Layer 7 observability with Prometheus, Grafana, and Kubernetes

Consul service mesh deploys an Envoy sidecar proxy alongside each service instance in a datacenter. The sidecar proxy brokers traffic between the local service instance and other services registered with Consul. The proxy is aware of all traffic that passes through it. In addition to securing inter-service communication, the proxy can also collect and expose data about the service instance. Starting with version 1.5, Consul service mesh is able to configure Envoy to expose layer 7 metrics, such as HTTP status codes or request latency, and as of 1.10 is able to export full mesh telemetry for Consul native resources as well as services including Consul agents and gateways.

In this tutorial, you will:

• Configure Consul to expose Envoy metrics to Prometheus.
• Deploy Prometheus and Grafana using their official Helm charts.
• Deploy Consul using the official Helm chart or the Consul K8S CLI.
• Deploy a multi-tier demo application that is configured to be scraped by Prometheus.
• Start a traffic simulation deployment, and observe the application traffic in Grafana.

Prerequisites

If you already have a Kubernetes cluster running with helm and kubectl installed, you can start on the tutorial right away. If not, set up a Kubernetes cluster using your favorite method that supports persistent volume claims, or install and start Minikube v1.10.1+ or kind v0.8.1+.

You must also install kubectl, and both install and initialize Helm. Also, to ensure you have the latest helm charts for Consul, Prometheus, and Grafana, you can run the following command.

$ helm repo add prometheus-community https://prometheus-community.github.io/helm-charts && \helm repo add grafana https://grafana.github.io/helm-charts && \helm repo add hashicorp https://helm.releases.hashicorp.com && \helm repo update

Next, clone the GitHub repository that contains the files you'll use with this tutorial.

$ git clone https://github.com/hashicorp-education/learn-consul-kubernetes

$ git clone git@github.com:hashicorp/learn-consul-kubernetes.git

Change directories into the tutorial specific folder in the repository you just cloned.

$ cd learn-consul-kubernetes/layer7-observability

Checkout the tagged version verified for this tutorial.

$ git checkout tags/v0.0.15

We'll refer to this directory as your working directory, and you'll run the rest of the commands in this tutorial from this directory.

Deploy Consul

You can deploy a complete Consul datacenter using the official Consul Helm chart or the Consul K8S CLI. Feel free to review the Consul Kubernetes installation documentation to learn more about these installation options.

Review the custom values file

Once you have installed the prerequisites, you're ready to install Consul. Open the file in your working directory called helm/consul-values.yaml and review the configuration. It should match the YAML shown below.

global:  enabled: true  name: consul  datacenter: dc1  metrics:    enabled: true    enableAgentMetrics: true    agentMetricsRetentionTime: "1m"server:  replicas: 1ui:  enabled: true  metrics:    enabled: true    provider: "prometheus"    baseURL: http://prometheus-server.default.svc.cluster.localconnectInject:  enabled: true  default: truecontroller:  enabled: true

Install Consul in your cluster

You can now deploy a complete Consul datacenter in your Kubernetes cluster using the official Consul Helm chart or the Consul K8S CLI.

$ helm repo add hashicorp https://helm.releases.hashicorp.com"hashicorp" has been added to your repositories

$ helm install --values helm/consul-values.yaml consul hashicorp/consul --create-namespace --namespace consul --version "0.43.0"

$ brew tap hashicorp/tap

$ brew install hashicorp/tap/consul-k8s

$ consul-k8s install -config-file=helm/consul-values.yaml -set global.image=hashicorp/consul:1.12.0

Check that Consul is running in your Kubernetes cluster using kubectl. Consul setup is complete when all pods have a status of Running, as illustrated in the following output.

