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docs/gitbook/tutorials/crossover-progressive-delivery.md
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# Crossover Canary Deployments
This guide shows you how to use Envoy, [Crossover](https://github.com/mumoshu/crossover) and Flagger to automate canary deployments.
Crossover is a minimal Envoy xDS implementation supports [Service Mesh Interface](https://smi-spec.io/).
## Prerequisites
Flagger requires a Kubernetes cluster **v1.16** or newer and Envoy paired with [Crossover](https://github.com/mumoshu/crossover) sidecar.
Create a test namespace:
```bash
kubectl create ns test
```
Install Envoy along with the Crossover sidecar with Helm:
```bash
helm repo add crossover https://mumoshu.github.io/crossover
helm upgrade --install envoy crossover/envoy \
--namespace test \
-f <(cat <<EOF
smi:
apiVersions:
trafficSplits: v1alpha1
upstreams:
podinfo:
smi:
enabled: true
backends:
podinfo-primary:
port: 9898
weight: 100
podinfo-canary:
port: 9898
weight: 0
EOF
)
```
Install Flagger and the Prometheus add-on in the same namespace as Envoy:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger flagger/flagger \
--namespace test \
--set prometheus.install=true \
--set meshProvider=smi:crossover
```
## Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler \(HPA\), then creates a series of objects \(Kubernetes deployments, ClusterIP services, SMI traffic splits\). These objects expose the application on the mesh and drive the canary analysis and promotion. There's no SMI object you need to create by yourself.
Create a deployment and a horizontal pod autoscaler:
```bash
kubectl apply -k https://github.com/fluxcd/flagger//kustomize/podinfo?ref=main
```
Deploy the load testing service to generate traffic during the canary analysis:
```bash
helm upgrade -i flagger-loadtester flagger/loadtester \
--namespace=test
```
Create a metric template to measure the HTTP requests error rate:
```yaml
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: error-rate
namespace: test
spec:
provider:
address: http://flagger-prometheus:9090
type: prometheus
query: |
100 - rate(
envoy_cluster_upstream_rq{
kubernetes_namespace="{{ namespace }}",
envoy_cluster_name="{{ target }}-canary",
envoy_response_code!~"5.*"
}[{{ interval }}])
/
rate(
envoy_cluster_upstream_rq{
kubernetes_namespace="{{ namespace }}",
envoy_cluster_name="{{ target }}-canary"
}[{{ interval }}]
) * 100
```
Create a metric template to measure the HTTP requests average duration:
```yaml
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: latency
namespace: test
spec:
provider:
address: http://flagger-prometheus:9090
type: prometheus
query: |
histogram_quantile(0.99,
sum(
rate(
envoy_cluster_upstream_rq_time_bucket{
kubernetes_namespace="{{ namespace }}",
envoy_cluster_name="{{ target }}-canary"
}[{{ interval }}]
)
) by (le)
)
```
Create a canary custom resource:
```yaml
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
name: podinfo
namespace: test
spec:
provider: "smi:crossover"
# deployment reference
targetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
progressDeadlineSeconds: 60
# HPA reference (optional)
autoscalerRef:
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
name: podinfo
service:
port: 9898
# define the canary analysis timing and KPIs
analysis:
# schedule interval (default 60s)
interval: 1m
# 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: 5
metrics:
- name: error-rate
templateRef:
name: error-rate
thresholdRange:
max: 1
interval: 30s
- name: latency
templateRef:
name: latency
thresholdRange:
max: 0.5
interval: 30s
webhooks:
- name: acceptance-test
type: pre-rollout
url: http://flagger-loadtester.test/
timeout: 30s
metadata:
type: bash
cmd: "curl -sd 'test' http://podinfo-canary.test:9898/token | grep token"
- name: load-test
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
cmd: "hey -z 1m -q 10 -c 2 -H 'Host: podinfo.test' http://envoy.test:10000/"
```
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
```
After the boostrap, the podinfo deployment will be scaled to zero and the traffic to `podinfo.test` will be routed to the primary pods. During the canary analysis, the `podinfo-canary.test` address can be used to target directly the canary pods.
## 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/fluxcd/flagger/main/docs/diagrams/flagger-canary-steps.png)
A canary deployment is triggered by changes in any of the following objects:
* Deployment PodSpec \(container image, command, ports, env, resources, etc\)
* ConfigMaps and Secrets mounted as volumes or mapped to environment variables
Trigger a canary deployment by updating the container image:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.5
```
Flagger detects that the deployment revision changed and starts a new rollout:
```text
kubectl -n test describe canary/podinfo
Status:
Canary Weight: 0
Failed Checks: 0
Phase: Succeeded
Events:
New revision detected! Scaling up podinfo.test
Waiting for podinfo.test rollout to finish: 0 of 1 updated replicas are available
Pre-rollout check acceptance-test passed
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
Routing all traffic to primary
Promotion completed! Scaling down podinfo.test
```
When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary.
