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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Tutorials</a><ul class="current">
<li class="toctree-l2 current"><a class="current reference internal" href="#">Fundamental Concepts</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#example-pipeline-definition">Example Pipeline definition</a></li>
<li class="toctree-l3"><a class="reference internal" href="#it-s-a-dag-definition-file">It’s a DAG definition file</a></li>
<li class="toctree-l3"><a class="reference internal" href="#importing-modules">Importing Modules</a></li>
<li class="toctree-l3"><a class="reference internal" href="#default-arguments">Default Arguments</a></li>
<li class="toctree-l3"><a class="reference internal" href="#instantiate-a-dag">Instantiate a DAG</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#tasks">Tasks</a></li>
<li class="toctree-l3"><a class="reference internal" href="#templating-with-jinja">Templating with Jinja</a></li>
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<div class="section" id="fundamental-concepts">
<h1>Fundamental Concepts<a class="headerlink" href="#fundamental-concepts" title="Permalink to this heading"></a></h1>
<p>This tutorial walks you through some of the fundamental Airflow concepts,
objects, and their usage while writing your first DAG.</p>
<div class="section" id="example-pipeline-definition">
<h2>Example Pipeline definition<a class="headerlink" href="#example-pipeline-definition" title="Permalink to this heading"></a></h2>
<p>Here is an example of a basic pipeline definition. Do not worry if this looks
complicated, a line by line explanation follows below.</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
<span class="kn">from</span> <span class="nn">textwrap</span> <span class="kn">import</span> <span class="n">dedent</span>
<span class="c1"># The DAG object; we&#39;ll need this to instantiate a DAG</span>
<span class="kn">from</span> <span class="nn">airflow</span> <span class="kn">import</span> <span class="n">DAG</span>
<span class="c1"># Operators; we need this to operate!</span>
<span class="kn">from</span> <span class="nn">airflow.operators.bash</span> <span class="kn">import</span> <span class="n">BashOperator</span>
<span class="k">with</span> <span class="n">DAG</span><span class="p">(</span>
<span class="s1">&#39;tutorial&#39;</span><span class="p">,</span>
<span class="c1"># These args will get passed on to each operator</span>
<span class="c1"># You can override them on a per-task basis during operator initialization</span>
<span class="n">default_args</span><span class="o">=</span><span class="p">{</span>
<span class="s1">&#39;depends_on_past&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;airflow@example.com&#39;</span><span class="p">],</span>
<span class="s1">&#39;email_on_failure&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email_on_retry&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;retries&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">&#39;retry_delay&#39;</span><span class="p">:</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">minutes</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
<span class="c1"># &#39;queue&#39;: &#39;bash_queue&#39;,</span>
<span class="c1"># &#39;pool&#39;: &#39;backfill&#39;,</span>
<span class="c1"># &#39;priority_weight&#39;: 10,</span>
<span class="c1"># &#39;end_date&#39;: datetime(2016, 1, 1),</span>
<span class="c1"># &#39;wait_for_downstream&#39;: False,</span>
<span class="c1"># &#39;sla&#39;: timedelta(hours=2),</span>
<span class="c1"># &#39;execution_timeout&#39;: timedelta(seconds=300),</span>
<span class="c1"># &#39;on_failure_callback&#39;: some_function,</span>
<span class="c1"># &#39;on_success_callback&#39;: some_other_function,</span>
<span class="c1"># &#39;on_retry_callback&#39;: another_function,</span>
<span class="c1"># &#39;sla_miss_callback&#39;: yet_another_function,</span>
<span class="c1"># &#39;trigger_rule&#39;: &#39;all_success&#39;</span>
<span class="p">},</span>
<span class="n">description</span><span class="o">=</span><span class="s1">&#39;A simple tutorial DAG&#39;</span><span class="p">,</span>
<span class="n">schedule</span><span class="o">=</span><span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">start_date</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">catchup</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">tags</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;example&#39;</span><span class="p">],</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">dag</span><span class="p">:</span>
<span class="c1"># t1, t2 and t3 are examples of tasks created by instantiating operators</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;print_date&#39;</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="s1">&#39;date&#39;</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;sleep&#39;</span><span class="p">,</span>
<span class="n">depends_on_past</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="s1">&#39;sleep 5&#39;</span><span class="p">,</span>
<span class="n">retries</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t1</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="n">dedent</span><span class="p">(</span>
<span class="sd">&quot;&quot;&quot;\</span>
<span class="sd"> #### Task Documentation</span>
<span class="sd"> You can document your task using the attributes `doc_md` (markdown),</span>
<span class="sd"> `doc` (plain text), `doc_rst`, `doc_json`, `doc_yaml` which gets</span>
<span class="sd"> rendered in the UI&#39;s Task Instance Details page.</span>
<span class="sd"> ![img](http://montcs.bloomu.edu/~bobmon/Semesters/2012-01/491/import%20soul.png)</span>
<span class="sd"> **Image Credit:** Randall Munroe, [XKCD](https://xkcd.com/license.html)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="p">)</span>
<span class="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="vm">__doc__</span> <span class="c1"># providing that you have a docstring at the beginning of the DAG; OR</span>
<span class="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
<span class="s2"> This is a documentation placed anywhere</span>
