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The main differences have to do with passing functions to RDD +operations (e.g. map) and handling RDDs of different types, as discussed next.</p> + +<h1 id="key-differences-in-the-java-api">Key Differences in the Java API</h1> + +<p>There are a few key differences between the Java and Scala APIs:</p> + +<ul> + <li>Java does not support anonymous or first-class functions, so functions must +be implemented by extending the +<a href="api/core/index.html#spark.api.java.function.Function"><code>spark.api.java.function.Function</code></a>, +<a href="api/core/index.html#spark.api.java.function.Function2"><code>Function2</code></a>, etc. +classes.</li> + <li>To maintain type safety, the Java API defines specialized Function and RDD +classes for key-value pairs and doubles. For example, +<a href="api/core/index.html#spark.api.java.JavaPairRDD"><code>JavaPairRDD</code></a> +stores key-value pairs.</li> + <li>RDD methods like <code>collect()</code> and <code>countByKey()</code> return Java collections types, +such as <code>java.util.List</code> and <code>java.util.Map</code>.</li> + <li>Key-value pairs, which are simply written as <code>(key, value)</code> in Scala, are represented +by the <code>scala.Tuple2</code> class, and need to be created using <code>new Tuple2<K, V>(key, value)</code>.</li> +</ul> + +<h2 id="rdd-classes">RDD Classes</h2> + +<p>Spark defines additional operations on RDDs of key-value pairs and doubles, such +as <code>reduceByKey</code>, <code>join</code>, and <code>stdev</code>.</p> + +<p>In the Scala API, these methods are automatically added using Scala’s +<a href="http://www.scala-lang.org/node/130">implicit conversions</a> mechanism.</p> + +<p>In the Java API, the extra methods are defined in the +<a href="api/core/index.html#spark.api.java.JavaPairRDD"><code>JavaPairRDD</code></a> +and <a href="api/core/index.html#spark.api.java.JavaDoubleRDD"><code>JavaDoubleRDD</code></a> +classes. RDD methods like <code>map</code> are overloaded by specialized <code>PairFunction</code> +and <code>DoubleFunction</code> classes, allowing them to return RDDs of the appropriate +types. Common methods like <code>filter</code> and <code>sample</code> are implemented by +each specialized RDD class, so filtering a <code>PairRDD</code> returns a new <code>PairRDD</code>, +etc (this acheives the “same-result-type” principle used by the <a href="http://docs.scala-lang.org/overviews/core/architecture-of-scala-collections.html">Scala collections +framework</a>).</p> + +<h2 id="function-classes">Function Classes</h2> + +<p>The following table lists the function classes used by the Java API. Each +class has a single abstract method, <code>call()</code>, that must be implemented.</p> + +<table class="table"> +<tr><th>Class</th><th>Function Type</th></tr> + +<tr><td>Function<T, R></td><td>T => R </td></tr> +<tr><td>DoubleFunction<T></td><td>T => Double </td></tr> +<tr><td>PairFunction<T, K, V></td><td>T => Tuple2<K, V> </td></tr> + +<tr><td>FlatMapFunction<T, R></td><td>T => Iterable<R> </td></tr> +<tr><td>DoubleFlatMapFunction<T></td><td>T => Iterable<Double> </td></tr> +<tr><td>PairFlatMapFunction<T, K, V></td><td>T => Iterable<Tuple2<K, V>> </td></tr> + +<tr><td>Function2<T1, T2, R></td><td>T1, T2 => R (function of two arguments)</td></tr> +</table> + +<h2 id="storage-levels">Storage Levels</h2> + +<p>RDD <a href="scala-programming-guide.html#rdd-persistence">storage level</a> constants, such as <code>MEMORY_AND_DISK</code>, are +declared in the <a href="api/core/index.html#spark.api.java.StorageLevels">spark.api.java.StorageLevels</a> class.</p> + +<h1 id="other-features">Other Features</h1> + +<p>The Java API supports other Spark features, including +<a href="scala-programming-guide.html#accumulators">accumulators</a>, +<a href="scala-programming-guide.html#broadcast-variables">broadcast variables</a>, and +<a href="scala-programming-guide.html#rdd-persistence">caching</a>.</p> + +<h1 id="example">Example</h1> + +<p>As an example, we will implement word count using the Java API.</p> + +<div class="highlight"><pre><code class="java"><span class="kn">import</span> <span class="nn">spark.api.java.*</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">spark.api.java.function.*</span><span class="o">;</span> + +<span class="n">JavaSparkContext</span> <span class="n">sc</span> <span class="o">=</span> <span class="k">new</span> <span class="n">JavaSparkContext</span><span class="o">(...);</span> +<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">lines</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">);</span> +<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">words</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="na">flatMap</span><span class="o">(</span> + <span class="k">new</span> <span class="n">FlatMapFunction</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Iterable</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> + <span class="k">return</span> <span class="n">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">" "</span><span class="o">));</span> + <span class="o">}</span> + <span class="o">}</span> +<span class="o">);</span> +</code></pre> +</div> + +<p>The word count program starts by creating a <code>JavaSparkContext</code>, which accepts +the same parameters as its Scala counterpart. <code>JavaSparkContext</code> supports the +same data loading methods as the regular <code>SparkContext</code>; here, <code>textFile</code> +loads lines from text files stored in HDFS.