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<h2>Apache Spark Examples</h2>
<p>These examples give a quick overview of the Spark API.
Spark is built on the concept of <em>distributed datasets</em>, which contain arbitrary Java or
Python objects. You create a dataset from external data, then apply parallel operations
to it. The building block of the Spark API is its <a href="http://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds">RDD API</a>.
In the RDD API,
there are two types of operations: <em>transformations</em>, which define a new dataset based on previous ones,
and <em>actions</em>, which kick off a job to execute on a cluster.
On top of Spark’s RDD API, high level APIs are provided, e.g.
<a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#dataframes">DataFrame API</a> and
<a href="http://spark.apache.org/docs/latest/mllib-guide.html">Machine Learning API</a>.
These high level APIs provide a concise way to conduct certain data operations.
In this page, we will show examples using RDD API as well as examples using high level APIs.</p>
<h2>RDD API Examples</h2>
<h3>Word Count</h3>
<p>In this example, we use a few transformations to build a dataset of (String, Int) pairs called <code>counts</code> and then save it to a file.</p>
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<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">text_file</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"hdfs://..."</span><span class="p">)</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">text_file</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">" "</span><span class="p">))</span> \
<span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">word</span><span class="p">:</span> <span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> \
<span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>
<span class="n">counts</span><span class="o">.</span><span class="n">saveAsTextFile</span><span class="p">(</span><span class="s">"hdfs://..."</span><span class="p">)</span></code></pre></figure>
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<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">textFile</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">)</span>
<span class="k">val</span> <span class="n">counts</span> <span class="k">=</span> <span class="n">textFile</span><span class="o">.</span><span class="n">flatMap</span><span class="o">(</span><span class="n">line</span> <span class="k">=></span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="s">" "</span><span class="o">))</span>
<span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">word</span> <span class="k">=></span> <span class="o">(</span><span class="n">word</span><span class="o">,</span> <span class="mi">1</span><span class="o">))</span>
<span class="o">.</span><span class="n">reduceByKey</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)</span>
<span class="n">counts</span><span class="o">.</span><span class="n">saveAsTextFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">)</span></code></pre></figure>
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<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">textFile</span> <span class="o">=</span> <span class="n">sc</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">textFile</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="nf">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">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">pairs</span> <span class="o">=</span> <span class="n">words</span><span class="o">.</span><span class="na">mapToPair</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="nf">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="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">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="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">pairs</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">a</span><span class="o">,</span> <span class="n">Integer</span> <span class="n">b</span><span class="o">)</span> <span class="o">{</span> <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="o">;</span> <span class="o">}</span>
<span class="o">});</span>
<span class="n">counts</span><span class="o">.</span><span class="na">saveAsTextFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">);</span></code></pre></figure>
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<h3>Pi Estimation</h3>
<p>Spark can also be used for compute-intensive tasks. This code estimates <span style="font-family: serif; font-size: 120%;">π</span> by "throwing darts" at a circle. We pick random points in the unit square ((0, 0) to (1,1)) and see how many fall in the unit circle. The fraction should be <span style="font-family: serif; font-size: 120%;">π / 4</span>, so we use this to get our estimate.</p>
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<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">random</span><span class="p">(),</span> <span class="n">random</span><span class="p">()</span>
<span class="k">return</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="o">*</span><span class="n">y</span> <span class="o"><</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">count</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="nb">xrange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">NUM_SAMPLES</span><span class="p">))</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span> \
<span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"Pi is roughly </span><span class="si">%f</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="mf">4.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="n">NUM_SAMPLES</span><span class="p">)</span></code></pre></figure>
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<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">count</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="mi">1</span> <span class="n">to</span> <span class="nc">NUM_SAMPLES</span><span class="o">).</span><span class="n">map</span><span class="o">{</span><span class="n">i</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">x</span> <span class="k">=</span> <span class="nc">Math</span><span class="o">.</span><span class="n">random</span><span class="o">()</span>
<span class="k">val</span> <span class="n">y</span> <span class="k">=</span> <span class="nc">Math</span><span class="o">.</span><span class="n">random</span><span class="o">()</span>
<span class="k">if</span> <span class="o">(</span><span class="n">x</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="o">*</span><span class="n">y</span> <span class="o"><</span> <span class="mi">1</span><span class="o">)</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span>
<span class="o">}.</span><span class="n">reduce</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Pi is roughly "</span> <span class="o">+</span> <span class="mf">4.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="nc">NUM_SAMPLES</span><span class="o">)</span></code></pre></figure>
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</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">List</span><span class="o"><</span><span class="n">Integer</span><span class="o">></span> <span class="n">l</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><</span><span class="n">Integer</span><span class="o">>(</span><span class="n">NUM_SAMPLES</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">NUM_SAMPLES</span><span class="o">;</span> <span class="n">i</span><span class="o">++)</span> <span class="o">{</span>
<span class="n">l</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">i</span><span class="o">);</span>
<span class="o">}</span>
