Source code for airflow.example_dags.tutorial

#
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"""
### Tutorial Documentation
Documentation that goes along with the Airflow tutorial located
[here](https://airflow.apache.org/tutorial.html)
"""

from __future__ import annotations

# [START tutorial]
# [START import_module]
import textwrap
from datetime import datetime, timedelta

# Operators; we need this to operate!
from airflow.providers.standard.operators.bash import BashOperator

# The DAG object; we'll need this to instantiate a DAG
from airflow.sdk import DAG

# [END import_module]


# [START instantiate_dag]
with DAG(
    "tutorial",
    # [START default_args]
    # These args will get passed on to each operator
    # You can override them on a per-task basis during operator initialization
    default_args={
        "depends_on_past": False,
        "retries": 1,
        "retry_delay": timedelta(minutes=5),
        # 'queue': 'bash_queue',
        # 'pool': 'backfill',
        # 'priority_weight': 10,
        # 'end_date': datetime(2016, 1, 1),
        # 'wait_for_downstream': False,
        # 'execution_timeout': timedelta(seconds=300),
        # 'on_failure_callback': some_function, # or list of functions
        # 'on_success_callback': some_other_function, # or list of functions
        # 'on_retry_callback': another_function, # or list of functions
        # 'sla_miss_callback': yet_another_function, # or list of functions
        # 'on_skipped_callback': another_function, #or list of functions
        # 'trigger_rule': 'all_success'
    },
    # [END default_args]
    description="A simple tutorial DAG",
    schedule=timedelta(days=1),
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example"],
) as dag:
    # [END instantiate_dag]

    # t1, t2 and t3 are examples of tasks created by instantiating operators
    # [START basic_task]

[docs]
    t1 = BashOperator(
        task_id="print_date",
        bash_command="date",
    )


    t2 = BashOperator(
        task_id="sleep",
        depends_on_past=False,
        bash_command="sleep 5",
        retries=3,
    )
    # [END basic_task]

    # [START documentation]
    t1.doc_md = textwrap.dedent(
        """\
    #### Print the current date

    This task runs `date` by using the `bash_command` argument on `BashOperator`.
    In the Task Instance Details page, Airflow renders this documentation from
    the task's `doc_md` field. After this task succeeds, Airflow can run both
    downstream tasks: `sleep` and `templated`.

    ```bash
    date
    ```

    Math fences are rendered with KaTeX. This tutorial starts one task and then
    branches into two downstream tasks:

    ```math
    1\\ \\text{upstream task} + 2\\ \\text{downstream tasks} = 3\\ \\text{tasks}
    ```

    The same dependency is shown as a Mermaid diagram:

    ```mermaid
    graph LR
        print_date[print_date] --> sleep[sleep]
        print_date --> templated[templated]
    ```
    """
    )

    dag.doc_md = __doc__  # providing that you have a docstring at the beginning of the Dag; OR
    dag.doc_md = """
    This is a documentation placed anywhere
    """  # otherwise, type it like this
    # [END documentation]

    # [START jinja_template]
    templated_command = textwrap.dedent(
        """
    {% for i in range(5) %}
        echo "{{ ds }}"
        echo "{{ macros.ds_add(ds, 7)}}"
    {% endfor %}
    """
    )

    t3 = BashOperator(
        task_id="templated",
        depends_on_past=False,
        bash_command=templated_command,
    )
    # [END jinja_template]

    t1 >> [t2, t3]
# [END tutorial]