![]() Please note that the time returned by macros.time is in the timezone specified in your Airflow configuration. In this example, the print_current_time function prints the current time, which is obtained by calling time(). With DAG('time_dag', start_date=datetime(2022, 1, 1)) as dag: Here's an example of how you can use macros.time in your DAG: from airflow import DAGįrom _operator import PythonOperator It's useful when you need to perform operations that are dependent on the current time. The macros.time variable is a function that returns the current time in the timezone specified in your Airflow configuration. One of these is the macros.time variable. In Apache Airflow, the macros module provides a set of predefined variables and functions that can be used in your DAGs. Using the macros.time Variable in Airflow For more information on how to use macros in Airflow, refer to the Airflow documentation on macros. In this example, ('') is used to parse a string into a datetime object, which is then used as the start date for the DAG.įor more information on the dateutil module and its capabilities, you can refer to the official dateutil documentation. Start = DummyOperator(task_id='start', dag=dag) Here is an example of how you might use macros.dateutil in an Airflow DAG: from airflow import DAGįrom _operator import DummyOperator Computing the number of business days between two dates.Computing relative deltas (next Monday, next month, next year, etc.).The dateutil module in Airflow provides several useful features for manipulating dates and times, such as: It is part of the macros module, which is a set of predefined variables that can be used in your Airflow DAGs. In Apache Airflow, macros.dateutil is a module that provides powerful extensions to the standard datetime module. If the task runs for longer than this, Airflow will raise an error.įor more information on Airflow macros, you can refer to the official Airflow documentation. In this example, timedelta(minutes=2) is used to specify that the maximum execution time for task1 is 2 minutes. Here's an example of how you can use macros.timedelta: from _operator import DummyOperatorĭag = DAG('my_dag', default_args=default_args)Įxecution_timeout=timedelta(minutes=2) # Using timedelta here This macro is part of the Airflow's Jinja templating mechanism, which allows you to inject dynamic information into your task parameters. It represents a duration or difference between two dates or times. In Apache Airflow, macros.timedelta is a utility that helps in performing operations on dates. For more details on the datetime module and the strftime method, you can refer to the official Python documentation. The execution_date is formatted as a string using the strftime method from the datetime module, which is accessed through .įor more information on using macros in Airflow, you can refer to the official Airflow documentation. In this example, the print_execution_date function prints the execution date of the task. Here's an example of how you might use the ds_nodash filter in a BashOperator: from _operator import BashOperatorīash_command='echo "Execution date is , This filter is particularly useful when you need to pass the execution date as a parameter to a function or a command that does not accept date strings with dashes. The ds stands for "date stamp" and nodash implies that this date stamp should not contain any dashes.įor instance, if the execution date is, applying the ds_nodash filter would result in 20220301. ![]() In Apache Airflow, the ds_nodash filter is used to format the execution date of a task into a string without dashes. Note the placeholder /log.Understanding the ds_nodash Filter in Airflow Here is a template: Īnd the data model: title_to_insert="My Super Page" Templates are widely used for creating dynamic HTML pages. A template engine is a library that combines templates with data models to produce documents. Stick with me □ What is Jinja templating? Jinja templating! You will discover the solution later in the article. What if you miss a day and want to rerun the task for that day? Are you gonna hardcode that date for this specific day? What if you missed a week? A month? You get it. However, this solution has a severe limitation. It is a little better as the date isn’t hardcoded anymore but based on the current day. What about that instead: from datetime import extract_data(): Hardcoding the date means changing it every day, which makes no sense. How would you implement the task that extracts the data from these directories? I’m sure we all agree the code below won’t work: extract_data():
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