Merge pull request #203 from JoshRosen/docs/java-programming-guide

Java Programming Guide

docs/bagel-programming-guide.md

@@ -19,7 +19,7 @@ To write a Bagel application, you will need to add Spark, its dependencies, and
 
 ## Programming Model
 
-Bagel operates on a graph represented as a [distributed dataset]({{HOME_PATH}}programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.
+Bagel operates on a graph represented as a [distributed dataset]({{HOME_PATH}}scala-programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.
 
 For example, we can use Bagel to implement PageRank. Here, vertices represent pages, edges represent links between pages, and messages represent shares of PageRank sent to the pages that a particular page links to. 
 

docs/index.md

@@ -54,7 +54,7 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
 
 # Where to Go from Here
 
-* [Spark Programming Guide]({{HOME_PATH}}programming-guide.html): how to get started using Spark, and details on the API
+* [Spark Programming Guide]({{HOME_PATH}}scala-programming-guide.html): how to get started using Spark, and details on the API
 * [Running Spark on Amazon EC2]({{HOME_PATH}}ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
 * [Running Spark on Mesos]({{HOME_PATH}}running-on-mesos.html): instructions on how to deploy to a private cluster
 * [Running Spark on YARN]({{HOME_PATH}}running-on-yarn.html): instructions on how to run Spark on top of a YARN cluster