<span class="s2"> &quot;&quot;&quot;</span> <span class="c1"># otherwise, type it like this</span>
<span class="n">templated_command</span> <span class="o">=</span> <span class="n">dedent</span><span class="p">(</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> {% for i in range(5) %}</span>
<span class="sd"> echo &quot;{{ ds }}&quot;</span>
<span class="sd"> echo &quot;{{ macros.ds_add(ds, 7)}}&quot;</span>
<span class="sd"> {% endfor %}</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="p">)</span>
<span class="n">t3</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;templated&#39;</span><span class="p">,</span>
<span class="n">depends_on_past</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="n">templated_command</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t1</span> <span class="o">&gt;&gt;</span> <span class="p">[</span><span class="n">t2</span><span class="p">,</span> <span class="n">t3</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="it-s-a-dag-definition-file">
<h2>It’s a DAG definition file<a class="headerlink" href="#it-s-a-dag-definition-file" title="Permalink to this heading"></a></h2>
<p>One thing to wrap your head around (it may not be very intuitive for everyone
at first) is that this Airflow Python script is really
just a configuration file specifying the DAG’s structure as code.
The actual tasks defined here will run in a different context from
the context of this script. Different tasks run on different workers
at different points in time, which means that this script cannot be used
to cross communicate between tasks. Note that for this
purpose we have a more advanced feature called <a class="reference internal" href="../concepts/xcoms.html"><span class="doc">XComs</span></a>.</p>
<p>People sometimes think of the DAG definition file as a place where they
can do some actual data processing - that is not the case at all!
The script’s purpose is to define a DAG object. It needs to evaluate
quickly (seconds, not minutes) since the scheduler will execute it
periodically to reflect the changes if any.</p>
</div>
<div class="section" id="importing-modules">
<h2>Importing Modules<a class="headerlink" href="#importing-modules" title="Permalink to this heading"></a></h2>
<p>An Airflow pipeline is just a Python script that happens to define an
Airflow DAG object. Let’s start by importing the libraries we will need.</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
<span class="kn">from</span> <span class="nn">textwrap</span> <span class="kn">import</span> <span class="n">dedent</span>
<span class="c1"># The DAG object; we&#39;ll need this to instantiate a DAG</span>
<span class="kn">from</span> <span class="nn">airflow</span> <span class="kn">import</span> <span class="n">DAG</span>
<span class="c1"># Operators; we need this to operate!</span>
<span class="kn">from</span> <span class="nn">airflow.operators.bash</span> <span class="kn">import</span> <span class="n">BashOperator</span>
</pre></div>
</div>
</div>
<p>See <a class="reference internal" href="../modules_management.html"><span class="doc">Modules Management</span></a> for details on how Python and Airflow manage modules.</p>
</div>
<div class="section" id="default-arguments">
<h2>Default Arguments<a class="headerlink" href="#default-arguments" title="Permalink to this heading"></a></h2>
<p>We’re about to create a DAG and some tasks, and we have the choice to
explicitly pass a set of arguments to each task’s constructor
(which would become redundant), or (better!) we can define a dictionary
of default parameters that we can use when creating tasks.</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># These args will get passed on to each operator</span>
<span class="c1"># You can override them on a per-task basis during operator initialization</span>
<span class="n">default_args</span><span class="o">=</span><span class="p">{</span>
<span class="s1">&#39;depends_on_past&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;airflow@example.com&#39;</span><span class="p">],</span>
<span class="s1">&#39;email_on_failure&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email_on_retry&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;retries&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">&#39;retry_delay&#39;</span><span class="p">:</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">minutes</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
<span class="c1"># &#39;queue&#39;: &#39;bash_queue&#39;,</span>
<span class="c1"># &#39;pool&#39;: &#39;backfill&#39;,</span>
<span class="c1"># &#39;priority_weight&#39;: 10,</span>
<span class="c1"># &#39;end_date&#39;: datetime(2016, 1, 1),</span>
<span class="c1"># &#39;wait_for_downstream&#39;: False,</span>
<span class="c1"># &#39;sla&#39;: timedelta(hours=2),</span>
<span class="c1"># &#39;execution_timeout&#39;: timedelta(seconds=300),</span>
<span class="c1"># &#39;on_failure_callback&#39;: some_function,</span>
<span class="c1"># &#39;on_success_callback&#39;: some_other_function,</span>
<span class="c1"># &#39;on_retry_callback&#39;: another_function,</span>
<span class="c1"># &#39;sla_miss_callback&#39;: yet_another_function,</span>
<span class="c1"># &#39;trigger_rule&#39;: &#39;all_success&#39;</span>
<span class="p">},</span>
</pre></div>
</div>
</div>
<p>For more information about the BaseOperator’s parameters and what they do,
refer to the <code class="xref py py-class docutils literal notranslate"><span class="pre">airflow.models.BaseOperator</span></code> documentation.</p>
<p>Also, note that you could easily define different sets of arguments that
would serve different purposes. An example of that would be to have
different settings between a production and development environment.</p>
</div>
<div class="section" id="instantiate-a-dag">
<h2>Instantiate a DAG<a class="headerlink" href="#instantiate-a-dag" title="Permalink to this heading"></a></h2>
<p>We’ll need a DAG object to nest our tasks into. Here we pass a string
that defines the <code class="docutils literal notranslate"><span class="pre">dag_id</span></code>, which serves as a unique identifier for your DAG.
