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| <blockquote> |
| <div></div></blockquote> |
| <div class="section" id="best-practices"> |
| <h1>Best Practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h1> |
| <p>Running Airflow in production is seamless. It comes bundled with all the plugins and configs |
| necessary to run most of the DAGs. However, you can come across certain pitfalls, which can cause occasional errors. |
| Let’s take a look at what you need to do at various stages to avoid these pitfalls, starting from writing the DAG |
| to the actual deployment in the production environment.</p> |
| <div class="section" id="writing-a-dag"> |
| <h2>Writing a DAG<a class="headerlink" href="#writing-a-dag" title="Permalink to this headline">¶</a></h2> |
| <p>Creating a new DAG in Airflow is quite simple. However, there are many things that you need to take care of |
| to ensure the DAG run or failure does not produce unexpected results.</p> |
| <div class="section" id="creating-a-task"> |
| <h3>Creating a task<a class="headerlink" href="#creating-a-task" title="Permalink to this headline">¶</a></h3> |
| <p>You should treat tasks in Airflow equivalent to transactions in a database. It implies that you should never produce |
| incomplete results from your tasks. An example is not to produce incomplete data in <code class="docutils literal notranslate"><span class="pre">HDFS</span></code> or <code class="docutils literal notranslate"><span class="pre">S3</span></code> at the end of a task.</p> |
| <p>Airflow can retry a task if it fails. Thus, the tasks should produce the same outcome on every re-run. |
| Some of the ways you can avoid producing a different result -</p> |
| <ul class="simple"> |
| <li><p>Do not use INSERT during a task re-run, an INSERT statement might lead to duplicate rows in your database. |
| Replace it with UPSERT.</p></li> |
| <li><p>Read and write in a specific partition. Never read the latest available data in a task. |
| Someone may update the input data between re-runs, which results in different outputs. |
| A better way is to read the input data from a specific partition. You can use <code class="docutils literal notranslate"><span class="pre">execution_date</span></code> as a partition. |
| You should follow this partitioning method while writing data in S3/HDFS, as well.</p></li> |
| <li><p>The python datetime <code class="docutils literal notranslate"><span class="pre">now()</span></code> function gives the current datetime object. |
| This function should never be used inside a task, especially to do the critical computation, as it leads to different outcomes on each run. |
| It’s fine to use it, for example, to generate a temporary log.</p></li> |
| </ul> |
| <div class="admonition tip"> |
| <p class="admonition-title">Tip</p> |
| <p>You should define repetitive parameters such as <code class="docutils literal notranslate"><span class="pre">connection_id</span></code> or S3 paths in <code class="docutils literal notranslate"><span class="pre">default_args</span></code> rather than declaring them for each task. |
| The <code class="docutils literal notranslate"><span class="pre">default_args</span></code> help to avoid mistakes such as typographical errors.</p> |
| </div> |
| </div> |
| <div class="section" id="deleting-a-task"> |
| <h3>Deleting a task<a class="headerlink" href="#deleting-a-task" title="Permalink to this headline">¶</a></h3> |
| <p>Never delete a task from a DAG. In case of deletion, the historical information of the task disappears from the Airflow UI. |
| It is advised to create a new DAG in case the tasks need to be deleted.</p> |
| </div> |
| <div class="section" id="communication"> |
| <h3>Communication<a class="headerlink" href="#communication" title="Permalink to this headline">¶</a></h3> |
| <p>Airflow executes tasks of a DAG on different servers in case you are using <a class="reference internal" href="executor/kubernetes.html"><span class="doc">Kubernetes executor</span></a> or <a class="reference internal" href="executor/celery.html"><span class="doc">Celery executor</span></a>. |
| Therefore, you should not store any file or config in the local filesystem as the next task is likely to run on a different server without access to it — for example, a task that downloads the data file that the next task processes. |
| In the case of <a class="reference internal" href="_api/airflow/executors/local_executor/index.html#airflow.executors.local_executor.LocalExecutor" title="airflow.executors.local_executor.LocalExecutor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Local</span> <span class="pre">executor</span></code></a>, |
| storing a file on disk can make retries harder e.g., your task requires a config file that is deleted by another task in DAG.</p> |
| <p>If possible, use <code class="docutils literal notranslate"><span class="pre">XCom</span></code> to communicate small messages between tasks and a good way of passing larger data between tasks is to use a remote storage such as S3/HDFS. |
| For example, if we have a task that stores processed data in S3 that task can push the S3 path for the output data in <code class="docutils literal notranslate"><span class="pre">Xcom</span></code>, |
| and the downstream tasks can pull the path from XCom and use it to read the data.</p> |
| <p>The tasks should also not store any authentication parameters such as passwords or token inside them. |
