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Metrics Analysis
As part of the analysis process, Flagger can validate service level objectives (SLOs) like availability, error rate percentage, average response time and any other objective based on app specific metrics. If a drop in performance is noticed during the SLOs analysis, the release will be automatically rolled back with minimum impact to end-users.
Builtin metrics
Flagger comes with two builtin metric checks: HTTP request success rate and duration.
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analysis:
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metrics:
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- name: request-success-rate
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interval: 1m
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# minimum req success rate (non 5xx responses)
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# percentage (0-100)
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thresholdRange:
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min: 99
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- name: request-duration
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interval: 1m
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# maximum req duration P99
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# milliseconds
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thresholdRange:
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max: 500
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For each metric you can specify a range of accepted values with
thresholdRange and the window size or the time series with interval. The builtin checks are available for every service mesh / ingress controlle and are implemented with Prometheus queries.Custom metrics
The canary analysis can be extended with custom metric checks. Using a
MetricTemplate custom resource, you configure Flagger to connect to a metric provider and run a query that returns a float64 value. The query result is used to validate the canary based on the specified threshold range.1
apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: my-metric
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spec:
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provider:
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type: # can be prometheus, datadog, etc
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address: # API URL
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insecureSkipVerify: # if set to true, disables the TLS cert validation
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secretRef:
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name: # name of the secret containing the API credentials
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query: # metric query
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The following variables are available in query templates:
name(canary.metadata.name)namespace(canary.metadata.namespace)target(canary.spec.targetRef.name)service(canary.spec.service.name)ingress(canary.spec.ingresRef.name)interval(canary.spec.analysis.metrics[].interval)
A canary analysis metric can reference a template with
templateRef:1
analysis:
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metrics:
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- name: "my metric"
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templateRef:
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name: my-metric
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# namespace is optional
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# when not specified, the canary namespace will be used
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namespace: flagger
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# accepted values
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thresholdRange:
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min: 10
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max: 1000
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# metric query time window
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interval: 1m
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Prometheus
You can create custom metric checks targeting a Prometheus server by setting the provider type to
prometheus and writing the query in PromQL.Prometheus template example:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: not-found-percentage
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namespace: istio-system
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spec:
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provider:
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type: prometheus
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address: http://prometheus.istio-system:9090
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query: |
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100 - sum(
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rate(
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istio_requests_total{
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reporter="destination",
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destination_workload_namespace="{{ namespace }}",
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destination_workload="{{ target }}",
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response_code!="404"
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}[{{ interval }}]
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)
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)
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/
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sum(
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rate(
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istio_requests_total{
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reporter="destination",
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destination_workload_namespace="{{ namespace }}",
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destination_workload="{{ target }}"
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}[{{ interval }}]
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)
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) * 100
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Reference the template in the canary analysis:
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analysis:
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metrics:
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- name: "404s percentage"
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templateRef:
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name: not-found-percentage
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namespace: istio-system
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thresholdRange:
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max: 5
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interval: 1m
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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.
Prometheus gRPC error rate example:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: grpc-error-rate-percentage
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namespace: flagger
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spec:
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provider:
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type: prometheus
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address: http://flagger-prometheus.flagger-system:9090
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query: |
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100 - sum(
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rate(
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grpc_server_handled_total{
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grpc_code!="OK",
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kubernetes_namespace="{{ namespace }}",
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kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
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}[{{ interval }}]
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)
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)
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/
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sum(
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rate(
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grpc_server_started_total{
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kubernetes_namespace="{{ namespace }}",
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kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
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}[{{ interval }}]
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)
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) * 100
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Prometheus authentication
If your Prometheus API requires basic authentication, you can create a secret in the same namespace as the
MetricTemplate with the basic-auth credentials:1
apiVersion: v1
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kind: Secret
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metadata:
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name: prom-basic-auth
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namespace: flagger
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data:
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username: your-user
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password: your-password
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Then reference the secret in the
MetricTemplate:1
apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: my-metric
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namespace: flagger
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spec:
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provider:
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type: prometheus
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address: http://prometheus.monitoring:9090
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secretRef:
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name: prom-basic-auth
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Datadog
You can create custom metric checks using the Datadog provider.