We also pass the default argument dictionary that we just defined and
define a <code class="docutils literal notranslate"><span class="pre">schedule</span></code> of 1 day for the DAG.</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">DAG</span><span class="p">(</span>
<span class="s1">&#39;tutorial&#39;</span><span class="p">,</span>
<span class="c1"># These args will get passed on to each operator</span>
<span class="c1"># You can override them on a per-task basis during operator initialization</span>
<span class="n">default_args</span><span class="o">=</span><span class="p">{</span>
<span class="s1">&#39;depends_on_past&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;airflow@example.com&#39;</span><span class="p">],</span>
<span class="s1">&#39;email_on_failure&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email_on_retry&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;retries&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">&#39;retry_delay&#39;</span><span class="p">:</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">minutes</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
<span class="c1"># &#39;queue&#39;: &#39;bash_queue&#39;,</span>
<span class="c1"># &#39;pool&#39;: &#39;backfill&#39;,</span>
<span class="c1"># &#39;priority_weight&#39;: 10,</span>
<span class="c1"># &#39;end_date&#39;: datetime(2016, 1, 1),</span>
<span class="c1"># &#39;wait_for_downstream&#39;: False,</span>
<span class="c1"># &#39;sla&#39;: timedelta(hours=2),</span>
<span class="c1"># &#39;execution_timeout&#39;: timedelta(seconds=300),</span>
<span class="c1"># &#39;on_failure_callback&#39;: some_function,</span>
<span class="c1"># &#39;on_success_callback&#39;: some_other_function,</span>
<span class="c1"># &#39;on_retry_callback&#39;: another_function,</span>
<span class="c1"># &#39;sla_miss_callback&#39;: yet_another_function,</span>
<span class="c1"># &#39;trigger_rule&#39;: &#39;all_success&#39;</span>
<span class="p">},</span>
<span class="n">description</span><span class="o">=</span><span class="s1">&#39;A simple tutorial DAG&#39;</span><span class="p">,</span>
<span class="n">schedule</span><span class="o">=</span><span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">start_date</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">catchup</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">tags</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;example&#39;</span><span class="p">],</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">dag</span><span class="p">:</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="operators">
<h2>Operators<a class="headerlink" href="#operators" title="Permalink to this heading"></a></h2>
<p>An operator defines a unit of work for Airflow to complete. Using operators is the classic approach
to defining work in Airflow. For some use cases, it’s better to use the TaskFlow API to define
work in a Pythonic context as described in <a class="reference internal" href="taskflow.html"><span class="doc">Working with TaskFlow</span></a>. For now, using operators helps to
visualize task dependencies in our DAG code.</p>
<p>All operators inherit from the BaseOperator, which includes all of the required arguments for
running work in Airflow. From here, each operator includes unique arguments for
the type of work it’s completing. Some of the most popular operators are the PythonOperator, the BashOperator, and the
KubernetesPodOperator.</p>
<p>Airflow completes work based on the arguments you pass to your operators. In this tutorial, we
use the BashOperator to run a few bash scripts.</p>
</div>
<div class="section" id="tasks">
<h2>Tasks<a class="headerlink" href="#tasks" title="Permalink to this heading"></a></h2>
<p>To use an operator in a DAG, you have to instantiate it as a task. Tasks
determine how to execute your operator’s work within the context of a DAG.</p>
<p>In the following example, we instantiate the BashOperator as two separate tasks in order to run two
separate bash scripts. The first argument for each instantiation, <code class="docutils literal notranslate"><span class="pre">task_id</span></code>,
acts as a unique identifier for the task.</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">t1</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;print_date&#39;</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="s1">&#39;date&#39;</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;sleep&#39;</span><span class="p">,</span>
<span class="n">depends_on_past</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="s1">&#39;sleep 5&#39;</span><span class="p">,</span>
