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[SPARK-3619] Part 2. Upgrade to Mesos 0.21 to work around MESOS-1688 #4361

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[SPARK-3619] Part 2. Upgrade to Mesos 0.21 to work around MESOS-1688 #4361

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0dace7b
[SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688
jongyoul Feb 4, 2015
2e15a21
[SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688
jongyoul Feb 22, 2015
a6a00c2
[SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688
jongyoul Feb 22, 2015
f1ea91f
Merge branch 'SPARK-3619-1' of https://github.com/jongyoul/spark into…
jongyoul Feb 22, 2015
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2 changes: 1 addition & 1 deletion conf/spark-env.sh.template
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Expand Up @@ -15,7 +15,7 @@
# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program
# - SPARK_CLASSPATH, default classpath entries to append
# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data
# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos
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@tnachen I've found only this setting for mesos. Do you think there is another comment?

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I think this is it, thanks !

this PR LGTM.

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@tnachen Thanks.


# Options read in YARN client mode
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
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5 changes: 1 addition & 4 deletions docs/running-on-mesos.md
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Expand Up @@ -110,7 +110,7 @@ cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooK
The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos:

1. In `spark-env.sh` set some environment variables:
* `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is typically
* `export MESOS_NATIVE_JAVA_LIBRARY=<path to libmesos.so>`. This path is typically
`<prefix>/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation
instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of
`libmesos.so`.
Expand Down Expand Up @@ -167,9 +167,6 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere
only makes sense if you run just one application at a time. You can cap the maximum number of cores
using `conf.set("spark.cores.max", "10")` (for example).

# Known issues
- When using the "fine-grained" mode, make sure that your executors always leave 32 MB free on the slaves. Otherwise it can happen that your Spark job does not proceed anymore. Currently, Apache Mesos only offers resources if there are at least 32 MB memory allocatable. But as Spark allocates memory only for the executor and cpu only for tasks, it can happen on high slave memory usage that no new tasks will be started anymore. More details can be found in [MESOS-1688](https://issues.apache.org/jira/browse/MESOS-1688). Alternatively use the "coarse-gained" mode, which is not affected by this issue.

# Running Alongside Hadoop

You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a
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2 changes: 1 addition & 1 deletion repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala
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Expand Up @@ -281,7 +281,7 @@ class ReplSuite extends FunSuite {
assertDoesNotContain("Exception", output)
}

if (System.getenv("MESOS_NATIVE_LIBRARY") != null) {
if (System.getenv("MESOS_NATIVE_JAVA_LIBRARY") != null) {
test("running on Mesos") {
val output = runInterpreter("localquiet",
"""
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2 changes: 1 addition & 1 deletion repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala
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Expand Up @@ -289,7 +289,7 @@ class ReplSuite extends FunSuite {
assertDoesNotContain("Exception", output)
}

if (System.getenv("MESOS_NATIVE_LIBRARY") != null) {
if (System.getenv("MESOS_NATIVE_JAVA_LIBRARY") != null) {
test("running on Mesos") {
val output = runInterpreter("localquiet",
"""
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