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| <div class="section" id="scaling-out-with-celery"> |
| <h1>Scaling Out with Celery<a class="headerlink" href="#scaling-out-with-celery" title="Permalink to this headline">¶</a></h1> |
| <p><code class="docutils literal notranslate"><span class="pre">CeleryExecutor</span></code> is one of the ways you can scale out the number of workers. For this |
| to work, you need to setup a Celery backend (<strong>RabbitMQ</strong>, <strong>Redis</strong>, …) and |
| change your <code class="docutils literal notranslate"><span class="pre">airflow.cfg</span></code> to point the executor parameter to |
| <code class="docutils literal notranslate"><span class="pre">CeleryExecutor</span></code> and provide the related Celery settings.</p> |
| <p>For more information about setting up a Celery broker, refer to the |
| exhaustive <a class="reference external" href="http://docs.celeryproject.org/en/latest/getting-started/brokers/index.html">Celery documentation on the topic</a>.</p> |
| <p>Here are a few imperative requirements for your workers:</p> |
| <ul class="simple"> |
| <li><code class="docutils literal notranslate"><span class="pre">airflow</span></code> needs to be installed, and the CLI needs to be in the path</li> |
| <li>Airflow configuration settings should be homogeneous across the cluster</li> |
| <li>Operators that are executed on the worker need to have their dependencies |
| met in that context. For example, if you use the <code class="docutils literal notranslate"><span class="pre">HiveOperator</span></code>, |
| the hive CLI needs to be installed on that box, or if you use the |
| <code class="docutils literal notranslate"><span class="pre">MySqlOperator</span></code>, the required Python library needs to be available in |
| the <code class="docutils literal notranslate"><span class="pre">PYTHONPATH</span></code> somehow</li> |
| <li>The worker needs to have access to its <code class="docutils literal notranslate"><span class="pre">DAGS_FOLDER</span></code>, and you need to |
| synchronize the filesystems by your own means. A common setup would be to |
| store your DAGS_FOLDER in a Git repository and sync it across machines using |
| Chef, Puppet, Ansible, or whatever you use to configure machines in your |
| environment. If all your boxes have a common mount point, having your |
| pipelines files shared there should work as well</li> |
| </ul> |
| <p>To kick off a worker, you need to setup Airflow and kick off the worker |
| subcommand</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>airflow worker |
| </pre></div> |
| </div> |
| <p>Your worker should start picking up tasks as soon as they get fired in |
| its direction.</p> |
| <p>Note that you can also run “Celery Flower”, a web UI built on top of Celery, |
| to monitor your workers. You can use the shortcut command <code class="docutils literal notranslate"><span class="pre">airflow</span> <span class="pre">flower</span></code> |
| to start a Flower web server.</p> |
| <p>Please note that you must have the <code class="docutils literal notranslate"><span class="pre">flower</span></code> python library already installed on your system. The recommend way is to install the airflow celery bundle.</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install <span class="s1">'apache-airflow[celery]'</span> |
| </pre></div> |
| </div> |
| <p>Some caveats:</p> |
| <ul class="simple"> |
| <li>Make sure to use a database backed result backend</li> |
| <li>Make sure to set a visibility timeout in [celery_broker_transport_options] that exceeds the ETA of your longest running task</li> |
| <li>Tasks can consume resources. Make sure your worker has enough resources to run <cite>worker_concurrency</cite> tasks</li> |
| </ul> |
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