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| <div class="section" id="tutorial-on-the-taskflow-api"> |
| <h1>Tutorial on the TaskFlow API<a class="headerlink" href="#tutorial-on-the-taskflow-api" title="Permalink to this headline">¶</a></h1> |
| <p>This tutorial builds on the regular Airflow Tutorial and focuses specifically |
| on writing data pipelines using the TaskFlow API paradigm which is introduced as |
| part of Airflow 2.0 and contrasts this with DAGs written using the traditional paradigm.</p> |
| <p>The data pipeline chosen here is a simple ETL pattern with |
| three separate tasks for Extract, Transform, and Load.</p> |
| <div class="section" id="example-taskflow-api-etl-pipeline"> |
| <h2>Example “TaskFlow API” ETL Pipeline<a class="headerlink" href="#example-taskflow-api-etl-pipeline" title="Permalink to this headline">¶</a></h2> |
| <p>Here is a very simple ETL pipeline using the TaskFlow API paradigm. A more detailed |
| explanation is given below.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span> |
| <span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span> |
| |
| <span class="kn">from</span> <span class="nn">airflow.decorators</span> <span class="kn">import</span> <span class="n">dag</span><span class="p">,</span> <span class="n">task</span> |
| <span class="nd">@dag</span><span class="p">(</span><span class="n">schedule_interval</span><span class="o">=</span><span class="kc">None</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">'example'</span><span class="p">])</span> |
| <span class="k">def</span> <span class="nf">tutorial_taskflow_api_etl</span><span class="p">():</span> |
| <span class="sd">"""</span> |
| <span class="sd"> ### TaskFlow API Tutorial Documentation</span> |
| <span class="sd"> This is a simple ETL data pipeline example which demonstrates the use of</span> |
| <span class="sd"> the TaskFlow API using three simple tasks for Extract, Transform, and Load.</span> |
| <span class="sd"> Documentation that goes along with the Airflow TaskFlow API tutorial is</span> |
| <span class="sd"> located</span> |
| <span class="sd"> [here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html)</span> |
| <span class="sd"> """</span> |
| <span class="nd">@task</span><span class="p">()</span> |
| <span class="k">def</span> <span class="nf">extract</span><span class="p">():</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Extract task</span> |
| <span class="sd"> A simple Extract task to get data ready for the rest of the data</span> |
| <span class="sd"> pipeline. In this case, getting data is simulated by reading from a</span> |
| <span class="sd"> hardcoded JSON string.</span> |
| <span class="sd"> """</span> |
| <span class="n">data_string</span> <span class="o">=</span> <span class="s1">'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'</span> |
| |
| <span class="n">order_data_dict</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">data_string</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">order_data_dict</span> |
| <span class="nd">@task</span><span class="p">(</span><span class="n">multiple_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">order_data_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Transform task</span> |
| <span class="sd"> A simple Transform task which takes in the collection of order data and</span> |
| <span class="sd"> computes the total order value.</span> |
| <span class="sd"> """</span> |
| <span class="n">total_order_value</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">order_data_dict</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">total_order_value</span> <span class="o">+=</span> <span class="n">value</span> |
| |
| <span class="k">return</span> <span class="p">{</span><span class="s2">"total_order_value"</span><span class="p">:</span> <span class="n">total_order_value</span><span class="p">}</span> |
| <span class="nd">@task</span><span class="p">()</span> |
| <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">total_order_value</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Load task</span> |
| <span class="sd"> A simple Load task which takes in the result of the Transform task and</span> |
| <span class="sd"> instead of saving it to end user review, just prints it out.</span> |
| <span class="sd"> """</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total order value is: </span><span class="si">{</span><span class="n">total_order_value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="n">order_data</span> <span class="o">=</span> <span class="n">extract</span><span class="p">()</span> |
| <span class="n">order_summary</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">order_data</span><span class="p">)</span> |
| <span class="n">load</span><span class="p">(</span><span class="n">order_summary</span><span class="p">[</span><span class="s2">"total_order_value"</span><span class="p">])</span> |
| <span class="n">tutorial_etl_dag</span> <span class="o">=</span> <span class="n">tutorial_taskflow_api_etl</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 headline">¶</a></h2> |
| <p>If this is the first DAG file you are looking at, please note that this Python script |
| is interpreted by Airflow and is a configuration file for your data pipeline. |
| For a complete introduction to DAG files, please look at the core <a class="reference internal" href="tutorial.html"><span class="doc">Airflow tutorial</span></a> |
| which covers DAG structure and definitions extensively.</p> |
| </div> |
| <div class="section" id="instantiate-a-dag"> |
| <h2>Instantiate a DAG<a class="headerlink" href="#instantiate-a-dag" title="Permalink to this headline">¶</a></h2> |
| <p>We are creating a DAG which is the collection of our tasks with dependencies between |
| the tasks. This is a very simple definition, since we just want the DAG to be run |
| when we set this up with Airflow, without any retries or complex scheduling. |
