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| Authoring Workflow in Apache Airflow. Airflow makes it easy to author workflows using python scripts." /> |
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| <span class="bodytext__medium--brownish-grey">Mon, Aug 17, 2020</span> |
| </div> |
| <p class="blogpost-content__metadata--title">Apache Airflow For Newcomers</p> |
| <div class="blogpost-content__metadata--author"> |
| <span class="blogpost-content__metadata--author"> |
| Ephraim Anierobi |
| </span> |
| |
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| <p class="blogpost-content__metadata--description"></p> |
| </div> |
| <div class="markdown-content"> |
| <p>Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. |
| A workflow is a sequence of tasks that processes a set of data. You can think of workflow as the |
| path that describes how tasks go from being undone to done. Scheduling, on the other hand, is the |
| process of planning, controlling, and optimizing when a particular task should be done.</p> |
| <h3 id="authoring-workflow-in-apache-airflow">Authoring Workflow in Apache Airflow.</h3> |
| <p>Airflow makes it easy to author workflows using python scripts. A <a href="https://en.wikipedia.org/wiki/Directed_acyclic_graph">Directed Acyclic Graph</a> |
| (DAG) represents a workflow in Airflow. It is a collection of tasks in a way that shows each task’s |
| relationships and dependencies. You can have as many DAGs as you want, and Airflow will execute |
| them according to the task’s relationships and dependencies. If task B depends on the successful |
| execution of another task A, it means Airflow will run task A and only run task B after task A. |
| This dependency is very easy to express in Airflow. For example, the above scenario is expressed as</p> |
| <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="n">task_A</span> <span class="o">>></span> <span class="n">task_B</span> |
| </code></pre></div><p>Also equivalent to</p> |
| <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="n">task_A</span><span class="o">.</span><span class="n">set_downstream</span><span class="p">(</span><span class="n">task_B</span><span class="p">)</span> |
| </code></pre></div><p><img src="Simple_dag.png" alt="Simple Dag"></p> |
| <p>That helps Airflow to know that it needs to execute task A before task B. Tasks can have far more complex |
| relationships to each other than expressed above and Airflow figures out how and when to execute the tasks following |
| their relationships and dependencies. |
| <img src="semicomplex.png" alt="Complex Dag"></p> |
| <p>Before we discuss the architecture of Airflow that makes scheduling, executing, and monitoring of |
| workflow an easy thing, let us discuss the <a href="https://github.com/apache/airflow/blob/master/BREEZE.rst">Breeze environment</a>.</p> |
| <h3 id="breeze-environment">Breeze Environment</h3> |
| <p>The breeze environment is the development environment for Airflow where you can run tests, build images, |
| build documentations and so many other things. There are excellent |
| <a href="https://github.com/apache/airflow/blob/master/BREEZE.rst">documentation and video</a> on Breeze environment. |
| Please check them out. You enter the Breeze environment by running the <code>./breeze</code> script. You can run all |
| the commands mentioned here in the Breeze environment.</p> |
| <h3 id="scheduler">Scheduler</h3> |
| <p>The scheduler is the component that monitors DAGs and triggers those tasks whose dependencies have |
| been met. It watches over the DAG folder, checking the tasks in each DAG and triggers them once they |
| are ready. It accomplishes this by spawning a process that runs periodically(every minute or so) |
| reading the metadata database to check the status of each task and decides what needs to be done. |
| The metadata database is where the status of all tasks are recorded. The status can be one of running, |
| success, failed, etc.</p> |
| <p>A task is said to be ready when its dependencies have been met. The dependencies include all the data |
| necessary for the task to be executed. It should be noted that the scheduler won’t trigger your tasks until |
| the period it covers has ended. If a task’s <code>schedule_interval</code> is <code>@daily</code>, the scheduler triggers the task |
| at the end of the day and not at the beginning. This is to ensure that the necessary data needed for the tasks |
| are ready. It is also possible to trigger tasks manually on the UI.</p> |
| <p>In the <a href="https://github.com/apache/airflow/blob/master/BREEZE.rst">Breeze environment</a>, the scheduler is started by running the command <code>airflow scheduler</code>. It uses |
| the configured production environment. The configuration can be specified in <code>airflow.cfg</code></p> |
| <h3 id="executor">Executor</h3> |
| <p>Executors are responsible for running tasks. They work with the scheduler to get information about |
