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.

analysis:
metrics:
- name: request-success-rate
interval: 1m
# minimum req success rate (non 5xx responses)
# percentage (0-100)
thresholdRange:
min: 99
- name: request-duration
interval: 1m
# maximum req duration P99
# milliseconds
thresholdRange:
max: 500

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 controller 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.

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: my-metric
spec:
provider:
type: # can be prometheus or datadog
address: # API URL
secretRef:
name: # name of the secret containing the API credentials
query: # metric query

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:

analysis:
metrics:
- name: "my metric"
templateRef:
name: my-metric
# namespace is optional
# when not specified, the canary namespace will be used
namespace: flagger
# accepted values
thresholdRange:
min: 10
max: 1000
# metric query time window
interval: 1m

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:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: not-found-percentage
namespace: istio-system
spec:
provider:
type: prometheus
address: http://prometheus.istio-system:9090
query: |
100 - sum(
rate(
istio_requests_total{
reporter="destination",
destination_workload_namespace="{{ namespace }}",
destination_workload="{{ target }}",
response_code!="404"
}[{{ interval }}]
)
)
/
sum(
rate(
istio_requests_total{
reporter="destination",
destination_workload_namespace="{{ namespace }}",
destination_workload="{{ target }}"
}[{{ interval }}]
)
) * 100

Reference the template in the canary analysis:

analysis:
metrics:
- name: "404s percentage"
templateRef:
name: not-found-percentage
namespace: istio-system
thresholdRange:
max: 5
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.

Prometheus gRPC error rate example:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: grpc-error-rate-percentage
namespace: flagger
spec:
provider:
type: prometheus
address: http://flagger-prometheus.flagger-system:9090
query: |
100 - sum(
rate(
grpc_server_handled_total{
grpc_code!="OK",
kubernetes_namespace="{{ namespace }}",
kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
}[{{ interval }}]
)
)
/
sum(
rate(
grpc_server_started_total{
kubernetes_namespace="{{ namespace }}",
kubernetes_pod_name=~"{{ target }}-[0-9a-zA-Z]+(-[0-9a-zA-Z]+)"
}[{{ interval }}]
)
) * 100

The above template is for gRPC services instrumented with go-grpc-prometheus.

Datadog

You can create custom metric checks using the Datadog provider.

Create a secret with your Datadog API credentials:

apiVersion: v1
kind: Secret
metadata:
name: datadog
namespace: istio-system
data:
datadog_api_key: your-datadog-api-key
datadog_application_key: your-datadog-application-key

Datadog template example:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: not-found-percentage
namespace: istio-system
spec:
provider:
type: datadog
address: https://api.datadoghq.com
secretRef:
name: datadog
query: |
100 - (
sum:istio.mesh.request.count{
reporter:destination,
destination_workload_namespace:{{ namespace }},
destination_workload:{{ target }},
!response_code:404
}.as_count()
/
sum:istio.mesh.request.count{
reporter:destination,
destination_workload_namespace:{{ namespace }},
destination_workload:{{ target }}
}.as_count()
) * 100

Reference the template in the canary analysis:

analysis:
metrics:
- name: "404s percentage"
templateRef:
name: not-found-percentage
namespace: istio-system
thresholdRange:
max: 5
interval: 1m

Amazon CloudWatch

You can create custom metric checks using the CloudWatch metrics provider.

CloudWatch template example:

apiVersion: flagger.app/v1alpha1
kind: MetricTemplate
metadata:
name: cloudwatch-error-rate
spec:
provider:
type: cloudwatch
region: ap-northeast-1 # specify the region of your metrics
query: |
[
{
"Id": "e1",
"Expression": "m1 / m2",
"Label": "ErrorRate"
},
{
"Id": "m1",
"MetricStat": {
"Metric": {
"Namespace": "MyKubernetesCluster",
"MetricName": "ErrorCount",
"Dimensions": [
{
"Name": "appName",
"Value": "{{ name }}.{{ namespace }}"
}
]
},
"Period": 60,
"Stat": "Sum",
"Unit": "Count"
},
"ReturnData": false
},
{
"Id": "m2",
"MetricStat": {
"Metric": {
"Namespace": "MyKubernetesCluster",
"MetricName": "RequestCount",
"Dimensions": [
{
"Name": "appName",
"Value": "{{ name }}.{{ namespace }}"
}
]
},
"Period": 60,
"Stat": "Sum",
"Unit": "Count"
},
"ReturnData": false
}
]

The query format documentation can be found here.

Reference the template in the canary analysis:

analysis:
metrics:
- name: "app error rate"
templateRef:
name: cloudwatch-error-rate
thresholdRange:
max: 0.1
interval: 1m

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:

apiVersion: v1
kind: Secret
metadata:
name: newrelic
namespace: istio-system
data:
newrelic_account_id: your-account-id
newrelic_query_key: your-insights-query-key

New Relic template example:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: newrelic-error-rate
namespace: ingress-nginx
spec:
provider:
type: newrelic
secretRef:
name: newrelic
query: |
SELECT
filter(sum(nginx_ingress_controller_requests), WHERE status >= '500') /
sum(nginx_ingress_controller_requests) * 100
FROM Metric
WHERE metricName = 'nginx_ingress_controller_requests'
AND ingress = '{{ ingress }}' AND namespace = '{{ namespace }}'

Reference the template in the canary analysis:

analysis:
metrics:
- name: "error rate"
templateRef:
name: newrelic-error-rate
namespace: ingress-nginx
thresholdRange:
max: 5
interval: 1m