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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.

Make sure that you've properly configured metrics-server (as is default in Kubernetes 1.9+), or enabling custom metrics autoscaling support will disable CPU autoscaling support.

Note that most of the API versions in this walkthrough target Kubernetes 1.9+. Note that current versions of the adapter only work with Kubernetes 1.8+. Version 0.1.0 works with Kubernetes 1.7, but is significantly different.

Binaries and Images

In order to follow this walkthrough, you'll need container images for Prometheus and the custom metrics adapter.

Prometheus can be found at prom/prometheus on Dockerhub. The adapter has different images for each arch, and can be found at directxman12/k8s-prometheus-adapter-${ARCH}. For instance, if you're on an x86_64 machine, use the directxman12/k8s-prometheus-adapter-amd64 image.

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

It's reccomended to use the Prometheus Operator to deploy Prometheus. It's a lot easier than configuring Prometheus by hand. Note that the Prometheus operator rules rename some labels if they conflict with its automatic labels, so you may have to tweak the adapter configuration slightly.

If you don't want to use the Prometheus Operator, you'll have to deploy Prometheus with a hand-written configuration. Below, you can find the relevant parts of the configuration that are expected for this walkthrough. See the Prometheus documentation on configuring Prometheus for more information.

For the purposes of this walkthrough, you'll need the following configuration options to be set:

prom-cfg.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: (.+)

Ensure that your Prometheus is up and running by accessing the Prometheus dashboard, and checking on the labels on those metrics. You'll need the label names for configuring the adapter.

Creating the Resources and Launching the Deployment

The deploy/manifests directory contains the appropriate files for creating the Kubernetes objects to deploy the adapter.

See the deployment README for more information about the steps to deploy the adapter. Note that if you're deploying on a non-x86_64 (amd64) platform, you'll need to change the image field in the Deployment to be the appropriate image for your platform.

You may also need to modify the ConfigMap containing the metrics discovery configuration. If you're using the Prometheus configuration described above, it should work out of the box in common cases. Otherwise, read the configuration documentation to learn how to configure the adapter for your particular metrics and labels.

The Registered API

As part of the creation of the adapter Deployment and associated objects (performed above), we registered the API with the API aggregator (part of the main Kubernetes API server).

The API is registered as custom.metrics.k8s.io/v1beta1, and you can find more information about aggregation at Concepts: Aggregation.

If you're deploying into production, you'll probably want to modify the APIService object to contain the CA used to sign your serving certificates.

To do this, first base64-encode the CA (assuming it's stored in /tmp/ca.crt):

$ base64 -w 0 < /tmp/ca.crt

Then, edit the APIService and place the encoded contents into the caBundle field under spec, and removing the insecureSkipTLSVerify field in the same location:

$ kubectl edit apiservice v1beta1.custom.metrics.k8s.io

This ensures that the API aggregator checks that the API is being served by the server that you expect, by verifying the certificates.

Double-Checking Your Work

With that all set, your custom metrics API should show up in discovery.

Try fetching the discovery information for it:

$ 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's simple HTTP counter in the luxas/autoscale-demo image on Dockerhub:

sample-app.deploy.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:
        # if you're not using the Operator, you'll need these annotations
        # otherwise, configure the operator to collect metrics from
        # the sample-app service on port 80 at /metrics
        prometheus.io/scrape: true
        prometheus.io/port: 8080
        prometheus.io/path: "/metrics"
    spec:
      containers:
      - image: luxas/autoscale-demo:v0.1.2
        name: metrics-provider
      ports:
      - name: http
        port: 8080

Create this deployment, and expose it so that you can easily trigger increases in metrics:

$ 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. The cumulative Prometheus metric http_requests_total should have become the custom-metrics-API rate metric pods/http_requests. Check out its value:

$ 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:

$ 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.

Troubleshooting Missing Metrics

If the metric does not appear, or is not registered with the right resources, you might need to modify your metrics discovery configuration, as mentioned above. Check your labels via the Prometheus dashboard, and then modify the configuration appropriately.

As noted in the main README, you'll need to also make sure your metrics relist interval is at least your Prometheus scrape interval. If it's less that that, you'll see metrics periodically appear and disappear from the adapter.

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, 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 
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

$ 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.

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 config documentation, and check the default configuration.