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** This tutorial was originally published on Datawire.io in 2017. As a result, some of the tools mentioned may no longer be actively maintained. Please join our Slack if you have any questions.

Prometheus - Monitoring Envoy and Ambassador on Kubernetes

With the Prometheus Operator

In the Kubernetes ecosystem, one of the emerging themes is how applications can best take advantage of the various capabilities of Kubernetes. The Kubernetes community has also introduced new concepts, such as Custom Resources, to make it easier to build Kubernetes-native software.

In late 2016, CoreOS introduced the Operator pattern and released the Prometheus Operator as a working pattern example. The Prometheus Operator automatically creates and manages Prometheus monitoring instances.

The operator model is especially powerful for cloud-native organizations deploying multiple services. In this model, each team can deploy its own Prometheus instance as necessary instead of relying on a central SRE team to implement monitoring

Envoy, Ambassador, and Prometheus

In this tutorial, we'll show how the Prometheus Operator can be used to monitor an Envoy proxy deployed at the edge. Envoy is an open source L7 proxy. One of the many reasons for Envoy's growing popularity is its emphasis on observability. Envoy uses statsD as its output format.

Instead of using Envoy directly, we'll use Ambassador. Ambassador is a Kubernetes-native API Gateway built on Envoy. Similar to the Prometheus Operator, Ambassador configures and manages Envoy instances in Kubernetes so that the end user doesn't need to do that work directly.

Prerequisites

This tutorial assumes you're running Kubernetes 1.8 or later, with RBAC enabled.

Note: If you're running on Google Kubernetes Engine, you'll need to grant cluster-admin privileges to the account that will be installing Prometheus and Ambassador. You can do this with the commands below:

Deploy the Prometheus Operator

The Prometheus Operator is configured as a Kubernetes deployment. We'll first deploy the Prometheus operator.

kubectl apply -f prom-operator.yaml

We'll also want to create an additional ServiceAccounts for the actual Prometheus instances.

kubectl apply -f prom-rbac.yaml

The Operator functions as your virtual SRE. At all times, the Prometheus operator insures that you have a set of Prometheus servers running with the appropriate configuration.

Deploy Ambassador

Ambassador also functions as your virtual SRE. At all times, Ambassador insures that you have a set of Envoy proxies running the appropriate configuration.

We're going to deploy Ambassador into Kubernetes. On each Ambassador pod, we'll also deploy an additional container that runs the Prometheus statsd exporter. The exporter will collect the statsd metrics emitted by Envoy over UDP, and proxy them to Prometheus over TCP in Prometheus metrics format.

kubectl apply -f ambassador-rbac.yaml

Ambassador is typically deployed as an API Gateway at the edge of your network. We'll deploy a service to map to the Ambassador deployment. Note: if you're not on AWS or GKE, you'll need to update the service below to be a NodePort instead of a LoadBalancer.

kubectl apply -f ambassador.yaml

You should now have a working Ambassador and StatsD/Prometheus exporter that is accessible from outside your cluster.

Configure Prometheus

We now have Ambassador/Envoy running, along with the Prometheus Operator. How do we hook this all together? Logically, all the metrics data flows from Envoy to Prometheus in the following way:

So far, we've deployed Envoy and the StatsD exporter, so now it's time to deploy the other components of this flow.

We'll first create a Kubernetes service that points to the StatsD exporter. We'll then create a ServiceMonitor that tells Prometheus to add the service as a target.

kubectl apply -f statsd-sink-svc.yaml

Next, we need to tell the Prometheus Operator to create a Prometheus cluster for us. The Prometheus cluster is configured to collect data from any ServiceMonitor with the ambassador:monitoring label.

kubectl apply -f prometheus.yaml

Finally, we can create a service to expose Prometheus to the rest of the world. Again, if you're not on AWS or GKE, you'll want to use a NodePort instead.

kubectl apply -f prom-svc.yaml

Testing

We've now configured Prometheus to monitor Envoy, so now let's test this out. Get the external IP address for Prometheus.

In the example above, this is 35.191.39.173. Now, go to http://$PROM_IP:9090 to see the Prometheus UI. You should see a number of metrics automatically populate in Prometheus.

Troubleshooting

If the above doesn't work, there are a few things to investigate:

  • Make sure all your pods are running (kubectl get pods)
  • Check the logs on the Prometheus cluster (kubectl logs $PROM_POD prometheus)
  • Check Ambassador diagnostics to verify Ambassador is working correctly

Get a service running in Envoy

The metrics so far haven't been very interesting, since we haven't routed any traffic through Envoy. We'll use Ambassador to set up a route from Envoy to the httpbin service. Ambassador is configured using Kubernetes annotations, so we'll do that here.

kubectl apply -f httpbin.yaml

Now, if we get the external IP address of Ambassador, we can route requests through Ambassador to the httpbin service:

Run a curl command a few times, as shown above. Going back to the Prometheus dashboard, you'll see that a bevy of new metrics that contain httpbin have appeared. Pick any of these metrics to explore further. For more information on Envoy stats, Matt Klein has written a detailed overview of Envoy's stats architecture. If you are interested in setting up a Grafana dashboard, Alex Gervais has published a sample Grafana/Ambassador dashboard.

Conclusion

Microservices, as you know, are distributed systems. The key to scaling distributed systems is creating loose coupling between each of the components. In a microservices architecture, the most painful source of coupling is actually organizational and not architectural. Design patterns such as the Prometheus Operator enable teams to be more self-sufficient, and reduce organizational coupling, enabling teams to code faster.

Next Steps