| In this example, please notice that we are creating this DAG using the <code class="docutils literal notranslate"><span class="pre">@dag</span></code> decorator |
| as shown below, with the Python function name acting as the DAG identifier.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@dag</span><span class="p">(</span><span class="n">schedule_interval</span><span class="o">=</span><span class="kc">None</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">'example'</span><span class="p">])</span> |
| <span class="k">def</span> <span class="nf">tutorial_taskflow_api_etl</span><span class="p">():</span> |
| <span class="sd">"""</span> |
| <span class="sd"> ### TaskFlow API Tutorial Documentation</span> |
| <span class="sd"> This is a simple ETL data pipeline example which demonstrates the use of</span> |
| <span class="sd"> the TaskFlow API using three simple tasks for Extract, Transform, and Load.</span> |
| <span class="sd"> Documentation that goes along with the Airflow TaskFlow API tutorial is</span> |
| <span class="sd"> located</span> |
| <span class="sd"> [here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html)</span> |
| <span class="sd"> """</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="tasks"> |
| <h2>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">¶</a></h2> |
| <p>In this data pipeline, tasks are created based on Python functions using the <code class="docutils literal notranslate"><span class="pre">@task</span></code> decorator |
| as shown below. The function name acts as a unique identifier for the task.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span><span class="p">()</span> |
| <span class="k">def</span> <span class="nf">extract</span><span class="p">():</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Extract task</span> |
| <span class="sd"> A simple Extract task to get data ready for the rest of the data</span> |
| <span class="sd"> pipeline. In this case, getting data is simulated by reading from a</span> |
| <span class="sd"> hardcoded JSON string.</span> |
| <span class="sd"> """</span> |
| <span class="n">data_string</span> <span class="o">=</span> <span class="s1">'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'</span> |
| |
| <span class="n">order_data_dict</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">data_string</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">order_data_dict</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>The returned value, which in this case is a dictionary, will be made available for use in later tasks.</p> |
| <p>The Transform and Load tasks are created in the same manner as the Extract task shown above.</p> |
| </div> |
| <div class="section" id="main-flow-of-the-dag"> |
| <h2>Main flow of the DAG<a class="headerlink" href="#main-flow-of-the-dag" title="Permalink to this headline">¶</a></h2> |
| <p>Now that we have the Extract, Transform, and Load tasks defined based on the Python functions, |
| we can move to the main part of the DAG.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">order_data</span> <span class="o">=</span> <span class="n">extract</span><span class="p">()</span> |
| <span class="n">order_summary</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">order_data</span><span class="p">)</span> |
| <span class="n">load</span><span class="p">(</span><span class="n">order_summary</span><span class="p">[</span><span class="s2">"total_order_value"</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>That’s it, we are done! |
| We have invoked the Extract task, obtained the order data from there and sent it over to |
| the Transform task for summarization, and then invoked the Load task with the summarized data. |
| The dependencies between the tasks and the passing of data between these tasks which could be |
| running on different workers on different nodes on the network is all handled by Airflow.</p> |
| <p>Now to actually enable this to be run as a DAG, we invoke the Python function |
| <code class="docutils literal notranslate"><span class="pre">tutorial_taskflow_api_etl</span></code> set up using the <code class="docutils literal notranslate"><span class="pre">@dag</span></code> decorator earlier, as shown below.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">tutorial_etl_dag</span> <span class="o">=</span> <span class="n">tutorial_taskflow_api_etl</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="but-how"> |
| <h2>But how?<a class="headerlink" href="#but-how" title="Permalink to this headline">¶</a></h2> |
| <p>For experienced Airflow DAG authors, this is startlingly simple! Let’s contrast this with |
| how this DAG had to be written before Airflow 2.0 below:</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_etl_dag.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_etl_dag.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span> |
| <span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</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'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.python</span> <span class="kn">import</span> <span class="n">PythonOperator</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">'owner'</span><span class="p">:</span> <span class="s1">'airflow'</span><span class="p">,</span> |
| <span class="p">}</span> |
| <span class="k">with</span> <span class="n">DAG</span><span class="p">(</span> |
| <span class="s1">'tutorial_etl_dag'</span><span class="p">,</span> |
| <span class="n">default_args</span><span class="o">=</span><span class="n">default_args</span><span class="p">,</span> |
| <span class="n">description</span><span class="o">=</span><span class="s1">'ETL DAG tutorial'</span><span class="p">,</span> |
| <span class="n">schedule_interval</span><span class="o">=</span><span class="kc">None</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">'example'</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="n">dag</span><span class="o">.</span><span class="n">doc_md</span> <span class="o">=</span> <span class="vm">__doc__</span> |
