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, datadog, etc
    address: # API URL
    insecureSkipVerify: # if set to true, disables the TLS cert validation
    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)
variables (canary.spec.analysis.metrics[].templateVariables)

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

A canary analysis metric can reference a set of custom variables with templateVariables. These variables will be then injected into the query defined in the referred MetricTemplate object during canary analysis:

  analysis:
    metrics:
      - name: "my metric"
        templateRef:
          name: my-metric
          namespace: flagger
        # accepted values
        thresholdRange:
          min: 10
          max: 1000
        # metric query time window
        interval: 1m
        # custom variables used within the referenced metric template
        templateVariables:
          direction: inbound

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: my-metric
spec:
  provider:
    type: prometheus
    address: http://prometheus.linkerd-viz:9090
  query: |
    histogram_quantile(
      0.99,
      sum(
        rate(
          response_latency_ms_bucket{
            namespace="{{ namespace }}",
            deployment=~"{{ target }}",
            direction="{{ variables.direction }}"
          }[{{ interval }}]
        )
      ) by (le)
    )

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.

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:

apiVersion: v1
kind: Secret
metadata:
  name: prom-auth
  namespace: flagger
data:
  username: your-user
  password: your-password

or if you require bearer token authentication (via a SA token):

apiVersion: v1
kind: Secret
metadata:
  name: prom-auth
  namespace: flagger
data:
  token: ey1234...

Then reference the secret in the MetricTemplate:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: my-metric
  namespace: flagger
spec:
  provider:
    type: prometheus
    address: http://prometheus.monitoring:9090
    secretRef:
      name: prom-auth

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

Graphite

You can create custom metric checks using the Graphite provider.

Graphite template example:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: graphite-request-success-rate
spec:
  provider:
    type: graphite
    address: http://graphite.monitoring
  query: |
    target=summarize(
      asPercent(
        sumSeries(
          stats.timers.httpServerRequests.app.{{target}}.exception.*.method.*.outcome.{CLIENT_ERROR,INFORMATIONAL,REDIRECTION,SUCCESS}.status.*.uri.*.count
        ),
        sumSeries(
          stats.timers.httpServerRequests.app.{{target}}.exception.*.method.*.outcome.*.status.*.uri.*.count
        )
      ),
      {{interval}},
      'avg'
    )

Reference the template in the canary analysis:

  analysis:
    metrics:
      - name: "success rate"
        templateRef:
          name: graphite-request-success-rate
        thresholdRange:
          min: 90
        interval: 1min

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:

apiVersion: v1
kind: Secret
metadata:
  name: graphite-basic-auth
  namespace: flagger
data:
  username: your-user
  password: your-password

Then, reference the secret in the MetricTemplate:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: my-metric
  namespace: flagger
spec:
  provider:
    type: graphite
    address: http://graphite.monitoring
    secretRef:
      name: graphite-basic-auth

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

kubectl annotate serviceaccount flagger \
    --namespace <namespace> \
    iam.gke.io/gcp-service-account=<gcp-serviceaccount-name>@<project-id>.iam.gserviceaccount.com

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).

 kubectl create secret generic gcloud-sa --from-literal=project=<project-id>

Then reference the secret in the metric template. Note: The particular MQL query used here works if Istio is installed on GKE.

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: bytes-sent
  namespace: test
spec:
  provider:
    type: stackdriver
    secretRef: 
      name: gcloud-sa
  query: |
    fetch k8s_container
    | metric 'istio.io/service/server/response_latencies'
    | filter
        (metric.destination_service_name == '{{ service }}-canary'
        && metric.destination_service_namespace == '{{ namespace }}')
    | align delta(1m)
    | every 1m
    | group_by [],
        [value_response_latencies_percentile:
          percentile(value.response_latencies, 99)]

The reference for the query language can be found here

InfluxDB

The InfluxDB provider uses the flux query language.

Create a secret that contains your authentication token that can be found in the InfluxDB UI.

 kubectl create secret generic influx-token --from-literal=token=<token>

Then reference the secret in the metric template.

Note: The particular MQL query used here works if Istio is installed on GKE.

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: not-found
  namespace: test
spec:
  provider:
    type: influxdb
    secretRef:
      name: influx-token
  query: |
    from(bucket: "default")
    |> range(start: -2h)
    |> filter(fn: (r) => r["_measurement"] == "istio_requests_total")
    |> filter(fn: (r) => r[" destination_workload_namespace"] == "{{ namespace }}")
    |> filter(fn: (r) => r["destination_workload"] == "{{ target }}")
    |> filter(fn: (r) => r["response_code"] == "500")
    |> count()
    |> yield(name: "count")

Dynatrace

You can create custom metric checks using the Dynatrace provider.

Create a secret with your Dynatrace token:

apiVersion: v1
kind: Secret
metadata:
  name: dynatrace
  namespace: istio-system
data:
  dynatrace_token: ZHQwYz...

Dynatrace metric template example:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: response-time-95pct
  namespace: istio-system
spec:
  provider:
    type: dynatrace
    address: https://xxxxxxxx.live.dynatrace.com
    secretRef:
      name: dynatrace
  query: |
    builtin:service.response.time:filter(eq(dt.entity.service,SERVICE-ABCDEFG0123456789)):percentile(95)

Reference the template in the canary analysis:

  analysis:
    metrics:
      - name: "response-time-95pct"
        templateRef:
          name: response-time-95pct
          namespace: istio-system
        thresholdRange:
          max: 1000
        interval: 1m

Keptn

You can create custom metric checks using the Keptn provider. This Provider allows to verify either the value of a single KeptnMetric, representing the value of a single metric, or of a Keptn Analysis, which provides a flexible grading logic for analysing and prioritising a number of different metric values coming from different data sources.

This provider requires Keptn to be installed in the cluster.

Example for a Keptn metric template:

apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
  name: response-time
  namespace: istio-system
spec:
  provider:
    type: keptn
  query: keptnmetric/my-namespace/response-time/2m/reporter=destination

This will reference the KeptnMetric with the name response-time in the namespace my-namespace, which could look like the following:

apiVersion: metrics.keptn.sh/v1beta1
kind: KeptnMetric
metadata:
  name: response-time
  namespace: my-namespace
spec:
  fetchIntervalSeconds: 10
  provider:
    name: my-prometheus-keptn-provider
  query: histogram_quantile(0.8, sum by(le) (rate(http_server_request_latency_seconds_bucket{status_code='200',
    job='simple-go-backend'}[5m[])))

The query contains the following components, which are divided by / characters:

<type>/<namespace>/<resource-name>/<timeframe>/<arguments>

type (required): Must be either keptnmetric or analysis.
namespace (required): The namespace of the referenced KeptnMetric/AnalysisDefinition.
resource-name (required): The name of the referenced KeptnMetric/AnalysisDefinition.
timeframe (optional): The timeframe used for the Analysis. This will usually be set to the same value as the analysis interval of a Canary. Only relevant if the type is set to analysis.
arguments (optional): Arguments to be passed to an Analysis. Arguments are passed as a list of key value pairs, separated by ; characters, e.g. foo=bar;bar=foo. Only relevant if the type is set to analysis.

For the type analysis, the value returned by the provider is either 0 (if the analysis failed), or 1 (analysis passed).

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