Walkthrough =========== This walkthrough will go over the basics of setting up the Prometheus adapter on your cluster and configuring an autoscaler to use application metrics sourced from the adapter. Prerequisites ------------- ### Cluster Configuration ### Before getting started, ensure that the main components of your cluster are configured for autoscaling on custom metrics. As of Kubernetes 1.7, this requires enabling the aggregation layer on the API server and configuring the controller manager to use the metrics APIs via their REST clients. Detailed instructions can be found in the Kubernetes documentation under [Horizontal Pod Autoscaling](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-custom-metrics). Make sure that you've properly configured metrics-server (as is default in Kubernetes 1.9+), or enabling custom metrics autoscaling support with disable CPU autoscaling support. Note that most of the API versions in this walkthrough target Kubernetes 1.9. It should still work with 1.7 and 1.8, but you might have to change some minor details. ### Binaries and Images ### In order to follow this walkthrough, you'll need container images for Prometheus and the custom metrics adapter. Both can be found on Dockerhub under `prom/prometheus` and `directxman12/k8s-prometheus-adapter`, respectively. If you're feeling adventurous, you can build the latest version of the custom metrics adapter by running `make docker-build`. Launching Prometheus and the Adapter ------------------------------------ In this walkthrough, it's assumed that you're deploying Prometheus into its own namespace called `prom`. Most of the sample commands and files are namespace-agnostic, but there are a few commands that rely on namespace. If you're using a different namespace, simply substitute that in for `prom` when it appears. ### Prometheus Configuration ### If you've never deployed Prometheus before, you'll need an appropriate Prometheus configuration. There's a extensive sample Prometheus configuration in the Prometheus repository [here](https://github.com/prometheus/prometheus/blob/master/documentation/examples/prometheus-kubernetes.yml). Be sure to also read the Prometheus documentation on [configuring Prometheus](https://prometheus.io/docs/operating/configuration/). For the purposes of this walkthrough, you'll need the following configuration options to be set:
prom-cfg.yaml ```yaml # a short scrape interval means you can respond to changes in # metrics more quickly global: scrape_interval: 15s # you need a scrape configuration for scraping from pods scrape_configs: - job_name: 'kubernetes-pods' # if you want to use metrics on jobs, set the below field to # true to prevent Prometheus from setting the `job` label # automatically. honor_labels: false kubernetes_sd_configs: - role: pod # skip verification so you can do HTTPS to pods tls_config: insecure_skip_verify: true # make sure your labels are in order relabel_configs: # these labels tell Prometheus to automatically attach source # pod and namespace information to each collected sample, so # that they'll be exposed in the custom metrics API automatically. - source_labels: [__meta_kubernetes_namespace] action: replace target_label: namespace - source_labels: [__meta_kubernetes_pod_name] action: replace target_label: pod # these labels tell Prometheus to look for # prometheus.io/{scrape,path,port} annotations to configure # how to scrape - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape] action: keep regex: true - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path] action: replace target_label: __metrics_path__ regex: (.+) - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port] action: replace regex: ([^:]+)(?::\d+)?;(\d+) replacement: $1:$2 target_label: __address__ - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scheme] action: replace target_label: __scheme__ regex: (.+) ```
Place your full configuration (it might just be the above) in a file (call it `prom-cfg.yaml`, for example), and then create a ConfigMap containing it: ```shell $ kubectl -n prom create configmap prometheus --from-file=prometheus.yml=prom-cfg.yaml ``` You'll be using this later when you launch Prometheus. ### Launching the Deployment ### It's generally easiest to launch the adapter and Prometheus as two containers in the same pod. You can use a deployment to manage your adapter and Prometheus instance. First, we'll create a ServiceAccount for the deployment to run as: ```yaml $ kubectl -n prom create serviceaccount prom-cm-adapter ``` Start out with a fairly straightforward Prometheus deployment using the ConfigMap from above, and proceed from there:
prom-adapter.deployment.yaml [Prometheus only] ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: prometheus spec: replicas: 1 selector: matchLabels: app: prometheus template: metadata: labels: app: prometheus spec: serviceAccountName: prom-cm-adapter containers: - image: prom/prometheus:v2.2.0-rc.0 name: prometheus args: # point prometheus at the configuration that you mount in below - --config.file=/etc/prometheus/prometheus.yml ports: # this port exposes the dashboard and the HTTP API - containerPort: 9090 name: prom-web protocol: TCP volumeMounts: # you'll mount your ConfigMap volume at /etc/prometheus - mountPath: /etc/prometheus name: prom-config volumes: # make your configmap available as a volume, so that you can # mount in the Prometheus config from earlier - name: prom-config configMap: name: prometheus ```
