Only pip installation is currently officially supported. Starting with Airflow 2.3.0, Airflow is tested with Python 3.7, 3.8, 3.9, 3.10. If you are looking for the best way to deal with Airflow job, you can try this way and hope it will be useful for you.Successful installation requires a Python 3 environment. I don't recommend this for Production but it is great for local development. This is just an introduction of Airflow 2 developing on Docker technology. # stop all containers and remove everything Stop the running terminal ( control + c on Mac) and down them using this command. It shows some basic stats about the DAGs at a side.Īnd the graph is not much differ, yet better, right? 6. It's more modern, clearer, and neater.ĭAG history is quite easier to read. When we click one and go see the UI of DAG history is improved. There will be a table showing all error messages in every DAGs files. We can check the failed one by this command. In some cases, the web show some errors, for example we programmatically put wrong syntaxes or imports. It will automatically compile DAGs files and display on the web if all is successful. The airflow-worker-container-name can be retrieved by docker ps -a and choose one with worker in its name.Ģ. Access the worker instance with the command. It wouldn't show in the DAGs list instantly. I example the DAG from and save it in the folder /dags like this. It should be successful and now we can see the first page of Airflow here. Open a browser then go to Use the username/password as airflow/ airflow to login this page. We see this means we are ready to view the airflow webpage now. Next are to create the scheduler which is for scheduling our jobs, and the worker that execute them.Īnd the last one is webserver. Seconds after this will show the necessary images are downloading. Make sure all dependencies of our works are listed in requirements.txt and constraints.txt (if any) before go next.įor example, I put the package pysftp in the file so when we build an image up, the following packages will be installed in the worker instance and be ready to use.Īlso find the available packages via. Prepare all necessary directory: /dags, /logs, /plugins.Disable sample DAGs at line 59 of docker-compose.yaml.You can add yours at requirements.txt and constraint.txt if needed. The original docker-compose.yaml relies on default image but I want ability to add additional python packages, so that I create a simple dockerfile.If you want to get more familiar with this, can visit my latest blog below. The installation starts from docker-compose.yaml.I have assembled the steps defined at the Airflow documentation page into a single repo below. Now the official docker compose for Airflow 2 has been launched here so I have no need to find out an other more reliable one. So I ended up using Puckel's image instead. Kind of information is lacking in the website and configurations are lots to go. I do have time back then to build a container of Airflow 1 in my macbook but found the official image at that time isn't good enough. I will write this later.Ĭan visit the official page here to read all changes. This feature is new to me as well in order to make code cleaner and easier to read. Now we can add the decorator on top of a method and assign them as a python operator. Of course, it comes with new cleaner user interface, more understandable history page. As far as I read the changelog, I can summarize the big points that we can reach and use it as a consumer here.
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