| <span class="k">def</span> <span class="nf">extract</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">ti</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">'ti'</span><span class="p">]</span> |
| <span class="n">data_string</span> <span class="o">=</span> <span class="s1">'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'</span> |
| <span class="n">ti</span><span class="o">.</span><span class="n">xcom_push</span><span class="p">(</span><span class="s1">'order_data'</span><span class="p">,</span> <span class="n">data_string</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">ti</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">'ti'</span><span class="p">]</span> |
| <span class="n">extract_data_string</span> <span class="o">=</span> <span class="n">ti</span><span class="o">.</span><span class="n">xcom_pull</span><span class="p">(</span><span class="n">task_ids</span><span class="o">=</span><span class="s1">'extract'</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s1">'order_data'</span><span class="p">)</span> |
| <span class="n">order_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">extract_data_string</span><span class="p">)</span> |
| |
| <span class="n">total_order_value</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">order_data</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">total_order_value</span> <span class="o">+=</span> <span class="n">value</span> |
| |
| <span class="n">total_value</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"total_order_value"</span><span class="p">:</span> <span class="n">total_order_value</span><span class="p">}</span> |
| <span class="n">total_value_json_string</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">total_value</span><span class="p">)</span> |
| <span class="n">ti</span><span class="o">.</span><span class="n">xcom_push</span><span class="p">(</span><span class="s1">'total_order_value'</span><span class="p">,</span> <span class="n">total_value_json_string</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">ti</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">'ti'</span><span class="p">]</span> |
| <span class="n">total_value_string</span> <span class="o">=</span> <span class="n">ti</span><span class="o">.</span><span class="n">xcom_pull</span><span class="p">(</span><span class="n">task_ids</span><span class="o">=</span><span class="s1">'transform'</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s1">'total_order_value'</span><span class="p">)</span> |
| <span class="n">total_order_value</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">total_value_string</span><span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="n">total_order_value</span><span class="p">)</span> |
| <span class="n">extract_task</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'extract'</span><span class="p">,</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">extract</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">extract_task</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">"""\</span> |
| <span class="sd"> #### Extract task</span> |
| <span class="sd"> A simple Extract task to get data ready for the rest of the data pipeline.</span> |
| <span class="sd"> In this case, getting data is simulated by reading from a hardcoded JSON string.</span> |
| <span class="sd"> This data is then put into xcom, so that it can be processed by the next task.</span> |
| <span class="sd"> """</span> |
| <span class="p">)</span> |
| |
| <span class="n">transform_task</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'transform'</span><span class="p">,</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">transform_task</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">"""\</span> |
| <span class="sd"> #### Transform task</span> |
| <span class="sd"> A simple Transform task which takes in the collection of order data from xcom</span> |
| <span class="sd"> and computes the total order value.</span> |
| <span class="sd"> This computed value is then put into xcom, so that it can be processed by the next task.</span> |
| <span class="sd"> """</span> |
| <span class="p">)</span> |
| |
| <span class="n">load_task</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'load'</span><span class="p">,</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">load</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">load_task</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">"""\</span> |
| <span class="sd"> #### Load task</span> |
| <span class="sd"> A simple Load task which takes in the result of the Transform task, by reading it</span> |
| <span class="sd"> from xcom and instead of saving it to end user review, just prints it out.</span> |
| <span class="sd"> """</span> |
| <span class="p">)</span> |
| |
| <span class="n">extract_task</span> <span class="o">>></span> <span class="n">transform_task</span> <span class="o">>></span> <span class="n">load_task</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>All of the processing shown above is being done in the new Airflow 2.0 dag as well, but |
| it is all abstracted from the DAG developer.</p> |
| <p>Let’s examine this in detail by looking at the Transform task in isolation since it is |
| in the middle of the data pipeline. In Airflow 1.x, this task is defined as shown below:</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_etl_dag.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_etl_dag.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">ti</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">'ti'</span><span class="p">]</span> |
| <span class="n">extract_data_string</span> <span class="o">=</span> <span class="n">ti</span><span class="o">.</span><span class="n">xcom_pull</span><span class="p">(</span><span class="n">task_ids</span><span class="o">=</span><span class="s1">'extract'</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s1">'order_data'</span><span class="p">)</span> |
| <span class="n">order_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">extract_data_string</span><span class="p">)</span> |
