Merge remote-tracking branch 'old/master'

1 files changed, 52 insertions, 25 deletions

diff --git a/README.md b/README.md
index 4c91013f7e..11bf8edb3b 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,12 @@
-# Spark
+# Apache Spark
 
-Lightning-Fast Cluster Computing - <http://www.spark-project.org/>
+Lightning-Fast Cluster Computing - <http://spark.incubator.apache.org/>
 
 
 ## Online Documentation
 
 You can find the latest Spark documentation, including a programming
-guide, on the project webpage at <http://spark-project.org/documentation.html>.
+guide, on the project webpage at <http://spark.incubator.apache.org/documentation.html>.
 This README file only contains basic setup instructions.
 
 
@@ -16,26 +16,24 @@ Spark requires Scala 2.9.3 (Scala 2.10 is not yet supported). The project is
 built using Simple Build Tool (SBT), which is packaged with it. To build
 Spark and its example programs, run:
 
-    sbt/sbt package
+    sbt/sbt assembly
 
-Spark also supports building using Maven. If you would like to build using Maven,
-see the [instructions for building Spark with Maven](http://spark-project.org/docs/latest/building-with-maven.html)
-in the spark documentation..
+Once you've built Spark, the easiest way to start using it is the shell:
 
-To run Spark, you will need to have Scala's bin directory in your `PATH`, or
-you will need to set the `SCALA_HOME` environment variable to point to where
-you've installed Scala. Scala must be accessible through one of these
-methods on your cluster's worker nodes as well as its master.
+    ./spark-shell
 
-To run one of the examples, use `./run <class> <params>`. For example:
+Or, for the Python API, the Python shell (`./pyspark`).
 
-    ./run spark.examples.SparkLR local[2]
+Spark also comes with several sample programs in the `examples` directory.
+To run one of them, use `./run-example <class> <params>`. For example:
+
+    ./run-example org.apache.spark.examples.SparkLR local[2]
 
 will run the Logistic Regression example locally on 2 CPUs.
 
 Each of the example programs prints usage help if no params are given.
 
-All of the Spark samples take a `<host>` parameter that is the cluster URL
+All of the Spark samples take a `<master>` parameter that is the cluster URL
 to connect to. This can be a mesos:// or spark:// URL, or "local" to run
 locally with one thread, or "local[N]" to run locally with N threads.
 
@@ -43,23 +41,52 @@ locally with one thread, or "local[N]" to run locally with N threads.
 ## A Note About Hadoop Versions
 
 Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
-storage systems. Because the HDFS API has changed in different versions of
+storage systems. Because the protocols have changed in different versions of
 Hadoop, you must build Spark against the same version that your cluster runs.
-You can change the version by setting the `HADOOP_VERSION` variable at the top
-of `project/SparkBuild.scala`, then rebuilding Spark.
+You can change the version by setting the `SPARK_HADOOP_VERSION` environment
+when building Spark.
 
+For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop
+versions without YARN, use:
 
-## Configuration
+    # Apache Hadoop 1.2.1
+    $ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
+
+    # Cloudera CDH 4.2.0 with MapReduce v1
+    $ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly
+
+For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
+with YARN, also set `SPARK_YARN=true`:
+
+    # Apache Hadoop 2.0.5-alpha
+    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
+
+    # Cloudera CDH 4.2.0 with MapReduce v2
+    $ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly
 
-Please refer to the "Configuration" guide in the online documentation for a
-full overview on how to configure Spark. At the minimum, you will need to
-create a `conf/spark-env.sh` script (copy `conf/spark-env.sh.template`) and
-set the following two variables:
+For convenience, these variables may also be set through the `conf/spark-env.sh` file
+described below.
 
-- `SCALA_HOME`: Location where Scala is installed.
+When developing a Spark application, specify the Hadoop version by adding the
+"hadoop-client" artifact to your project's dependencies. For example, if you're
+using Hadoop 1.0.1 and build your application using SBT, add this entry to
+`libraryDependencies`:
+
+    "org.apache.hadoop" % "hadoop-client" % "1.2.1"
+
+If your project is built with Maven, add this to your POM file's `<dependencies>` section:
+
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <version>1.2.1</version>
+    </dependency>
+
+
+## Configuration
 
-- `MESOS_NATIVE_LIBRARY`: Your Mesos library (only needed if you want to run
-  on Mesos). For example, this might be `/usr/local/lib/libmesos.so` on Linux.
+Please refer to the [Configuration guide](http://spark.incubator.apache.org/docs/latest/configuration.html)
+in the online documentation for an overview on how to configure Spark.
 
 
 ## Contributing to Spark