docs/java-programming-guide.md

@@ -2,4 +2,172 @@
 layout: global
 title: Java Programming Guide
 ---
-TODO: Write Java programming guide!
+
+The Spark Java API
+([spark.api.java]({{HOME_PATH}}api/core/index.html#spark.api.java.package)) defines
+[`JavaSparkContext`]({{HOME_PATH}}api/core/index.html#spark.api.java.JavaSparkContext) and
+[`JavaRDD`]({{HOME_PATH}}api/core/index.html#spark.api.java.JavaRDD) clases,
+which support
+the same methods as their Scala counterparts but take Java functions and return
+Java data and collection types.
+
+Because Java API is similar to the Scala API, this programming guide only
+covers Java-specific features;
+the [Scala Programming Guide]({{HOME_PATH}}scala-programming-guide.html)
+provides a more general introduction to Spark concepts and should be read
+first.
+
+
+# Key differences in the Java API
+There are a few key differences between the Java and Scala APIs:
+
+* Java does not support anonymous or first-class functions, so functions must
+  be implemented by extending the
+  [`spark.api.java.function.Function`]({{HOME_PATH}}api/core/index.html#spark.api.java.function.Function),
+  [`Function2`]({{HOME_PATH}}api/core/index.html#spark.api.java.function.Function2), etc.
+  classes.
+* To maintain type safety, the Java API defines specialized Function and RDD
+  classes for key-value pairs and doubles.
+* RDD methods like `collect` and `countByKey` return Java collections types,
+  such as `java.util.List` and `java.util.Map`.
+
+
+## RDD Classes
+Spark defines additional operations on RDDs of doubles and key-value pairs, such
+as `stdev` and `join`.
+
+In the Scala API, these methods are automatically added using Scala's
+[implicit conversions](http://www.scala-lang.org/node/130) mechanism.
+
+In the Java API, the extra methods are defined in
+[`JavaDoubleRDD`]({{HOME_PATH}}api/core/index.html#spark.api.java.JavaDoubleRDD) and
+[`JavaPairRDD`]({{HOME_PATH}}api/core/index.html#spark.api.java.JavaPairRDD)
+classes.  RDD methods like `map` are overloaded by specialized `PairFunction`
+and `DoubleFunction` classes, allowing them to return RDDs of the appropriate
+types.  Common methods like `filter` and `sample` are implemented by
+each specialized RDD class, so filtering a `PairRDD` returns a new `PairRDD`,
+etc (this acheives the "same-result-type" principle used by the [Scala collections
+framework](http://docs.scala-lang.org/overviews/core/architecture-of-scala-collections.html)).
+
+## Function Classes
+
+The following table lists the function classes used by the Java API.  Each
+class has a single abstract method, `call()`, that must be implemented.
+
+<table class="table">
+<tr><th>Class</th><th>Function Type</th></tr>
+
+<tr><td>Function&lt;T, R&gt;</td><td>T -&gt; R </td></tr>
+<tr><td>DoubleFunction&lt;T&gt;</td><td>T -&gt; Double </td></tr>
+<tr><td>PairFunction&lt;T, K, V&gt;</td><td>T -&gt; Tuple2&lt;K, V&gt; </td></tr>
+
+<tr><td>FlatMapFunction&lt;T, R&gt;</td><td>T -&gt; Iterable&lt;R&gt; </td></tr>
+<tr><td>DoubleFlatMapFunction&lt;T&gt;</td><td>T -&gt; Iterable&lt;Double&gt; </td></tr>
+<tr><td>PairFlatMapFunction&lt;T, K, V&gt;</td><td>T -&gt; Iterable&lt;Tuple2&lt;K, V&gt;&gt; </td></tr>
+
+<tr><td>Function2&lt;T1, T2, R&gt;</td><td>T1, T2 -&gt; R (function of two arguments)</td></tr>
+</table>
+
+# Other Features
+The Java API supports other Spark features, including
+[accumulators]({{HOME_PATH}}scala-programming-guide.html#accumulators),
+[broadcast variables]({{HOME_PATH}}scala-programming-guide.html#broadcast_variables), and
+[caching]({{HOME_PATH}}scala-programming-guide.html#caching).
+
+# Example
+
+As an example, we will implement word count using the Java API.
+
+{% highlight java %}
+import spark.api.java.*;
+import spark.api.java.function.*;
+
+JavaSparkContext sc = new JavaSparkContext(...);
+JavaRDD<String> lines = ctx.textFile("hdfs://...");
+JavaRDD<String> words = lines.flatMap(
+  new FlatMapFunction<String, String>() {
+    public Iterable<String> call(String s) {
+      return Arrays.asList(s.split(" "));
+    }
+  }
+);
+{% endhighlight %}
+
+The word count program starts by creating a `JavaSparkContext`, which accepts
+the same parameters as its Scala counterpart.  `JavaSparkContext` supports the
+same data loading methods as the regular `SparkContext`; here, `textFile`
+loads lines from text files stored in HDFS.
+
+To split the lines into words, we use `flatMap` to split each line on
+whitespace.  `flatMap` is passed a `FlatMapFunction` that accepts a string and
+returns an `java.lang.Iterable` of strings.
+
+Here, the `FlatMapFunction` was created inline; another option is to subclass
+`FlatMapFunction` and pass an instance to `flatMap`:
+
+{% highlight java %}
+class Split extends FlatMapFunction<String, String> {
+  public Iterable<String> call(String s) {
+    return Arrays.asList(s.split(" "));
+  }
+);
+JavaRDD<String> words = lines.flatMap(new Split());
+{% endhighlight %}
+
+Continuing with the word count example, we map each word to a `(word, 1)` pair:
+
+{% highlight java %}
+import scala.Tuple2;
+JavaPairRDD<String, Integer> ones = words.map(
+  new PairFunction<String, String, Integer>() {
+    public Tuple2<String, Integer> call(String s) {
+      return new Tuple2(s, 1);
+    }
+  }
+);
+{% endhighlight %}
+
+Note that `map` was passed a `PairFunction<String, String, Integer>` and
+returned a `JavaPairRDD<String, Integer>`.
+
+
+
+To finish the word count program, we will use `reduceByKey` to count the
+occurrences of each word:
+
+{% highlight java %}
+JavaPairRDD<String, Integer> counts = ones.reduceByKey(
+  new Function2<Integer, Integer, Integer>() {
+    public Integer call(Integer i1, Integer i2) {
+      return i1 + i2;
+    }
+  }
+);
+{% endhighlight %}
+
+Here, `reduceByKey` is passed a `Function2`, which implements a function with
+two arguments.  The resulting `JavaPairRDD` contains `(word, count)` pairs.
+
+In this example, we explicitly showed each intermediate RDD.  It is also
+possible to chain the RDD transformations, so the word count example could also
+be written as:
+
+{% highlight java %}
+JavaPairRDD<String, Integer> counts = lines.flatMap(
+    ...
+  ).map(
+    ...
+  ).reduceByKey(
+    ...
+  );
+{% endhighlight %}
+There is no performance difference between these approaches; the choice is
+a matter of style.
+
+
+# Where to go from here
+Spark includes several sample jobs using the Java API in
+`examples/src/main/java`.  You can run them by passing the class name to the
+`run` script included in Spark -- for example, `./run
+spark.examples.JavaWordCount`.  Each example program prints usage help when run
+without any arguments.