**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.
Flagger comes with a Grafana dashboard made for canary analysis. Install Grafana with Helm:
```bash
helm upgrade -i flagger-grafana flagger/grafana \
--namespace=test \
--set url=http://flagger-prometheus:9090
```
Run:
```bash
kubectl port-forward --namespace test svc/flagger-grafana 3000:80
```
The Envoy dashboard URL is [http://localhost:3000/d/flagger-envoy/envoy-canary?refresh=10s&orgId=1&var-namespace=test&var-target=podinfo](http://localhost:3000/d/flagger-envoy/envoy-canary?refresh=10s&orgId=1&var-namespace=test&var-target=podinfo)
![Envoy Canary Dashboard](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/screens/flagger-grafana-appmesh.png)
You can monitor all canaries with:
```bash
watch kubectl get canaries --all-namespaces
NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
test podinfo Progressing 15 2019-10-02T14:05:07Z
prod frontend Succeeded 0 2019-10-02T16:15:07Z
prod backend Failed 0 2019-10-02T17:05:07Z
```
If you’ve enabled the Slack notifications, you should receive the following messages:
![Flagger Slack Notifications](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/screens/slack-canary-notifications.png)
## Automated rollback
During the canary analysis you can generate HTTP 500 errors or high latency to test if Flagger pauses the rollout.
Trigger a canary deployment:
```bash
kubectl -n test set image deployment/podinfo \
podinfod=stefanprodan/podinfo:3.1.2
```
Exec into the load tester pod with:
```bash
kubectl -n test exec -it deploy/flagger-loadtester bash
```
Generate HTTP 500 errors:
```bash
hey -z 1m -c 5 -q 5 -H 'Host: podinfo.test' http://envoy.test:10000/status/500
```
Generate latency:
```bash
watch -n 1 curl -H 'Host: podinfo.test' http://envoy.test:10000/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 logs deploy/flagger -f | jq .msg
New revision detected! progressing canary analysis for podinfo.test
Pre-rollout check acceptance-test passed
Advance podinfo.test canary weight 5
Advance podinfo.test canary weight 10
Advance podinfo.test canary weight 15
Halt podinfo.test advancement success rate 69.17% < 99%
Halt podinfo.test advancement success rate 61.39% < 99%
Halt podinfo.test advancement success rate 55.06% < 99%
Halt podinfo.test advancement request duration 1.20s > 0.5s
Halt podinfo.test advancement request duration 1.45s > 0.5s
Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
```
If you’ve enabled the Slack notifications, you’ll receive a message if the progress deadline is exceeded, or if the analysis reached the maximum number of failed checks:
![Flagger Slack Notifications](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/screens/slack-canary-failed.png)

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@@ -1,16 +1,18 @@
# Gloo Canary Deployments
This guide shows you how to use the [Gloo Edge](https://gloo.solo.io/) ingress controller and Flagger to automate canary deployments.
This guide shows you how to use the [Gloo Edge](https://gloo.solo.io/) ingress controller
and Flagger to automate canary releases and A/B testing.
![Flagger Gloo Ingress Controller](https://raw.githubusercontent.com/fluxcd/flagger/main/docs/diagrams/flagger-gloo-overview.png)
## Prerequisites
This guide was written for Flagger version **1.5.0** or higher. Prior versions of Flagger used Gloo upstream groups to handle
canaries, but newer versions of Flagger use Gloo route tables to handle canaries as well as A/B testing.
Flagger requires a Kubernetes cluster **v1.16** or newer and Gloo Edge ingress **1.6.0** or newer.
This guide was written for Flagger version **1.6.0** or higher. Prior versions of Flagger
used Gloo upstream groups to handle canaries, but newer versions of Flagger use Gloo
route tables to handle canaries as well as A/B testing.
Install Gloo with Helm v3:
```bash
@@ -33,7 +35,9 @@ helm upgrade -i flagger flagger/flagger \
## Bootstrap
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler \(HPA\), then creates a series of objects \(Kubernetes deployments, ClusterIP services and Gloo route tables groups\). These objects expose the application outside the cluster and drive the canary analysis and promotion.
Flagger takes a Kubernetes deployment and optionally a horizontal pod autoscaler (HPA),
then creates a series of objects (Kubernetes deployments, ClusterIP services and Gloo route tables groups).
These objects expose the application outside the cluster and drive the canary analysis and promotion.
