Merge branch 'master' into spark-1002-remove-jars

1 files changed, 38 insertions, 14 deletions

diff --git a/docs/quick-start.md b/docs/quick-start.md
index 5c55def398..1882ea75c0 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -124,7 +124,7 @@ object SimpleApp {
 }
 {% endhighlight %}
 
-This program simply counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
+This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
 
 This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on:
 
@@ -193,7 +193,7 @@ public class SimpleApp {
 }
 {% endhighlight %}
 
-This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail.
+This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail.
 
 To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
 
@@ -224,11 +224,11 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a
 If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS:
 
 {% highlight xml %}
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-client</artifactId>
-      <version>...</version>
-    </dependency>
+<dependency>
+  <groupId>org.apache.hadoop</groupId>
+  <artifactId>hadoop-client</artifactId>
+  <version>...</version>
+</dependency>
 {% endhighlight %}
 
 We lay out these files according to the canonical Maven directory structure:
@@ -270,7 +270,7 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs)
 {% endhighlight %}
 
 
-This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file.
+This program just counts the number of lines containing 'a' and the number containing 'b' in a text file.
 Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. 
 As with the Scala and Java examples, we use a SparkContext to create RDDs.
 We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference.
@@ -301,14 +301,38 @@ assembly jar (or "uber" jar) containing your code and its dependencies. Both
 have assembly plugins. When creating assembly jars, list Spark 
 itself as a `provided` dependency; it need not be bundled since it is 
 already present on the slaves. Once you have an assembled jar, 
-add it to the SparkContext as shown here. It is also possible to submit 
-your dependent jars one-by-one when creating a SparkContext.
+add it to the SparkContext as shown here. It is also possible to add
+your dependent jars one-by-one using the `addJar` method of `SparkContext`.
+
+For Python, you can use the `pyFiles` argument of SparkContext
+or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed.
 
 ### Setting Configuration Options
-Spark includes several configuration options which influence the behavior
-of your application. These should be set as 
-[JVM system properties](configuration.html#system-properties) in your 
-program. The options will be captured and shipped to all slave nodes.
+Spark includes several [configuration options](configuration.html#spark-properties)
+that influence the behavior of your application.
+These should be set by building a [SparkConf](api/core/index.html#org.apache.spark.SparkConf)
+object and passing it to the SparkContext constructor.
+For example, in Java and Scala, you can do:
+
+{% highlight scala %}
+import org.apache.spark.{SparkConf, SparkContext}
+val conf = new SparkConf()
+             .setMaster("local")
+             .setAppName("My application")
+             .set("spark.executor.memory", "1g")
+val sc = new SparkContext(conf)
+{% endhighlight %}
+
+Or in Python:
+
+{% highlight scala %}
+from pyspark import SparkConf, SparkContext
+conf = SparkConf()
+conf.setMaster("local")
+conf.setAppName("My application")
+conf.set("spark.executor.memory", "1g"))
+sc = SparkContext(conf = conf)
+{% endhighlight %}
 
 ### Accessing Hadoop Filesystems