| Where at all possible, use <a class="reference internal" href="concepts.html#concepts-connections"><span class="std std-ref">Connections</span></a> to store data securely in Airflow backend and retrieve them using a unique connection id.</p> |
| </div> |
| <div class="section" id="variables"> |
| <h3>Variables<a class="headerlink" href="#variables" title="Permalink to this headline">¶</a></h3> |
| <p>You should avoid usage of Variables outside an operator’s <code class="docutils literal notranslate"><span class="pre">execute()</span></code> method or Jinja templates if possible, |
| as Variables create a connection to metadata DB of Airflow to fetch the value, which can slow down parsing and place extra load on the DB.</p> |
| <p>Airflow parses all the DAGs in the background at a specific period. |
| The default period is set using <code class="docutils literal notranslate"><span class="pre">processor_poll_interval</span></code> config, which is by default 1 second. During parsing, Airflow creates a new connection to the metadata DB for each DAG. |
| It can result in a lot of open connections.</p> |
| <p>The best way of using variables is via a Jinja template which will delay reading the value until the task execution. The template synaxt to do this is:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{{</span> <span class="n">var</span><span class="o">.</span><span class="n">value</span><span class="o">.<</span><span class="n">variable_name</span><span class="o">></span> <span class="p">}}</span> |
| </pre></div> |
| </div> |
| <p>or if you need to deserialize a json object from the variable :</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{{</span> <span class="n">var</span><span class="o">.</span><span class="n">json</span><span class="o">.<</span><span class="n">variable_name</span><span class="o">></span> <span class="p">}}</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>In general, you should not write any code outside the tasks. The code outside the tasks runs every time Airflow parses the DAG, which happens every second by default.</p> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="testing-a-dag"> |
| <h2>Testing a DAG<a class="headerlink" href="#testing-a-dag" title="Permalink to this headline">¶</a></h2> |
| <p>Airflow users should treat DAGs as production level code. The DAGs should have various tests to ensure that it produces expected results. |
| You can write a wide variety of tests for a DAG. Let’s take a look at some of them.</p> |
| <div class="section" id="dag-loader-test"> |
| <h3>DAG Loader Test<a class="headerlink" href="#dag-loader-test" title="Permalink to this headline">¶</a></h3> |
| <p>This test should ensure that your DAG does not contain a piece of code that raises error while loading. |
| No additional code needs to be written by the user to run this test.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">your</span><span class="o">-</span><span class="n">dag</span><span class="o">-</span><span class="n">file</span><span class="o">.</span><span class="n">py</span> |
| </pre></div> |
| </div> |
| <p>Running the above command without any error ensures your DAG does not contain any uninstalled dependency, syntax errors, etc.</p> |
| <p>You can look into <a class="reference internal" href="tutorial.html#testing"><span class="std std-ref">Testing a DAG</span></a> for details on how to test individual operators.</p> |
| </div> |
| <div class="section" id="unit-tests"> |
| <h3>Unit tests<a class="headerlink" href="#unit-tests" title="Permalink to this headline">¶</a></h3> |
| <p>Unit tests ensure that there is no incorrect code in your DAG. You can write a unit test for your tasks as well as your DAG.</p> |
| <p><strong>Unit test for loading a DAG:</strong></p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">airflow.models</span> <span class="kn">import</span> <span class="n">DagBag</span> |
| <span class="kn">import</span> <span class="nn">unittest</span> |
| |
| <span class="k">class</span> <span class="nc">TestHelloWorldDAG</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span> |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">setUpClass</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span> |
| <span class="bp">cls</span><span class="o">.</span><span class="n">dagbag</span> <span class="o">=</span> <span class="n">DagBag</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">test_dag_loaded</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">dag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dagbag</span><span class="o">.</span><span class="n">get_dag</span><span class="p">(</span><span class="n">dag_id</span><span class="o">=</span><span class="s1">'hello_world'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assertDictEqual</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dagbag</span><span class="o">.</span><span class="n">import_errors</span><span class="p">,</span> <span class="p">{})</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assertIsNotNone</span><span class="p">(</span><span class="n">dag</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dag</span><span class="o">.</span><span class="n">tasks</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><strong>Unit test for custom operator:</strong></p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span> |
| <span class="kn">from</span> <span class="nn">airflow.utils.state</span> <span class="kn">import</span> <span class="n">State</span> |
| |
| <span class="n">DEFAULT_DATE</span> <span class="o">=</span> <span class="s1">'2019-10-03'</span> |
