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Merge branch 'master' into release-1.12

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zacharysarah
2018-10-17 19:10:16 -07:00
parent 01677e2710 548b538f72
commit 9a2e390e59
558 changed files with 71868 additions and 2594 deletions
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content/en/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough.md
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@@ -440,7 +440,7 @@ the quantity `10500m` would be written as `10.5` in decimal notation. The metri
will return whole numbers without a suffix when possible, and will generally return
quantities in milli-units otherwise. This means you might see your metric value fluctuate
between `1` and `1500m`, or `1` and `1.5` when written in decimal notation. See the
[glossary entry on quantities](/docs/reference/glossary/quantity.md) for more information.
[glossary entry on quantities](/docs/reference/glossary?core-object=true#term-quantity) for more information.
## Appendix: Other possible scenarios
@@ -458,4 +458,4 @@ $ kubectl create -f https://k8s.io/examples/application/hpa/php-apache.yaml
horizontalpodautoscaler.autoscaling/php-apache created
```
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content/en/docs/tasks/run-application/horizontal-pod-autoscale.md
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@@ -64,7 +64,7 @@ or the custom metrics API (for all other metrics).
except that it works with raw values, not utilization values.
* For object metrics and external metrics, a single metric is fetched, which describes
the object in question. This metric is compared compared to the target
the object in question. This metric is compared to the target
value, to produce a ratio as above. In the `autoscaling/v2beta2` API
version, this value can optionally be divided by the number of pods before the
comparison is made.
@@ -99,7 +99,7 @@ desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricVal
For example, if the current metric value is `200m`, and the desired value
is `100m`, the number of replicas will be doubled, since `200.0 / 100.0 ==
2.0` If the the current value is instead `50m`, we'll halve the number of
2.0` If the current value is instead `50m`, we'll halve the number of
replicas, since `50.0 / 100.0 == 0.5`. We'll skip scaling if the ratio is
sufficiently close to 1.0 (within a globally-configurable tolerance, from
the `--horizontal-pod-autoscaler-tolerance` flag, which defaults to 0.1).
@@ -122,7 +122,7 @@ became ready, that pod is set aside as well.
Due to technical constraints, the HorizontalPodAutoscaler controller
cannot exactly determine the first time a pod becomes ready when
determinining whether to set aside certain CPU metrics. Instead, it
determining whether to set aside certain CPU metrics. Instead, it
considers a Pod "not yet ready" if it's unready and transitioned to
unready within a short, configurable window of time since it started.
This value is configured with the `--horizontal-pod-autoscaler-initial-readiness-delay` flag, and its default is 30
@@ -139,9 +139,9 @@ conservatively, assuming those pods were consuming 100% of the desired
value in case of a scale down, and 0% in case of a scale up. This dampens
the magnitude of any potential scale.
Futhermore, if any not-yet-ready pods were present, and we would have
Furthermore, if any not-yet-ready pods were present, and we would have
scaled up without factoring in missing metrics or not-yet-ready pods, we
conservatively assume the non-yet-ready pods are consuming 0% of the
conservatively assume the not-yet-ready pods are consuming 0% of the
desired metric, further dampening the magnitude of a scale up.
After factoring in the not-yet-ready pods and missing metrics, we
@@ -161,9 +161,9 @@ into a desired replica count (e.g. due to an error fetching the metrics
from the metrics APIs), scaling is skipped.
Finally, just before HPA scales the target, the scale reccomendation is recorded. The
controller considers all reccomendations within a configurable window choosing the
controller considers all recommendations within a configurable window choosing the
highest recommendation from within that window. This value can be configured using the `--horizontal-pod-autoscaler-downscale-stabilization-window` flag, which defaults to 5 minutes.
This means that scaledowns will occur gradually, smothing out the impact of rapidly
This means that scaledowns will occur gradually, smoothing out the impact of rapidly
fluctuating metric values.
## API Object
@@ -278,4 +278,4 @@ APIs, cluster administrators must ensure that:
* kubectl autoscale command: [kubectl autoscale](/docs/reference/generated/kubectl/kubectl-commands/#autoscale).
* Usage example of [Horizontal Pod Autoscaler](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/).
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