Outlier detection

Configure Gloo to remove unhealthy destinations from the connection pool, and add the destinations back when they become healthy again.

Outlier detection is an important part of building resilient apps. An outlier detection policy sets up several conditions, such as retries and ejection percentages, that Gloo Mesh uses to determine if a service is unhealthy. In case an unhealthy service is detected, the outlier policy defines how Gloo Mesh removes (ejects) services from the pool of healthy destinations to send traffic to. Your apps then have time to recover before they are added back to the load-balancing pool and checked again for consecutive errors.

You can use outlier detection policies in combination with other policies, such as failover or retry policies. In case of a failure, the outlier detection policy tells Gloo Mesh when and for how long to remove unhealthy services. The retry policy tells Gloo Mesh how many times to retry requests before the outlier detection policy considers the request as failing and removes the service from the pool of healthy destinations. The failover policy tells Gloo Mesh which healthy destinations to reroute traffic to, based on the closest locality.

For more information, see the following resources.

Before you begin

This guide assumes that you use the same names for components like clusters, workspaces, and namespaces as in the getting started. If you have different names, make sure to update the sample configuration files in this guide.
  1. Complete the multicluster getting started guide to set up the following testing environment.
    • Three clusters along with environment variables for the clusters and their Kubernetes contexts.
    • The Gloo Platform CLI, meshctl, along with other CLI tools such as kubectl and istioctl.
    • The Gloo management server in the management cluster, and the Gloo agents in the workload clusters.
    • Istio installed in the workload clusters.
    • A simple Gloo workspace setup.
  2. Install Bookinfo and other sample apps.

Configure outlier detection policies

You can apply an outlier detection policy at the destination level. For more information, see Applying policies.

Review the following sample configuration file.

apiVersion: resilience.policy.gloo.solo.io/v2
kind: OutlierDetectionPolicy
metadata:
  name: outlier-detection
  namespace: bookinfo
spec:
  applyToDestinations:
  - kind: VIRTUAL_DESTINATION
    selector: {}
  config:
    baseEjectionTime: 30s
    consecutiveErrors: 2
    interval: 1s
    maxEjectionPercent: 100

Review the following table to understand this configuration. For more information, see the API docs.

Setting Description
applyToDestinations Configure which destinations to apply the policy to, by using labels. Destinations can be a Kubernetes service, VirtualDestination, or ExternalService. If you do not specify any destinations or routes, the rate limit policy applies to all destinations in the workspace by default. If you do not specify any destinations but you do specify a route, the rate limit applies to the route but to no destinations. This example selects all virtual destinations in the workspace, including the one that you previously created.
baseEjectionTime The minimum time duration for ejection, or the time when a destination is considered unhealthy and not used for load balancing. Set this value as an integer plus a unit of time, in the format 1h, 1m, 1s, or 1ms. The value must be at least 1ms, and defaults to 30s.
consecutiveErrors The number of errors before a destination is removed from the healthy connection pool. The default is 5.
interval The amount of time between analyzing destinations for ejection. Set this value as an integer plus a unit of time, in the format 1h, 1m, 1s, or 1ms. The value must be at least 1ms, and defaults to 10s.
maxEjectionPercent The maximum percentage of destinations that can be removed from the healthy connection pool at a time. For example, if you have 10 total destinations that the policy selects, and set this value to 50 percent, 5 destinations can be removed at once. At least 1 destination can always be removed, regardless of the value you set. You can set this value between 0 and 100, with a default of 100.

Verify outlier detection policies

You can test how outlier detection works by opening the Bookinfo app in your browser and observing the reviews app behavior after applying various resources.

