Various broken links in documentation

docs/bagel-programming-guide.md

@@ -106,7 +106,7 @@ _Example_
 
 ## Operations
 
-Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/incubator-spark/blob/master/bagel/src/main/scala/spark/bagel/Bagel.scala) for details.
+Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/incubator-spark/blob/master/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala) for details.
 
 ### Actions
 

docs/hadoop-third-party-distributions.md

@@ -10,7 +10,7 @@ with these distributions:
 # Compile-time Hadoop Version
 
 When compiling Spark, you'll need to 
-[set the SPARK_HADOOP_VERSION flag](http://localhost:4000/index.html#a-note-about-hadoop-versions):
+[set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions):
 
     SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
 

docs/index.md

@@ -58,7 +58,7 @@ By default, Spark links to Hadoop 1.0.4. You can change this by setting the
 
     SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly
 
-In addition, if you wish to run Spark on [YARN](running-on-yarn.md), set
+In addition, if you wish to run Spark on [YARN](running-on-yarn.html), set
 `SPARK_YARN` to `true`:
 
     SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly

docs/job-scheduling.md

@@ -91,7 +91,7 @@ The fair scheduler also supports grouping jobs into _pools_, and setting differe
 (e.g. weight) for each pool. This can be useful to create a "high-priority" pool for more important jobs,
 for example, or to group the jobs of each user together and give _users_ equal shares regardless of how
 many concurrent jobs they have instead of giving _jobs_ equal shares. This approach is modeled after the
-[Hadoop Fair Scheduler](http://hadoop.apache.org/docs/stable/fair_scheduler.html).
+[Hadoop Fair Scheduler](http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/FairScheduler.html).
 
 Without any intervention, newly submitted jobs go into a _default pool_, but jobs' pools can be set by
 adding the `spark.scheduler.pool` "local property" to the SparkContext in the thread that's submitting them.

docs/running-on-yarn.md

@@ -116,7 +116,7 @@ For example:
 
 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.
+See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process.
 
 # Important Notes
 
@@ -124,4 +124,4 @@ See [Building Spark with Maven](building-with-maven.md) for instructions on how
 - 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.  
\ No newline at end of file
+- 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.  

docs/streaming-programming-guide.md

@@ -214,7 +214,7 @@ ssc.stop()
 {% endhighlight %}
 
 # Example
-A simple example to start off is the [NetworkWordCount](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/NetworkWordCount.scala` .
+A simple example to start off is the [NetworkWordCount](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala` .
 
 {% highlight scala %}
 import org.apache.spark.streaming.{Seconds, StreamingContext}
@@ -283,7 +283,7 @@ Time: 1357008430000 ms
 </td>
 </table>
 
-You can find more examples in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
+You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
 
 # DStream Persistence
 Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`.
@@ -483,7 +483,7 @@ Similar to [Spark's Java API](java-programming-guide.html), we also provide a Ja
 1. Functions for transformations must be implemented as subclasses of [Function](api/core/index.html#org.apache.spark.api.java.function.Function) and [Function2](api/core/index.html#org.apache.spark.api.java.function.Function2)
 1. Unlike the Scala API, the Java API handles DStreams for key-value pairs using a separate [JavaPairDStream](api/streaming/index.html#org.apache.spark.streaming.api.java.JavaPairDStream) class(similar to [JavaRDD and JavaPairRDD](java-programming-guide.html#rdd-classes). DStream functions like `map` and `filter` are implemented separately by JavaDStreams and JavaPairDStream to return DStreams of appropriate types.
 
-Spark's [Java Programming Guide](java-programming-guide.html) gives more ideas about using the Java API. To extends the ideas presented for the RDDs to DStreams, we present parts of the Java version of the same NetworkWordCount example presented above. The full source code is given at `<spark repo>/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java`
+Spark's [Java Programming Guide](java-programming-guide.html) gives more ideas about using the Java API. To extends the ideas presented for the RDDs to DStreams, we present parts of the Java version of the same NetworkWordCount example presented above. The full source code is given at `<spark repo>/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java`
 
 The streaming context and the socket stream from input source is started by using a `JavaStreamingContext`, that has the same parameters and provides the same input streams as its Scala counterpart.
 
@@ -527,5 +527,5 @@ JavaPairDStream<String, Integer> wordCounts = words.map(
 # Where to Go from Here
 
 * API docs - [Scala](api/streaming/index.html#org.apache.spark.streaming.package) and [Java](api/streaming/index.html#org.apache.spark.streaming.api.java.package)
-* More examples - [Scala](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/spark/streaming/examples)
+* More examples - [Scala](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples) and [Java](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/org/apache/spark/streaming/examples)
 * [Paper describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)