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.
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.
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
.
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.
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.
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.
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.
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.
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.
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.
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.
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.