[MINOR][K8S][DOCS] Add all resource managers in `Scheduling Within an…

… Application` section ### What changes were proposed in this pull request? `Job Scheduling` document doesn't mention `K8s resource manager` so far because `Scheduling Across Applications` section only mentions all resource managers except K8s. This PR aims to add all supported resource managers in `Scheduling Within an Application section` section. ### Why are the changes needed? K8s also supports `FAIR` schedule within an application. ### Does this PR introduce _any_ user-facing change? No. This is a doc-only update. ### How was this patch tested? N/A Closes apache#39704 from dongjoon-hyun/minor_job_scheduling. Authored-by: Dongjoon Hyun <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]> (cherry picked from commit 45dbc44) Signed-off-by: Dongjoon Hyun <[email protected]>
peter-toth · Jan 24, 2023 · 0fff27d · 0fff27d
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diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
@@ -191,6 +191,9 @@ of cluster resources. This means that short jobs submitted while a long job is r
 resources right away and still get good response times, without waiting for the long job to finish. This
 mode is best for multi-user settings.
 
+This feature is disabled by default and available on all coarse-grained cluster managers, i.e.
+[standalone mode](spark-standalone.html), [YARN mode](running-on-yarn.html),
+[K8s mode](running-on-kubernetes.html) and [Mesos coarse-grained mode](running-on-mesos.html#mesos-run-modes).
 To enable the fair scheduler, simply set the `spark.scheduler.mode` property to `FAIR` when configuring
 a SparkContext: