[SPARK-1105] fix site scala version error in docs

https://spark-project.atlassian.net/browse/SPARK-1105

fix site scala version error

Author: CodingCat <zhunansjtu@gmail.com>

Closes #618 from CodingCat/doc_version and squashes the following commits:

39bb8aa [CodingCat] more fixes
65bedb0 [CodingCat] fix site scala version error in doc

docs/_config.yml

@@ -5,7 +5,8 @@ markdown: kramdown
 # of Spark, Scala, and Mesos.
 SPARK_VERSION: 1.0.0-incubating-SNAPSHOT
 SPARK_VERSION_SHORT: 1.0.0
-SCALA_VERSION: "2.10"
+SCALA_BINARY_VERSION: "2.10"
+SCALA_VERSION: "2.10.3"
 MESOS_VERSION: 0.13.0
 SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net
 SPARK_GITHUB_URL: https://github.com/apache/incubator-spark

docs/bagel-programming-guide.md

@@ -16,7 +16,7 @@ This guide shows the programming model and features of Bagel by walking through
 To use Bagel in your program, add the following SBT or Maven dependency:
 
     groupId = org.apache.spark
-    artifactId = spark-bagel_{{site.SCALA_VERSION}}
+    artifactId = spark-bagel_{{site.SCALA_BINARY_VERSION}}
     version = {{site.SPARK_VERSION}}
 
 # Programming Model

docs/building-with-maven.md

@@ -17,10 +17,10 @@ You'll need to configure Maven to use more memory than usual by setting `MAVEN_O
 
 If you don't run this, you may see errors like the following:
 
-    [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_VERSION}}/classes...
+    [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_BINARY_VERSION}}/classes...
     [ERROR] PermGen space -> [Help 1]
 
-    [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_VERSION}}/classes...
+    [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_BINARY_VERSION}}/classes...
     [ERROR] Java heap space -> [Help 1]
 
 You can fix this by setting the `MAVEN_OPTS` variable as discussed before.

docs/index.md

@@ -19,7 +19,7 @@ Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with
 
     sbt/sbt assembly
 
-For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you will need to use this same version of Scala in your own program -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
+For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
 
 # Running the Examples and Shell
 

docs/quick-start.md

@@ -115,7 +115,7 @@ object SimpleApp {
   def main(args: Array[String]) {
     val logFile = "$YOUR_SPARK_HOME/README.md" // Should be some file on your system
     val sc = new SparkContext("local", "Simple App", "YOUR_SPARK_HOME",
-      List("target/scala-{{site.SCALA_VERSION}}/simple-project_{{site.SCALA_VERSION}}-1.0.jar"))
+      List("target/scala-{{site.SCALA_BINARY_VERSION}}/simple-project_{{site.SCALA_BINARY_VERSION}}-1.0.jar"))
     val logData = sc.textFile(logFile, 2).cache()
     val numAs = logData.filter(line => line.contains("a")).count()
     val numBs = logData.filter(line => line.contains("b")).count()
@@ -214,7 +214,7 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a
   <dependencies>
     <dependency> <!-- Spark dependency -->
       <groupId>org.apache.spark</groupId>
-      <artifactId>spark-core_{{site.SCALA_VERSION}}</artifactId>
+      <artifactId>spark-core_{{site.SCALA_BINARY_VERSION}}</artifactId>
       <version>{{site.SPARK_VERSION}}</version>
     </dependency>
   </dependencies>

docs/running-on-yarn.md

@@ -15,7 +15,7 @@ This can be built by setting the Hadoop version and `SPARK_YARN` environment var
     SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
 The assembled JAR will be something like this:
-`./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
+`./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
 
 The build process now also supports new YARN versions (2.2.x). See below.
 
@@ -25,7 +25,7 @@ The build process now also supports new YARN versions (2.2.x). See below.
 - The assembled jar can be installed into HDFS or used locally.
 - Your application code must be packaged into a separate JAR file.
 