| what resources are needed to run a task as the task is queued.</p> |
| <p>By default, Airflow uses the <a href="https://airflow.apache.org/docs/stable/executor/sequential.html#sequential-executor">SequentialExecutor</a>. |
| However, this executor is limited and it is the only executor that can be used with SQLite.</p> |
| <p>There are many other <a href="https://airflow.apache.org/docs/stable/executor/index.html">executors</a>, |
| the difference is on the resources they have and how they choose to use the resources. The available executors |
| are:</p> |
| <ul> |
| <li>Sequential Executor</li> |
| <li>Debug Executor</li> |
| <li>Local Executor</li> |
| <li>Dask Executor</li> |
| <li>Celery Executor</li> |
| <li>Kubernetes Executor</li> |
| <li>Scaling Out with Mesos (community contributed)</li> |
| </ul> |
| <p>CeleryExecutor is a better executor compared to the SequentialExecutor. The CeleryExecutor uses several |
| workers to execute a job in a distributed way. If a worker node is ever down, the CeleryExecutor assign its |
| task to another worker node. This ensures high availability.</p> |
| <p>The CeleryExecutor works closely with the scheduler which adds a message to the queue and the Celery broker |
| which delivers the message to a Celery worker to execute. |
| You can find more information about the CeleryExecutor and how to configure it at the |
| <a href="https://airflow.apache.org/docs/stable/executor/celery.html#celery-executor">documentation</a></p> |
| <h3 id="webserver">Webserver</h3> |
| <p>The webserver is the web interface (UI) for Airflow. The UI is feature-rich. It makes it easy to |
| monitor and troubleshoot DAGs and Tasks.</p> |
| <p><img src="airflow-ui.png" alt="airflow UI"></p> |
| <p>There are many actions you can perform on the UI. You can trigger a task, monitor the execution |
| including the duration of the task. The UI makes it possible to view the task’s dependencies in a |
| tree view and graph view. You can view task logs in the UI.</p> |
| <p>The web UI is started with the command <code>airflow webserver</code> in the breeze environment.</p> |
| <h3 id="backend">Backend</h3> |
| <p>By default, Airflow uses the SQLite backend for storing the configuration information, DAG states, |
| and much other useful information. This should not be used in production as SQLite can cause a data |
| loss.</p> |
| <p>You can use PostgreSQL or MySQL as a backend for airflow. It is easy to change to PostgreSQL or MySQL.</p> |
| <p>The command <code>./breeze --backend mysql</code> selects MySQL as the backend when starting the breeze environment.</p> |
| <h3 id="operators">Operators</h3> |
| <p>Operators determine what gets done by a task. Airflow has a lot of builtin Operators. Each operator |
| does a specific task. There’s a BashOperator that executes a bash command, the PythonOperator which |
| calls a python function, AwsBatchOperator which executes a job on AWS Batch and <a href="https://airflow.apache.org/docs/stable/concepts.html#operators">many more</a>.</p> |
| <h4 id="sensors">Sensors</h4> |
| <p>Sensors can be described as special operators that are used to monitor a long-running task. |
| Just like Operators, there are many predefined sensors in Airflow. These includes</p> |
| <ul> |
| <li>AthenaSensor: Asks for the state of the Query until it reaches a failure state or success state.</li> |
| <li>AzureCosmosDocumentSensor: Checks for the existence of a document which matches the given query in CosmosDB</li> |
| <li>GoogleCloudStorageObjectSensor: Checks for the existence of a file in Google Cloud Storage</li> |
| </ul> |
| <p>A list of most of the available sensors can be found in this <a href="https://airflow.apache.org/docs/stable/_api/airflow/contrib/sensors/index.html?highlight=sensors#module-airflow.contrib.sensors">module</a></p> |
| <h3 id="contributing-to-airflow">Contributing to Airflow</h3> |
| <p>Airflow is an open source project, everyone is welcome to contribute. It is easy to get started thanks |
| to the excellent <a href="https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst">documentation on how to get started</a>.</p> |
| <p>I joined the community about 12 weeks ago through the <a href="https://www.outreachy.org/">Outreachy Program</a> and have |
| completed about <a href="https://github.com/apache/airflow/pulls/ephraimbuddy">40 PRs</a>.</p> |
| <p>It has been an amazing experience! Thanks to my mentors <a href="https://github.com/potiuk">Jarek</a> and |
| <a href="https://github.com/kaxil">Kaxil</a>, and the community members especially <a href="https://github.com/mik-laj">Kamil</a> |
| and <a href="https://github.com/turbaszek">Tomek</a> for all their support. I’m grateful!</p> |
| <p>Thank you so much, <a href="https://github.com/leahecole">Leah E. Cole</a>, for your wonderful reviews.</p> |
| |
| </div> |
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