mirror of
https://github.com/fluxcd/flagger.git
synced 2026-04-15 06:57:34 +00:00
5d3ab056f0
Adding support for overriding the primary scaler replica count via .spec.autoscalerRef.primaryScalerReplicas, a feature which would enable users to define a different scaling configurations for the primary. This can be useful in the situation where the user does not want to scale the canary workload to the exact same size as the primary, especially when opting for a canary deployment pattern where only a small portion of traffic is routed to the canary workload pods. Signed-off-by: Aurel Canciu <aurelcanciu@gmail.com>
243 lines
8.4 KiB
Markdown
243 lines
8.4 KiB
Markdown
# Canary analysis with KEDA ScaledObjects
|
|
|
|
This guide shows you how to use Flagger with KEDA ScaledObjects to autoscale workloads during a Canary analysis run.
|
|
We will be using a Blue/Green deployment strategy with the Kubernetes provider for the sake of this tutorial, but
|
|
you can use any deployment strategy combined with any supported provider.
|
|
|
|
## Prerequisites
|
|
|
|
Flagger requires a Kubernetes cluster **v1.16** or newer. For this tutorial, we'll need KEDA **2.7.1** or newer.
|
|
|
|
Install KEDA:
|
|
|
|
```bash
|
|
helm repo add kedacore https://kedacore.github.io/charts
|
|
kubectl create namespace keda
|
|
helm install keda kedacore/keda --namespace keda
|
|
```
|
|
|
|
Install Flagger:
|
|
```bash
|
|
helm repo add flagger https://flagger.app
|
|
|
|
helm upgrade -i flagger flagger/flagger \
|
|
--namespace flagger \
|
|
--set prometheus.install=true \
|
|
--set meshProvider=kubernetes
|
|
```
|
|
|
|
## Bootstrap
|
|
|
|
Flagger takes a Kubernetes deployment and a KEDA ScaledObject targeting the deployment. It then creates a series of objects
|
|
(Kubernetes deployments, ClusterIP services and another KEDA ScaledObject targeting the created Deployment).
|
|
These objects expose the application inside the mesh and drive the Canary analysis and Blue/Green promotion.
|
|
|
|
Create a test namespace:
|
|
|
|
```bash
|
|
kubectl create ns test
|
|
```
|
|
|
|
Create a deployment named `podinfo`:
|
|
|
|
```bash
|
|
kubectl apply -n test -f https://raw.githubusercontent.com/fluxcd/flagger/main/kustomize/podinfo/deployment.yaml
|
|
```
|
|
|
|
Deploy the load testing service to generate traffic during the analysis:
|
|
|
|
```bash
|
|
kubectl apply -k https://github.com/fluxcd/flagger//kustomize/tester?ref=main
|
|
```
|
|
|
|
Create a ScaledObject which targets the `podinfo` deployment and uses Prometheus as a trigger:
|
|
```yaml
|
|
apiVersion: keda.sh/v1alpha1
|
|
kind: ScaledObject
|
|
metadata:
|
|
name: podinfo-so
|
|
namespace: test
|
|
spec:
|
|
scaleTargetRef:
|
|
name: podinfo
|
|
pollingInterval: 10
|
|
cooldownPeriod: 20
|
|
minReplicaCount: 1
|
|
maxReplicaCount: 3
|
|
triggers:
|
|
- type: prometheus
|
|
metadata:
|
|
name: prom-trigger
|
|
serverAddress: http://flagger-prometheus.flagger-system:9090
|
|
metricName: http_requests_total
|
|
query: sum(rate(http_requests_total{ app="podinfo" }[30s]))
|
|
threshold: '5'
|
|
```
|
|
|
|
Create a canary custom resource for the `podinfo` deployment:
|
|
|
|
```yaml
|
|
apiVersion: flagger.app/v1beta1
|
|
kind: Canary
|
|
metadata:
|
|
name: podinfo
|
|
namespace: test
|
|
spec:
|
|
provider: kubernetes
|
|
# deployment reference
|
|
targetRef:
|
|
apiVersion: apps/v1
|
|
kind: Deployment
|
|
name: podinfo
|
|
# Scaler reference
|
|
autoscalerRef:
|
|
apiVersion: keda.sh/v1alpha1
|
|
kind: ScaledObject
|
|
# ScaledObject targeting the canary deployment
|
|
name: podinfo-so
|
|
# Mapping between trigger names and the related query to use for the generated
|
|
# ScaledObject targeting the primary deployment. (Optional)
|
|
primaryScalerQueries:
|
|
prom-trigger: sum(rate(http_requests_total{ app="podinfo-primary" }[30s]))
|
|
# Overriding replica scaling configuration for the generated ScaledObject
|
|
# targeting the primary deployment. (Optional)
|
|
primaryScalerReplicas:
|
|
minReplicas: 2
|
|
maxReplicas: 5
|
|
# the maximum time in seconds for the canary deployment
|
|
# to make progress before rollback (default 600s)
|
|
progressDeadlineSeconds: 60
|
|
service:
|
|
port: 80
|
|
targetPort: 9898
|
|
name: podinfo-svc
|
|
portDiscovery: true
|
|
analysis:
|
|
# schedule interval (default 60s)
|
|
interval: 15s
|
|
# max number of failed checks before rollback
|
|
threshold: 5
|
|
# number of checks to run before promotion
|
|
iterations: 5
|
|
# Prometheus checks based on
|
|
# http_request_duration_seconds histogram
|
|
metrics:
|
|
- name: request-success-rate
|
|
interval: 1m
|
|
thresholdRange:
|
|
min: 99
|
|
- name: request-duration
|
|
interval: 30s
|
|
thresholdRange:
|
|
max: 500
|
|
# load testing hooks
|
|
webhooks:
|
|
- name: load-test
|
|
url: http://flagger-loadtester.test/
|
|
timeout: 5s
|
|
metadata:
|
|
type: cmd
|
|
cmd: "hey -z 2m -q 20 -c 2 http://podinfo-svc-canary.test/"
|
|
```
|
|
|
|
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
|
|
scaledobject.keda.sh/podinfo-so
|
|
canary.flagger.app/podinfo
|
|
|
|
# generated
|
|
deployment.apps/podinfo-primary
|
|
horizontalpodautoscaler.autoscaling/podinfo-primary
|
|
service/podinfo
|
|
service/podinfo-canary
|
|
service/podinfo-primary
|
|
scaledobject.keda.sh/podinfo-so-primary
|
|
```
|
|
|
|
We refer to our ScaledObject for the canary deployment using `.spec.autoscalerRef`. Flagger will use this to generate a ScaledObject which will scale the primary deployment.