| <span class="n">TEST_DAG_ID</span> <span class="o">=</span> <span class="s1">'test_my_custom_operator'</span> |
| |
| <span class="k">class</span> <span class="nc">MyCustomOperatorTest</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">setUp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dag</span> <span class="o">=</span> <span class="n">DAG</span><span class="p">(</span><span class="n">TEST_DAG_ID</span><span class="p">,</span> <span class="n">schedule_interval</span><span class="o">=</span><span class="s1">'@daily'</span><span class="p">,</span> <span class="n">default_args</span><span class="o">=</span><span class="p">{</span><span class="s1">'start_date'</span> <span class="p">:</span> <span class="n">DEFAULT_DATE</span><span class="p">})</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="n">MyCustomOperator</span><span class="p">(</span> |
| <span class="n">dag</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dag</span><span class="p">,</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'test'</span><span class="p">,</span> |
| <span class="n">prefix</span><span class="o">=</span><span class="s1">'s3://bucket/some/prefix'</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">ti</span> <span class="o">=</span> <span class="n">TaskInstance</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="n">execution_date</span><span class="o">=</span><span class="n">DEFAULT_DATE</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">test_execute_no_trigger</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">ti</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">ignore_ti_state</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ti</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="n">State</span><span class="o">.</span><span class="n">SUCCESS</span><span class="p">)</span> |
| <span class="c1">#Assert something related to tasks results</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="self-checks"> |
| <h3>Self-Checks<a class="headerlink" href="#self-checks" title="Permalink to this headline">¶</a></h3> |
| <p>You can also implement checks in a DAG to make sure the tasks are producing the results as expected. |
| As an example, if you have a task that pushes data to S3, you can implement a check in the next task. For example, the check could |
| make sure that the partition is created in S3 and perform some simple checks to see if the data is correct or not.</p> |
| <p>Similarly, if you have a task that starts a microservice in Kubernetes or Mesos, you should check if the service has started or not using <a class="reference internal" href="_api/airflow/sensors/http_sensor/index.html#airflow.sensors.http_sensor.HttpSensor" title="airflow.sensors.http_sensor.HttpSensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">airflow.sensors.http_sensor.HttpSensor</span></code></a>.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">task</span> <span class="o">=</span> <span class="n">PushToS3</span><span class="p">(</span><span class="o">...</span><span class="p">)</span> |
| <span class="n">check</span> <span class="o">=</span> <span class="n">S3KeySensor</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="s1">'check_parquet_exists'</span><span class="p">,</span> |
| <span class="n">bucket_key</span><span class="o">=</span><span class="s2">"s3://bucket/key/foo.parquet"</span><span class="p">,</span> |
| <span class="n">poke_interval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">timeout</span><span class="o">=</span><span class="mi">0</span> |
| <span class="p">)</span> |
| <span class="n">task</span> <span class="o">>></span> <span class="n">check</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="staging-environment"> |
| <h3>Staging environment<a class="headerlink" href="#staging-environment" title="Permalink to this headline">¶</a></h3> |
| <p>If possible, keep a staging environment to test the complete DAG run before deploying in the production. |
| Make sure your DAG is parameterized to change the variables, e.g., the output path of S3 operation or the database used to read the configuration. |
| Do not hard code values inside the DAG and then change them manually according to the environment.</p> |
| <p>You can use environment variables to parameterize the DAG.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span> |
| |
| <span class="n">dest</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span> |
| <span class="s2">"MY_DAG_DEST_PATH"</span><span class="p">,</span> |
| <span class="s2">"s3://default-target/path/"</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="deployment-in-production"> |
| <h2>Deployment in Production<a class="headerlink" href="#deployment-in-production" title="Permalink to this headline">¶</a></h2> |
| <p>Once you have completed all the mentioned checks, it is time to deploy your DAG in production. |
| To do this, first, you need to make sure that the Airflow is itself production-ready. |
| Let’s see what precautions you need to take.</p> |
| <div class="section" id="database-backend"> |
| <h3>Database backend<a class="headerlink" href="#database-backend" title="Permalink to this headline">¶</a></h3> |
| <p>Airflow comes with an <code class="docutils literal notranslate"><span class="pre">SQLite</span></code> backend by default. It allows the user to run Airflow without any external database. |
| However, such a setup is meant to be for testing purposes only. Running the default setup can lead to data loss in multiple scenarios. |