<span class="n">retries</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
<p>Notice how we pass a mix of operator specific arguments (<code class="docutils literal notranslate"><span class="pre">bash_command</span></code>) and
an argument common to all operators (<code class="docutils literal notranslate"><span class="pre">retries</span></code>) inherited
from BaseOperator to the operator’s constructor. This is simpler than
passing every argument for every constructor call. Also, notice that in
the second task we override the <code class="docutils literal notranslate"><span class="pre">retries</span></code> parameter with <code class="docutils literal notranslate"><span class="pre">3</span></code>.</p>
<p>The precedence rules for a task are as follows:</p>
<ol class="arabic simple">
<li><p>Explicitly passed arguments</p></li>
<li><p>Values that exist in the <code class="docutils literal notranslate"><span class="pre">default_args</span></code> dictionary</p></li>
<li><p>The operator’s default value, if one exists</p></li>
</ol>
<p>A task must include or inherit the arguments <code class="docutils literal notranslate"><span class="pre">task_id</span></code> and <code class="docutils literal notranslate"><span class="pre">owner</span></code>,
otherwise Airflow will raise an exception.</p>
</div>
<div class="section" id="templating-with-jinja">
<h2>Templating with Jinja<a class="headerlink" href="#templating-with-jinja" title="Permalink to this heading"></a></h2>
<p>Airflow leverages the power of
<a class="reference external" href="https://jinja.palletsprojects.com/en/2.11.x/">Jinja Templating</a> and provides
the pipeline author
with a set of built-in parameters and macros. Airflow also provides
hooks for the pipeline author to define their own parameters, macros and
templates.</p>
<p>This tutorial barely scratches the surface of what you can do with
templating in Airflow, but the goal of this section is to let you know
this feature exists, get you familiar with double curly brackets, and
point to the most common template variable: <code class="docutils literal notranslate"><span class="pre">{{</span> <span class="pre">ds</span> <span class="pre">}}</span></code> (today’s “date
stamp”).</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">templated_command</span> <span class="o">=</span> <span class="n">dedent</span><span class="p">(</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">{% for i in range(5) %}</span>
<span class="sd"> echo &quot;{{ ds }}&quot;</span>
<span class="sd"> echo &quot;{{ macros.ds_add(ds, 7)}}&quot;</span>
<span class="sd">{% endfor %}</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="p">)</span>
<span class="n">t3</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;templated&#39;</span><span class="p">,</span>
<span class="n">depends_on_past</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="n">templated_command</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
<p>Notice that the <code class="docutils literal notranslate"><span class="pre">templated_command</span></code> contains code logic in <code class="docutils literal notranslate"><span class="pre">{%</span> <span class="pre">%}</span></code> blocks,
references parameters like <code class="docutils literal notranslate"><span class="pre">{{</span> <span class="pre">ds</span> <span class="pre">}}</span></code>, and calls a function as in
<code class="docutils literal notranslate"><span class="pre">{{</span> <span class="pre">macros.ds_add(ds,</span> <span class="pre">7)}}</span></code>.</p>
<p>Files can also be passed to the <code class="docutils literal notranslate"><span class="pre">bash_command</span></code> argument, like
<code class="docutils literal notranslate"><span class="pre">bash_command='templated_command.sh'</span></code>, where the file location is relative to
the directory containing the pipeline file (<code class="docutils literal notranslate"><span class="pre">tutorial.py</span></code> in this case). This
may be desirable for many reasons, like separating your script’s logic and
pipeline code, allowing for proper code highlighting in files composed in
different languages, and general flexibility in structuring pipelines. It is
also possible to define your <code class="docutils literal notranslate"><span class="pre">template_searchpath</span></code> as pointing to any folder
locations in the DAG constructor call.</p>
<p>Using that same DAG constructor call, it is possible to define
<code class="docutils literal notranslate"><span class="pre">user_defined_macros</span></code> which allow you to specify your own variables.
