nomad can automatically deploy and configure these proxies to give you the exact metrics that you need. Nic Jackson: So now we have these metrics, we've identified that there are a number of periods where the resources in the cluster are under utilized. Now we reach our first peak around lunchtime, and then we see a little drop off before things start to rise again as folks start to enjoy their free time. After this, we start to see a downward curve until the pattern repeats itself.

What this presents us, however, is opportunity. If you look at the area above the curve, you're going to see that we have unused capacity. We can leverage this capacity to save money, drive maximum efficiency out of the system and handle these peak load moments during the day. How would we do this? Is that we're going to use the metrics provided by Consul service mesh. And we're going to feed them into Nomad's autoscaler. This is going to allow us to dynamically shape the different workloads based on our traffic demands.

Erik Veld: Because if we look at this Grafana dashboard, we can see that we currently have one instance of the API service running and the traffic is fairly stable. But as soon as the traffic starts to peak, we're going to have issues, so we need to start scaling those applications. So let me show you how to do that. So what you see here is the API job and specifically the task that we'll be scaling up and down.

Now, currently this is running with count equals one, but we want to define a policy that the autoscaler can use to scale this up and down for us. So we need to add the scaling stanza and then a policy for that autoscaler.

Now, as Jono showed before we have these metrics coming in from the Envoy proxies, and we're grabbing these to send to our Prometheus instance. So we're going to set source to Prometheus. And then we need to define a query, if I could type. Erik Veld: Now the auto scaler wants a singular value to act upon. So that's why I'm wrapping this query in a scaler. Now, if you are like me, you hate writing Prometheus queries. So we're lucky that we already have these dashboards and we can use these queries that we've already defined there.

So we're going to be grabbing the average amount of requests per second, going into each of these API instances.

And we can just copy paste that query in directly. Erik Veld: So let's head on over to the dashboard and then grab that query out of here. And then we can paste that in, and now what we need to do, now that we have that value, the system needs to be able to react to that. So we're going to need to add a strategy. And a strategy we want to be using is the target value strategy. And we're going to configure that so that it will try to make the factor equal to one. And that's mostly because I'm running this on my Tao desktop, and I don't want to overload it, otherwise I couldn't be recording this video.

So we're going to set the value to one.

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Erik Veld: Now, if I would apply this, the autoscaler would go crazy and start scaling up infinitely. So I want to add some guard rails. Now I want to be running a minimum of one instance of this job, and let's just say, I want a maximum of three API instances running. Now, all that's left to do is to run the API. HCL instance to Nomad. Okay, cool. So now we're scaling the API service dynamically up and down to handle for that peak load.

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But what do we do with that headroom that we still have left? We're paying for that compute. So let's use that. Let's add a scaling stanza to the miner to actually fill up those gaps.

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