| |
| <span class="n">total_order_value</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">order_data</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">total_order_value</span> <span class="o">+=</span> <span class="n">value</span> |
| |
| <span class="n">total_value</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"total_order_value"</span><span class="p">:</span> <span class="n">total_order_value</span><span class="p">}</span> |
| <span class="n">total_value_json_string</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">total_value</span><span class="p">)</span> |
| <span class="n">ti</span><span class="o">.</span><span class="n">xcom_push</span><span class="p">(</span><span class="s1">'total_order_value'</span><span class="p">,</span> <span class="n">total_value_json_string</span><span class="p">)</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>As we see here, the data being processed in the Transform function is passed to it using XCom |
| variables. In turn, the summarized data from the Transform function is also placed |
| into another XCom variable which will then be used by the Load task.</p> |
| <p>Contrasting that with TaskFlow API in Airflow 2.0 as shown below.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span><span class="p">(</span><span class="n">multiple_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">order_data_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Transform task</span> |
| <span class="sd"> A simple Transform task which takes in the collection of order data and</span> |
| <span class="sd"> computes the total order value.</span> |
| <span class="sd"> """</span> |
| <span class="n">total_order_value</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">order_data_dict</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">total_order_value</span> <span class="o">+=</span> <span class="n">value</span> |
| |
| <span class="k">return</span> <span class="p">{</span><span class="s2">"total_order_value"</span><span class="p">:</span> <span class="n">total_order_value</span><span class="p">}</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>All of the XCom usage for data passing between these tasks is abstracted away from the DAG author |
| in Airflow 2.0. However, XCom variables are used behind the scenes and can be viewed using |
| the Airflow UI as necessary for debugging or DAG monitoring.</p> |
| <p>Similarly, task dependencies are automatically generated within TaskFlows based on the |
| functional invocation of tasks. In Airflow 1.x, tasks had to be explicitly created and |
| dependencies specified as shown below.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_etl_dag.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_etl_dag.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">extract_task</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'extract'</span><span class="p">,</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">extract</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">extract_task</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">"""\</span> |
| <span class="sd">#### Extract task</span> |
| <span class="sd">A simple Extract task to get data ready for the rest of the data pipeline.</span> |
| <span class="sd">In this case, getting data is simulated by reading from a hardcoded JSON string.</span> |
| <span class="sd">This data is then put into xcom, so that it can be processed by the next task.</span> |
| <span class="sd">"""</span> |
| <span class="p">)</span> |
| |
| <span class="n">transform_task</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'transform'</span><span class="p">,</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">transform_task</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">"""\</span> |
| <span class="sd">#### Transform task</span> |
| <span class="sd">A simple Transform task which takes in the collection of order data from xcom</span> |
| <span class="sd">and computes the total order value.</span> |
| <span class="sd">This computed value is then put into xcom, so that it can be processed by the next task.</span> |
| <span class="sd">"""</span> |
| <span class="p">)</span> |
| |
| <span class="n">load_task</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'load'</span><span class="p">,</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">load</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">load_task</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">"""\</span> |
| <span class="sd">#### Load task</span> |
| <span class="sd">A simple Load task which takes in the result of the Transform task, by reading it</span> |
| <span class="sd">from xcom and instead of saving it to end user review, just prints it out.</span> |
| <span class="sd">"""</span> |
| <span class="p">)</span> |
| |
| <span class="n">extract_task</span> <span class="o">>></span> <span class="n">transform_task</span> <span class="o">>></span> <span class="n">load_task</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>In contrast, with the TaskFlow API in Airflow 2.0, the invocation itself automatically generates |
| the dependencies as shown below.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/example_dags/tutorial_taskflow_api_etl.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/example_dags/tutorial_taskflow_api_etl.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">order_data</span> <span class="o">=</span> <span class="n">extract</span><span class="p">()</span> |
| <span class="n">order_summary</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">order_data</span><span class="p">)</span> |
| <span class="n">load</span><span class="p">(</span><span class="n">order_summary</span><span class="p">[</span><span class="s2">"total_order_value"</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="using-the-taskflow-api-with-docker-or-virtual-environments"> |
