Question

有没有办法在Airflow中创建一个用户定义的宏，它本身是从其他宏计算的？

from airflow import DAG
from airflow.operators.bash_operator import BashOperator

dag = DAG(
    'simple',
    schedule_interval='0 21 * * *',
    user_defined_macros={
        'next_execution_date': '{{ dag.following_schedule(execution_date) }}',
    },
)

task = BashOperator(
    task_id='bash_op',
    bash_command='echo "{{ next_execution_date }}"',
    dag=dag,
)

此处的用例是将新的Airflow v1.8 next_execution_date宏反向移植到Airflow v1.7中。不幸的是，这个模板在没有宏扩展的情况下呈现：

$ airflow render simple bash_op 2017-08-09 21:00:00
    # ----------------------------------------------------------
    # property: bash_command
    # ----------------------------------------------------------
    echo "{{ dag.following_schedule(execution_date) }}"

Answer 1

以下是一些解决方案：

1。覆盖`BashOperator`以向上下文添加一些值

class NextExecutionDateAwareBashOperator(BashOperator):
    def render_template(self, attr, content, context):
        dag = context['dag']
        execution_date = context['execution_date']
        context['next_execution_date'] = dag.following_schedule(execution_date)

        return super().render_templates(attr, content, context)
        # or in python 2:
        # return super(NextExecutionDateAwareBashOperator, self).render_templates(attr, content, context)

这种方法很有用：您可以在自定义运算符中捕获一些重复的代码。

不好的部分：在渲染模板化字段之前，您必须编写自定义运算符以向上下文添加值。

2。在用户定义的宏

中进行计算

Macros不一定是值。它们可以是功能。

在你的dag中：

def compute_next_execution_date(dag, execution_date):
    return dag.following_schedule(execution_date)

dag = DAG(
    'simple',
    schedule_interval='0 21 * * *',
    user_defined_macros={
        'next_execution_date': compute_next_execution_date,
    },
)

task = BashOperator(
    task_id='bash_op',
    bash_command='echo "{{ next_execution_date(dag, execution_date) }}"',
    dag=dag,
)

好的部分：您可以定义可重用的函数来处理运行时可用的值（XCom values，作业实例属性，任务实例属性等...），并使您的函数结果可用于呈现模板。

坏的部分（但不是那么烦人）：你必须在每个dag中导入这样一个用户定义宏的功能。

3。直接在模板中调用您的陈述

此解决方案是最简单的（如Ardan's answer所述），可能是您案例中的好方法。

BashOperator(
    task_id='bash_op',
    bash_command='echo "{{ dag.following_schedule(execution_date) }}"',
    dag=dag,
)

非常适合像这样简单的通话。它们是macros直接提供的其他一些对象（如task，task_instance等...）;甚至可以使用一些标准模块（例如macros.time，...）。

Answer 2

默认情况下，

user_defined_macros不会作为模板处理。如果您想在user_defined_macro中保留模板（或者如果您在params变量中使用模板），则可以随时手动重新运行模板功能：

class DoubleTemplatedBashOperator(BashOperator):
    def pre_execute(self, context):
        context['ti'].render_templates()

这适用于不会引用其他参数或UDM的模板。通过这种方式，你可以拥有两个深度的＆＃34;模板。

或者将您的UDM直接放在BashOperator命令中（最简单的解决方案）：

BashOperator(
    task_id='bash_op',
    bash_command='echo "{{ dag.following_schedule(execution_date) }}"',
    dag=dag,
)

Answer 3

我投票支持制作Airflow插件以注入您的预定义宏。使用此方法，您可以在任何运算符中使用预定义的宏，而无需声明任何内容。

下面是一些我们正在使用的自定义宏。使用示例：{{ macros.dagtz_next_execution_date(ti) }}

from airflow.plugins_manager import AirflowPlugin
from datetime import datetime, timedelta
from airflow.utils.db import provide_session
from airflow.models import DagRun
import pendulum


@provide_session
def _get_dag_run(ti, session=None):
    """Get DagRun obj of the TaskInstance ti

    Args:
        ti (TYPE): the TaskInstance object
        session (None, optional): Not in use

    Returns:
        DagRun obj: the DagRun obj of the TaskInstance ti
    """
    task = ti.task
    dag_run = None
    if hasattr(task, 'dag'):
        dag_run = (
            session.query(DagRun)
            .filter_by(
                dag_id=task.dag.dag_id,
                execution_date=ti.execution_date)
            .first()
        )
        session.expunge_all()
        session.commit()
    return dag_run


def ds_add_no_dash(ds, days):
    """
    Add or subtract days from a YYYYMMDD
    :param ds: anchor date in ``YYYYMMDD`` format to add to
    :type ds: str
    :param days: number of days to add to the ds, you can use negative values
    :type days: int
    >>> ds_add('20150101', 5)
    '20150106'
    >>> ds_add('20150106', -5)
    '20150101'
    """

    ds = datetime.strptime(ds, '%Y%m%d')
    if days:
        ds = ds + timedelta(days)
    return ds.isoformat()[:10].replace('-', '')


def dagtz_execution_date(ti):
    """get the TaskInstance execution date (in DAG timezone) in pendulum obj

