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flagger/docs/gitbook/how-it-works.md
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# How it works
[Flagger](https://github.com/weaveworks/flagger) takes a Kubernetes deployment and optionally
a horizontal pod autoscaler \(HPA\) and creates a series of objects
\(Kubernetes deployments, ClusterIP services and Istio or App Mesh virtual services\) to drive the canary analysis and promotion.
![Flagger Canary Process](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-canary-hpa.png)
### Canary Custom Resource
For a deployment named _podinfo_, a canary promotion can be defined using Flagger's custom resource:
```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
# service port name (optional, will default to "http")
portName: http-podinfo
# Istio gateways (optional)
gateways:
- public-gateway.istio-system.svc.cluster.local
# Istio virtual service host names (optional)
hosts:
- podinfo.example.com
# promote the canary without analysing it (default false)
skipAnalysis: false
# define the canary analysis timing and KPIs
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# max number of failed metric checks before rollback
threshold: 10
# max traffic percentage routed to canary
# percentage (0-100)
maxWeight: 50
# canary increment step
# percentage (0-100)
stepWeight: 5
# Prometheus checks
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
# external checks (optional)
webhooks:
- name: integration-tests
url: http://podinfo.test:9898/echo
timeout: 1m
# key-value pairs (optional)
metadata:
test: "all"
token: "16688eb5e9f289f1991c"
```
**Note** that the target deployment must have a single label selector in the format `app: <DEPLOYMENT-NAME>`:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: podinfo
spec:
selector:
matchLabels:
app: podinfo
template:
metadata:
labels:
app: podinfo
```
Besides `app` Flagger supports `name` and `app.kubernetes.io/name` selectors. If you use a different
convention you can specify your label with the `-selector-labels` flag.
The target deployment should expose a TCP port that will be used by Flagger to create the ClusterIP Service and
the Istio Virtual Service. The container port from the target deployment should match the `service.port` value.
### Istio routing
Flagger creates an Istio Virtual Service based on the Canary service spec. The service configuration lets you expose
an app inside or outside the mesh.
You can also define HTTP match conditions, URI rewrite rules, CORS policies, timeout and retries.
The following spec exposes the `frontend` workload inside the mesh on `frontend.test.svc.cluster.local:9898`
and outside the mesh on `frontend.example.com`. You'll have to specify an Istio ingress gateway for external hosts.
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: frontend
namespace: test
spec:
service:
# container port
port: 9898
# service port name (optional, will default to "http")
portName: http-frontend
# Istio gateways (optional)
gateways:
- public-gateway.istio-system.svc.cluster.local
- mesh
# Istio virtual service host names (optional)
hosts:
- frontend.example.com
# HTTP match conditions (optional)
match:
- uri:
prefix: /
# HTTP rewrite (optional)
rewrite:
uri: /
# Envoy timeout and retry policy (optional)
headers:
request:
add:
x-envoy-upstream-rq-timeout-ms: "15000"
x-envoy-max-retries: "10"
x-envoy-retry-on: "gateway-error,connect-failure,refused-stream"
# cross-origin resource sharing policy (optional)
corsPolicy:
allowOrigin:
- example.com
allowMethods:
- GET
allowCredentials: false
allowHeaders:
- x-some-header
maxAge: 24h
```
For the above spec Flagger will generate the following virtual service:
```yaml
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: frontend
namespace: test
ownerReferences:
- apiVersion: flagger.app/v1alpha3
blockOwnerDeletion: true
controller: true
kind: Canary
name: podinfo
uid: 3a4a40dd-3875-11e9-8e1d-42010a9c0fd1
spec:
gateways:
- public-gateway.istio-system.svc.cluster.local
- mesh
hosts:
- frontend.example.com
- frontend
http:
- appendHeaders:
x-envoy-max-retries: "10"
x-envoy-retry-on: gateway-error,connect-failure,refused-stream
x-envoy-upstream-rq-timeout-ms: "15000"
corsPolicy:
allowHeaders:
- x-some-header
allowMethods:
- GET
allowOrigin:
- example.com
maxAge: 24h
match:
- uri:
prefix: /
rewrite:
uri: /
route:
- destination:
host: podinfo-primary
port:
number: 9898
weight: 100
- destination:
host: podinfo-canary
port:
number: 9898
weight: 0
```
Flagger keeps in sync the virtual service with the canary service spec. Any direct modification to the virtual
service spec will be overwritten.
