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<div class="section" id="quick-start">
<h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this headline"></a></h1>
<p>The installation is quick and straightforward.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># airflow needs a home, ~/airflow is the default,</span>
<span class="c1"># but you can lay foundation somewhere else if you prefer</span>
<span class="c1"># (optional)</span>
<span class="nb">export</span> <span class="nv">AIRFLOW_HOME</span><span class="o">=</span>~/airflow
<span class="c1"># install from pypi using pip</span>
pip install apache-airflow
<span class="c1"># initialize the database</span>
airflow initdb
<span class="c1"># start the web server, default port is 8080</span>
airflow webserver -p <span class="m">8080</span>
<span class="c1"># start the scheduler</span>
airflow scheduler
<span class="c1"># visit localhost:8080 in the browser and enable the example dag in the home page</span>
</pre></div>
</div>
<p>Upon running these commands, Airflow will create the <code class="docutils literal notranslate"><span class="pre">$AIRFLOW_HOME</span></code> folder
and lay an “airflow.cfg” file with defaults that get you going fast. You can
inspect the file either in <code class="docutils literal notranslate"><span class="pre">$AIRFLOW_HOME/airflow.cfg</span></code>, or through the UI in
the <code class="docutils literal notranslate"><span class="pre">Admin-&gt;Configuration</span></code> menu. The PID file for the webserver will be stored
in <code class="docutils literal notranslate"><span class="pre">$AIRFLOW_HOME/airflow-webserver.pid</span></code> or in <code class="docutils literal notranslate"><span class="pre">/run/airflow/webserver.pid</span></code>
if started by systemd.</p>
<p>Out of the box, Airflow uses a sqlite database, which you should outgrow
fairly quickly since no parallelization is possible using this database
backend. It works in conjunction with the <a class="reference internal" href="_api/airflow/executors/sequential_executor/index.html#airflow.executors.sequential_executor.SequentialExecutor" title="airflow.executors.sequential_executor.SequentialExecutor"><code class="xref py py-class docutils literal notranslate"><span class="pre">airflow.executors.sequential_executor.SequentialExecutor</span></code></a> which will
only run task instances sequentially. While this is very limiting, it allows
you to get up and running quickly and take a tour of the UI and the
command line utilities.</p>
<p>Here are a few commands that will trigger a few task instances. You should
be able to see the status of the jobs change in the <code class="docutils literal notranslate"><span class="pre">example1</span></code> DAG as you
run the commands below.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># run your first task instance</span>
airflow run example_bash_operator runme_0 <span class="m">2015</span>-01-01
<span class="c1"># run a backfill over 2 days</span>
airflow backfill example_bash_operator -s <span class="m">2015</span>-01-01 -e <span class="m">2015</span>-01-02
</pre></div>
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<div class="section" id="what-s-next">
<h2>What’s Next?<a class="headerlink" href="#what-s-next" title="Permalink to this headline"></a></h2>
<p>From this point, you can head to the <a class="reference internal" href="tutorial.html"><span class="doc">Tutorial</span></a> section for further examples or the <a class="reference internal" href="howto/index.html"><span class="doc">How-to Guides</span></a> section if you’re ready to get your hands dirty.</p>
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