![]() If you click on them, you can access their corresponding logs. You'll see small colored boxes that represent the executions of each of your DAG's tasks. Open the detail page of your DAG and select the Tree View. You can access the DAG execution logs via the Airflow UI as well. So for your dag to run, you should turn it on using the ON/OFF toggle in the Airflow UI. By default, new dags are turned off in Airflow. After a bit, you should see your new dag under the DAGs tab in the Airflow UI (refresh page). Scp dagFile.py :airflow/dags/dagFile.pyĦ. To add a new dag to your Airflow instance, just scp the dag Python file to the corresponding stats machine dag folder. After that, you should be able to see Airflow's UI if you open (change port if needed) on your browser.ĥ. Use the port that you specified when launching the web server. On your local machine, create an ssh tunnel to the stats machine you are running Airflow. This will spin up the service that executes the dags.Ĥ. Execute the Airflow scheduler inside a screen/tmux. Execute the Airflow web server inside a screen/tmux. If not, make sure your environment is setup correctly.Ģ. If you just configured Airflow following the previous section, skip this step. Whenever you want to test an Airflow DAG, just jump to the next section.ġ. The steps followed so far don't need to be repeated. Airflow is now configured to be able to access Hive. You can now stop the Airflow web server in your stats machine. Set the following configurations and save changes:Ĭonn id = analytics-test-hive (Same string defined from your configuration. Open (change port if needed) on your browser, and click on the edit button for the connection with Conn Type = hive_metastore. For that, spin up the Airflow web server. The Hive metastore configurations need to be set from the Airflow UI. Edit ~/airflow/airflow.cfg and assign the following configuration values.Ĥ. # copy your credentials cache path and Service principalģ. You'll find the path to your credentials cache directory under Ticket cache: FILE. Obtain your Kerberos credentials cache path. If you just installed Airflow following the previous section, skip this step. The installation is finished at this point.ġ. Airflow will create a SQLite database file, a logs folder and a config file, all under your Airflow directory. Flask-admin version needs to be 1.4.0, because newer versions break when spinning up the Airflow web server (). Note Airflow is installed together with its Hdfs, Hive and Kerberos extensions. Make sure your environment variable https_proxy is set to allow you to download Python packages from the internet.ħ. This will set some environment variables that control which Python executable and packages are going to be used, and will display a different command line prompt.Ħ. ![]() This will allow you to install all required Python packages for Airflow without altering other Python systems you may have.ĥ. Change directory to your Airflow folder.Ĥ. This will tell Airflow where to setup configuration, database files and where to find your dag files.ģ. Set the environment variable AIRFLOW_HOME to your Airflow folder. It should contain a subfolder named dags, where you will put your dag files.Ģ. Make sure you have a dedicated directory for Airflow in your home folder. So, consider renewing it for long tests.ġ. Note that you'll be able to execute tests in airflow only for as long as your ticket is valid. Also, make sure at all times that your Kerberos authentication ticket is fresh. All steps in this tutorial assume you are logged in your preferred stats machine via ssh.Ģ.
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