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author | Reynold Xin <rxin@databricks.com> | 2015-11-18 12:50:29 -0800 |
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committer | Reynold Xin <rxin@databricks.com> | 2015-11-18 12:50:29 -0800 |
commit | a416e41e285700f861559d710dbf857405bfddf6 (patch) | |
tree | a0d2f70568985f6c98c3bbf1a7435ea7b731e726 /docs/job-scheduling.md | |
parent | 31921e0f0bd559d042148d1ea32f865fb3068f38 (diff) | |
download | spark-a416e41e285700f861559d710dbf857405bfddf6.tar.gz spark-a416e41e285700f861559d710dbf857405bfddf6.tar.bz2 spark-a416e41e285700f861559d710dbf857405bfddf6.zip |
[SPARK-11809] Switch the default Mesos mode to coarse-grained mode
Based on my conversions with people, I believe the consensus is that the coarse-grained mode is more stable and easier to reason about. It is best to use that as the default rather than the more flaky fine-grained mode.
Author: Reynold Xin <rxin@databricks.com>
Closes #9795 from rxin/SPARK-11809.
Diffstat (limited to 'docs/job-scheduling.md')
-rw-r--r-- | docs/job-scheduling.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md index a3c34cb679..36327c6efe 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -47,7 +47,7 @@ application is not running tasks on a machine, other applications may run tasks is useful when you expect large numbers of not overly active applications, such as shell sessions from separate users. However, it comes with a risk of less predictable latency, because it may take a while for an application to gain back cores on one node when it has work to do. To use this mode, simply use a -`mesos://` URL without setting `spark.mesos.coarse` to true. +`mesos://` URL and set `spark.mesos.coarse` to false. Note that none of the modes currently provide memory sharing across applications. If you would like to share data this way, we recommend running a single server application that can serve multiple requests by querying |