For example, passing <code class="docutils literal notranslate"><span class="pre">dict(foo='bar')</span></code> to this argument allows you
to use <code class="docutils literal notranslate"><span class="pre">{{</span> <span class="pre">foo</span> <span class="pre">}}</span></code> in your templates. Moreover, specifying
<code class="docutils literal notranslate"><span class="pre">user_defined_filters</span></code> allows you to register your own filters. For example,
passing <code class="docutils literal notranslate"><span class="pre">dict(hello=lambda</span> <span class="pre">name:</span> <span class="pre">'Hello</span> <span class="pre">%s'</span> <span class="pre">%</span> <span class="pre">name)</span></code> to this argument allows
you to use <code class="docutils literal notranslate"><span class="pre">{{</span> <span class="pre">'world'</span> <span class="pre">|</span> <span class="pre">hello</span> <span class="pre">}}</span></code> in your templates. For more information
regarding custom filters have a look at the
<a class="reference external" href="https://jinja.palletsprojects.com/en/latest/api/#custom-filters">Jinja Documentation</a>.</p>
<p>For more information on the variables and macros that can be referenced
in templates, make sure to read through the <a class="reference internal" href="../templates-ref.html#templates-ref"><span class="std std-ref">Templates reference</span></a>.</p>
</div>
<div class="section" id="adding-dag-and-tasks-documentation">
<h2>Adding DAG and Tasks documentation<a class="headerlink" href="#adding-dag-and-tasks-documentation" title="Permalink to this heading"></a></h2>
<p>We can add documentation for DAG or each single task. DAG documentation only supports
markdown so far, while task documentation supports plain text, markdown, reStructuredText,
json, and yaml. The DAG documentation can be written as a doc string at the beginning
of the DAG file (recommended), or anywhere else in the file. Below you can find some examples
on how to implement task and DAG docs, as well as screenshots:</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">t1</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="n">dedent</span><span class="p">(</span>
<span class="sd">&quot;&quot;&quot;\</span>
<span class="sd">#### Task Documentation</span>
<span class="sd">You can document your task using the attributes `doc_md` (markdown),</span>
<span class="sd">`doc` (plain text), `doc_rst`, `doc_json`, `doc_yaml` which gets</span>
<span class="sd">rendered in the UI&#39;s Task Instance Details page.</span>
<span class="sd">![img](http://montcs.bloomu.edu/~bobmon/Semesters/2012-01/491/import%20soul.png)</span>
<span class="sd">**Image Credit:** Randall Munroe, [XKCD](https://xkcd.com/license.html)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="p">)</span>
<span class="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="vm">__doc__</span> <span class="c1"># providing that you have a docstring at the beginning of the DAG; OR</span>
<span class="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
<span class="s2">This is a documentation placed anywhere</span>
<span class="s2">&quot;&quot;&quot;</span> <span class="c1"># otherwise, type it like this</span>
</pre></div>
</div>
</div>
<img alt="../_images/task_doc.png" src="../_images/task_doc.png" />
<img alt="../_images/dag_doc.png" src="../_images/dag_doc.png" />
</div>
<div class="section" id="setting-up-dependencies">
<h2>Setting up Dependencies<a class="headerlink" href="#setting-up-dependencies" title="Permalink to this heading"></a></h2>
<p>We have tasks <code class="docutils literal notranslate"><span class="pre">t1</span></code>, <code class="docutils literal notranslate"><span class="pre">t2</span></code> and <code class="docutils literal notranslate"><span class="pre">t3</span></code> that do not depend on each other. Here’s a few ways
you can define dependencies between them:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">t1</span><span class="o">.</span><span class="n">set_downstream</span><span class="p">(</span><span class="n">t2</span><span class="p">)</span>
<span class="c1"># This means that t2 will depend on t1</span>
<span class="c1"># running successfully to run.</span>
<span class="c1"># It is equivalent to:</span>
<span class="n">t2</span><span class="o">.</span><span class="n">set_upstream</span><span class="p">(</span><span class="n">t1</span><span class="p">)</span>
<span class="c1"># The bit shift operator can also be</span>
<span class="c1"># used to chain operations:</span>
<span class="n">t1</span> <span class="o">&gt;&gt;</span> <span class="n">t2</span>
<span class="c1"># And the upstream dependency with the</span>
<span class="c1"># bit shift operator:</span>
<span class="n">t2</span> <span class="o">&lt;&lt;</span> <span class="n">t1</span>
<span class="c1"># Chaining multiple dependencies becomes</span>
<span class="c1"># concise with the bit shift operator:</span>
<span class="n">t1</span> <span class="o">&gt;&gt;</span> <span class="n">t2</span> <span class="o">&gt;&gt;</span> <span class="n">t3</span>
<span class="c1"># A list of tasks can also be set as</span>
<span class="c1"># dependencies. These operations</span>
<span class="c1"># all have the same effect:</span>
<span class="n">t1</span><span class="o">.</span><span class="n">set_downstream</span><span class="p">([</span><span class="n">t2</span><span class="p">,</span> <span class="n">t3</span><span class="p">])</span>
<span class="n">t1</span> <span class="o">&gt;&gt;</span> <span class="p">[</span><span class="n">t2</span><span class="p">,</span> <span class="n">t3</span><span class="p">]</span>
