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| <div class="section" id="installation"> |
| <h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1> |
| <p>PySpark is included in the official releases of Spark available in the <a class="reference external" href="https://spark.apache.org/downloads.html">Apache Spark website</a>. |
| For Python users, PySpark also provides <code class="docutils literal notranslate"><span class="pre">pip</span></code> installation from PyPI. This is usually for local usage or as |
| a client to connect to a cluster instead of setting up a cluster itself.</p> |
| <p>This page includes instructions for installing PySpark by using pip, Conda, downloading manually, |
| and building from the source.</p> |
| <div class="section" id="python-versions-supported"> |
| <h2>Python Versions Supported<a class="headerlink" href="#python-versions-supported" title="Permalink to this headline">¶</a></h2> |
| <p>Python 3.8 and above.</p> |
| </div> |
| <div class="section" id="using-pypi"> |
| <h2>Using PyPI<a class="headerlink" href="#using-pypi" title="Permalink to this headline">¶</a></h2> |
| <p>PySpark installation using <a class="reference external" href="https://pypi.org/project/pyspark/">PyPI</a> is as follows:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>pyspark |
| </pre></div> |
| </div> |
| <p>If you want to install extra dependencies for a specific component, you can install it as below:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Spark SQL</span> |
| pip<span class="w"> </span>install<span class="w"> </span>pyspark<span class="o">[</span>sql<span class="o">]</span> |
| <span class="c1"># pandas API on Spark</span> |
| pip<span class="w"> </span>install<span class="w"> </span>pyspark<span class="o">[</span>pandas_on_spark<span class="o">]</span><span class="w"> </span>plotly<span class="w"> </span><span class="c1"># to plot your data, you can install plotly together.</span> |
| <span class="c1"># Spark Connect</span> |
| pip<span class="w"> </span>install<span class="w"> </span>pyspark<span class="o">[</span>connect<span class="o">]</span> |
| </pre></div> |
| </div> |
| <p>For PySpark with/without a specific Hadoop version, you can install it by using <code class="docutils literal notranslate"><span class="pre">PYSPARK_HADOOP_VERSION</span></code> environment variables as below:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">PYSPARK_HADOOP_VERSION</span><span class="o">=</span><span class="m">3</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>pyspark |
| </pre></div> |
| </div> |
| <p>The default distribution uses Hadoop 3.3 and Hive 2.3. If users specify different versions of Hadoop, the pip installation automatically |
| downloads a different version and uses it in PySpark. Downloading it can take a while depending on |
| the network and the mirror chosen. <code class="docutils literal notranslate"><span class="pre">PYSPARK_RELEASE_MIRROR</span></code> can be set to manually choose the mirror for faster downloading.</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">PYSPARK_RELEASE_MIRROR</span><span class="o">=</span>http://mirror.apache-kr.org<span class="w"> </span><span class="nv">PYSPARK_HADOOP_VERSION</span><span class="o">=</span><span class="m">3</span><span class="w"> </span>pip<span class="w"> </span>install |
| </pre></div> |
| </div> |
| <p>It is recommended to use <code class="docutils literal notranslate"><span class="pre">-v</span></code> option in <code class="docutils literal notranslate"><span class="pre">pip</span></code> to track the installation and download status.</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">PYSPARK_HADOOP_VERSION</span><span class="o">=</span><span class="m">3</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>pyspark<span class="w"> </span>-v |
| </pre></div> |
| </div> |
| <p>Supported values in <code class="docutils literal notranslate"><span class="pre">PYSPARK_HADOOP_VERSION</span></code> are:</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">without</span></code>: Spark pre-built with user-provided Apache Hadoop</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">3</span></code>: Spark pre-built for Apache Hadoop 3.3 and later (default)</p></li> |
| </ul> |
| <p>Note that this installation of PySpark with/without a specific Hadoop version is experimental. It can change or be removed between minor releases.</p> |
| </div> |
| <div class="section" id="using-conda"> |
| <h2>Using Conda<a class="headerlink" href="#using-conda" title="Permalink to this headline">¶</a></h2> |
| <p>Conda is an open-source package management and environment management system (developed by |
| <a class="reference external" href="https://www.anaconda.com/">Anaconda</a>), which is best installed through |
| <a class="reference external" href="https://docs.conda.io/en/latest/miniconda.html/">Miniconda</a> or <a class="reference external" href="https://github.com/conda-forge/miniforge/">Miniforge</a>. |
| The tool is both cross-platform and language agnostic, and in practice, conda can replace both |
| <a class="reference external" href="https://pip.pypa.io/en/latest/">pip</a> and <a class="reference external" href="https://virtualenv.pypa.io/en/latest/">virtualenv</a>.</p> |
| <p>Conda uses so-called channels to distribute packages, and together with the default channels by |
| Anaconda itself, the most important channel is <a class="reference external" href="https://conda-forge.org/">conda-forge</a>, which |
| is the community-driven packaging effort that is the most extensive & the most current (and also |