$ kubectl get pods --namespace consulNAME                                           READY   STATUS    RESTARTS   AGEconsul-client-72kt4                            1/1     Running   0          42sconsul-connect-injector-67d6495b75-rmx2f       1/1     Running   0          42sconsul-connect-injector-67d6495b75-xck5f       1/1     Running   0          42sconsul-controller-559465fd96-zq869             1/1     Running   0          42sconsul-server-0                                1/1     Running   0          42sconsul-webhook-cert-manager-7cf6df6c4f-bhsqq   1/1     Running   0          42s

Deploy the metrics pipeline

Consul service mesh can integrate with a variety of metrics tooling, but in this tutorial, you will use Prometheus and Grafana to collect and visualize metrics. In this tutorial, Envoy is not injected into the Prometheus and Grafana pod. Leaving Envoy out of the pod is due to Prometheus and Grafana not conducting an application-type workload but instead serving as support tooling. Keeping Prometheus and Grafana out of the service catalog more accurately reflects a list of application services but you may inject Envoy into the Prometheus and Grafana pod if you so choose.

You can disable Envoy by configuring podAnnotations with the "consul.hashicorp.com/connect-inject": "false" annotation for each of those applications. Refer to their respect values files in the helm directory for more information.

Deploy Prometheus with Helm

Install the official Prometheus Helm chart using the values in helm/prometheus-values.yaml.

$ helm install -f helm/prometheus-values.yaml prometheus prometheus-community/prometheus --version "15.5.3" --waitNAME: prometheusLAST DEPLOYED: Thu Mar  3 13:57:57 2022NAMESPACE: defaultSTATUS: deployed...TRUNCATED...For more information on running Prometheus, visit:https://prometheus.io/

Check that Prometheus is running in your Kubernetes cluster using kubectl. Prometheus setup is complete when all pods have a status of Running, as illustrated in the following output.

$ kubectl get pods --namespace defaultNAME                                             READY   STATUS    RESTARTS   AGEprometheus-kube-state-metrics-644f869f97-hgxr8   1/1     Running   0          3m21sprometheus-node-exporter-v8jsj                   1/1     Running   0          3m20sprometheus-pushgateway-67cf8576b7-vsjfl          1/1     Running   0          3m20sprometheus-server-84dfcc8695-mkds2               2/2     Running   0          3m20s

Deploy Grafana with Helm

Installing Grafana will follow a similar process. Install the official Grafana Helm chart using the values in helm/grafana-values.yaml. This configuration will tell Grafana to use Prometheus as a datasource, and set the admin password to password.

$ helm install -f helm/grafana-values.yaml grafana grafana/grafana --version "6.23.1" --waitNAME: grafanaLAST DEPLOYED: Thu Mar  3 14:03:25 2022NAMESPACE: defaultSTATUS: deployed...TRUNCATED...

Check that Grafana is running in your Kubernetes cluster using kubectl. Grafana setup is complete when all pods have a status of Running, as illustrated in the following output.

$ kubectl get pods --namespace defaultNAME                                             READY   STATUS    RESTARTS   AGEgrafana-d7fcbc6b8-7v5zz                          1/1     Running   0          74sprometheus-kube-state-metrics-644f869f97-hgxr8   1/1     Running   0          6m42sprometheus-node-exporter-v8jsj                   1/1     Running   0          6m41sprometheus-pushgateway-67cf8576b7-vsjfl          1/1     Running   0          6m41sprometheus-server-84dfcc8695-mkds2               2/2     Running   0          6m41s

To expose the Grafana UI outside the cluster, issue the following command.

$ kubectl port-forward svc/grafana 3000:3000Forwarding from 127.0.0.1:3000 -> 3000Forwarding from [::1]:3000 -> 3000

Leave this port-forward session active so that you can visit the UI again later once metrics are being collected.

Navigate to http://localhost:3000 in a browser tab and log in to the Grafana UI using admin as the username and password as the password. Once you have logged into the Grafana UI, hover over the dashboards icon (four squares in the left-hand menu), and then click the Browse option.

This will take you to a page that gives you some choices about how to upload Grafana dashboards. Click the Import button on the right-hand side of the screen. Open the file in the grafana subdirectory named hashicups-dashboard.json and copy the contents into the JSON window of the Grafana UI. Click through the rest of the options, and you will end up with a dashboard waiting for data to display.