Save this file (for example, as `prom-adapter.deployment.yaml`). Now, you'll need to modify the deployment to include the adapter. The adapter has several options, most of which it shares with any other standard Kubernetes addon API server. This means that you'll need proper API server certificates for it to function properly. To learn more about which certificates are needed, and what they mean, see [Concepts: Auth and Certificates](https://github.com/kubernetes-incubator/apiserver-builder/blob/master/docs/concepts/auth.md). Once you've generated the necessary certificates, you'll need to place them in a secret. This walkthrough assumes that you've set up your cluster to automatically inject client certificates and CA certificates (see the above concepts documentation). Make sure you've granted the default service account for your namespace permission to fetch the authentication CA ConfigMap: ```shell $ kubectl create rolebinding prom-ext-auth-reader --role="extension-apiserver-authentication-reader" --serviceaccount=prom:prom-cm-adapter ``` Then, store your serving certificates in a secret: ```shell $ kubectl -n prom create secret tls serving-cm-adapter --cert=/path/to/cm-adapter/serving.crt --key=/path/to/cm-adapter/serving.key ``` Next, you'll need to make sure that the service account used to launch the Deployment has permission to list resources in the cluster: ```shell $ kubectl create clusterrole resource-lister --verb=list --resource="*" $ kubectl create clusterrolebinding cm-adapter-resource-lister --clusterrole=resource-lister -- serviceaccount=prom:prom-cm-adapter ``` Finally, ensure the deployment has all the necessary permissions to delegate authentication and authorization decisions to the main API server. See [Concepts: Auth and Certificates](https://github.com/kubernetes-incubator/apiserver-builder/blob/master/docs/concepts/auth.md) for more information. Next, amend the file above to run the adapter as well. You may need to modify this part if you wish to inject the needed certificates a different way.
prom-adapter.deployment.yaml [Adapter & Prometheus] ```yaml ... spec: containers: ... - image: directxman12/k8s-prometheus-adapter name: cm-adapter args: - --secure-port=6443 - --logtostderr=true # use your serving certs - --tls-cert-file=/var/run/serving-certs/tls.crt - --tls-private-key-file=/var/run/serving-certs/tls.key # Prometheus is running in the same pod, so you can say your Prometheus # is at `localhost` - --prometheus-url=http://localhost:9090 # relist available Prometheus metrics every 1m - --metrics-relist-interval=1m # calculate rates for cumulative metrics over 30s periods. This should be *at least* # as much as your collection interval for Prometheus. - --rate-interval=30s # re-discover new available resource names every 10m. You probably # won't need to have this be particularly often, but if you add # additional addons to your cluster, the adapter will discover there # existance at this interval - --discovery-interval=10m - --v=4 ports: # this port exposes the custom metrics API - containerPort: 6443 name: https protocol: TCP volumeMounts: - mountPath: /var/run/serving-certs name: serving-certs readOnly: true volumes: ... - name: serving-certs secret: secretName: serving-cm-adapter ```
Next, create the deployment and expose it as a service, mapping port 443 to your pod's port 443, on which you've exposed the custom metrics API: ```shell $ kubectl -n prom create -f prom-adapter.deployment.yaml $ kubectl -n prom create service clusterip prometheus --tcp=443:6443 ``` ### Registering the API ### Now that you have a running deployment of Prometheus and the adapter, you'll need to register it as providing the `custom.metrics.k8s.io/v1beta1` API. For more information on how this works, see [Concepts: Aggregation](https://github.com/kubernetes-incubator/apiserver-builder/blob/master/docs/concepts/aggregation.md). You'll need to create an API registration record for the `custom.metrics.k8s.io/v1beta1` API. In order to do this, you'll need the base64 encoded version of the CA certificate used to sign the serving certificates you created above. If the CA certificate is stored in `/tmp/ca.crt`, you can get the base64-encoded form like this: ```shell $ base64 --w 0 < /tmp/ca.crt ``` Take the resulting value, and place it into the following file:
cm-registration.yaml *Note that apiregistration moved to stable in 1.10, so you'll need to use the `apiregistration.k8s.io/v1` API version there*. ```yaml apiVersion: apiregistration.k8s.io/v1beta1 kind: APIService metadata: name: v1beta1.custom.metrics.k8s.io spec: # this tells the aggregator how to verify that your API server is # actually who it claims to be caBundle: # these specify which group and version you're registering the API # server for group: custom.metrics.k8s.io version: v1beta1 # these control how the aggregator prioritizes your registration. # it's not particularly relevant in this case. groupPriorityMinimum: 1000 versionPriority: 10 # finally, this points the aggregator at the service for your # API server that you created service: name: prometheus namespace: prom ```