| <h2>Using the TaskFlow API with Docker or Virtual Environments<a class="headerlink" href="#using-the-taskflow-api-with-docker-or-virtual-environments" title="Permalink to this headline">¶</a></h2> |
| <p>If you have tasks that require complex or conflicting requirements then you will have the ability to use the |
| TaskFlow API with either a Docker container (since version 2.2.0) or Python virtual environment (since 2.0.2). |
| This added functionality will allow a much more |
| comprehensive range of use-cases for the TaskFlow API, as you will not be limited to the |
| packages and system libraries of the Airflow worker.</p> |
| <p>To use a docker image with the TaskFlow API, change the decorator to <code class="docutils literal notranslate"><span class="pre">@task.docker</span></code> |
| and add any needed arguments to correctly run the task. Please note that the docker |
| image must have a working Python installed and take in a bash command as the <code class="docutils literal notranslate"><span class="pre">command</span></code> argument.</p> |
| <p>Below is an example of using the <code class="docutils literal notranslate"><span class="pre">@task.docker</span></code> decorator to run a Python task.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/providers/docker/example_dags/tutorial_taskflow_api_etl_docker_virtualenv.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/providers/docker/example_dags/tutorial_taskflow_api_etl_docker_virtualenv.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span><span class="o">.</span><span class="n">docker</span><span class="p">(</span><span class="n">image</span><span class="o">=</span><span class="s1">'python:3.9-slim-buster'</span><span class="p">,</span> <span class="n">multiple_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">order_data_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Transform task</span> |
| <span class="sd"> A simple Transform task which takes in the collection of order data and</span> |
| <span class="sd"> computes the total order value.</span> |
| <span class="sd"> """</span> |
| <span class="n">total_order_value</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">order_data_dict</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">total_order_value</span> <span class="o">+=</span> <span class="n">value</span> |
| |
| <span class="k">return</span> <span class="p">{</span><span class="s2">"total_order_value"</span><span class="p">:</span> <span class="n">total_order_value</span><span class="p">}</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>It is worth noting that the Python source code (extracted from the decorated function) and any callable args are sent to the container via (encoded and pickled) environment variables so the length of these is not boundless (the exact limit depends on system settings).</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Using <code class="docutils literal notranslate"><span class="pre">@task.docker</span></code> decorator in one of the earlier Airflow versions</p> |
| <p>Since <code class="docutils literal notranslate"><span class="pre">@task.docker</span></code> decorator is available in the docker provider, you might be tempted to use it in |
| Airflow version before 2.2, but this is not going to work. You will get this error if you try:</p> |
| <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>AttributeError: '_TaskDecorator' object has no attribute 'docker' |
| </pre></div> |
| </div> |
| <p>You should upgrade to Airflow 2.2 or above in order to use it.</p> |
| </div> |
| <p>If you don’t want to run your image on a Docker environment, and instead want to create a separate virtual |
| environment on the same machine, you can use the <code class="docutils literal notranslate"><span class="pre">@task.virtualenv</span></code> decorator instead. The <code class="docutils literal notranslate"><span class="pre">@task.virtualenv</span></code> |
| decorator will allow you to create a new virtualenv with custom libraries and even a different |
| Python version to run your function.</p> |
| <div class="example-block-wrapper docutils container"> |
| <p class="example-header example-header--with-button"><span class="example-title">airflow/providers/docker/example_dags/tutorial_taskflow_api_etl_docker_virtualenv.py</span><a class="example-header-button viewcode-button reference internal" href="_modules/airflow/providers/docker/example_dags/tutorial_taskflow_api_etl_docker_virtualenv.html"><span>View Source</span></a></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span><span class="o">.</span><span class="n">virtualenv</span><span class="p">(</span> |
| <span class="n">use_dill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">system_site_packages</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">requirements</span><span class="o">=</span><span class="p">[</span><span class="s1">'funcsigs'</span><span class="p">],</span> |
| <span class="p">)</span> |
| <span class="k">def</span> <span class="nf">extract</span><span class="p">():</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Extract task</span> |
| <span class="sd"> A simple Extract task to get data ready for the rest of the data</span> |
| <span class="sd"> pipeline. In this case, getting data is simulated by reading from a</span> |
| <span class="sd"> hardcoded JSON string.</span> |
| <span class="sd"> """</span> |
| <span class="kn">import</span> <span class="nn">json</span> |
| |
| <span class="n">data_string</span> <span class="o">=</span> <span class="s1">'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'</span> |
| |
| <span class="n">order_data_dict</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">data_string</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">order_data_dict</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>These two options should allow for far greater flexibility for users who wish to keep their workflows more simple |
| and Pythonic.</p> |
| </div> |
| <div class="section" id="multiple-outputs-inference"> |