Deploy a demo application on Kubernetes

Now that your monitoring pipeline is set up, deploy a demo application that will generate data. We will be using HashiCups, an application that emulates an online order app for a coffee shop. For this tutorial, the HashiCups application includes a React front end, a GraphQL API, a REST API and a Postgres database.

All the files defining HashiCups are in the hashicups directory. Open a new terminal, and deploy the demo application.

$ kubectl apply -f hashicupsservice/frontend createdserviceaccount/frontend createdservicedefaults.consul.hashicorp.com/frontend createdconfigmap/nginx-configmap createddeployment.apps/frontend createdservice/postgres createdserviceaccount/postgres createdservicedefaults.consul.hashicorp.com/postgres createddeployment.apps/postgres createdservice/product-api createdserviceaccount/product-api createdservicedefaults.consul.hashicorp.com/product-api createdconfigmap/db-configmap createddeployment.apps/product-api createdservice/public-api createdserviceaccount/public-api createdservicedefaults.consul.hashicorp.com/public-api createddeployment.apps/public-api created

Check that HashiCups is running in your Kubernetes cluster using kubectl. HashiCups setup is complete when all pods have a status of Running, as illustrated in the following output.

$ kubectl get pods --namespace defaultNAME                                             READY   STATUS    RESTARTS   AGEfrontend-65f9ff786f-ppkf6                        2/2     Running   0          83sgrafana-d7fcbc6b8-7v5zz                          1/1     Running   0          6m12spostgres-f6f5ff9d5-d96lt                         2/2     Running   0          83sproduct-api-5f869745dd-tsb79                     2/2     Running   0          83sprometheus-kube-state-metrics-644f869f97-hgxr8   1/1     Running   0          11mprometheus-node-exporter-v8jsj                   1/1     Running   0          11mprometheus-pushgateway-67cf8576b7-vsjfl          1/1     Running   0          11mprometheus-server-84dfcc8695-mkds2               2/2     Running   0          11mpublic-api-5b7c6cf5cc-5c8rw                      2/2     Running   0          83s

Test the application by viewing the React front end. You can do this by forwarding the frontend deployment's port 80 to your development host.

$ kubectl port-forward deploy/frontend 8080:80Forwarding from 127.0.0.1:8080 -> 80Forwarding from [::1]:8080 -> 80

Navigate to http://localhost:8080 in a browser window. You should observe the following screen.

Now that the application is running let's verify that Consul did, in fact, configure Envoy to publish metrics at port 20200. The Envoy side-car proxy can be reached at port 19000. Enter CTRL-C to stop the port forward tunnel for the frontend service on port 80, and then issue the following command which will open a tunnel to the Envoy proxy.

$ kubectl port-forward deploy/frontend 19000:19000Forwarding from 127.0.0.1:19000 -> 19000Forwarding from [::1]:19000 -> 19000

Navigate to http://localhost:19000/config_dump in a browser window. You should observe what looks like a raw JSON document dumped to the screen. This is the Envoy configuration. Search for 20200 and you should find two different stanzas that reference this port. One of them is included next for reference.

{  "name": "envoy_prometheus_metrics_listener",  "address": {  "socket_address": {    "address": "0.0.0.0",    "port_value": 20200  }}

This confirms that Consul has configured Envoy to publish Prometheus metrics. Enter CTRL-C to stop the port-forward session from the side-car proxy. You will not need to reference it again for the remainder of the tutorial.

Visualize application metrics

While Grafana is optimized for at a glance observability, the Prometheus UI can be useful as well. Issue the following command to expose the Prometheus UI to your development host.

$ kubectl port-forward deploy/prometheus-server 9090:9090Forwarding from 127.0.0.1:9090 -> 9090Forwarding from [::1]:9090 -> 9090

Discover available metrics with Prometheus

Navigate to http://localhost:9090 in a browser window, and you should observe the default Prometheus UI. In the textbox at the top of the screen paste sum by(__name__)({app="product-api"})!= 0 and then click the button labeled Execute. Your screen will now look similar to the following.