<span class="p">[</span><span class="n">t2</span><span class="p">,</span> <span class="n">t3</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">t1</span>
</pre></div>
</div>
<p>Note that when executing your script, Airflow will raise exceptions when
it finds cycles in your DAG or when a dependency is referenced more
than once.</p>
</div>
<div class="section" id="using-time-zones">
<h2>Using time zones<a class="headerlink" href="#using-time-zones" title="Permalink to this heading"></a></h2>
<p>Creating a time zone aware DAG is quite simple. Just make sure to supply a time zone aware dates
using <code class="docutils literal notranslate"><span class="pre">pendulum</span></code>. Don’t try to use standard library
<a class="reference external" href="https://docs.python.org/3/library/datetime.html#timezone-objects">timezone</a> as they are known to
have limitations and we deliberately disallow using them in DAGs.</p>
</div>
<div class="section" id="recap">
<h2>Recap<a class="headerlink" href="#recap" title="Permalink to this heading"></a></h2>
<p>Alright, so we have a pretty basic DAG. At this point your code should look
something like this:</p>
<div class="example-block-wrapper compound">
<p class="example-header example-header--with-button compound-first"><span class="example-title">airflow/example_dags/tutorial.py</span><a class="example-header-button viewcode-button reference internal" href="../_modules/airflow/example_dags/tutorial.html"><span class="viewcode-link">[source]</span></a></p>
<div class="compound-last highlight-python notranslate"><div class="highlight"><pre><span></span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
<span class="kn">from</span> <span class="nn">textwrap</span> <span class="kn">import</span> <span class="n">dedent</span>
<span class="c1"># The DAG object; we&#39;ll need this to instantiate a DAG</span>
<span class="kn">from</span> <span class="nn">airflow</span> <span class="kn">import</span> <span class="n">DAG</span>
<span class="c1"># Operators; we need this to operate!</span>
<span class="kn">from</span> <span class="nn">airflow.operators.bash</span> <span class="kn">import</span> <span class="n">BashOperator</span>
<span class="k">with</span> <span class="n">DAG</span><span class="p">(</span>
<span class="s1">&#39;tutorial&#39;</span><span class="p">,</span>
<span class="c1"># These args will get passed on to each operator</span>
<span class="c1"># You can override them on a per-task basis during operator initialization</span>
<span class="n">default_args</span><span class="o">=</span><span class="p">{</span>
<span class="s1">&#39;depends_on_past&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;airflow@example.com&#39;</span><span class="p">],</span>
<span class="s1">&#39;email_on_failure&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;email_on_retry&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">&#39;retries&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">&#39;retry_delay&#39;</span><span class="p">:</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">minutes</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
<span class="c1"># &#39;queue&#39;: &#39;bash_queue&#39;,</span>
<span class="c1"># &#39;pool&#39;: &#39;backfill&#39;,</span>
<span class="c1"># &#39;priority_weight&#39;: 10,</span>
<span class="c1"># &#39;end_date&#39;: datetime(2016, 1, 1),</span>
<span class="c1"># &#39;wait_for_downstream&#39;: False,</span>
<span class="c1"># &#39;sla&#39;: timedelta(hours=2),</span>
<span class="c1"># &#39;execution_timeout&#39;: timedelta(seconds=300),</span>
<span class="c1"># &#39;on_failure_callback&#39;: some_function,</span>
<span class="c1"># &#39;on_success_callback&#39;: some_other_function,</span>
<span class="c1"># &#39;on_retry_callback&#39;: another_function,</span>
<span class="c1"># &#39;sla_miss_callback&#39;: yet_another_function,</span>
<span class="c1"># &#39;trigger_rule&#39;: &#39;all_success&#39;</span>
<span class="p">},</span>
<span class="n">description</span><span class="o">=</span><span class="s1">&#39;A simple tutorial DAG&#39;</span><span class="p">,</span>
<span class="n">schedule</span><span class="o">=</span><span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">start_date</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">catchup</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">tags</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;example&#39;</span><span class="p">],</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">dag</span><span class="p">:</span>
<span class="c1"># t1, t2 and t3 are examples of tasks created by instantiating operators</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;print_date&#39;</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="s1">&#39;date&#39;</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;sleep&#39;</span><span class="p">,</span>
<span class="n">depends_on_past</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="s1">&#39;sleep 5&#39;</span><span class="p">,</span>
<span class="n">retries</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t1</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="n">dedent</span><span class="p">(</span>
<span class="sd">&quot;&quot;&quot;\</span>
<span class="sd"> #### Task Documentation</span>
<span class="sd"> You can document your task using the attributes `doc_md` (markdown),</span>
<span class="sd"> `doc` (plain text), `doc_rst`, `doc_json`, `doc_yaml` which gets</span>
<span class="sd"> rendered in the UI&#39;s Task Instance Details page.</span>