Create a secret with your Datadog API credentials:
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apiVersion: v1
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kind: Secret
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metadata:
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name: datadog
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namespace: istio-system
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data:
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datadog_api_key: your-datadog-api-key
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datadog_application_key: your-datadog-application-key
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Datadog template example:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: not-found-percentage
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namespace: istio-system
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spec:
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provider:
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type: datadog
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address: https://api.datadoghq.com
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secretRef:
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name: datadog
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query: |
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100 - (
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sum:istio.mesh.request.count{
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reporter:destination,
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destination_workload_namespace:{{ namespace }},
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destination_workload:{{ target }},
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!response_code:404
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}.as_count()
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/
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sum:istio.mesh.request.count{
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reporter:destination,
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destination_workload_namespace:{{ namespace }},
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destination_workload:{{ target }}
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}.as_count()
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) * 100
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Reference the template in the canary analysis:
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analysis:
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metrics:
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- name: "404s percentage"
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templateRef:
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name: not-found-percentage
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namespace: istio-system
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thresholdRange:
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max: 5
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interval: 1m
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Amazon CloudWatch
You can create custom metric checks using the CloudWatch metrics provider.
CloudWatch template example:
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apiVersion: flagger.app/v1alpha1
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kind: MetricTemplate
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metadata:
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name: cloudwatch-error-rate
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spec:
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provider:
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type: cloudwatch
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region: ap-northeast-1 # specify the region of your metrics
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query: |
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[
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{
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"Id": "e1",
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"Expression": "m1 / m2",
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"Label": "ErrorRate"
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},
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{
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"Id": "m1",
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"MetricStat": {
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"Metric": {
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"Namespace": "MyKubernetesCluster",
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"MetricName": "ErrorCount",
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"Dimensions": [
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{
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"Name": "appName",
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"Value": "{{ name }}.{{ namespace }}"
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}
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]
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},
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"Period": 60,
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"Stat": "Sum",
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"Unit": "Count"
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},
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"ReturnData": false
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},
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{
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"Id": "m2",
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"MetricStat": {
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"Metric": {
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"Namespace": "MyKubernetesCluster",
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"MetricName": "RequestCount",
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"Dimensions": [
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{
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"Name": "appName",
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"Value": "{{ name }}.{{ namespace }}"
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}
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]
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},
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"Period": 60,
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"Stat": "Sum",
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"Unit": "Count"
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},
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"ReturnData": false
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}
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]
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Reference the template in the canary analysis:
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analysis:
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metrics:
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- name: "app error rate"
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templateRef:
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name: cloudwatch-error-rate
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thresholdRange:
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max: 0.1
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interval: 1m
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Note that Flagger need AWS IAM permission to perform
cloudwatch:GetMetricData to use this provider.New Relic
You can create custom metric checks using the New Relic provider.
Create a secret with your New Relic Insights credentials:
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apiVersion: v1
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kind: Secret
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metadata:
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name: newrelic
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namespace: istio-system
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data:
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newrelic_account_id: your-account-id
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newrelic_query_key: your-insights-query-key
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New Relic template example:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: newrelic-error-rate
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namespace: ingress-nginx
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spec:
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provider:
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type: newrelic
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secretRef:
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name: newrelic
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query: |
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SELECT
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filter(sum(nginx_ingress_controller_requests), WHERE status >= '500') /
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sum(nginx_ingress_controller_requests) * 100
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FROM Metric
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WHERE metricName = 'nginx_ingress_controller_requests'
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AND ingress = '{{ ingress }}' AND namespace = '{{ namespace }}'
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Reference the template in the canary analysis:
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analysis:
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metrics:
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- name: "error rate"
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templateRef:
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name: newrelic-error-rate
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namespace: ingress-nginx
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thresholdRange:
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max: 5
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interval: 1m
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Graphite
You can create custom metric checks using the Graphite provider.