To expose a workload inside the mesh on `http://backend.test.svc.cluster.local:9898`,
the service spec can contain only the container port:
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: backend
namespace: test
spec:
service:
port: 9898
```
Based on the above spec, Flagger will create several ClusterIP services like:
```yaml
apiVersion: v1
kind: Service
metadata:
name: backend-primary
ownerReferences:
- apiVersion: flagger.app/v1alpha3
blockOwnerDeletion: true
controller: true
kind: Canary
name: backend
uid: 2ca1a9c7-2ef6-11e9-bd01-42010a9c0145
spec:
type: ClusterIP
ports:
- name: http
port: 9898
protocol: TCP
targetPort: 9898
selector:
app: backend-primary
```
Flagger works for user facing apps exposed outside the cluster via an ingress gateway
and for backend HTTP APIs that are accessible only from inside the mesh.
### Canary Stages
![Flagger Canary Stages](https://raw.githubusercontent.com/weaveworks/flagger/master/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 mounted as volumes or mapped to environment variables
* Secrets mounted as volumes or mapped to environment variables
Gated canary promotion stages:
* scan for canary deployments
* check Istio virtual service routes are mapped to primary and canary ClusterIP services
* check primary and canary deployments status
* halt advancement if a rolling update is underway
* halt advancement if pods are unhealthy
* call pre-rollout webhooks are check results
* halt advancement if any hook returned a non HTTP 2xx result
* increment the failed checks counter
* increase canary traffic weight percentage from 0% to 5% (step weight)
* call rollout webhooks and check results
* check canary HTTP request success rate and latency
* halt advancement if any metric is under the specified threshold
* increment the failed checks counter
* check if the number of failed checks reached the threshold
* route all traffic to primary
* scale to zero the canary deployment and mark it as failed
* call post-rollout webhooks
* post the analysis result to Slack
* wait for the canary deployment to be updated and start over
* increase canary traffic weight by 5% (step weight) till it reaches 50% (max weight)
* halt advancement if any webhook call fails
* halt advancement while canary request success rate is under the threshold
* halt advancement while canary request duration P99 is over the threshold
* halt advancement if the primary or canary deployment becomes unhealthy
* halt advancement while canary deployment is being scaled up/down by HPA
* promote canary to primary
* copy ConfigMaps and Secrets from canary to primary
* copy canary deployment spec template over primary
* wait for primary rolling update to finish
* halt advancement if pods are unhealthy
* route all traffic to primary
* scale to zero the canary deployment
* mark rollout as finished
* call post-rollout webhooks
* post the analysis result to Slack
* wait for the canary deployment to be updated and start over
### Canary Analysis
The canary analysis runs periodically until it reaches the maximum traffic weight or the failed checks threshold.
Spec:
```yaml
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# max number of failed metric checks before rollback
threshold: 10
# max traffic percentage routed to canary
# percentage (0-100)
maxWeight: 50
# canary increment step
# percentage (0-100)
stepWeight: 2
# deploy straight to production without
# the metrics and webhook checks
skipAnalysis: false
```
The above analysis, if it succeeds, will run for 25 minutes while validating the HTTP metrics and webhooks every minute.
You can determine the minimum time that it takes to validate and promote a canary deployment using this formula:
```
interval * (maxWeight / stepWeight)
```
And the time it takes for a canary to be rollback when the metrics or webhook checks are failing:
```
interval * threshold
```
In emergency cases, you may want to skip the analysis phase and ship changes directly to production.
At any time you can set the `spec.skipAnalysis: true`.
When skip analysis is enabled, Flagger checks if the canary deployment is healthy and
promotes it without analysing it. If an analysis is underway, Flagger cancels it and runs the promotion.
### A/B Testing
Besides weighted routing, Flagger can be configured to route traffic to the canary based on HTTP match conditions.
In an A/B testing scenario, you'll be using HTTP headers or cookies to target a certain segment of your users.
This is particularly useful for frontend applications that require session affinity.
You can enable A/B testing by specifying the HTTP match conditions and the number of iterations:
```yaml
canaryAnalysis:
# schedule interval (default 60s)
interval: 1m
# total number of iterations
iterations: 10
# max number of failed iterations before rollback
threshold: 2
# canary match condition
match:
- headers:
user-agent:
regex: "^(?!.*Chrome).*Safari.*"
- headers:
cookie:
regex: "^(.*?;)?(user=test)(;.*)?$"
```
If Flagger finds a HTTP match condition, it will ignore the `maxWeight` and `stepWeight` settings.