<span class="kt">long</span> <span class="n">count</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">l</span><span class="o">).</span><span class="na">filter</span><span class="o">(</span><span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">Integer</span><span class="o">,</span> <span class="n">Boolean</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Boolean</span> <span class="nf">call</span><span class="o">(</span><span class="n">Integer</span> <span class="n">i</span><span class="o">)</span> <span class="o">{</span>
<span class="kt">double</span> <span class="n">x</span> <span class="o">=</span> <span class="n">Math</span><span class="o">.</span><span class="na">random</span><span class="o">();</span>
<span class="kt">double</span> <span class="n">y</span> <span class="o">=</span> <span class="n">Math</span><span class="o">.</span><span class="na">random</span><span class="o">();</span>
<span class="k">return</span> <span class="n">x</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="o">*</span><span class="n">y</span> <span class="o"><</span> <span class="mi">1</span><span class="o">;</span>
<span class="o">}</span>
<span class="o">}).</span><span class="na">count</span><span class="o">();</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Pi is roughly "</span> <span class="o">+</span> <span class="mf">4.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="n">NUM_SAMPLES</span><span class="o">);</span></code></pre></figure>
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<h2>DataFrame API Examples</h2>
<p>
In Spark, a <a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#dataframes">DataFrame</a>
is a distributed collection of data organized into named columns.
Users can use DataFrame API to perform various relational operations on both external
data sources and Spark’s built-in distributed collections without providing specific procedures for processing data.
Also, programs based on DataFrame API will be automatically optimized by Spark’s built-in optimizer, Catalyst.
</p>
<h3>Text Search</h3>
<p>In this example, we search through the error messages in a log file.</p>
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<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">textFile</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"hdfs://..."</span><span class="p">)</span>
<span class="c"># Creates a DataFrame having a single column named "line"</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">textFile</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="n">Row</span><span class="p">(</span><span class="n">r</span><span class="p">))</span><span class="o">.</span><span class="n">toDF</span><span class="p">([</span><span class="s">"line"</span><span class="p">])</span>
<span class="n">errors</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s">"line"</span><span class="p">)</span><span class="o">.</span><span class="n">like</span><span class="p">(</span><span class="s">"</span><span class="si">%E</span><span class="s">RROR%"</span><span class="p">))</span>
<span class="c"># Counts all the errors</span>
<span class="n">errors</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="c"># Counts errors mentioning MySQL</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s">"line"</span><span class="p">)</span><span class="o">.</span><span class="n">like</span><span class="p">(</span><span class="s">"%MySQL%"</span><span class="p">))</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="c"># Fetches the MySQL errors as an array of strings</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s">"line"</span><span class="p">)</span><span class="o">.</span><span class="n">like</span><span class="p">(</span><span class="s">"%MySQL%"</span><span class="p">))</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">textFile</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"hdfs://..."</span><span class="o">)</span>
<span class="c1">// Creates a DataFrame having a single column named "line"</span>
<span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">textFile</span><span class="o">.</span><span class="n">toDF</span><span class="o">(</span><span class="s">"line"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">errors</span> <span class="k">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="n">like</span><span class="o">(</span><span class="s">"%ERROR%"</span><span class="o">))</span>
<span class="c1">// Counts all the errors</span>
<span class="n">errors</span><span class="o">.</span><span class="n">count</span><span class="o">()</span>
<span class="c1">// Counts errors mentioning MySQL</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="n">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="n">count</span><span class="o">()</span>
<span class="c1">// Fetches the MySQL errors as an array of strings</span>
<span class="n">errors</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="n">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="n">collect</span><span class="o">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Creates a DataFrame having a single column named "line"</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">textFile</span> <span class="o">=</span> <span class="n">sc</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">Row</span><span class="o">></span> <span class="n">rowRDD</span> <span class="o">=</span> <span class="n">textFile</span><span class="o">.</span><span class="na">map</span><span class="o">(</span>
<span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Row</span><span class="o">>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Row</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">line</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
<span class="k">return</span> <span class="n">RowFactory</span><span class="o">.</span><span class="na">create</span><span class="o">(</span><span class="n">line</span><span class="o">);</span>
<span class="o">}</span>
<span class="o">});</span>
<span class="n">List</span><span class="o"><</span><span class="n">StructField</span><span class="o">></span> <span class="n">fields</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><</span><span class="n">StructField</span><span class="o">>();</span>
<span class="n">fields</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">DataTypes</span><span class="o">.</span><span class="na">createStructField</span><span class="o">(</span><span class="s">"line"</span><span class="o">,</span> <span class="n">DataTypes</span><span class="o">.</span><span class="na">StringType</span><span class="o">,</span> <span class="kc">true</span><span class="o">));</span>
<span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="n">DataTypes</span><span class="o">.</span><span class="na">createStructType</span><span class="o">(</span><span class="n">fields</span><span class="o">);</span>
<span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rowRDD</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>
<span class="n">DataFrame</span> <span class="n">errors</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="na">like</span><span class="o">(</span><span class="s">"%ERROR%"</span><span class="o">));</span>
<span class="c1">// Counts all the errors</span>
<span class="n">errors</span><span class="o">.</span><span class="na">count</span><span class="o">();</span>
<span class="c1">// Counts errors mentioning MySQL</span>
<span class="n">errors</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="na">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="na">count</span><span class="o">();</span>
<span class="c1">// Fetches the MySQL errors as an array of strings</span>
<span class="n">errors</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="n">col</span><span class="o">(</span><span class="s">"line"</span><span class="o">).</span><span class="na">like</span><span class="o">(</span><span class="s">"%MySQL%"</span><span class="o">)).</span><span class="na">collect</span><span class="o">();</span></code></pre></figure>
</div>
</div>
</div>
<h3>Simple Data Operations</h3>
<p>
In this example, we read a table stored in a database and calculate the number of people for every age.