| <h2>Multiple outputs inference<a class="headerlink" href="#multiple-outputs-inference" title="Permalink to this headline">¶</a></h2> |
| <p>Tasks can also infer multiple outputs by using dict Python typing.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span> |
| <span class="k">def</span> <span class="nf">identity_dict</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span> |
| <span class="k">return</span> <span class="p">{</span><span class="s2">"x"</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="n">y</span><span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>By using the typing <code class="docutils literal notranslate"><span class="pre">Dict</span></code> for the function return type, the <code class="docutils literal notranslate"><span class="pre">multiple_outputs</span></code> parameter |
| is automatically set to true.</p> |
| <p>Note, If you manually set the <code class="docutils literal notranslate"><span class="pre">multiple_outputs</span></code> parameter the inference is disabled and |
| the parameter value is used.</p> |
| </div> |
| <div class="section" id="adding-dependencies-between-decorated-and-traditional-tasks"> |
| <h2>Adding dependencies between decorated and traditional tasks<a class="headerlink" href="#adding-dependencies-between-decorated-and-traditional-tasks" title="Permalink to this headline">¶</a></h2> |
| <p>The above tutorial shows how to create dependencies between TaskFlow functions. However, dependencies can also |
| be set between traditional tasks (such as <a class="reference internal" href="_api/airflow/operators/bash/index.html#airflow.operators.bash.BashOperator" title="airflow.operators.bash.BashOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">BashOperator</span></code></a> |
| or <a class="reference internal" href="_api/airflow/sensors/filesystem/index.html#airflow.sensors.filesystem.FileSensor" title="airflow.sensors.filesystem.FileSensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">FileSensor</span></code></a>) and TaskFlow functions.</p> |
| <p>Building this dependency is shown in the code below:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span><span class="p">()</span> |
| <span class="k">def</span> <span class="nf">extract_from_file</span><span class="p">():</span> |
| <span class="sd">"""</span> |
| <span class="sd"> #### Extract from file task</span> |
| <span class="sd"> A simple Extract task to get data ready for the rest of the data</span> |
| <span class="sd"> pipeline, by reading the data from a file into a pandas dataframe</span> |
| <span class="sd"> """</span> |
| <span class="n">order_data_file</span> <span class="o">=</span> <span class="s2">"/tmp/order_data.csv"</span> |
| <span class="n">order_data_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">order_data_file</span><span class="p">)</span> |
| |
| |
| <span class="n">file_task</span> <span class="o">=</span> <span class="n">FileSensor</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">"check_file"</span><span class="p">,</span> <span class="n">filepath</span><span class="o">=</span><span class="s2">"/tmp/order_data.csv"</span><span class="p">)</span> |
| <span class="n">order_data</span> <span class="o">=</span> <span class="n">extract_from_file</span><span class="p">()</span> |
| |
| <span class="n">file_task</span> <span class="o">>></span> <span class="n">order_data</span> |
| </pre></div> |
| </div> |
| <p>In the above code block, a new TaskFlow function is defined as <code class="docutils literal notranslate"><span class="pre">extract_from_file</span></code> which |
| reads the data from a known file location. |
| In the main DAG, a new <code class="docutils literal notranslate"><span class="pre">FileSensor</span></code> task is defined to check for this file. Please note |
| that this is a Sensor task which waits for the file. |
| Finally, a dependency between this Sensor task and the TaskFlow function is specified.</p> |
| </div> |
| <div class="section" id="consuming-xcoms-between-decorated-and-traditional-tasks"> |
| <h2>Consuming XComs between decorated and traditional tasks<a class="headerlink" href="#consuming-xcoms-between-decorated-and-traditional-tasks" title="Permalink to this headline">¶</a></h2> |
| <p>As noted above, the TaskFlow API allows XComs to be consumed or passed between tasks in a manner that is |
| abstracted away from the DAG author. This section dives further into detailed examples of how this is |
| possible not only between TaskFlow functions but between both TaskFlow functions <em>and</em> traditional tasks.</p> |
| <p>You may find it necessary to consume an XCom from traditional tasks, either pushed within the task’s execution |
| or via its return value, as an input into downstream tasks. You can access the pushed XCom (also known as an |
| <code class="docutils literal notranslate"><span class="pre">XComArg</span></code>) by utilizing the <code class="docutils literal notranslate"><span class="pre">.output</span></code> property exposed for all operators.</p> |
| <p>By default, using the <code class="docutils literal notranslate"><span class="pre">.output</span></code> property to retrieve an XCom result is the equivalent of:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">task_instance</span><span class="o">.</span><span class="n">xcom_pull</span><span class="p">(</span><span class="n">task_ids</span><span class="o">=</span><span class="s2">"my_task_id"</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s2">"return_value"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>To retrieve an XCom result for a key other than <code class="docutils literal notranslate"><span class="pre">return_value</span></code>, you can use:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">my_op</span> <span class="o">=</span> <span class="n">MyOperator</span><span class="p">(</span><span class="o">...</span><span class="p">)</span> |
| <span class="n">my_op_output</span> <span class="o">=</span> <span class="n">my_op</span><span class="o">.</span><span class="n">output</span><span class="p">[</span><span class="s2">"some_other_xcom_key"</span><span class="p">]</span> |