The above configuration will run an analysis for ten minutes targeting the Safari users and those that have a test cookie.
You can determine the minimum time that it takes to validate and promote a canary deployment using this formula:
```
interval * iterations
```
And the time it takes for a canary to be rollback when the metrics or webhook checks are failing:
```
interval * threshold
```
Make sure that the analysis threshold is lower than the number of iterations.
### HTTP Metrics
The canary analysis is using the following Prometheus queries:
**HTTP requests success rate percentage**
Spec:
```yaml
canaryAnalysis:
metrics:
- name: request-success-rate
# minimum req success rate (non 5xx responses)
# percentage (0-100)
threshold: 99
interval: 1m
```
Istio query:
```javascript
sum(
rate(
istio_requests_total{
reporter="destination",
destination_workload_namespace=~"$namespace",
destination_workload=~"$workload",
response_code!~"5.*"
}[$interval]
)
)
/
sum(
rate(
istio_requests_total{
reporter="destination",
destination_workload_namespace=~"$namespace",
destination_workload=~"$workload"
}[$interval]
)
)
```
App Mesh query:
```javascript
sum(
rate(
envoy_cluster_upstream_rq{
kubernetes_namespace="$namespace",
kubernetes_pod_name=~"$workload",
response_code!~"5.*"
}[$interval]
)
)
/
sum(
rate(
envoy_cluster_upstream_rq{
kubernetes_namespace="$namespace",
kubernetes_pod_name=~"$workload"
}[$interval]
)
)
```
**HTTP requests milliseconds duration P99**
Spec:
```yaml
canaryAnalysis:
metrics:
- name: request-duration
# maximum req duration P99
# milliseconds
threshold: 500
interval: 1m
```
Istio query:
```javascript
histogram_quantile(0.99,
sum(
irate(
istio_request_duration_seconds_bucket{
reporter="destination",
destination_workload=~"$workload",
destination_workload_namespace=~"$namespace"
}[$interval]
)
) by (le)
)
```
App Mesh query:
```javascript
histogram_quantile(0.99,
sum(
irate(
envoy_cluster_upstream_rq_time_bucket{
kubernetes_pod_name=~"$workload",
kubernetes_namespace=~"$namespace"
}[$interval]
)
) by (le)
)
```
> **Note** that the metric interval should be lower or equal to the control loop interval.
### Custom Metrics
The canary analysis can be extended with custom Prometheus queries.
```yaml
canaryAnalysis:
threshold: 1
maxWeight: 50
stepWeight: 5
metrics:
- name: "404s percentage"
threshold: 5
query: |
100 - sum(
rate(
istio_requests_total{
reporter="destination",
destination_workload_namespace="test",
destination_workload="podinfo",
response_code!="404"
}[1m]
)
)
/
sum(
rate(
istio_requests_total{
reporter="destination",
destination_workload_namespace="test",
destination_workload="podinfo"
}[1m]
)
) * 100
```
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.
```yaml
canaryAnalysis:
threshold: 1
maxWeight: 50
stepWeight: 5
metrics:
- name: "rpc error rate"
threshold: 5
query: |
100 - (sum
rate(
grpc_server_handled_total{
grpc_service="my.TestService",
grpc_code!="OK"
}[1m]
)
)
/
sum(
rate(
grpc_server_started_total{
grpc_service="my.TestService"
}[1m]
)
) * 100
```
The above configuration validates the canary by checking if the percentage of
non-OK GRPC req/sec is below 5 percent of the total requests. If the non-OK
rate reaches the 5% threshold, then the canary fails.
When specifying a query, Flagger will run the promql query and convert the result to float64.
Then it compares the query result value with the metric threshold value.
### Webhooks
The canary analysis can be extended with webhooks. Flagger will call each webhook URL and
determine from the response status code (HTTP 2xx) if the canary is failing or not.
There are three types of hooks:
* Pre-rollout hooks are executed before routing traffic to canary.
The canary advancement is paused if a pre-rollout hook fails and if the number of failures reach the
threshold the canary will be rollback.
* Rollout hooks are executed during the analysis on each iteration before the metric checks.
If a rollout hook call fails the canary advancement is paused and eventfully rolled back.
* Post-rollout hooks are executed after the canary has been promoted or rolled back.
If a post rollout hook fails the error is logged.