Finally, we save the calculated result to S3 in the format of JSON.
A simple MySQL table "people" is used in the example and this table has two columns,
"name" and "age".
</p>
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<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Creates a DataFrame based on a table named "people"</span>
<span class="c"># stored in a MySQL database.</span>
<span class="n">url</span> <span class="o">=</span> \
<span class="s">"jdbc:mysql://yourIP:yourPort/test?user=yourUsername;password=yourPassword"</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span> \
<span class="o">.</span><span class="n">read</span> \
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s">"jdbc"</span><span class="p">)</span> \
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s">"url"</span><span class="p">,</span> <span class="n">url</span><span class="p">)</span> \
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s">"dbtable"</span><span class="p">,</span> <span class="s">"people"</span><span class="p">)</span> \
<span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="c"># Looks the schema of this DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
<span class="c"># Counts people by age</span>
<span class="n">countsByAge</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s">"age"</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c"># Saves countsByAge to S3 in the JSON format.</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s">"json"</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">"s3a://..."</span><span class="p">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// Creates a DataFrame based on a table named "people"</span>
<span class="c1">// stored in a MySQL database.</span>
<span class="k">val</span> <span class="n">url</span> <span class="k">=</span>
<span class="s">"jdbc:mysql://yourIP:yourPort/test?user=yourUsername;password=yourPassword"</span>
<span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span>
<span class="o">.</span><span class="n">read</span>
<span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="s">"jdbc"</span><span class="o">)</span>
<span class="o">.</span><span class="n">option</span><span class="o">(</span><span class="s">"url"</span><span class="o">,</span> <span class="n">url</span><span class="o">)</span>
<span class="o">.</span><span class="n">option</span><span class="o">(</span><span class="s">"dbtable"</span><span class="o">,</span> <span class="s">"people"</span><span class="o">)</span>
<span class="o">.</span><span class="n">load</span><span class="o">()</span>
<span class="c1">// Looks the schema of this DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
<span class="c1">// Counts people by age</span>
<span class="k">val</span> <span class="n">countsByAge</span> <span class="k">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="s">"age"</span><span class="o">).</span><span class="n">count</span><span class="o">()</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">show</span><span class="o">()</span>
<span class="c1">// Saves countsByAge to S3 in the JSON format.</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="s">"json"</span><span class="o">).</span><span class="n">save</span><span class="o">(</span><span class="s">"s3a://..."</span><span class="o">)</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Creates a DataFrame based on a table named "people"</span>
<span class="c1">// stored in a MySQL database.</span>
<span class="n">String</span> <span class="n">url</span> <span class="o">=</span>
<span class="s">"jdbc:mysql://yourIP:yourPort/test?user=yourUsername;password=yourPassword"</span><span class="o">;</span>
<span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span>
<span class="o">.</span><span class="na">read</span><span class="o">()</span>
<span class="o">.</span><span class="na">format</span><span class="o">(</span><span class="s">"jdbc"</span><span class="o">)</span>
<span class="o">.</span><span class="na">option</span><span class="o">(</span><span class="s">"url"</span><span class="o">,</span> <span class="n">url</span><span class="o">)</span>
<span class="o">.</span><span class="na">option</span><span class="o">(</span><span class="s">"dbtable"</span><span class="o">,</span> <span class="s">"people"</span><span class="o">)</span>
<span class="o">.</span><span class="na">load</span><span class="o">();</span>
<span class="c1">// Looks the schema of this DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="na">printSchema</span><span class="o">();</span>
<span class="c1">// Counts people by age</span>
<span class="n">DataFrame</span> <span class="n">countsByAge</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="na">groupBy</span><span class="o">(</span><span class="s">"age"</span><span class="o">).</span><span class="na">count</span><span class="o">();</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="na">show</span><span class="o">();</span>
<span class="c1">// Saves countsByAge to S3 in the JSON format.</span>
<span class="n">countsByAge</span><span class="o">.</span><span class="na">write</span><span class="o">().</span><span class="na">format</span><span class="o">(</span><span class="s">"json"</span><span class="o">).</span><span class="na">save</span><span class="o">(</span><span class="s">"s3a://..."</span><span class="o">);</span></code></pre></figure>
</div>
</div>
</div>
<h2>Machine Learning Example</h2>
<p>
<a href="http://spark.apache.org/docs/latest/mllib-guide.html">MLlib</a>, Spark’s Machine Learning (ML) library, provides many distributed ML algorithms.
These algorithms cover tasks such as feature extraction, classification, regression, clustering,
recommendation, and more.
MLlib also provides tools such as ML Pipelines for building workflows, CrossValidator for tuning parameters,
and model persistence for saving and loading models.
</p>
<h3>Prediction with Logistic Regression</h3>
<p>
In this example, we take a dataset of labels and feature vectors.
We learn to predict the labels from feature vectors using the Logistic Regression algorithm.
</p>
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</ul>
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<div class="tab-pane tab-pane-python active">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Every record of this DataFrame contains the label and</span>
<span class="c"># features represented by a vector.</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">])</span>
<span class="c"># Set parameters for the algorithm.</span>
<span class="c"># Here, we limit the number of iterations to 10.</span>
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c"># Fit the model to the data.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="c"># Given a dataset, predict each point's label, and show the results.</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-scala">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// Every record of this DataFrame contains the label and</span>
<span class="c1">// features represented by a vector.</span>
<span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="o">(</span><span class="n">data</span><span class="o">).</span><span class="n">toDF</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">)</span>
<span class="c1">// Set parameters for the algorithm.</span>
<span class="c1">// Here, we limit the number of iterations to 10.</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">().</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="c1">// Fit the model to the data.</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">df</span><span class="o">)</span>
<span class="c1">// Inspect the model: get the feature weights.</span>
<span class="k">val</span> <span class="n">weights</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">weights</span>
<span class="c1">// Given a dataset, predict each point's label, and show the results.</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">df</span><span class="o">).</span><span class="n">show</span><span class="o">()</span></code></pre></figure>
</div>
</div>
<div class="tab-pane tab-pane-java">
<div class="code code-tab">
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="c1">// Every record of this DataFrame contains the label and</span>
<span class="c1">// features represented by a vector.</span>
<span class="n">StructType</span> <span class="n">schema</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StructType</span><span class="o">(</span><span class="k">new</span> <span class="n">StructField</span><span class="o">[]{</span>
<span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">"label"</span><span class="o">,</span> <span class="n">DataTypes</span><span class="o">.</span><span class="na">DoubleType</span><span class="o">,</span> <span class="kc">false</span><span class="o">,</span> <span class="n">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">()),</span>
<span class="k">new</span> <span class="nf">StructField</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">VectorUDT</span><span class="o">(),</span> <span class="kc">false</span><span class="o">,</span> <span class="n">Metadata</span><span class="o">.</span><span class="na">empty</span><span class="o">()),</span>
<span class="o">});</span>
<span class="n">DataFrame</span> <span class="n">df</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">data</span><span class="o">,</span> <span class="n">schema</span><span class="o">);</span>
<span class="c1">// Set parameters for the algorithm.</span>
<span class="c1">// Here, we limit the number of iterations to 10.</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">().</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span>
<span class="c1">// Fit the model to the data.</span>
<span class="n">LogisticRegressionModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">df</span><span class="o">);</span>
<span class="c1">// Inspect the model: get the feature weights.</span>
<span class="n">Vector</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">weights</span><span class="o">();</span>
<span class="c1">// Given a dataset, predict each point's label, and show the results.</span>
<span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">df</span><span class="o">).</span><span class="na">show</span><span class="o">();</span></code></pre></figure>
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<p><a name="additional"></a></p>
<h1>Additional Examples</h1>
<p>Many additional examples are distributed with Spark:</p>
<ul>
<li>Basic Spark: <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples">Scala examples</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples">Java examples</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/python">Python examples</a></li>
<li>Spark Streaming: <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming">Scala examples</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples/streaming">Java examples</a></li>
</ul>
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