Register that registration object with the aggregator: ```shell $ kubectl create -f cm-registration.yaml ``` ### Double-Checking Your Work ### With that all set, your custom metrics API should show up in discovery. Try fetching the discovery information for it: ```shell $ kubectl get --raw /apis/custom.metrics.k8s.io/v1beta1 ``` Since you don't have any metrics collected yet, you shouldn't see any available resources, but the request should return successfully. Keep this command in mind -- you'll want to use it later once you have a pod producing custom metrics. Collecting Application Metrics ------------------------------ Now that you have a working pipeline for ingesting application metrics, you'll need an application that produces some metrics. Any application which produces Prometheus-formatted metrics will do. For the purposes of this walkthrough, try out [@luxas](https://github.com/luxas)'s simple HTTP counter in the `luxas/autoscale-demo` image on Dockerhub:
sample-app.deploy.yaml ```yaml apiVersion: apps/v1beta1 kind: Deployment metadata: name: sample-app spec: replicas: 1 selector: matchLabels: app: sample-app template: metadata: labels: app: sample-app annotations: # based on your Prometheus config above, this tells prometheus # to scrape this pod for metrics on port 8080 at "/metrics" prometheus.io/scrape: true prometheus.io/port: 8080 prometheus.io/path: "/metrics" spec: containers: - image: luxas/autoscale-demo name: metrics-provider ports: - name: http port: 8080 ```
Create this deployment, and expose it so that you can easily trigger increases in metrics: ```yaml $ kubectl create -f sample-app.deploy.yaml $ kubectl create service clusterip sample-app --tcp=80:8080 ``` This sample application provides some metrics on the number of HTTP requests it receives. Consider the metric `http_requests_total`. First, check that it appears in discovery using the command from [Double-Checking Yor Work](#double-checking-your-work). The cumulative Prometheus metric `http_requests_total` should have become the custom-metrics-API rate metric `pods/http_requests`. Check out its value: ```shell $ kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/*/http_requests?selector=app%3Dsample-app" ``` It should be zero, since you're not currently accessing it. Now, create a few requests with curl: ```shell $ curl http://$(kubectl get service sample-app -o jsonpath='{ .spec.clusterIP }')/metrics ``` Try fetching the metrics again. You should see an increase in the rate after the collection interval specified in your Prometheus configuration has elapsed. If you leave it for a bit, the rate will go back down again. Autoscaling ----------- Now that you have an application which produces custom metrics, you'll be able to autoscale on it. As noted in the [HorizontalPodAutoscaler walkthrough](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-multiple-metrics-and-custom-metrics), there are three different types of metrics that the HorizontalPodAutoscaler can handle. In this walkthrough, you've exposed some metrics that can be consumed using the `Pods` metric type. Create a description for the HorizontalPodAutoscaler (HPA):
sample-app-hpa.yaml ```yaml kind: HorizontalPodAutoscaler apiVersion: autoscaling/v2beta1 metadata: name: sample-app spec: scaleTargetRef: # point the HPA at the sample application # you created above apiVersion: apps/v1 kind: Deployment name: sample-app # autoscale between 1 and 10 replicas minReplicas: 1 maxReplicas: 10 metrics: # use a "Pods" metric, which takes the average of the # given metric across all pods controlled by the autoscaling target - type: Pods pods: # use the metric that you used above: pods/http_requests metricName: http_requests # target 500 milli-requests per second, # which is 1 request every two seconds targetAverageValue: 500m ```
Create the HorizontalPodAutoscaler with ``` $ kubectl create -f sample-app-hpa.yaml ``` Then, like before, make some requests to the sample app's service. If you describe the HPA, after the HPA sync interval has elapsed, you should see the number of pods increase proportionally to the ratio between the actual requests per second and your target of 1 request every 2 seconds. You can examine the HPA with ```shell $ kubectl describe hpa sample-app ``` You should see the HPA's last observed metric value, which should roughly correspond to the rate of requests that you made. Next Steps ---------- For more information on how the HPA controller consumes different kinds of metrics, take a look at the [HPA walkthrough](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-multiple-metrics-and-custom-metrics). Also try exposing a non-cumulative metric from your own application, or scaling on application on a metric provided by another application by setting different labels or using the `Object` metric source type. For more information on how metrics are exposed by the Prometheus adapter, see the [format documentation](./format.md).