Graphite template example:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: graphite-request-success-rate
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spec:
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provider:
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type: graphite
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address: http://graphite.monitoring
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query: |
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target=summarize(
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asPercent(
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sumSeries(
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stats.timers.httpServerRequests.app.{{target}}.exception.*.method.*.outcome.{CLIENT_ERROR,INFORMATIONAL,REDIRECTION,SUCCESS}.status.*.uri.*.count
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),
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sumSeries(
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stats.timers.httpServerRequests.app.{{target}}.exception.*.method.*.outcome.*.status.*.uri.*.count
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)
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),
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{{interval}},
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'avg'
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)
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Reference the template in the canary analysis:
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analysis:
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metrics:
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- name: "success rate"
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templateRef:
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name: graphite-request-success-rate
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thresholdRange:
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min: 90
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interval: 1min
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Graphite authentication
If your Graphite API requires basic authentication, you can create a secret in the same namespace as the
MetricTemplate with the basic-auth credentials:1
apiVersion: v1
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kind: Secret
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metadata:
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name: graphite-basic-auth
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namespace: flagger
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data:
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username: your-user
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password: your-password
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Then, reference the secret in the
MetricTemplate:1
apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: my-metric
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namespace: flagger
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spec:
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provider:
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type: graphite
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address: http://graphite.monitoring
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secretRef:
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name: graphite-basic-auth
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Google Cloud Monitoring (Stackdriver)
Enable Workload Identity on your cluster, create a service account key that has read access to the Cloud Monitoring API and then create an IAM policy binding between the GCP service account and the Flagger service account on Kubernetes. You can take a look at this guide​
Annotate the flagger service account
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kubectl annotate serviceaccount flagger \
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--namespace <namespace> \
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iam.gke.io/gcp-service-account=<gcp-serviceaccount-name>@<project-id>.iam.gserviceaccount.com
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Alternatively, you can download the json keys and add it to your secret with the key
serviceAccountKey (This method is not recommended).Create a secret that contains your project-id (and, if workload identity is not enabled on your cluster, your service account json).
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kubectl create secret generic gcloud-sa --from-literal=project=<project-id>
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Then reference the secret in the metric template. Note: The particular MQL query used here works if Istio is installed on GKE.
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: bytes-sent
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namespace: test
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spec:
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provider:
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type: stackdriver
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secretRef:
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name: gcloud-sa
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query: |
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fetch k8s_container
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| metric 'istio.io/service/server/response_latencies'
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| filter
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(metric.destination_service_name == '{{ service }}-canary'
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&& metric.destination_service_namespace == '{{ namespace }}')
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| align delta(1m)
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| every 1m
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| group_by [],
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[value_response_latencies_percentile:
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percentile(value.response_latencies, 99)]
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InfluxDB
Create a secret that contains your authentication token that can be found in the InfluxDB UI.
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kubectl create secret generic influx-token --from-literal=token=<token>
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Then reference the secret in the metric template.
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: not-found
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namespace: test
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spec:
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provider:
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type: influxdb
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secretRef:
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name: influx-token
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query: |
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from(bucket: "default")
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|> range(start: -2h)
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|> filter(fn: (r) => r["_measurement"] == "istio_requests_total")
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|> filter(fn: (r) => r[" destination_workload_namespace"] == "{{ namespace }}")
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|> filter(fn: (r) => r["destination_workload"] == "{{ target }}")
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|> filter(fn: (r) => r["response_code"] == "500")
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|> count()
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|> yield(name: "count")
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Dynatrace
You can create custom metric checks using the Dynatrace provider.
Create a secret with your Dynatrace token:
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apiVersion: v1
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kind: Secret
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metadata:
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name: dynatrace
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namespace: istio-system
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data:
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dynatrace_token: ZHQwYz...
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Dynatrace metric template example:
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apiVersion: flagger.app/v1beta1
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kind: MetricTemplate
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metadata:
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name: response-time-95pct
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namespace: istio-system
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spec:
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provider:
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type: dynatrace
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address: https://xxxxxxxx.live.dynatrace.com
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secretRef:
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name: dynatrace
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query: |
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builtin:service.response.time:filter(eq(dt.entity.service,SERVICE-ABCDEFG0123456789)):percentile(95)
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Reference the template in the canary analysis:
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analysis:
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metrics:
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- name: "response-time-95pct"
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templateRef:
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name: response-time-95pct
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namespace: istio-system
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thresholdRange:
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max: 1000
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interval: 1m
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Last modified 1mo ago