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| <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 style="font-size: 16px;">Install with 'pip' |
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| <p>$ pip install pyspark</p> |
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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> |
| |
| </div> |
| <div class="tab-pane" id="nav-analytics" role="tabpanel" aria-labelledby="nav-ad-tab"> |
| |
| <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> |
| |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="tab-pane fade" id="nav-sql" role="tabpanel" aria-labelledby="nav-sql-tab"> |
| <div class="mb-2 title">Run now</div> |
| <div class="code"> |
| <p>$ docker run -it --rm spark /opt/spark/bin/spark-sql</p> |
| <p>spark-sql></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-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> |
| |
| </div> |
| </div> |
| </div> |
| <div class="tab-pane fade" id="nav-scala" role="tabpanel" aria-labelledby="nav-scala-tab"> |
| <div class="mb-2 title">Run now</div> |
| <div class="code"> |
| <p>$ docker run -it --rm spark /opt/spark/bin/spark-shell</p> |
| <p>scala></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-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> |
| |
| </div> |
| </div> |
| </div> |
| <div class="tab-pane fade" id="nav-java" role="tabpanel" aria-labelledby="nav-java-tab"> |
| <div class="mb-2 title">Run now</div> |
| <div class="code"> |
| <p>$ docker run -it --rm spark /opt/spark/bin/spark-shell</p> |
| <p>scala></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-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> |
| |
| </div> |
| </div> |
| </div> |
| <div class="tab-pane fade" id="nav-r" role="tabpanel" aria-labelledby="nav-r-tab"> |
| <div class="mb-2 title">Run now</div> |
| <div class="code"> |
| <p>$ docker run -it --rm spark:r /opt/spark/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> |
| |
| </div> |
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| <section class="py-5" style="background: rgba(186, 231, 253, 0.17);"> |
| <div class="container text-center"> |
| <div class="row" style="line-height: 42px;"> |
| <div style="font-style: normal;font-weight: bold;font-size: 32px;line-height: 42px;text-align: center;">The most widely-used |
| engine for scalable computing |
| </div> |
| <div style="font-style: normal;font-weight: normal;font-size: 24px;line-height: 30px;text-align: center;">Thousands of |
| companies, including 80% of the Fortune 500, use Apache Spark<span class="tm">™</span>.<br />Over 2,000 contributors to |
| the open source project from industry and academia. |
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| <div style="font-style: normal;font-weight: normal;font-size: 24px;line-height: 30px;text-align: center;">Apache Spark<span class="tm">™</span> integrates with your favorite frameworks, helping to scale them to thousands of machines. |
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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> |
| <div style="font-style: normal;font-weight: normal;font-size: 24px;line-height: 42px;text-align: center;">Apache Spark<span class="tm">™</span> is built on an advanced distributed SQL engine |
| for large-scale data |
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| <div class="scalable-data-science"> |
| <a href="/docs/latest/sql-performance-tuning.html#adaptive-query-execution" alt="Adaptive Query Execution">Adaptive Query Execution</a> |
| <p>Spark SQL adapts the execution plan at runtime, such as automatically setting the number of reducers and join algorithms.</p> |
| </div> |
| <div class="mt-5 scalable-data-science"> |
| <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> |
| </div> |
| <div class="mt-5 scalable-data-science"> |
| <a href="/docs/latest/sql-data-sources-json.html" alt="Structured and unstructured data">Structured and unstructured data</a> |
| <p>Spark SQL works on structured tables and unstructured data such as JSON or images.</p> |
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| <div class="col-12 col-lg-6 order-1 order-lg-21 text-center ms-auto"> |
| <div class="fw-bold mb-2">TPC-DS 1TB No-Stats With vs. Without Adaptive Query Execution</div> |
| <img src="/images/AQE-compersion.png" width="100%" /> |
| <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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