| serves as the upstream for the Anaconda channels in most cases).</p> |
| <p>To create a new conda environment from your terminal and activate it, proceed as shown below:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>pyspark_env |
| conda<span class="w"> </span>activate<span class="w"> </span>pyspark_env |
| </pre></div> |
| </div> |
| <p>After activating the environment, use the following command to install pyspark, |
| a python version of your choice, as well as other packages you want to use in |
| the same session as pyspark (you can install in several steps too).</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>pyspark<span class="w"> </span><span class="c1"># can also add "python=3.8 some_package [etc.]" here</span> |
| </pre></div> |
| </div> |
| <p>Note that <a class="reference external" href="https://anaconda.org/conda-forge/pyspark">PySpark for conda</a> is maintained |
| separately by the community; while new versions generally get packaged quickly, the |
| availability through conda(-forge) is not directly in sync with the PySpark release cycle.</p> |
| <p>While using pip in a conda environment is technically feasible (with the same command as |
| <a class="reference external" href="#using-pypi">above</a>), this approach is <a class="reference external" href="https://www.anaconda.com/blog/using-pip-in-a-conda-environment/">discouraged</a>, |
| because pip does not interoperate with conda.</p> |
| <p>For a short summary about useful conda commands, see their |
| <a class="reference external" href="https://docs.conda.io/projects/conda/en/latest/user-guide/cheatsheet.html/">cheat sheet</a>.</p> |
| </div> |
| <div class="section" id="manually-downloading"> |
| <h2>Manually Downloading<a class="headerlink" href="#manually-downloading" title="Permalink to this headline">¶</a></h2> |
| <p>PySpark is included in the distributions available at the <a class="reference external" href="https://spark.apache.org/downloads.html">Apache Spark website</a>. |
| You can download a distribution you want from the site. After that, uncompress the tar file into the directory where you want |
| to install Spark, for example, as below:</p> |
| <pre class="literal-block">tar xzvf spark-3.5.4-bin-hadoop3.tgz</pre> |
| <p>Ensure the <code class="docutils literal notranslate"><span class="pre">SPARK_HOME</span></code> environment variable points to the directory where the tar file has been extracted. |
| Update <code class="docutils literal notranslate"><span class="pre">PYTHONPATH</span></code> environment variable such that it can find the PySpark and Py4J under <code class="docutils literal notranslate"><span class="pre">SPARK_HOME/python/lib</span></code>. |
| One example of doing this is shown below:</p> |
| <pre class="literal-block">cd spark-3.5.4-bin-hadoop3 |
| export SPARK_HOME=`pwd` |
| export PYTHONPATH=$(ZIPS=("$SPARK_HOME"/python/lib/<em>.zip); IFS=:; echo "${ZIPS[</em>]}"):$PYTHONPATH</pre> |
| </div> |
| <div class="section" id="installing-from-source"> |
| <h2>Installing from Source<a class="headerlink" href="#installing-from-source" title="Permalink to this headline">¶</a></h2> |
| <p>To install PySpark from source, refer to <a class="reference external" href="https://spark.apache.org/docs/3.5.4/building-spark.html">Building Spark</a>.</p> |
| </div> |
| <div class="section" id="dependencies"> |
| <h2>Dependencies<a class="headerlink" href="#dependencies" title="Permalink to this headline">¶</a></h2> |
| <table class="table"> |
| <colgroup> |
| <col style="width: 19%" /> |
| <col style="width: 18%" /> |
| <col style="width: 63%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Package</p></th> |
| <th class="head"><p>Supported version Note</p></th> |
| <th class="head"></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><cite>py4j</cite></p></td> |
| <td><p>>=0.10.9.7</p></td> |
| <td><p>Required</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><cite>pandas</cite></p></td> |
| <td><p>>=1.0.5</p></td> |
| <td><p>Required for pandas API on Spark and Spark Connect; Optional for Spark SQL</p></td> |
| </tr> |
| <tr class="row-even"><td><p><cite>pyarrow</cite></p></td> |
| <td><p>>=4.0.0,<13.0.0</p></td> |
| <td><p>Required for pandas API on Spark and Spark Connect; Optional for Spark SQL</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><cite>numpy</cite></p></td> |
| <td><p>>=1.15</p></td> |
| <td><p>Required for pandas API on Spark and MLLib DataFrame-based API; Optional for Spark SQL</p></td> |
| </tr> |
| <tr class="row-even"><td><p><cite>grpcio</cite></p></td> |
| <td><p>>=1.48,<1.57</p></td> |
| <td><p>Required for Spark Connect</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><cite>grpcio-status</cite></p></td> |
| <td><p>>=1.48,<1.57</p></td> |
| <td><p>Required for Spark Connect</p></td> |
| </tr> |
| <tr class="row-even"><td><p><cite>googleapis-common-protos</cite></p></td> |
| <td><p>==1.56.4</p></td> |
| <td><p>Required for Spark Connect</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Note that PySpark requires Java 8 (except prior to 8u371), 11 or 17 with <code class="docutils literal notranslate"><span class="pre">JAVA_HOME</span></code> properly set. |
| If using JDK 11, set <code class="docutils literal notranslate"><span class="pre">-Dio.netty.tryReflectionSetAccessible=true</span></code> for Arrow related features and refer |
| to <a class="reference external" href="https://spark.apache.org/docs/3.5.4/#downloading">Downloading</a>.</p> |
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