Merge pull request #26472 from kbhawkey/cleanup-usage-just

clean up use of word: just

content/en/docs/tasks/run-application/delete-stateful-set.md

@@ -43,8 +43,8 @@ You may need to delete the associated headless service separately after the Stat
 kubectl delete service <service-name>
 ```
 
-Deleting a StatefulSet through kubectl will scale it down to 0, thereby deleting all pods that are a part of it. 
-If you want to delete just the StatefulSet and not the pods, use `--cascade=false`.
+When deleting a StatefulSet through `kubectl`, the StatefulSet scales down to 0. All Pods that are part of this workload are also deleted. If you want to delete only the StatefulSet and not the Pods, use `--cascade=false`.
+For example:
 
 ```shell
 kubectl delete -f <file.yaml> --cascade=false

content/en/docs/tasks/run-application/force-delete-stateful-set-pod.md

@@ -44,7 +44,7 @@ for StatefulSet Pods. Graceful deletion is safe and will ensure that the Pod
 [shuts down gracefully](/docs/concepts/workloads/pods/pod-lifecycle/#pod-termination)
 before the kubelet deletes the name from the apiserver.
 
-Kubernetes (versions 1.5 or newer) will not delete Pods just because a Node is unreachable.
+A Pod is not deleted automatically when a node is unreachable.
 The Pods running on an unreachable Node enter the 'Terminating' or 'Unknown' state after a
 [timeout](/docs/concepts/architecture/nodes/#condition).
 Pods may also enter these states when the user attempts graceful deletion of a Pod

content/en/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough.md

@@ -382,7 +382,7 @@ with *external metrics*.
 
 Using external metrics requires knowledge of your monitoring system; the setup is
 similar to that required when using custom metrics. External metrics allow you to autoscale your cluster
-based on any metric available in your monitoring system. Just provide a `metric` block with a
+based on any metric available in your monitoring system. Provide a `metric` block with a
 `name` and `selector`, as above, and use the `External` metric type instead of `Object`.
 If multiple time series are matched by the `metricSelector`,
 the sum of their values is used by the HorizontalPodAutoscaler.

content/en/docs/tasks/run-application/horizontal-pod-autoscale.md

@@ -162,7 +162,7 @@ can be fetched, scaling is skipped. This means that the HPA is still capable
 of scaling up if one or more metrics give a `desiredReplicas` greater than
 the current value.
 
-Finally, just before HPA scales the target, the scale recommendation is recorded.  The
+Finally, right before HPA scales the target, the scale recommendation is recorded.  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` flag, which defaults to 5 minutes.
 This means that scaledowns will occur gradually, smoothing out the impact of rapidly