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flagger/docs/gitbook/usage/metrics.md
2020-02-28 18:20:57 +02:00

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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.
```yaml
canaryAnalysis:
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](../faq.md#metrics).
### 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.
```yaml
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.canaryAnalysis.metrics[].interval)
A canary analysis metric can reference a template with `templateRef`:
```yaml
canaryAnalysis:
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:
```yaml
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: not-found-percentage
namespace: istio-system
spec:
provider:
type: prometheus
address: http://promethues.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:
```yaml
canaryAnalysis:
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:
```yaml
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
name: grpc-error-rate-percentage
namespace: flagger
spec:
provider:
type: prometheus
address: http://flagger-promethues.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 gPRC services instrumented with [go-grpc-prometheus](https://github.com/grpc-ecosystem/go-grpc-prometheus).
### Datadog
You can create custom metric checks using the Datadog provider.
Create a secret with your Datadog API credentials:
```yaml
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:
```yaml
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:
```yaml
canaryAnalysis:
metrics:
- name: "404s percentage"
templateRef:
name: not-found-percentage
namespace: istio-system
thresholdRange:
max: 5
interval: 1m
```