  1. Create a virtual destination for the reviews app. The virtual destination allows for multicluster routing across clusters for the three different reviews apps in your Bookinfo setup.
    kubectl --context ${REMOTE_CONTEXT1} apply -f - <<EOF
    apiVersion: networking.gloo.solo.io/v2
    kind: VirtualDestination
    metadata:
      name: reviews-global
      namespace: bookinfo
    spec:
      hosts:
      - reviews.vd
      ports:
      - number: 80
        protocol: HTTP
        targetPort:
          name: http
      services:
      - labels:
          app: reviews
    EOF
    
  2. Send a request to the reviews app from the ratings app several times. Notice that you get responses with no stars (v1), black stars (v2), and red stars (v3) from all three reviews apps across clusters.
    kubectl exec $(kubectl get pod -l app=ratings -n bookinfo -o jsonpath='{.items[].metadata.name}' --context ${REMOTE_CONTEXT1}) -n bookinfo -c ratings --context ${REMOTE_CONTEXT1} -- curl -sS reviews.global:80/reviews/1 -v
    
  3. Create the outlier detection policy that you previously reviewed.
    kubectl --context ${REMOTE_CONTEXT1} apply -f - <<EOF
    apiVersion: resilience.policy.gloo.solo.io/v2
    kind: OutlierDetectionPolicy
    metadata:
      name: outlier-detection
      namespace: bookinfo
    spec:
      applyToDestinations:
      - kind: VIRTUAL_DESTINATION
        selector: {}
      config:
        baseEjectionTime: 30s
        consecutiveErrors: 2
        interval: 1s
        maxEjectionPercent: 100
    EOF
    
  4. Repeat the request to the reviews app. Notice that you get responses with no stars (v1) and black stars (v2), but no longer any red stars (v3 in the second cluster). When you apply an outlier detection policy, Gloo enforces locality preference. The request is fulfilled by the reviews apps local to the requesting ratings app when the reviews apps are healthy.
    kubectl exec $(kubectl get pod -l app=ratings -n bookinfo -o jsonpath='{.items[].metadata.name}' --context ${REMOTE_CONTEXT1}) -n bookinfo -c ratings --context ${REMOTE_CONTEXT1} -- curl -sS reviews.global:80/reviews/1 -v
    
  5. Send the reviews v1 app to sleep, to mimic an app failure.
    kubectl --context ${REMOTE_CONTEXT1} -n bookinfo patch deploy reviews-v1 --patch '{"spec":{"template":{"spec":{"containers":[{"name":"reviews","command":["sleep","20h"]}]}}}}'
    
  6. Repeat the request to the reviews app. Now, you get responses from black stars (v2) and red stars (v3) because reviews v1 is unhealthy and the virtual destination permits load balancing.
    kubectl exec $(kubectl get pod -l app=ratings -n bookinfo -o jsonpath='{.items[].metadata.name}' --context ${REMOTE_CONTEXT1}) -n bookinfo -c ratings --context ${REMOTE_CONTEXT1} -- curl -sS reviews.global:80/reviews/1 -v
    
  7. Remove the sleep command from the reviews v1 app to restore normal behavior.
    kubectl --context ${REMOTE_CONTEXT1} -n bookinfo patch deploy reviews-v1 --patch '{"spec":{"template":{"spec":{"containers":[{"name":"reviews","command":[]}]}}}}'
    
  8. Keep sending requests to the reviews app. Notice that for the first 30 seconds, you only get responses from black stars (v2) and red stars (v3). After 30 seconds, you also get responses with no stars (v1). The outlier detection policy set the baseEjectionTime to 30s, so the reviews v1 is not returned to the healthy connection pool until after this time elapses.
    kubectl exec $(kubectl get pod -l app=ratings -n bookinfo -o jsonpath='{.items[].metadata.name}' --context ${REMOTE_CONTEXT1}) -n bookinfo -c ratings --context ${REMOTE_CONTEXT1} -- curl -sS reviews.global:80/reviews/1 -v
    
  9. Optional: Clean up the Gloo resources that you created.
    kubectl --context $REMOTE_CONTEXT1 -n bookinfo delete virtualdestination reviews-global
    kubectl --context $REMOTE_CONTEXT1 -n bookinfo delete outlierdetectionpolicy outlier-detection