| <span class="c1"># OR</span> |
| <span class="n">my_op_output</span> <span class="o">=</span> <span class="n">my_op</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"some_other_xcom_key"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Using the <code class="docutils literal notranslate"><span class="pre">.output</span></code> property as an input to another task is supported only for operator parameters |
| listed as a <code class="docutils literal notranslate"><span class="pre">template_field</span></code>.</p> |
| </div> |
| <p>In the code example below, a <a class="reference external" href="/docs/apache-airflow-providers-http/stable/_api/airflow/providers/http/operators/http/index.html#airflow.providers.http.operators.http.SimpleHttpOperator" title="(in apache-airflow-providers-http v2.0.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleHttpOperator</span></code></a> result |
| is captured via <a class="reference internal" href="concepts/xcoms.html"><span class="doc">XComs</span></a>. This XCom result, which is the task output, is then passed |
| to a TaskFlow function which parses the response as JSON.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_api_results_task</span> <span class="o">=</span> <span class="n">SimpleHttpOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"get_api_results"</span><span class="p">,</span> |
| <span class="n">endpoint</span><span class="o">=</span><span class="s2">"/api/query"</span><span class="p">,</span> |
| <span class="n">do_xcom_push</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">http_conn_id</span><span class="o">=</span><span class="s2">"http"</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| |
| <span class="nd">@task</span><span class="p">(</span><span class="n">max_retries</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">parse_results</span><span class="p">(</span><span class="n">api_results</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">api_results</span><span class="p">)</span> |
| |
| |
| <span class="n">parsed_results</span> <span class="o">=</span> <span class="n">parse_results</span><span class="p">(</span><span class="n">api_results</span><span class="o">=</span><span class="n">get_api_results_task</span><span class="o">.</span><span class="n">output</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>The reverse can also be done: passing the output of a TaskFlow function as an input to a traditional task.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span> |
| <span class="k">def</span> <span class="nf">create_queue</span><span class="p">():</span> |
| <span class="sd">"""This is a Python function that creates an SQS queue"""</span> |
| <span class="n">hook</span> <span class="o">=</span> <span class="n">SQSHook</span><span class="p">()</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">hook</span><span class="o">.</span><span class="n">create_queue</span><span class="p">(</span><span class="n">queue_name</span><span class="o">=</span><span class="s2">"sample-queue"</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">result</span><span class="p">[</span><span class="s2">"QueueUrl"</span><span class="p">]</span> |
| |
| |
| <span class="n">sqs_queue</span> <span class="o">=</span> <span class="n">create_queue</span><span class="p">()</span> |
| |
| <span class="n">publish_to_queue</span> <span class="o">=</span> <span class="n">SQSPublishOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"publish_to_queue"</span><span class="p">,</span> |
| <span class="n">sqs_queue</span><span class="o">=</span><span class="n">sqs_queue</span><span class="p">,</span> |
| <span class="n">message_content</span><span class="o">=</span><span class="s2">"{{ task_instance }}-{{ execution_date }}"</span><span class="p">,</span> |
| <span class="n">message_attributes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">delay_seconds</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Take note in the code example above, the output from the <code class="docutils literal notranslate"><span class="pre">create_queue</span></code> TaskFlow function, the URL of a |
| newly-created Amazon SQS Queue, is then passed to a <a class="reference external" href="/docs/apache-airflow-providers-amazon/stable/_api/airflow/providers/amazon/aws/operators/sqs/index.html#airflow.providers.amazon.aws.operators.sqs.SQSPublishOperator" title="(in apache-airflow-providers-amazon v2.5.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SQSPublishOperator</span></code></a> |
| task as the <code class="docutils literal notranslate"><span class="pre">sqs_queue</span></code> arg.</p> |
| <p>Finally, not only can you use traditional operator outputs as inputs for TaskFlow functions, but also as inputs to |
| other traditional operators. In the example below, the output from the <a class="reference external" href="/docs/apache-airflow-providers-amazon/stable/_api/airflow/providers/amazon/aws/transfers/salesforce_to_s3/index.html#airflow.providers.amazon.aws.transfers.salesforce_to_s3.SalesforceToS3Operator" title="(in apache-airflow-providers-amazon v2.5.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SalesforceToS3Operator</span></code></a> |
| task (which is an S3 URI for a destination file location) is used an input for the <code class="xref py py-class docutils literal notranslate"><span class="pre">S3CopyObjectOperator</span></code> |
| task to copy the same file to a date-partitioned storage location in S3 for long-term storage in a data lake.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">BASE_PATH</span> <span class="o">=</span> <span class="s2">"salesforce/customers"</span> |
| <span class="n">FILE_NAME</span> <span class="o">=</span> <span class="s2">"customer_daily_extract_{{ ds_nodash }}.csv"</span> |
| |
| |
| <span class="n">upload_salesforce_data_to_s3_landing</span> <span class="o">=</span> <span class="n">SalesforceToS3Operator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"upload_salesforce_data_to_s3"</span><span class="p">,</span> |