</p> + +<p>To split the lines into words, we use <code>flatMap</code> to split each line on +whitespace. <code>flatMap</code> is passed a <code>FlatMapFunction</code> that accepts a string and +returns an <code>java.lang.Iterable</code> of strings.</p> + +<p>Here, the <code>FlatMapFunction</code> was created inline; another option is to subclass +<code>FlatMapFunction</code> and pass an instance to <code>flatMap</code>:</p> + +<div class="highlight"><pre><code class="java"><span class="kd">class</span> <span class="nc">Split</span> <span class="kd">extends</span> <span class="n">FlatMapFunction</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">></span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Iterable</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> + <span class="k">return</span> <span class="n">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">" "</span><span class="o">));</span> + <span class="o">}</span> +<span class="o">);</span> +<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">words</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="na">flatMap</span><span class="o">(</span><span class="k">new</span> <span class="n">Split</span><span class="o">());</span> +</code></pre> +</div> + +<p>Continuing with the word count example, we map each word to a <code>(word, 1)</code> pair:</p> + +<div class="highlight"><pre><code class="java"><span class="kn">import</span> <span class="nn">scala.Tuple2</span><span class="o">;</span> +<span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="n">ones</span> <span class="o">=</span> <span class="n">words</span><span class="o">.</span><span class="na">map</span><span class="o">(</span> + <span class="k">new</span> <span class="n">PairFunction</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="n">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> + <span class="k">return</span> <span class="k">new</span> <span class="nf">Tuple2</span><span class="o">(</span><span class="n">s</span><span class="o">,</span> <span class="mi">1</span><span class="o">);</span> + <span class="o">}</span> + <span class="o">}</span> +<span class="o">);</span> +</code></pre> +</div> + +<p>Note that <code>map</code> was passed a <code>PairFunction<String, String, Integer></code> and +returned a <code>JavaPairRDD<String, Integer></code>.</p> + +<p>To finish the word count program, we will use <code>reduceByKey</code> to count the +occurrences of each word:</p> + +<div class="highlight"><pre><code class="java"><span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="n">counts</span> <span class="o">=</span> <span class="n">ones</span><span class="o">.</span><span class="na">reduceByKey</span><span class="o">(</span> + <span class="k">new</span> <span class="n">Function2</span><span class="o"><</span><span class="n">Integer</span><span class="o">,</span> <span class="n">Integer</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Integer</span> <span class="nf">call</span><span class="o">(</span><span class="n">Integer</span> <span class="n">i1</span><span class="o">,</span> <span class="n">Integer</span> <span class="n">i2</span><span class="o">)</span> <span class="o">{</span> + <span class="k">return</span> <span class="n">i1</span> <span class="o">+</span> <span class="n">i2</span><span class="o">;</span> + <span class="o">}</span> + <span class="o">}</span> +<span class="o">);</span> +</code></pre> +</div> + +<p>Here, <code>reduceByKey</code> is passed a <code>Function2</code>, which implements a function with +two arguments. The resulting <code>JavaPairRDD</code> contains <code>(word, count)</code> pairs.</p> + +<p>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:</p> + +<div class="highlight"><pre><code class="java"><span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="n">counts</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="na">flatMap</span><span class="o">(</span> + <span class="o">...</span> + <span class="o">).</span><span class="na">map</span><span class="o">(</span> + <span class="o">...</span> + <span class="o">).</span><span class="na">reduceByKey</span><span class="o">(</span> + <span class="o">...</span> + <span class="o">);</span> +</code></pre> +</div> + +<p>There is no performance difference between these approaches; the choice is +just a matter of style.</p> + +<h1 id="javadoc">Javadoc</h1> + +<p>We currently provide documentation for the Java API as Scaladoc, in the +<a href="api/core/index.html#spark.api.java.package"><code>spark.api.java</code> package</a>, because +some of the classes are implemented in Scala. The main downside is that the types and function +definitions show Scala syntax (for example, <code>def reduce(func: Function2[T, T]): T</code> instead of +<code>T reduce(Function2<T, T> func)</code>). +We hope to generate documentation with Java-style syntax in the future.</p> + +<h1 id="where-to-go-from-here">Where to Go from Here</h1> + +<p>Spark includes several sample programs using the Java API in +<code>examples/src/main/java</code>. You can run them by passing the class name to the +<code>run</code> script included in Spark – for example, <code>./run +spark.examples.JavaWordCount</code>. Each example program prints usage help when run +without any arguments.</p> + + <!-- Main hero unit for a primary marketing message or call to action --> + <!--<div class="hero-unit"> + <h1>Hello, world!</h1> + <p>This is a template for a simple marketing or informational website. It includes a large callout called the hero unit and three supporting pieces of content. 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