From 0dace7b20cbe48e931d2d657ff4807ce54df0665 Mon Sep 17 00:00:00 2001 From: Jongyoul Lee Date: Wed, 4 Feb 2015 17:44:32 +0900 Subject: [PATCH 1/3] [SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688 - MESOS_NATIVE_LIBRARY become deprecated - Chagned MESOS_NATIVE_LIBRARY to MESOS_NATIVE_JAVA_LIBRARY --- conf/spark-env.sh.template | 2 +- docs/running-on-mesos.md | 2 +- .../src/test/scala/org/apache/spark/repl/ReplSuite.scala | 2 +- .../src/test/scala/org/apache/spark/repl/ReplSuite.scala | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index 0886b0276fb90..67f81d33361e1 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -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 # Options read in YARN client mode # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 78358499fd01f..0436b35a1d2e6 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -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=`. This path is typically + * `export MESOS_NATIVE_JAVA_LIBRARY=`. This path is typically `/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`. diff --git a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala index e594ad868ea1c..4f2c4dc588bd0 100644 --- a/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/scala-2.10/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -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", """ diff --git a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala index f966f25c5a14c..0086e857d1458 100644 --- a/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/scala-2.11/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -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", """ From 2e15a212a1602bb2bc0232af529e68e1087b14d1 Mon Sep 17 00:00:00 2001 From: Jongyoul Lee Date: Mon, 23 Feb 2015 00:27:36 +0900 Subject: [PATCH 2/3] [SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688 - MESOS_NATIVE_LIBRARY become deprecated - Chagned MESOS_NATIVE_LIBRARY to MESOS_NATIVE_JAVA_LIBRARY --- docs/running-on-mesos.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 0436b35a1d2e6..b1db68f0292b8 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -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 From a6a00c26951d7737ab3bea9940c10d823b271a5c Mon Sep 17 00:00:00 2001 From: Jongyoul Lee Date: Mon, 23 Feb 2015 00:27:36 +0900 Subject: [PATCH 3/3] [SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688 - Removed 'Known issues' section --- docs/running-on-mesos.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 0436b35a1d2e6..b1db68f0292b8 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -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