|
|
By default, Flagger will try to guess the query to use for the primary ScaledObject, by replacing all mentions of `.spec.targetRef.Name` and `{.spec.targetRef.Name}-canary`
|
|
with `{.spec.targetRef.Name}-primary`, for all triggers.
|
|
For eg, if your ScaledObject has a trigger query defined as: `sum(rate(http_requests_total{ app="podinfo" }[30s]))` or `sum(rate(http_requests_total{ app="podinfo-primary" }[30s]))`, then the primary ScaledObject will have the same trigger with a query defined as `sum(rate(http_requests_total{ app="podinfo-primary" }[30s]))`.
|
|
|
|
If, the generated query does not meet your requirements, you can specify the query for autoscaling the primary deployment explicitly using
|
|
`.spec.autoscalerRef.primaryScalerQueries`, which lets you define a query for each trigger. Please note that, your ScaledObject's `.spec.triggers[@].name` must
|
|
not be blank, as Flagger needs that to identify each trigger uniquely.
|
|
|
|
In the situation when it is desired to have different scaling replica configuration between the canary and primary deployment ScaledObject you can use
|
|
the `.spec.autoscalerRef.primaryScalerReplicas` to override these values for the generated primary ScaledObject.
|
|
|
|
After the boostrap, the podinfo deployment will be scaled to zero and the traffic to `podinfo.test` will be routed to the primary pods. To keep the podinfo deployment
|
|
at 0 replicas and pause auto scaling, Flagger will add an annotation to your ScaledObject: `autoscaling.keda.sh/paused-replicas: 0`.
|
|
During the canary analysis, the annotation is removed, to enable auto scaling for the podinfo deployment.
|
|
The `podinfo-canary.test` address can be used to target directly the canary pods.
|
|
When the canary analysis starts, Flagger will call the pre-rollout webhooks before routing traffic to the canary. The Blue/Green deployment will run for five iterations while validating the HTTP metrics and rollout hooks every 15 seconds.
|
|
|
|
|
|
## Automated Blue/Green promotion
|
|
|
|
Trigger a deployment by updating the container image:
|
|
|
|
```bash
|
|
kubectl -n test set image deployment/podinfo \
|
|
podinfod=ghcr.io/stefanprodan/podinfo:6.0.1
|
|
```
|
|
|
|
Flagger detects that the deployment revision changed and starts a new rollout:
|
|
|
|
```text
|
|
kubectl -n test describe canary/podinfo
|
|
|
|
Events:
|
|
|
|
New revision detected 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 iteration 1/10
|
|
Advance podinfo.test canary iteration 2/10
|
|
Advance podinfo.test canary iteration 3/10
|
|
Advance podinfo.test canary iteration 4/10
|
|
Advance podinfo.test canary iteration 5/10
|
|
Advance podinfo.test canary iteration 6/10
|
|
Advance podinfo.test canary iteration 7/10
|
|
Advance podinfo.test canary iteration 8/10
|
|
Advance podinfo.test canary iteration 9/10
|
|
Advance podinfo.test canary iteration 10/10
|
|
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.
|
|
|
|
You can monitor all canaries with:
|
|
|
|
```bash
|
|
watch kubectl get canaries --all-namespaces
|
|
|
|
NAMESPACE NAME STATUS WEIGHT LASTTRANSITIONTIME
|
|
test podinfo Progressing 100 2019-06-16T14:05:07Z
|
|
```
|
|
|
|
You can monitor the scaling of the deployments with:
|
|
```bash
|
|
watch kubectl -n test get deploy podinfo
|
|
NAME READY UP-TO-DATE AVAILABLE AGE
|
|
flagger-loadtester 1/1 1 1 4m21s
|
|
podinfo 3/3 3 3 4m28s
|
|
podinfo-primary 3/3 3 3 3m14s
|
|
```
|
|
|
|
You can mointor how Flagger edits the annotations of your ScaledObject with:
|
|
```bash
|
|
watch "kubectl get -n test scaledobjects podinfo-so -o=jsonpath='{.metadata.annotations}'"
|
|
```
|