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317 lines
8.1 KiB
Markdown
317 lines
8.1 KiB
Markdown
# Metrics Analysis
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As part of the analysis process, Flagger can validate service level objectives (SLOs) like
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availability, error rate percentage, average response time and any other objective based on app specific metrics.
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If a drop in performance is noticed during the SLOs analysis,
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the release will be automatically rolled back with minimum impact to end-users.
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### Builtin metrics
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Flagger comes with two builtin metric checks: HTTP request success rate and duration.
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```yaml
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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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```
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For each metric you can specify a range of accepted values with `thresholdRange`
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and the window size or the time series with `interval`.
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The builtin checks are available for every service mesh / ingress controller
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and are implemented with [Prometheus queries](../faq.md#metrics).
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### Custom metrics
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The canary analysis can be extended with custom metric checks. Using a `MetricTemplate` custom resource, you
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configure Flagger to connect to a metric provider and run a query that returns a `float64` value.
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The query result is used to validate the canary based on the specified threshold range.
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```yaml
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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 or datadog
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address: # API URL
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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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```
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The following variables are available in query templates:
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- `name` (canary.metadata.name)
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- `namespace` (canary.metadata.namespace)
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- `target` (canary.spec.targetRef.name)
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- `service` (canary.spec.service.name)
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- `ingress` (canary.spec.ingresRef.name)
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- `interval` (canary.spec.analysis.metrics[].interval)
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A canary analysis metric can reference a template with `templateRef`:
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```yaml
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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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```
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### Prometheus
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You can create custom metric checks targeting a Prometheus server
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by setting the provider type to `prometheus` and writing the query in PromQL.
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Prometheus template example:
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```yaml
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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://promethues.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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```
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Reference the template in the canary analysis:
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```yaml
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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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```
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The above configuration validates the canary by checking
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if the HTTP 404 req/sec percentage is below 5 percent of the total traffic.
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If the 404s rate reaches the 5% threshold, then the canary fails.
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Prometheus gRPC error rate example:
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```yaml
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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-promethues.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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```
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The above template is for gRPC services instrumented with [go-grpc-prometheus](https://github.com/grpc-ecosystem/go-grpc-prometheus).
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### Datadog
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You can create custom metric checks using the Datadog provider.
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Create a secret with your Datadog API credentials:
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```yaml
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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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```
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Datadog template example:
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```yaml
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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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```
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Reference the template in the canary analysis:
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```yaml
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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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```
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### Amazon CloudWatch
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You can create custom metric checks using the CloudWatch metrics provider.
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CloudWatch template example:
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```yaml
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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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```
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The query format documentation can be found [here](https://aws.amazon.com/premiumsupport/knowledge-center/cloudwatch-getmetricdata-api/).
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Reference the template in the canary analysis:
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```yaml
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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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```
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**Note** that Flagger need AWS IAM permission to perform `cloudwatch:GetMetricData` to use this provider.
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