-If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
+If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_BINARY_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
 
 # Configuration
 
@@ -78,9 +78,9 @@ For example:
     $ cp conf/log4j.properties.template conf/log4j.properties
 
     # Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example
-    $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
+    $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
         ./bin/spark-class org.apache.spark.deploy.yarn.Client \
-          --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
+          --jar examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
           --class org.apache.spark.examples.SparkPi \
           --args yarn-standalone \
           --num-workers 3 \
@@ -117,13 +117,13 @@ In order to tune worker core/number/memory etc. You need to export environment v
 
 For example:
 
-    SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
-    SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
+    SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
+    SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
     ./bin/run-example org.apache.spark.examples.SparkPi yarn-client
 
 
-    SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
-    SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
+    SPARK_JAR=./assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
+    SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
     MASTER=yarn-client ./bin/spark-shell
 
 

docs/scala-programming-guide.md

@@ -17,12 +17,12 @@ This guide shows each of these features and walks through some samples. It assum
 
 # Linking with Spark
 
-Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you'll need to use this same version of Scala in your program -- newer major versions may not work.
+Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work.
 
 To write a Spark application, you need to add a dependency on Spark. If you use SBT or Maven, Spark is available through Maven Central at:
 
     groupId = org.apache.spark
-    artifactId = spark-core_{{site.SCALA_VERSION}}
+    artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
     version = {{site.SPARK_VERSION}} 
 
 In addition, if you wish to access an HDFS cluster, you need to add a dependency on `hadoop-client` for your version of HDFS:
@@ -31,7 +31,7 @@ In addition, if you wish to access an HDFS cluster, you need to add a dependency
     artifactId = hadoop-client
     version = <your-hdfs-version>
 
-For other build systems, you can run `sbt/sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
+For other build systems, you can run `sbt/sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
 
 Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
 

docs/streaming-programming-guide.md

@@ -275,23 +275,23 @@ To write your own Spark Streaming program, you will have to add the following de
  SBT or Maven project:
 
     groupId = org.apache.spark
-    artifactId = spark-streaming_{{site.SCALA_VERSION}}
+    artifactId = spark-streaming_{{site.SCALA_BINARY_VERSION}}
     version = {{site.SPARK_VERSION}}
 
 For ingesting data from sources like Kafka and Flume that are not present in the Spark
 Streaming core
  API, you will have to add the corresponding
-artifact `spark-streaming-xyz_{{site.SCALA_VERSION}}` to the dependencies. For example,
+artifact `spark-streaming-xyz_{{site.SCALA_BINARY_VERSION}}` to the dependencies. For example,
 some of the common ones are as follows.
 
 
 <table class="table">
 <tr><th>Source</th><th>Artifact</th></tr>
-<tr><td> Kafka </td><td> spark-streaming-kafka_{{site.SCALA_VERSION}} </td></tr>
-<tr><td> Flume </td><td> spark-streaming-flume_{{site.SCALA_VERSION}} </td></tr>
-<tr><td> Twitter </td><td> spark-streaming-twitter_{{site.SCALA_VERSION}} </td></tr>
-<tr><td> ZeroMQ </td><td> spark-streaming-zeromq_{{site.SCALA_VERSION}} </td></tr>
-<tr><td> MQTT </td><td> spark-streaming-mqtt_{{site.SCALA_VERSION}} </td></tr>
+<tr><td> Kafka </td><td> spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}} </td></tr>
+<tr><td> Flume </td><td> spark-streaming-flume_{{site.SCALA_BINARY_VERSION}} </td></tr>
+<tr><td> Twitter </td><td> spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}} </td></tr>
+<tr><td> ZeroMQ </td><td> spark-streaming-zeromq_{{site.SCALA_BINARY_VERSION}} </td></tr>
+<tr><td> MQTT </td><td> spark-streaming-mqtt_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> </td><td></td></tr>
 </table>
 
@@ -410,7 +410,7 @@ Scala and [JavaStreamingContext](api/streaming/index.html#org.apache.spark.strea
 Additional functionality for creating DStreams from sources such as Kafka, Flume, and Twitter
 can be imported by adding the right dependencies as explained in an
 [earlier](#linking) section. To take the
-case of Kafka, after adding the artifact `spark-streaming-kafka_{{site.SCALA_VERSION}}` to the
+case of Kafka, after adding the artifact `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` to the
 project dependencies, you can create a DStream from Kafka as
 
 <div class="codetabs">