<span class="sd"> ![img](http://montcs.bloomu.edu/~bobmon/Semesters/2012-01/491/import%20soul.png)</span>
<span class="sd"> **Image Credit:** Randall Munroe, [XKCD](https://xkcd.com/license.html)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="p">)</span>
<span class="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="vm">__doc__</span> <span class="c1"># providing that you have a docstring at the beginning of the DAG; OR</span>
<span class="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
<span class="s2"> This is a documentation placed anywhere</span>
<span class="s2"> &quot;&quot;&quot;</span> <span class="c1"># otherwise, type it like this</span>
<span class="n">templated_command</span> <span class="o">=</span> <span class="n">dedent</span><span class="p">(</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> {% for i in range(5) %}</span>
<span class="sd"> echo &quot;{{ ds }}&quot;</span>
<span class="sd"> echo &quot;{{ macros.ds_add(ds, 7)}}&quot;</span>
<span class="sd"> {% endfor %}</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="p">)</span>
<span class="n">t3</span> <span class="o">=</span> <span class="n">BashOperator</span><span class="p">(</span>
<span class="n">task_id</span><span class="o">=</span><span class="s1">&#39;templated&#39;</span><span class="p">,</span>
<span class="n">depends_on_past</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">bash_command</span><span class="o">=</span><span class="n">templated_command</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">t1</span> <span class="o">&gt;&gt;</span> <span class="p">[</span><span class="n">t2</span><span class="p">,</span> <span class="n">t3</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="testing">
<span id="id1"></span><h2>Testing<a class="headerlink" href="#testing" title="Permalink to this heading"></a></h2>
<div class="section" id="running-the-script">
<h3>Running the Script<a class="headerlink" href="#running-the-script" title="Permalink to this heading"></a></h3>
<p>Time to run some tests. First, let’s make sure the pipeline
is parsed successfully.</p>
<p>Let’s assume we are saving the code from the previous step in
<code class="docutils literal notranslate"><span class="pre">tutorial.py</span></code> in the DAGs folder referenced in your <code class="docutils literal notranslate"><span class="pre">airflow.cfg</span></code>.
The default location for your DAGs is <code class="docutils literal notranslate"><span class="pre">~/airflow/dags</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python ~/airflow/dags/tutorial.py
</pre></div>
</div>
<p>If the script does not raise an exception it means that you have not done
anything horribly wrong, and that your Airflow environment is somewhat
sound.</p>
</div>
<div class="section" id="command-line-metadata-validation">
<h3>Command Line Metadata Validation<a class="headerlink" href="#command-line-metadata-validation" title="Permalink to this heading"></a></h3>
<p>Let’s run a few commands to validate this script further.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># initialize the database tables</span>
airflow db init
<span class="c1"># print the list of active DAGs</span>
airflow dags list
<span class="c1"># prints the list of tasks in the &quot;tutorial&quot; DAG</span>
airflow tasks list tutorial
<span class="c1"># prints the hierarchy of tasks in the &quot;tutorial&quot; DAG</span>
airflow tasks list tutorial --tree
</pre></div>
</div>
</div>
<div class="section" id="id2">
<h3>Testing<a class="headerlink" href="#id2" title="Permalink to this heading"></a></h3>
<p>Let’s test by running the actual task instances for a specific date. The date
specified in this context is called the <em>logical date</em> (also called <em>execution
date</em> for historical reasons), which simulates the scheduler running your task
or DAG for a specific date and time, even though it <em>physically</em> will run now
(or as soon as its dependencies are met).</p>
<p>We said the scheduler runs your task <em>for</em> a specific date and time, not <em>at</em>.
This is because each run of a DAG conceptually represents not a specific date
and time, but an interval between two times, called a
<a class="reference internal" href="../dag-run.html#data-interval"><span class="std std-ref">data interval</span></a>. A DAG run’s logical date is the start of
its data interval.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># command layout: command subcommand [dag_id] [task_id] [(optional) date]</span>
<span class="c1"># testing print_date</span>
airflow tasks <span class="nb">test</span> tutorial print_date <span class="m">2015</span>-06-01
<span class="c1"># testing sleep</span>
airflow tasks <span class="nb">test</span> tutorial sleep <span class="m">2015</span>-06-01
</pre></div>
</div>
<p>Now remember what we did with templating earlier? See how this template
gets rendered and executed by running this command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># testing templated</span>
airflow tasks <span class="nb">test</span> tutorial templated <span class="m">2015</span>-06-01
</pre></div>
</div>
<p>This should result in displaying a verbose log of events and ultimately
running your bash command and printing the result.</p>
<p>Note that the <code class="docutils literal notranslate"><span class="pre">airflow</span> <span class="pre">tasks</span> <span class="pre">test</span></code> command runs task instances locally, outputs
their log to stdout (on screen), does not bother with dependencies, and
does not communicate state (running, success, failed, …) to the database.
