3/21/2023 0 Comments Dag airflow![]() ![]() ![]() While on the Airflow home page, go under Admin - Variables. You'll see how connections and dependencies work later, but first, let's write the code for the third task.Īirflow Task #3 - Save Processed Datetime The tasks are listed here but aren't connected in any way. Open Airflow home page - and see if your DAG appears in the list: Image 4 - Airflow web application home page (image by author)Ĭlick on it and go to the Graph view - you'll see our two tasks listed: Image 5 - Airflow DAG graph view (image by author) Let's test the thing: airflow tasks test first_airflow_dag process_datetime Image 3 - Testing the second Airflow task (image by author) Refer to Image 2 to see what a single datetime string looks like: import osĭt = ti.xcom_pull(task_ids=) The goal is to extract the year, month, day, time, and day of week information from it. We'll convert the datetime to a string and then split it into a list on blank spaces. The ti argument allows us to access the xcom_pull() method, which retrieves the return value from the previous task(s), specified by the task_ids parameter.ĭon't worry too much about xcoms, as we'll cover them extensively in the following articles. That's all we need to get started processing the datetime, so let's do that next.Īirflow Task #2 - Process Current Datetimeįor our second task, we'll use a PythonOperator that will call the process_datetime() function. You can see that Fri Feb 11 18:35: is returned, and the task has finished successfully. Our DAG is named first_airflow_dag and we're running a task with the ID of get_datetime, so the command boils down to this: airflow tasks test first_airflow_dag get_datetime Image 2 - Testing the first Airflow task (image by author) This is the command template you can use: airflow tasks test Right now, we can test if our first task works through Terminal. The returned value gets saved internally and you can retrieve it through Airflow's xcoms, but that's something we'll explore later. The bash_command argument allows you to specify the shell command that'll be executed: import os ![]() Each task in Airflow needs an ID, which must be unique across the DAG level. We'll use Airflow's BashOperator to execute a shell command. catchup - Boolean, whether or not Airflow should catch up for every scheduled interval between start_date and now.Īnd with that out of the way, we can proceed with writing our first task.start_date - The date at which your DAG will first run, I've set it in the past.For example, * * * * * means the DAG will run every minute. You can pass the strings or a cron-like expression. schedule_interval - Specifies the interval at which your DAG should run.dag_id - A unique ID that will represent the DAG inside the Airflow web application.We've made a lot of imports, and these are the modules and operators we'll use throughout the file.Įvery Airflow DAG is defined with Python's context manager syntax ( with). Write Your First Airflow DAG - The BoilerplateĬopy the following code to first_dag.py: import osįrom import BashOperatorįrom import PythonOperator You're ready to get started - let's begin with the boilerplate. Inside the dags folder create a new Python file called first_dag.py. I'm using P圜harm, but you're free to use anything else. Create the dags folder before starting and open it in any code editor. We'll start by creating a new file in ~/airflow/dags. You can get the current datetime information through the Terminal by running the following command: date Image 1 - How to get the current datetime information through Terminal (image by author) Saves the datetime information to a CSV file.Gets the current datetime information from the Terminal.Let's dive straight in!ĭon't feel like reading? Watch my video instead:Īs the title suggests, you'll write your first DAG that implements the following data pipeline: Pretty simple, but you'll learn how Airflow's Bash and Python operators work, and also how to communicate between tasks using Xcoms, and how to schedule your data pipelines. The data pipeline will get the current datetime from the Terminal, process it, and save it to a CSV file. Today you'll write your first data pipeline (DAG) in Airflow, and it won't take you more than 10 minutes. In the previous article, you've seen how to install Apache Airflow locally in a new Python virtual environment and how to do the initial setup. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |