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Apache Spark™ - Unified Engine for large-scale data analytics
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<h1 style="max-width: 680px;">Unified engine for large-scale data analytics</h1>
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<h2>What is Apache Spark<span class="tm" style="bottom: 14px;">™</span>?</h2>
<div class="what-is-spark">Apache Spark<span class="tm" style="bottom: 7px;">™</span> is a multi-language engine for executing data engineering,
data science, and machine learning on single-node machines or clusters.
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<div class="apache-spark-motto">Simple.<br />Fast.<br />Scalable.<br />Unified.</div>
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<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">json</span><span class="p">(</span><span class="s">"logs.json"</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="n">where</span><span class="p">(</span><span class="s">"age > 21"</span><span class="p">).</span><span class="n">select</span><span class="p">(</span><span class="s">"name.first"</span><span class="p">).</span><span class="n">show</span><span class="p">()</span></code></pre></figure>
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<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="c1"># Every record contains a label and feature vector
</span><span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</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="c1"># Split the data into train/test datasets
</span><span class="n">train_df</span><span class="p">,</span> <span class="n">test_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">.</span><span class="n">randomSplit</span><span class="p">([.</span><span class="mi">80</span><span class="p">,</span> <span class="p">.</span><span class="mi">20</span><span class="p">],</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="c1"># Set hyperparameters for the algorithm
</span><span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">numTrees</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="c1"># Fit the model to the training data
</span><span class="n">model</span> <span class="o">=</span> <span class="n">rf</span><span class="p">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_df</span><span class="p">)</span>
<span class="c1"># Generate predictions on the test dataset.
</span><span class="n">model</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test_df</span><span class="p">).</span><span class="n">show</span><span class="p">()</span></code></pre></figure>
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<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">csv</span><span class="p">(</span><span class="s">"accounts.csv"</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c1"># Select subset of features and filter for balance > 0
</span><span class="n">filtered_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">.</span><span class="n">select</span><span class="p">(</span><span class="s">"AccountBalance"</span><span class="p">,</span> <span class="s">"CountOfDependents"</span><span class="p">).</span><span class="nb">filter</span><span class="p">(</span><span class="s">"AccountBalance > 0"</span><span class="p">)</span>
<span class="c1"># Generate summary statistics
</span><span class="n">filtered_df</span><span class="p">.</span><span class="n">summary</span><span class="p">().</span><span class="n">show</span><span class="p">()</span></code></pre></figure>
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<p>$ docker run -it --rm apache/spark /opt/spark/bin/spark-sql</p>
<p>spark-sql></p>
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<figure class="highlight"><pre><code class="language-sql" data-lang="sql"><span class="k">SELECT</span>
<span class="n">name</span><span class="p">.</span><span class="k">first</span> <span class="k">AS</span> <span class="n">first_name</span><span class="p">,</span>
<span class="n">name</span><span class="p">.</span><span class="k">last</span> <span class="k">AS</span> <span class="n">last_name</span><span class="p">,</span>
<span class="n">age</span>
<span class="k">FROM</span> <span class="n">json</span><span class="p">.</span><span class="nv">`logs.json`</span>
<span class="k">WHERE</span> <span class="n">age</span> <span class="o">></span> <span class="mi">21</span><span class="p">;</span></code></pre></figure>
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<p>$ docker run -it --rm apache/spark /opt/spark/bin/spark-shell</p>
<p>scala></p>
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<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="nv">df</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">read</span><span class="o">.</span><span class="py">json</span><span class="o">(</span><span class="s">"logs.json"</span><span class="o">)</span>
<span class="nv">df</span><span class="o">.</span><span class="py">where</span><span class="o">(</span><span class="s">"age > 21"</span><span class="o">)</span>
<span class="o">.</span><span class="py">select</span><span class="o">(</span><span class="s">"name.first"</span><span class="o">).</span><span class="py">show</span><span class="o">()</span></code></pre></figure>
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<p>$ docker run -it --rm apache/spark /opt/spark/bin/spark-shell</p>
<p>scala></p>
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<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="nc">Dataset</span> <span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">read</span><span class="o">().</span><span class="na">json</span><span class="o">(</span><span class="s">"logs.json"</span><span class="o">);</span>
<span class="n">df</span><span class="o">.</span><span class="na">where</span><span class="o">(</span><span class="s">"age > 21"</span><span class="o">)</span>
<span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"name.first"</span><span class="o">).</span><span class="na">show</span><span class="o">();</span></code></pre></figure>
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<div class="mb-2 title">Run now</div>
<div class="code">
<p>$ SPARK-HOME/bin/sparkR</p>
<p>></p>
</div>
<div class="examples mt-5">
<div class="window"><span class="circle red"></span><span class="circle yellow"></span><span class="circle green"></span></div>
<div class="spark-code">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">read.json</span><span class="p">(</span><span class="n">path</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"logs.json"</span><span class="p">)</span><span class="w">
</span><span class="n">df</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">filter</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">age</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="m">21</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">name.first</span><span class="p">))</span></code></pre></figure>
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<div style="font-style: normal;font-weight: bold;font-size: 32px;line-height: 54px;text-align: center;">Spark SQL engine: under the hood</div>
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<p>Spark SQL adapts the execution plan at runtime, such as automatically setting the number of reducers and join algorithms.</p>
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<a href="/docs/latest/sql-ref-ansi-compliance.html" alt="Support for ANSI SQL">Support for ANSI SQL</a>
<p>Use the same SQL you’re already comfortable with.</p>
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<a href="/docs/latest/sql-data-sources-json.html" alt="Structured and unstructured data">Structured and unstructured data</a>
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<div class="fw-bold mt-2" style="font-size: 18px;">Accelerates TPC-DS queries up to <span style="color: #f55b15;">8x</span></div>
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