    Args:
        ti (TaskInstance): the TaskInstance object

    Returns:
        pendulum obj: execution_date in pendulum object (in DAG tz)
    """
    execution_date_pdl = pendulum.instance(ti.execution_date)
    dagtz_execution_date_pdl = execution_date_pdl.in_timezone(ti.task.dag.timezone)
    return dagtz_execution_date_pdl


def dagtz_next_execution_date(ti):
    """get the TaskInstance next execution date (in DAG timezone) in pendulum obj

    Args:
        ti (TaskInstance): the TaskInstance object

    Returns:
        pendulum obj: next execution_date in pendulum object (in DAG tz)
    """

    # For manually triggered dagruns that aren't run on a schedule, next/previous
    # schedule dates don't make sense, and should be set to execution date for
    # consistency with how execution_date is set for manually triggered tasks, i.e.
    # triggered_date == execution_date.
    dag_run = _get_dag_run(ti)
    if dag_run and dag_run.external_trigger:
        next_execution_date = ti.execution_date
    else:
        next_execution_date = ti.task.dag.following_schedule(ti.execution_date)

    next_execution_date_pdl = pendulum.instance(next_execution_date)
    dagtz_next_execution_date_pdl = next_execution_date_pdl.in_timezone(ti.task.dag.timezone)
    return dagtz_next_execution_date_pdl


def dagtz_next_ds(ti):
    """get the TaskInstance next execution date (in DAG timezone) in YYYY-MM-DD string
    """
    dagtz_next_execution_date_pdl = dagtz_next_execution_date(ti)
    return dagtz_next_execution_date_pdl.strftime('%Y-%m-%d')


def dagtz_next_ds_nodash(ti):
    """get the TaskInstance next execution date (in DAG timezone) in YYYYMMDD string
    """
    dagtz_next_ds_str = dagtz_next_ds(ti)
    return dagtz_next_ds_str.replace('-', '')


def dagtz_prev_execution_date(ti):
    """get the TaskInstance previous execution date (in DAG timezone) in pendulum obj

    Args:
        ti (TaskInstance): the TaskInstance object

    Returns:
        pendulum obj: previous execution_date in pendulum object (in DAG tz)
    """

    # For manually triggered dagruns that aren't run on a schedule, next/previous
    # schedule dates don't make sense, and should be set to execution date for
    # consistency with how execution_date is set for manually triggered tasks, i.e.
    # triggered_date == execution_date.
    dag_run = _get_dag_run(ti)
    if dag_run and dag_run.external_trigger:
        prev_execution_date = ti.execution_date
    else:
        prev_execution_date = ti.task.dag.previous_schedule(ti.execution_date)

    prev_execution_date_pdl = pendulum.instance(prev_execution_date)
    dagtz_prev_execution_date_pdl = prev_execution_date_pdl.in_timezone(ti.task.dag.timezone)
    return dagtz_prev_execution_date_pdl


def dagtz_prev_ds(ti):
    """get the TaskInstance prev execution date (in DAG timezone) in YYYY-MM-DD string
    """
    dagtz_prev_execution_date_pdl = dagtz_prev_execution_date(ti)
    return dagtz_prev_execution_date_pdl.strftime('%Y-%m-%d')


def dagtz_prev_ds_nodash(ti):
    """get the TaskInstance prev execution date (in DAG timezone) in YYYYMMDD string
    """
    dagtz_prev_ds_str = dagtz_prev_ds(ti)
    return dagtz_prev_ds_str.replace('-', '')


# Defining the plugin class
class AirflowTestPlugin(AirflowPlugin):
    name = "custom_macros"
    macros = [dagtz_execution_date, ds_add_no_dash,
              dagtz_next_execution_date, dagtz_next_ds, dagtz_next_ds_nodash,
              dagtz_prev_execution_date, dagtz_prev_ds, dagtz_prev_ds_nodash]

使自定义Airflow宏扩展其他宏

3 个答案:

1。覆盖`BashOperator`以向上下文添加一些值

2。在用户定义的宏

3。直接在模板中调用您的陈述

使自定义Airflow宏扩展其他宏

3 个答案:

1。覆盖BashOperator以向上下文添加一些值

2。在用户定义的宏

3。直接在模板中调用您的陈述

1。覆盖`BashOperator`以向上下文添加一些值