| If you want to run Airflow in production, make sure you <a class="reference internal" href="howto/initialize-database.html"><span class="doc">configure the backend</span></a> to be an external database such as PostgreSQL or MySQL.</p> |
| <p>You can change the backend using the following config</p> |
| <div class="highlight-ini notranslate"><div class="highlight"><pre><span></span><span class="k">[core]</span> |
| <span class="na">sql_alchemy_conn</span> <span class="o">=</span> <span class="s">my_conn_string</span> |
| </pre></div> |
| </div> |
| <p>Once you have changed the backend, airflow needs to create all the tables required for operation. |
| Create an empty DB and give airflow’s user the permission to <code class="docutils literal notranslate"><span class="pre">CREATE/ALTER</span></code> it. |
| Once that is done, you can run -</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">airflow</span> <span class="n">db</span> <span class="n">upgrade</span> |
| </pre></div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">upgrade</span></code> keeps track of migrations already applies, so it’s safe to run as often as you need.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Do not use <code class="docutils literal notranslate"><span class="pre">airflow</span> <span class="pre">db</span> <span class="pre">init</span></code> as it can create a lot of default connection, charts, etc. which are not required in production DB.</p> |
| </div> |
| </div> |
| <div class="section" id="multi-node-cluster"> |
| <h3>Multi-Node Cluster<a class="headerlink" href="#multi-node-cluster" title="Permalink to this headline">¶</a></h3> |
| <p>Airflow uses <a class="reference internal" href="_api/airflow/executors/sequential_executor/index.html#airflow.executors.sequential_executor.SequentialExecutor" title="airflow.executors.sequential_executor.SequentialExecutor"><code class="xref py py-class docutils literal notranslate"><span class="pre">airflow.executors.sequential_executor.SequentialExecutor</span></code></a> by default. However, by its nature, the user is limited to executing at most |
| one task at a time. <code class="docutils literal notranslate"><span class="pre">Sequential</span> <span class="pre">Executor</span></code> also pauses the scheduler when it runs a task, hence not recommended in a production setup. |
| You should use <a class="reference internal" href="_api/airflow/executors/local_executor/index.html#airflow.executors.local_executor.LocalExecutor" title="airflow.executors.local_executor.LocalExecutor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Local</span> <span class="pre">executor</span></code></a> for a single machine. |
| For multi-node setup, you should use <a class="reference internal" href="executor/kubernetes.html"><span class="doc">Kubernetes executor</span></a> or <a class="reference internal" href="executor/celery.html"><span class="doc">Celery executor</span></a>.</p> |
| <p>Once you have configured the executor, it is necessary to make sure that every node in the cluster contains the same configuration and dags. |
| Airflow only sends simple instructions such as “execute task X of dag Y” but does not send any dag files or configuration. You can use a simple cronjob or |
| any other mechanism to sync DAGs and configs across your nodes, e.g., checkout DAGs from git repo every 5 minutes on all nodes.</p> |
| </div> |
| <div class="section" id="logging"> |
| <h3>Logging<a class="headerlink" href="#logging" title="Permalink to this headline">¶</a></h3> |
| <p>If you are using disposable nodes in your cluster, configure the log storage to be a distributed file system (DFS) such as <code class="docutils literal notranslate"><span class="pre">S3</span></code> and <code class="docutils literal notranslate"><span class="pre">GCS</span></code>, or external services such as |
| Stackdriver Logging, Elasticsearch or Amazon CloudWatch. |
| This way, the logs are available even after the node goes down or gets replaced. See <a class="reference internal" href="howto/write-logs.html"><span class="doc">Writing Logs</span></a> for configurations.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>The logs only appear in your DFS after the task has finished. You can view the logs while the task is running in UI itself.</p> |
| </div> |
| </div> |
| <div class="section" id="configuration"> |
| <h3>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h3> |
| <p>Airflow comes bundles with a default <code class="docutils literal notranslate"><span class="pre">airflow.cfg</span></code> configuration file. |
| You should use environment variables for configurations that change across deployments |
| e.g. metadata DB, password. You can do it using the format <code class="docutils literal notranslate"><span class="pre">$AIRFLOW__{SECTION}__{KEY}</span></code></p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">AIRFLOW__CORE__SQL_ALCHEMY_CONN</span><span class="o">=</span><span class="n">my_conn_id</span> |
| <span class="n">AIRFLOW__WEBSERVER__BASE_URL</span><span class="o">=</span><span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">host</span><span class="p">:</span><span class="n">port</span> |
| </pre></div> |
| </div> |
| <p>Some configurations such as Airflow Backend connection URI can be derived from bash commands as well:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sql_alchemy_conn_cmd</span> <span class="o">=</span> <span class="n">bash_command_to_run</span> |
| </pre></div> |
| </div> |
| </div> |
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