[SPARK-9570][Docs][YARN]Consistent recommendation for submitting spark apps to YARN, -master yarn --deploy-mode x vs -master yarn-x'

R/README.md

@@ -63,5 +63,7 @@ You can also run the unit-tests for SparkR by running (you need to install the [
 The `./bin/spark-submit` and `./bin/sparkR` can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run
 ```
 export YARN_CONF_DIR=/etc/hadoop/conf
+./bin/spark-submit --master yarn --deploy-mode client examples/src/main/r/dataframe.R
+OR
 ./bin/spark-submit --master yarn examples/src/main/r/dataframe.R
 ```

README.md

@@ -58,8 +58,7 @@ To run one of them, use `./bin/run-example <class> [params]`. For example:
 will run the Pi example locally.
 
 You can set the MASTER environment variable when running examples to submit
-examples to a cluster. This can be a mesos:// or spark:// URL,
-"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
+examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN and "local" to run
 locally with one thread, or "local[N]" to run locally with N threads. You
 can also use an abbreviated class name if the class is in the `examples`
 package. For instance:

core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala

@@ -412,7 +412,8 @@ class SparkSubmitSuite
 
     // Test files and archives (Yarn)
     val clArgs2 = Seq(
-      "--master", "yarn-client",
+      "--master", "yarn",
+      "--deploy-mode","client",
       "--class", "org.SomeClass",
       "--files", files,
       "--archives", archives,
@@ -470,7 +471,8 @@ class SparkSubmitSuite
     writer2.println("spark.yarn.dist.archives " + archives)
     writer2.close()
     val clArgs2 = Seq(
-      "--master", "yarn-client",
+      "--master", "yarn",
+      "--deploy-mode","client",
       "--class", "org.SomeClass",
       "--properties-file", f2.getPath,
       "thejar.jar"

docs/running-on-yarn.md

@@ -16,37 +16,54 @@ containers used by the application use the same configuration. If the configurat
 Java system properties or environment variables not managed by YARN, they should also be set in the
 Spark application's configuration (driver, executors, and the AM when running in client mode).
 
-There are two deploy modes that can be used to launch Spark applications on YARN. In `yarn-cluster` mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In `yarn-client` mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
+There are two deploy modes that can be used to launch Spark applications on YARN. In `cluster` mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In `client` mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
 
-Unlike in Spark standalone and Mesos mode, in which the master's address is specified in the `--master` parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration. Thus, the `--master` parameter is `yarn-client` or `yarn-cluster`. 
-To launch a Spark application in `yarn-cluster` mode:
+Unlike in Spark standalone and Mesos mode, in which the master's address is specified in the `--master` parameter, in YARN mode the ResourceManager's address is picked up from the Hadoop configuration. Thus, the `--master` parameter is `yarn` and to specify the deployment `--deploy-mode` can be either `client` or `cluster`.
+To launch a Spark application in YARN in `cluster` mode:
 
-   `$ ./bin/spark-submit --class path.to.your.Class --master yarn-cluster [options] <app jar> [app options]`
-    
+   `$ ./bin/spark-submit --class path.to.your.Class --master yarn --deploy-mode cluster [options] <app jar> [app options]`
+
 For example:
 
     $ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
-        --master yarn-cluster \
+        --master yarn \
+        --deploy-mode cluster
         --num-executors 3 \
         --driver-memory 4g \
         --executor-memory 2g \
         --executor-cores 1 \
         --queue thequeue \
         lib/spark-examples*.jar \
-        10
 
-The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Debugging your Application" section below for how to see driver and executor logs.
+The above example starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running.  Refer to the "Debugging your Application" section below for how to see driver and executor logs.
+
+To launch a Spark application in `client` mode, do the same, but replace `cluster` with `client` in the --deploy-mode.  
+To run spark-shell:
 
-To launch a Spark application in `yarn-client` mode, do the same, but replace `yarn-cluster` with `yarn-client`.  To run spark-shell:
+    $ ./bin/spark-shell --master yarn --deploy-mode client
+
+The alternative to launching a Spark application on YARN is to set deployment mode for the YARN master in the `--master` itself.   
+`--master` can be `yarn-client` or `yarn-cluster`       
 
-    $ ./bin/spark-shell --master yarn-client
+For example:
 
+    $ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
+        --master yarn-cluster \
+        --num-executors 3 \
+        --driver-memory 4g \
+        --executor-memory 2g \
+        --executor-cores 1 \
+        --queue thequeue \
+        lib/spark-examples*.jar \
+        10
+
 ## Adding Other JARs
 
 In `yarn-cluster` mode, the driver runs on a different machine than the client, so `SparkContext.addJar` won't work out of the box with files that are local to the client. To make files on the client available to `SparkContext.addJar`, include them with the `--jars` option in the launch command. 
 
     $ ./bin/spark-submit --class my.main.Class \
-        --master yarn-cluster \
+        --master yarn
+        --deploy-mode cluster \
         --jars my-other-jar.jar,my-other-other-jar.jar
         my-main-jar.jar
         app_arg1 app_arg2
@@ -386,6 +403,6 @@ If you need a reference to the proper location to put log files in the YARN so t
 # Important notes
 
 - Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured.
-- In `yarn-cluster` mode, the local directories used by the Spark executors and the Spark driver will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored. In `yarn-client` mode, the Spark executors will use the local directories configured for YARN while the Spark driver will use those defined in `spark.local.dir`. This is because the Spark driver does not run on the YARN cluster in `yarn-client` mode, only the Spark executors do.
+- In `yarn-cluster`, the local directories used by the Spark executors and the Spark driver will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored. In `yarn-client` mode, the Spark executors will use the local directories configured for YARN while the Spark driver will use those defined in `spark.local.dir`. This is because the Spark driver does not run on the YARN cluster in `yarn-client` mode, only the Spark executors do.
 - The `--files` and `--archives` options support specifying file names with the # similar to Hadoop. For example you can specify: `--files localtest.txt#appSees.txt` and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name `appSees.txt`, and your application should use the name as `appSees.txt` to reference it when running on YARN.
 - The `--jars` option allows the `SparkContext.addJar` function to work if you are using it with local files and running in `yarn-cluster` mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.

docs/sql-programming-guide.md

@@ -1551,7 +1551,7 @@ on all of the worker nodes, as they will need access to the Hive serialization a
 (SerDes) in order to access data stored in Hive.
 
 Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running
-the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory
+the query on a YARN cluster (`--master yarn --deploy-mode cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory
 and `hive-site.xml` under `conf/` directory need to be available on the driver and all executors launched by the
 YARN cluster. The convenient way to do this is adding them through the `--jars` option and `--file` option of the
 `spark-submit` command.

docs/submitting-applications.md

@@ -103,12 +103,13 @@ run it with `--help`. Here are a few examples of common options:
 export HADOOP_CONF_DIR=XXX
 ./bin/spark-submit \
   --class org.apache.spark.examples.SparkPi \
-  --master yarn-cluster \  # can also be `yarn-client` for client mode
+  --master yarn \  
+  --deploy-mode cluster \
   --executor-memory 20G \
   --num-executors 50 \
   /path/to/examples.jar \
   1000
-
+  
 # Run a Python application on a Spark Standalone cluster
 ./bin/spark-submit \
   --master spark://207.184.161.138:7077 \
@@ -140,7 +141,6 @@ cluster mode. The cluster location will be found based on the HADOOP_CONF_DIR or
 </td></tr>
 </table>
 
-
 # Loading Configuration from a File
 
 The `spark-submit` script can load default [Spark configuration values](configuration.html) from a