stefanprodan / istio-hpa

Configure horizontal pod autoscaling with Istio metrics and Prometheus

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istio-hpa

One of the advantages of using a service mesh like Istio is the builtin monitoring capability. You don't have to instrument your web apps in order to monitor the L7 traffic. The Istio telemetry service collects stats like HTTP request rate, response status codes and duration form the Envoy sidecars that are running alongside your apps. Besides monitoring these metrics can be used to drive autoscaling and canary deployments.

Istio HPA

What follows is a step-by-step guide on configuring HPA v2 with metrics provided by Istio Mixer. When installing Istio make sure that the telemetry service and Prometheus are enabled. If you're using the GKE Istio add-on, you'll have to deploy Prometheus as described here.

In order to use the Istio metrics together with the Horizontal Pod Autoscaler you'll need an adapter that can run Prometheus queries. Zalando made a general purpose metrics adapter for Kubernetes called kube-metrics-adapter. The Zalando adapter scans the HPA objects, executes promql queries (specified with annotations) and stores the metrics in memory.

Installing the custom metrics adapter

Clone the istio-hpa repository:

git clone https://github.com/stefanprodan/istio-hpa
cd istio-hpa

Deploy the metrics adapter in the kube-system namespace:

kubectl apply -f ./kube-metrics-adapter/

When the adapter starts, it will generate a self-signed cert and will register itself under the custom.metrics.k8s.io group.

The adapter is configured to query the Prometheus instance that's running in the istio-system namespace.

Verify the install by checking the adapter logs:

kubectl -n kube-system logs deployment/kube-metrics-adapter

Installing the demo app

You will use podinfo, a small Golang-based web app to test the Horizontal Pod Autoscaler.

First create a test namespace with Istio sidecar injection enabled:

kubectl apply -f ./namespaces/

Create the podinfo deployment and ClusterIP service in the test namespace:

kubectl apply -f ./podinfo/deployment.yaml,./podinfo/service.yaml

In order to trigger the auto scaling, you'll need a tool to generate traffic. Deploy the load test service in the test namespace:

kubectl apply -f ./loadtester/

Verify the install by calling the podinfo API. Exec into the load tester pod and use hey to generate load for a couple of seconds:

export loadtester=$(kubectl -n test get pod -l "app=loadtester" -o jsonpath='{.items[0].metadata.name}')
kubectl -n test exec -it ${loadtester} -- sh

~ $ hey -z 5s -c 10 -q 2 http://podinfo.test:9898

Summary:
  Total:	5.0138 secs
  Requests/sec:	19.9451

Status code distribution:
  [200]	100 responses

  $ exit

The podinfo ClusterIP service exposes port 9898 under the http name. When using the http prefix, the Envoy sidecar will switch to L7 routing and the telemetry service will collect HTTP metrics.

Querying the Istio metrics

The Istio telemetry service collects metrics from the mesh and stores them in Prometheus. One such metric is istio_requests_total, with it you can determine the rate of requests per second a workload receives.

This is how you can query Prometheus for the req/sec rate received by podinfo in the last minute, excluding 404s:

  sum(
    rate(
      istio_requests_total{
        destination_workload="podinfo",
        destination_workload_namespace="test",
        reporter="destination",
        response_code!="404"
      }[1m]
    )
  )

The HPA needs to know the req/sec that each pod receives. You can use the container memory usage metric from kubelet to count the number of pods and calculate the Istio request rate per pod:

  sum(
    rate(
      istio_requests_total{
        destination_workload="podinfo",
        destination_workload_namespace="test"
      }[1m]
    )
  ) /
  count(
    count(
      container_memory_usage_bytes{
        namespace="test",
        pod=~"podinfo.*"
      }
    ) by (pod)
  )

Configuring the HPA with Istio metrics

Using the req/sec query you can define a HPA that will scale the podinfo workload based on the number of requests per second that each instance receives:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: podinfo
  namespace: test
  annotations:
    metric-config.object.istio-requests-total.prometheus/per-replica: "true"
    metric-config.object.istio-requests-total.prometheus/query: |
      sum(
        rate(
          istio_requests_total{
            destination_workload="podinfo",
            destination_workload_namespace="test"
          }[1m]
        )
      ) /
      count(
        count(
          container_memory_usage_bytes{
            namespace="test",
            pod=~"podinfo.*"
          }
        ) by (pod)
      )
spec:
  maxReplicas: 10
  minReplicas: 1
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  metrics:
    - type: Object
      object:
        metricName: istio-requests-total
        target:
          apiVersion: v1
          kind: Pod
          name: podinfo
        targetValue: 10

The above configuration will instruct the Horizontal Pod Autoscaler to scale up the deployment when the average traffic load goes over 10 req/sec per replica.

Create the HPA with:

kubectl apply -f ./podinfo/hpa.yaml

Start a load test and verify that the adapter computes the metric:

kubectl -n kube-system logs deployment/kube-metrics-adapter -f

Collected 1 new metric(s)
Collected new custom metric 'istio-requests-total' (44m) for Pod test/podinfo

List the custom metrics resources:

kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1" | jq .

The Kubernetes API should return a resource list containing the Istio metric:

{
  "kind": "APIResourceList",
  "apiVersion": "v1",
  "groupVersion": "custom.metrics.k8s.io/v1beta1",
  "resources": [
    {
      "name": "pods/istio-requests-total",
      "singularName": "",
      "namespaced": true,
      "kind": "MetricValueList",
      "verbs": [
        "get"
      ]
    }
  ]
}

After a couple of seconds the HPA will fetch the metric from the adapter:

kubectl -n test get hpa/podinfo

NAME      REFERENCE            TARGETS   MINPODS   MAXPODS   REPLICAS
podinfo   Deployment/podinfo   44m/10    1         10        1

Autoscaling based on HTTP traffic

To test the HPA you can use the load tester to trigger a scale up event.

Exec into the tester pod and use hey to generate load for a 5 minutes:

kubectl -n test exec -it ${loadtester} -- sh

~ $ hey -z 5m -c 10 -q 2 http://podinfo.test:9898

Press ctrl+c then exit to get out of load test terminal if you wanna stop prematurely.

After a minute the HPA will start to scale up the workload until the req/sec per pod drops under the target value:

watch kubectl -n test get hpa/podinfo

NAME      REFERENCE            TARGETS     MINPODS   MAXPODS   REPLICAS
podinfo   Deployment/podinfo   25272m/10   1         10        3

When the load test finishes, the number of requests per second will drop to zero and the HPA will start to scale down the workload. Note that the HPA has a back off mechanism that prevents rapid scale up/down events, the number of replicas will go back to one after a couple of minutes.

By default the metrics sync happens once every 30 seconds and scaling up/down can only happen if there was no rescaling within the last 3-5 minutes. In this way, the HPA prevents rapid execution of conflicting decisions and gives time for the Cluster Autoscaler to kick in.

Wrapping up

Scaling based on traffic is not something new to Kubernetes, an ingress controllers such as NGINX can expose Prometheus metrics for HPA. The difference in using Istio is that you can autoscale backend services as well, apps that are accessible only from inside the mesh. I'm not a big fan of embedding code in Kubernetes yaml but the Zalando metrics adapter is flexible enough to allow this kind of custom autoscaling.

If you have any suggestion on improving this guide please submit an issue or PR on GitHub at stefanprodan/istio-hpa. Contributions are more than welcome!

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Configure horizontal pod autoscaling with Istio metrics and Prometheus

License:MIT License


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