| <span class="n">salesforce_query</span><span class="o">=</span><span class="s2">"SELECT Id, Name, Company, Phone, Email, LastModifiedDate, IsActive FROM Customers"</span><span class="p">,</span> |
| <span class="n">s3_bucket_name</span><span class="o">=</span><span class="s2">"landing-bucket"</span><span class="p">,</span> |
| <span class="n">s3_key</span><span class="o">=</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">BASE_PATH</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">FILE_NAME</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> |
| <span class="n">salesforce_conn_id</span><span class="o">=</span><span class="s2">"salesforce"</span><span class="p">,</span> |
| <span class="n">aws_conn_id</span><span class="o">=</span><span class="s2">"s3"</span><span class="p">,</span> |
| <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| |
| <span class="n">store_to_s3_data_lake</span> <span class="o">=</span> <span class="n">S3CopyObjectOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s2">"store_to_s3_data_lake"</span><span class="p">,</span> |
| <span class="n">aws_conn_id</span><span class="o">=</span><span class="s2">"s3"</span><span class="p">,</span> |
| <span class="n">source_bucket_key</span><span class="o">=</span><span class="n">upload_salesforce_data_to_s3_landing</span><span class="o">.</span><span class="n">output</span><span class="p">,</span> |
| <span class="n">dest_bucket_name</span><span class="o">=</span><span class="s2">"data_lake"</span><span class="p">,</span> |
| <span class="n">dest_bucket_key</span><span class="o">=</span><span class="sa">f</span><span class="s2">"""</span><span class="si">{</span><span class="n">BASE_PATH</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="s2">"{{ execution_date.strftime('%Y/%m/</span><span class="si">%d</span><span class="s2">') }}"</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">FILE_NAME</span><span class="si">}</span><span class="s2">"""</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="accessing-context-variables-in-decorated-tasks"> |
| <h2>Accessing context variables in decorated tasks<a class="headerlink" href="#accessing-context-variables-in-decorated-tasks" title="Permalink to this headline">¶</a></h2> |
| <p>When running your callable, Airflow will pass a set of keyword arguments that can be used in your |
| function. This set of kwargs correspond exactly to what you can use in your Jinja templates. |
| For this to work, you need to define <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> in your function header, or you can add directly the |
| keyword arguments you would like to get - for example with the below code your callable will get |
| the values of <code class="docutils literal notranslate"><span class="pre">ti</span></code> and <code class="docutils literal notranslate"><span class="pre">next_ds</span></code> context variables. Note that when explicit keyword arguments are used, |
| they must be made optional in the function header to avoid <code class="docutils literal notranslate"><span class="pre">TypeError</span></code> exceptions during DAG parsing as |
| these values are not available until task execution.</p> |
| <p>With explicit arguments:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span> |
| <span class="k">def</span> <span class="nf">my_python_callable</span><span class="p">(</span><span class="n">ti</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">next_ds</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="k">pass</span> |
| </pre></div> |
| </div> |
| <p>With kwargs:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@task</span> |
| <span class="k">def</span> <span class="nf">my_python_callable</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">ti</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">"ti"</span><span class="p">]</span> |
| <span class="n">next_ds</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">"next_ds"</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>Also sometimes you might want to access the context somewhere deep the stack - and you do not want to pass |
| the context variables from the task callable. You can do it via <code class="docutils literal notranslate"><span class="pre">get_current_context</span></code> |
| method of the Python operator.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">airflow.operators.python</span> <span class="kn">import</span> <span class="n">get_current_context</span> |
| |
| |
| <span class="k">def</span> <span class="nf">some_function_in_your_library</span><span class="p">():</span> |
| <span class="n">context</span> <span class="o">=</span> <span class="n">get_current_context</span><span class="p">()</span> |
| <span class="n">ti</span> <span class="o">=</span> <span class="n">context</span><span class="p">[</span><span class="s2">"ti"</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>Current context is accessible only during the task execution. The context is not accessible during |
| <code class="docutils literal notranslate"><span class="pre">pre_execute</span></code> or <code class="docutils literal notranslate"><span class="pre">post_execute</span></code>. Calling this method outside execution context will raise an error.</p> |
| </div> |
| <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>You have seen how simple it is to write DAGs using the TaskFlow API paradigm within Airflow 2.0. Please do |
| read the <a class="reference internal" href="concepts/index.html"><span class="doc">Concepts section</span></a> for detailed explanation of Airflow concepts such as DAGs, Tasks, |
| Operators, and more. There’s also a whole section on the <a class="reference internal" href="concepts/taskflow.html"><span class="doc">TaskFlow API</span></a> and the <code class="docutils literal notranslate"><span class="pre">@task</span></code> decorator.</p> |
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