Spec:
```yaml
canaryAnalysis:
webhooks:
- name: "smoke test"
type: pre-rollout
url: http://migration-check.db/query
timeout: 30s
metadata:
key1: "val1"
key2: "val2"
- name: "load test"
type: rollout
url: http://flagger-loadtester.test/
timeout: 15s
metadata:
cmd: "hey -z 1m -q 5 -c 2 http://podinfo-canary.test:9898/"
- name: "notify"
type: post-rollout
url: http://telegram.bot:8080/
timeout: 5s
metadata:
some: "message"
```
> **Note** that the sum of all rollout webhooks timeouts should be lower than the analysis interval.
Webhook payload (HTTP POST):
```json
{
"name": "podinfo",
"namespace": "test",
"phase": "Progressing",
"metadata": {
"test": "all",
"token": "16688eb5e9f289f1991c"
}
}
```
Response status codes:
* 200-202 - advance canary by increasing the traffic weight
* timeout or non-2xx - halt advancement and increment failed checks
On a non-2xx response Flagger will include the response body (if any) in the failed checks log and Kubernetes events.
### Load Testing
For workloads that are not receiving constant traffic Flagger can be configured with a webhook,
that when called, will start a load test for the target workload.
If the target workload doesn't receive any traffic during the canary analysis,
Flagger metric checks will fail with "no values found for metric istio_requests_total".
Flagger comes with a load testing service based on [rakyll/hey](https://github.com/rakyll/hey)
that generates traffic during analysis when configured as a webhook.
![Flagger Load Testing Webhook](https://raw.githubusercontent.com/weaveworks/flagger/master/docs/diagrams/flagger-load-testing.png)
First you need to deploy the load test runner in a namespace with Istio sidecar injection enabled:
```bash
export REPO=https://raw.githubusercontent.com/weaveworks/flagger/master
kubectl -n test apply -f ${REPO}/artifacts/loadtester/deployment.yaml
kubectl -n test apply -f ${REPO}/artifacts/loadtester/service.yaml
```
Or by using Helm:
```bash
helm repo add flagger https://flagger.app
helm upgrade -i flagger-loadtester flagger/loadtester \
--namespace=test \
--set cmd.timeout=1h
```
When deployed the load tester API will be available at `http://flagger-loadtester.test/`.
Now you can add webhooks to the canary analysis spec:
```yaml
webhooks:
- name: load-test-get
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 1m -q 10 -c 2 http://podinfo.test:9898/"
- name: load-test-post
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 1m -q 10 -c 2 -m POST -d '{test: 2}' http://podinfo.test:9898/echo"
```
When the canary analysis starts, Flagger will call the webhooks and the load tester will run the `hey` commands
in the background, if they are not already running. This will ensure that during the
analysis, the `podinfo.test` virtual service will receive a steady stream of GET and POST requests.
If your workload is exposed outside the mesh with the Istio Gateway and TLS you can point `hey` to the
public URL and use HTTP2.
```yaml
webhooks:
- name: load-test-get
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
type: cmd
cmd: "hey -z 1m -q 10 -c 2 -h2 https://podinfo.example.com/"
```
The load tester can run arbitrary commands as long as the binary is present in the container image.
For example if you you want to replace `hey` with another CLI, you can create your own Docker image:
```dockerfile
FROM quay.io/stefanprodan/flagger-loadtester:<VER>
RUN curl -Lo /usr/local/bin/my-cli https://github.com/user/repo/releases/download/ver/my-cli \
&& chmod +x /usr/local/bin/my-cli
```
### Load Testing Delegation
The load tester can also forward testing tasks to external tools, by now [nGrinder](https://github.com/naver/ngrinder)
is supported.
To use this feature, add a load test task of type 'ngrinder' to the canary analysis spec:
```yaml
webhooks:
- name: load-test-post
url: http://flagger-loadtester.test/
timeout: 5s
metadata:
# type of this load test task, cmd or ngrinder
type: ngrinder
# base url of your nGrinder controller server
server: http://ngrinder-server:port
# id of the test to clone from, the test must have been defined.
clone: 100
# user name and base64 encoded password to authenticate against the nGrinder server
username: admin
passwd: YWRtaW4=
# the interval between between nGrinder test status polling, default to 1s
pollInterval: 5s
```
When the canary analysis starts, the load tester will initiate a [clone_and_start request](https://github.com/naver/ngrinder/wiki/REST-API-PerfTest)
to the nGrinder server and start a new performance test. the load tester will periodically poll the nGrinder server
for the status of the test, and prevent duplicate requests from being sent in subsequent analysis loops.
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