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The following commit(s) were added to refs/heads/branch-3.3 by this push:
new 0fff27ddc33 [MINOR][K8S][DOCS] Add all resource managers in
`Scheduling Within an Application` section
0fff27ddc33 is described below
commit 0fff27ddc3324c1bfd8e317771dbdd9a96839639
Author: Dongjoon Hyun <[email protected]>
AuthorDate: Tue Jan 24 00:06:25 2023 -0800
[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 #39704 from dongjoon-hyun/minor_job_scheduling.
Authored-by: Dongjoon Hyun <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>
(cherry picked from commit 45dbc44410f9bf74c7fb4431aad458db32960461)
Signed-off-by: Dongjoon Hyun <[email protected]>
---
docs/job-scheduling.md | 3 +++
1 file changed, 3 insertions(+)
diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
index f44ed8245e2..f8ffd0c170f 100644
--- 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:
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