# SMI Istio Canary Deployments

This guide shows you how to use the SMI Istio adapter and Flagger to automate canary deployments.

## Prerequisites

* Kubernetes > 1.13
* Istio > 1.0

## Install Istio SMI adapter

Install the SMI adapter:

```bash
kubectl apply -f https://raw.githubusercontent.com/deislabs/smi-adapter-istio/master/deploy/crds/crds.yaml
kubectl apply -f https://raw.githubusercontent.com/deislabs/smi-adapter-istio/master/deploy/operator-and-rbac.yaml
```

Create a generic Istio gateway to expose services outside the mesh on HTTP:

```yaml
apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: public-gateway
  namespace: istio-system
spec:
  selector:
    istio: ingressgateway
  servers:
    - port:
        number: 80
        name: http
        protocol: HTTP
      hosts:
        - "*"
```

Save the above resource as public-gateway.yaml and then apply it:

```bash
kubectl apply -f ./public-gateway.yaml
```

Find the Gateway load balancer IP and add a DNS record for it:

```bash
kubectl -n istio-system get svc/istio-ingressgateway -ojson | jq -r .status.loadBalancer.ingress[0].ip
```

## Install Flagger and Grafana

Add Flagger Helm repository:

```bash
helm repo add flagger https://flagger.app
```

Deploy Flagger in the _**istio-system**_ namespace:

```bash
helm upgrade -i flagger flagger/flagger \
--namespace=istio-system \
--set meshProvider=smi:istio
```

Flagger comes with a Grafana dashboard made for monitoring the canary deployments.

Deploy Grafana in the _**istio-system**_ namespace:

```bash
helm upgrade -i flagger-grafana flagger/grafana \
--namespace=istio-system \
--set url=http://prometheus.istio-system:9090
```

You can access Grafana using port forwarding:

```bash
kubectl -n istio-system port-forward svc/flagger-grafana 3000:80
```

## Workloads bootstrap

Create a test namespace with Istio sidecar injection enabled:

Create a test namespace and enable Linkerd proxy injection:

```bash
kubectl create ns test
kubectl label namespace test istio-injection=enabled
```

Create a deployment and a horizontal pod autoscaler:

```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/podinfo
```

Deploy the load testing service to generate traffic during the canary analysis:

```bash
kubectl apply -k github.com/weaveworks/flagger//kustomize/tester
```

Create a canary custom resource \(replace example.com with your own domain\):

```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
  name: podinfo
  namespace: test
spec:
  # deployment reference
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: podinfo
  # the maximum time in seconds for the canary deployment
  # to make progress before it is rollback (default 600s)
  progressDeadlineSeconds: 60
  # HPA reference (optional)
  autoscalerRef:
    apiVersion: autoscaling/v2beta1
    kind: HorizontalPodAutoscaler
    name: podinfo
  service:
    # container port
    port: 9898
    # Istio gateways (optional)
    gateways:
    - public-gateway.istio-system.svc.cluster.local
    # Istio virtual service host names (optional)
    hosts:
    - app.example.com
  canaryAnalysis:
    # schedule interval (default 60s)
    interval: 10s
    # max number of failed metric checks before rollback
    threshold: 5
    # max traffic percentage routed to canary
    # percentage (0-100)
    maxWeight: 50
    # canary increment step
    # percentage (0-100)
    stepWeight: 10
    metrics:
    - name: request-success-rate
      # minimum req success rate (non 5xx responses)
      # percentage (0-100)
      threshold: 99
      interval: 1m
    - name: request-duration
      # maximum req duration P99
      # milliseconds
      threshold: 500
      interval: 30s
    # generate traffic during analysis
    webhooks:
      - name: load-test
        url: http://flagger-loadtester.test/
        timeout: 5s
        metadata:
          cmd: "hey -z 1m -q 10 -c 2 http://podinfo.test:9898/"
```

Save the above resource as podinfo-canary.yaml and then apply it:

```bash
kubectl apply -f ./podinfo-canary.yaml
```

After a couple of seconds Flagger will create the canary objects:

```bash
# applied 
deployment.apps/podinfo
horizontalpodautoscaler.autoscaling/podinfo
canary.flagger.app/podinfo

# generated 
deployment.apps/podinfo-primary
horizontalpodautoscaler.autoscaling/podinfo-primary
service/podinfo
service/podinfo-canary
service/podinfo-primary
trafficsplits.split.smi-spec.io/podinfo
```

## Automated canary promotion

Flagger implements a control loop that gradually shifts traffic to the canary while measuring key performance indicators like HTTP requests success rate, requests average duration and pod health. Based on analysis of the KPIs a canary is promoted or aborted, and the analysis result is published to Slack.

![Flagger Canary Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-canary-steps.png)

Trigger a canary deployment by updating the container image:

```bash
kubectl -n test set image deployment/podinfo \
podinfod=quay.io/stefanprodan/podinfo:3.1.1
```

Flagger detects that the deployment revision changed and starts a new rollout:

```text
kubectl -n istio-system logs deployment/flagger -f | jq .msg


New revision detected podinfo.test
Scaling up podinfo.test
Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Advance podinfo.test canary weight 5
Advance podinfo.test canary weight 10
Advance podinfo.test canary weight 15
Advance podinfo.test canary weight 20
Advance podinfo.test canary weight 25
Advance podinfo.test canary weight 30
Advance podinfo.test canary weight 35
Advance podinfo.test canary weight 40
Advance podinfo.test canary weight 45
Advance podinfo.test canary weight 50
Copying podinfo.test template spec to podinfo-primary.test
Waiting for podinfo-primary.test rollout to finish: 1 of 2 updated replicas are available
Promotion completed! Scaling down podinfo.test
```

**Note** that if you apply new changes to the deployment during the canary analysis, Flagger will restart the analysis.

During the analysis the canary’s progress can be monitored with Grafana. The Istio dashboard URL is [http://localhost:3000/d/flagger-istio/istio-canary?refresh=10s&orgId=1&var-namespace=test&var-primary=podinfo-primary&var-canary=podinfo](http://localhost:3000/d/flagger-istio/istio-canary?refresh=10s&orgId=1&var-namespace=test&var-primary=podinfo-primary&var-canary=podinfo)

You can monitor all canaries with:

```bash
watch kubectl get canaries --all-namespaces

NAMESPACE   NAME      STATUS        WEIGHT   LASTTRANSITIONTIME
test        podinfo   Progressing   15       2019-05-16T14:05:07Z
prod        frontend  Succeeded     0        2019-05-15T16:15:07Z
prod        backend   Failed        0        2019-05-14T17:05:07Z
```

## Automated rollback

During the canary analysis you can generate HTTP 500 errors and high latency to test if Flagger pauses the rollout.

Create a tester pod and exec into it:

```bash
kubectl -n test run tester \
--image=quay.io/stefanprodan/podinfo:3.1.2 \
-- ./podinfo --port=9898

kubectl -n test exec -it tester-xx-xx sh
```

Generate HTTP 500 errors:

```bash
watch curl http://podinfo-canary:9898/status/500
```

Generate latency:

```bash
watch curl http://podinfo-canary:9898/delay/1
```

When the number of failed checks reaches the canary analysis threshold, the traffic is routed back to the primary, the canary is scaled to zero and the rollout is marked as failed.

```text
kubectl -n test describe canary/podinfo

Status:
  Canary Weight:         0
  Failed Checks:         10
  Phase:                 Failed
Events:
  Type     Reason  Age   From     Message
  ----     ------  ----  ----     -------
  Normal   Synced  3m    flagger  Starting canary deployment for podinfo.test
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 5
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 10
  Normal   Synced  3m    flagger  Advance podinfo.test canary weight 15
  Normal   Synced  3m    flagger  Halt podinfo.test advancement success rate 69.17% < 99%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 61.39% < 99%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 55.06% < 99%
  Normal   Synced  2m    flagger  Halt podinfo.test advancement success rate 47.00% < 99%
  Normal   Synced  2m    flagger  (combined from similar events): Halt podinfo.test advancement success rate 38.08% < 99%
  Warning  Synced  1m    flagger  Rolling back podinfo.test failed checks threshold reached 10
  Warning  Synced  1m    flagger  Canary failed! Scaling down podinfo.test
```