Create a test namespace:
@@ -53,7 +57,8 @@ Deploy the load testing service to generate traffic during the canary analysis:
kubectl -n test apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
```
Create a virtual service definition that references a route table that will be generated by Flagger \(replace `app.example.com` with your own domain\):
Create a virtual service definition that references a route table that will be generated by Flagger
(replace `app.example.com` with your own domain):
```yaml
apiVersion: gateway.solo.io/v1
@@ -80,7 +85,7 @@ Save the above resource as podinfo-virtualservice.yaml and then apply it:
kubectl apply -f ./podinfo-virtualservice.yaml
```
Create a canary custom resource \(replace `app.example.com` with your own domain\):
Create a canary custom resource (replace `app.example.com` with your own domain):
```yaml
apiVersion: flagger.app/v1beta1
@@ -182,7 +187,9 @@ podinfo Initialized 0 2019-05-17T08:09:51Z
## 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 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/fluxcd/flagger/main/docs/diagrams/flagger-canary-steps.png)
@@ -238,7 +245,8 @@ prod backend Failed 0 2019-05-17T17:05:07Z
## Automated rollback
During the canary analysis you can generate HTTP 500 errors and high latency to test if Flagger pauses and rolls back the faulted version.
During the canary analysis you can generate HTTP 500 errors and high latency to test if
Flagger pauses and rolls back the faulted version.
Trigger another canary deployment:
@@ -259,7 +267,8 @@ Generate high latency:
watch curl -H 'Host: app.example.com' http://gateway-proxy.gloo-system/delay/2
```
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.
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
@@ -288,7 +297,8 @@ Events:
The canary analysis can be extended with Prometheus queries.
The demo app is instrumented with Prometheus so you can create a custom check that will use the HTTP request duration histogram to validate the canary.
The demo app is instrumented with Prometheus so you can create a custom check that will use the HTTP request
duration histogram to validate the canary.
Create a metric template and apply it on the cluster:
@@ -336,7 +346,8 @@ Edit the canary analysis and add the following metric:
interval: 1m
```
The above configuration validates the canary by checking if the HTTP 404 req/sec percentage is below 5 percent of the total traffic. If the 404s rate reaches the 5% threshold, then the canary fails.
The above configuration validates the canary by checking if the HTTP 404 req/sec percentage
is below 5 percent of the total traffic. If the 404s rate reaches the 5% threshold, then the canary fails.
Trigger a canary deployment by updating the container image:
@@ -369,7 +380,8 @@ Rolling back podinfo.test failed checks threshold reached 5
Canary failed! Scaling down podinfo.test
```
If you have [alerting](../usage/alerting.md) configured, Flagger will send a notification with the reason why the canary failed.
If you have [alerting](../usage/alerting.md) configured,
Flagger will send a notification with the reason why the canary failed.
For an in-depth look at the analysis process read the [usage docs](../usage/how-it-works.md).

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# Canary analysis with Prometheus Operator
This guide show you how to use Prometheus Operator for canary analysis.
This guide show you how to use
[Prometheus Operator](https://github.com/prometheus-operator/prometheus-operator) for canary analysis.
## Prerequisites
Flagger requires a Kubernetes cluster **v1.16** or newer.
Flagger requires a Kubernetes cluster **v1.16** or newer and Prometheus Operator **v0.40** or newer.
Install Prometheus Operator with Helm v3:
@@ -19,7 +20,7 @@ helm upgrade -i prometheus prometheus-community/kube-prometheus-stack \
```
The `prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false`
option allows Prometheus operator to watch serviceMonitors outside of his namespace.
option allows Prometheus Operator to watch serviceMonitors outside of its namespace.
Install Flagger by setting the metrics server to Prometheus:
@@ -40,7 +41,7 @@ helm upgrade -i loadtester flagger/loadtester \
--namespace flagger-system
```
Install podinfo demo app:
Install [podinfo](https://github.com/stefanprodan/podinfo) demo app:
```bash
helm repo add podinfo https://stefanprodan.github.io/podinfo
@@ -53,23 +54,8 @@ helm upgrade -i podinfo podinfo/podinfo \
## Service monitors
The demo app is instrumented with Prometheus so you can create service monitors to scrape podinfo's metrics endpoint:
```yaml
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: podinfo-primary
namespace: test
spec:
endpoints:
- path: /metrics
port: http
interval: 5s
selector:
matchLabels:
app: podinfo
```
The demo app is instrumented with Prometheus,
so you can create a `ServiceMonitor` objects to scrape podinfo's metrics endpoint:
```yaml
apiVersion: monitoring.coreos.com/v1
@@ -85,10 +71,24 @@ spec:
selector:
matchLabels:
app: podinfo-canary
---
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: podinfo-primary
namespace: test
spec:
endpoints:
- path: /metrics
port: http
interval: 5s
selector:
matchLabels:
app: podinfo
```
We are setting `interval: 5s` to have a more aggressive scraping.
If you do not define it, you must to use a longer interval in the Canary object.
If you do not define it, you should use a longer interval in the Canary object.
## Metric templates
@@ -197,4 +197,3 @@ Based on the above specification, Flagger creates the primary and canary Kuberne
During the canary analysis, Prometheus will scrape the canary service and Flagger will use the HTTP error rate
and latency queries to determine if the release should be promoted or rolled back.

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# Rollout Weights