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author | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-09-08 13:36:50 -0700 |
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committer | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-09-08 13:36:50 -0700 |
commit | af8ffdb73c28012c9f5cf232ca7d4b4c6763628d (patch) | |
tree | 78f704de2adaf12c823ad743b4c3bc1303b0d034 /docs/job-scheduling.md | |
parent | c0d375107f414822d65eaff0e3a76dd3fe9e1570 (diff) | |
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-rw-r--r-- | docs/job-scheduling.md | 2 |
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diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md index 11b733137d..d304c5497b 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -25,7 +25,7 @@ different options to manage allocation, depending on the cluster manager. The simplest option, available on all cluster managers, is _static partitioning_ of resources. With this approach, each application is given a maximum amount of resources it can use, and holds onto them -for its whole duration. This is the only approach available in Spark's [standalone](spark-standalone.html) +for its whole duration. This is the approach used in Spark's [standalone](spark-standalone.html) and [YARN](running-on-yarn.html) modes, as well as the [coarse-grained Mesos mode](running-on-mesos.html#mesos-run-modes). Resource allocation can be configured as follows, based on the cluster type: |