It simply allows testing a single task instance.</p>
<p>The same applies to <code class="docutils literal notranslate"><span class="pre">airflow</span> <span class="pre">dags</span> <span class="pre">test</span></code>, but on a DAG
level. It performs a single DAG run of the given DAG id. While it does take task
dependencies into account, no state is registered in the database. It is
convenient for locally testing a full run of your DAG, given that e.g. if one of
your tasks expects data at some location, it is available.</p>
</div>
<div class="section" id="backfill">
<h3>Backfill<a class="headerlink" href="#backfill" title="Permalink to this heading"></a></h3>
<p>Everything looks like it’s running fine so let’s run a backfill.
<code class="docutils literal notranslate"><span class="pre">backfill</span></code> will respect your dependencies, emit logs into files and talk to
the database to record status. If you do have a webserver up, you will be able
to track the progress. <code class="docutils literal notranslate"><span class="pre">airflow</span> <span class="pre">webserver</span></code> will start a web server if you
are interested in tracking the progress visually as your backfill progresses.</p>
<p>Note that if you use <code class="docutils literal notranslate"><span class="pre">depends_on_past=True</span></code>, individual task instances
will depend on the success of their previous task instance (that is, previous
according to the logical date). Task instances with their logical dates equal to
<code class="docutils literal notranslate"><span class="pre">start_date</span></code> will disregard this dependency because there would be no past
task instances created for them.</p>
<p>You may also want to consider <code class="docutils literal notranslate"><span class="pre">wait_for_downstream=True</span></code> when using <code class="docutils literal notranslate"><span class="pre">depends_on_past=True</span></code>.
While <code class="docutils literal notranslate"><span class="pre">depends_on_past=True</span></code> causes a task instance to depend on the success
of its previous task_instance, <code class="docutils literal notranslate"><span class="pre">wait_for_downstream=True</span></code> will cause a task instance
to also wait for all task instances <em>immediately downstream</em> of the previous
task instance to succeed.</p>
<p>The date range in this context is a <code class="docutils literal notranslate"><span class="pre">start_date</span></code> and optionally an <code class="docutils literal notranslate"><span class="pre">end_date</span></code>,
which are used to populate the run schedule with task instances from this dag.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># optional, start a web server in debug mode in the background</span>
<span class="c1"># airflow webserver --debug &amp;</span>
<span class="c1"># start your backfill on a date range</span>
airflow dags backfill tutorial <span class="se">\</span>
--start-date <span class="m">2015</span>-06-01 <span class="se">\</span>
--end-date <span class="m">2015</span>-06-07
</pre></div>
</div>
</div>
</div>
<div class="section" id="what-s-next">
<h2>What’s Next?<a class="headerlink" href="#what-s-next" title="Permalink to this heading"></a></h2>
<p>That’s it! You have written, tested and backfilled your very first Airflow
pipeline. Merging your code into a repository that has a master scheduler
running against it should result in being triggered and run every day.</p>
<p>Here are a few things you might want to do next:</p>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<ul class="simple">
<li><p>Continue to the next step of the tutorial: <a class="reference internal" href="taskflow.html"><span class="doc">Working with TaskFlow</span></a></p></li>
<li><p>Skip to the the <a class="reference internal" href="../concepts/index.html"><span class="doc">Concepts</span></a> section for detailed explanation of Airflow concepts such as DAGs, Tasks, Operators, and more</p></li>
</ul>
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<li><a class="reference internal" href="#it-s-a-dag-definition-file">It’s a DAG definition file</a></li>
<li><a class="reference internal" href="#importing-modules">Importing Modules</a></li>
<li><a class="reference internal" href="#default-arguments">Default Arguments</a></li>
<li><a class="reference internal" href="#instantiate-a-dag">Instantiate a DAG</a></li>
<li><a class="reference internal" href="#operators">Operators</a></li>
<li><a class="reference internal" href="#tasks">Tasks</a></li>
<li><a class="reference internal" href="#templating-with-jinja">Templating with Jinja</a></li>
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<li><a class="reference internal" href="#setting-up-dependencies">Setting up Dependencies</a></li>
<li><a class="reference internal" href="#using-time-zones">Using time zones</a></li>
<li><a class="reference internal" href="#recap">Recap</a></li>
<li><a class="reference internal" href="#testing">Testing</a><ul>
<li><a class="reference internal" href="#running-the-script">Running the Script</a></li>
<li><a class="reference internal" href="#command-line-metadata-validation">Command Line Metadata Validation</a></li>
<li><a class="reference internal" href="#id2">Testing</a></li>
<li><a class="reference internal" href="#backfill">Backfill</a></li>
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<li><a class="reference internal" href="#what-s-next">What’s Next?</a></li>
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