You have now performed a PromQL query that will list all available metrics for resources that have the product-api label. In this case, that is the REST API resource you deployed from the app folder. This list of metrics can be used as constraints for further PromQL queries both here and in the Grafana UI.

Next, from within the Prometheus UI, click the dropdown menu item labeled Status in the menu bar at the top of the screen, and click on the option labeled Targets. This will navigate the UI to the Targets screen where you can review the resources that Prometheus is monitoring. The Prometheus Helm chart you installed earlier is configured to monitor the Kubernetes infrastructure as well as Prometheus itself.

Click on the button labeled show less next to each section header except the one labeled kubernetes-pods. Your screen should appear similar to the following.

Notice that several pods are running, and have a State of UP. Look at the first label for each pod in the Label column. You should have entries for app="frontend", app="postgres", app="products-api", and app="public-api". This confirms that Prometheus is collecting metrics from all of your annotated resources, and that they are all up and running.

Go back to the terminal where your port-forward session for the Prometheus UI is running, and type CTRL-C to end the session. You will not need to access the Prometheus UI for the remainder of the tutorial.

Simulate traffic

Now that you know the application is running, start generating some load so that you will have some metrics to look at in Grafana.

$ kubectl apply -f traffic.yamlconfigmap/k6-configmap createdservice/traffic createddeployment.apps/traffic createdserviceintentions.consul.hashicorp.com/traffic-to-frontend created

Envoy exposes a huge number of metrics. Which metrics are important to monitor will depend on your application. For this tutorial we have preconfigured a HashiCups-specific Grafana dashboard with a couple of basic metrics, but you should systematically consider what others you will need to collect as you move from testing into production.

View Grafana dashboard

Now that you have metrics flowing through your pipeline, and a traffic simulation deployment running, navigate back to your Grafana dashboard in the Grafana UI, and you should observe a screen similar to the following.

Notice that once you started the traffic simulation deployment Prometheus started to log active connections. This dashboard is simplistic, but illustrates that metrics are flowing through the pipeline, and should give you a reasonable starting point for setting up your own observability tooling.

Viewing traffic in Consul UI

You can also view service metrics in the Consul UI. In a new terminal session, issue the following command to expose the Consul UI to the development host.

$ kubectl port-forward consul-server-0 --namespace consul 8500:8500Forwarding from 127.0.0.1:8500 -> 8500Forwarding from [::1]:8500 -> 8500

Open http://localhost:8500 in a new browser tab, and navigate to the Services screen. Select the postgres service. You should observe that a chart with some basic metrics is embedded in the service tile.

Hover over the four tile elements to get tooltip descriptions of the different metrics.

You can also hover the timeline chart embedded in the tile, and review additional metrics for any point in time during the last 15 minutes.

Clean up

If you want to get rid of the configuration files and Consul Helm chart, recursively remove the learn-consul-kubernetes directory. `

$ cd ../.. && rm -rf learn-consul-kubernetes

Next steps

In this tutorial, you set up layer 7 metrics collection and visualization in a Kubernetes cluster using Consul service mesh, Prometheus, and Grafana, all deployed via Helm charts. Specifically, you:

• Configured Consul and Envoy to expose application metrics to Prometheus.
• Deployed Consul using the official helm chart.
• Deployed Prometheus and Grafana using their official Helm charts.
• Deployed a multi-tier demo application that was configured to be scraped by Prometheus.
• Started a traffic simulation deployment, and observed the metrics in Prometheus and Grafana.

Because all of these programs can run outside of Kubernetes, you can set this pipeline up in any environment or collect metrics from workloads running on mixed infrastructure.

To learn more about the configuration options in Consul that enable layer 7 metrics collection with or without Kubernetes, refer to our documentation. For more information on centrally configuring Consul, take a look at the centralized configuration documentation.