Merge pull request alteryx#234 from alig/master

pwendell · pwendell · commit 241336add5be · 2013-12-06T17:29:03.000-08:00
Updated documentation about the YARN v2.2 build process
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
@@ -45,6 +45,10 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with
     # Cloudera CDH 4.2.0 with MapReduce v2
     $ mvn -Phadoop2-yarn -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package
 
+Hadoop versions 2.2.x and newer can be built by setting the ```new-yarn``` and the ```yarn.version``` as follows:
+       mvn -Dyarn.version=2.2.0 -Dhadoop.version=2.2.0 -Pnew-yarn
+
+The build process handles Hadoop 2.2.x as a special case that uses the directory ```new-yarn```, which supports the new YARN API. Furthermore, for this version, the build depends on artifacts published by the spark-project to enable Akka 2.0.5 to work with protobuf 2.5. 
 
 ## Spark Tests in Maven ##
 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
@@ -45,7 +45,7 @@ The system currently supports three cluster managers:
   easy to set up a cluster.
 * [Apache Mesos](running-on-mesos.html) -- a general cluster manager that can also run Hadoop MapReduce
   and service applications.
-* [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2.0.
+* [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2.
 
 In addition, Spark's [EC2 launch scripts](ec2-scripts.html) make it easy to launch a standalone
 cluster on Amazon EC2.
diff --git a/docs/index.md b/docs/index.md
@@ -56,14 +56,16 @@ Hadoop, you must build Spark against the same version that your cluster uses.
 By default, Spark links to Hadoop 1.0.4. You can change this by setting the
 `SPARK_HADOOP_VERSION` variable when compiling:
 
-    SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
+    SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly
 
 In addition, if you wish to run Spark on [YARN](running-on-yarn.md), set
 `SPARK_YARN` to `true`:
 
     SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
-(Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.)
+Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.
+
+For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to build Spark and publish it locally. See [Launching Spark on YARN](running-on-yarn.md). This is needed because Hadoop 2.2 has non backwards compatible API changes.
 
 # Where to Go from Here
 
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
@@ -17,6 +17,7 @@ This can be built by setting the Hadoop version and `SPARK_YARN` environment var
 The assembled JAR will be something like this:
 `./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
 
+The build process now also supports new YARN versions (2.2.x). See below.
 
 # Preparations
 
@@ -111,9 +112,16 @@ For example:
     SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
     MASTER=yarn-client ./spark-shell
 
+# Building Spark for Hadoop/YARN 2.2.x
+
+Hadoop 2.2.x users must build Spark and publish it locally. The SBT build process handles Hadoop 2.2.x as a special case. This version of Hadoop has new YARN API changes and depends on a Protobuf version (2.5) that is not compatible with the Akka version (2.0.5) that Spark uses. Therefore, if the Hadoop version (e.g. set through ```SPARK_HADOOP_VERSION```) starts with 2.2.0 or higher then the build process will depend on Akka artifacts distributed by the Spark project compatible with Protobuf 2.5. Furthermore, the build process then uses the directory ```new-yarn``` (instead of ```yarn```), which supports the new YARN API. The build process should seamlessly work out of the box. 
+
+See [Building Spark with Maven](building-with-maven.md) for instructions on how to build Spark using the Maven process.
+
 # Important Notes
 
 - We do not requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed.
 - The local directories used for spark 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.
 - 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 --addJars option allows the SparkContext.addJar function to work if you are using it with local files. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
+- YARN 2.2.x users cannot simply depend on the Spark packages without building Spark, as the published Spark artifacts are compiled to work with the pre 2.2 API. Those users must build Spark and publish it locally.  
