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Add the documentation to the new scale-to-zero-pod-retention-period flag (#2478) 

* Add the documentation to the new scale-to-zero-pod-retention-period flag

* review
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Victor Agababov 2020-05-20 09:55:57 -07:00 committed by GitHub
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docs/serving/configuring-autoscaling.md
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@ -496,7 +496,7 @@ This period is an upper bound amount of time the system waits internally for the
* **Global key:** `scale-to-zero-grace-period`
* **Per-revision annotation key:** n/a
* **Possible values:** Duration
* **Possible values:** Duration (must be at least 6s).
* **Default:** `30s`
**Example:**
@ -526,6 +526,45 @@ spec:
{{< /tab >}}
{{< /tabs >}}
### Scale To Zero Last Pod Retention Period
The `scale-to-zero-pod-retention-period` flag determines the **minimum** amount of time that the last pod will remain active after the Autoscaler has decided to scale pods to zero.
This contrasts with the `scale-to-zero-grace-period` flag, which determines the **maximum** amount of time that the last pod will remain active after the Autoscaler has decided to scale pods to zero.
* **Global key:** `scale-to-zero-pod-retention-period`
* **Per-revision annotation key:** n/a
* **Possible values:** Non-negative duration string
* **Default:** `0s`
**Example:**
{{< tabs name="scale-to-zero-grace" default="Global (ConfigMap)" >}}
{{% tab name="Global (ConfigMap)" %}}
```yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: config-autoscaler
namespace: knative-serving
data:
scale-to-zero-pod-retention-period: "42s"
```
{{< /tab >}}
{{% tab name="Global (Operator)" %}}
```yaml
apiVersion: operator.knative.dev/v1alpha1
kind: KnativeServing
metadata:
name: knative-serving
spec:
config:
autoscaler:
scale-to-zero-pod-retention-period: "42s"
```
{{< /tab >}}
{{< /tabs >}}
## Modes: Stable and Panic
The KPA acts on the respective metrics (concurrency or RPS) aggregated over time-based windows. These windows define the amount of historical data the autoscaler takes into account and are used to smooth the data over the specified amount of time. The shorter these windows are, the more quickly the autoscaler will react but the more hysterically it will react as well.