In Airflow 2.0, when creating a DAG using the DAG constructor - I would like to use one of its trigger configuration parameters for naming its dag_id.
For example, as I use the Google Cloud Composer environment, I have something like the following code snippets:
trigger_dag.sh
DAG_VERSION=some_dag_v1.0.0
TRIGGER_PARAMS='{"dag_version":"'"${DAG_VERSION}"'"}';
gcloud beta composer environments run "${AIRFLOW_ENV_NAME}" --location=us-central1 dags trigger -- "${DAG_VERSION}" --conf "${TRIGGER_PARAMS}";
dag.py
dag = DAG(
dag_id=conf.dag_version, ## <- How do I access DAG config variables here?
schedule_interval=conf.dag_schedule_interval)
If I were inside a Python operator, I would have probably defined conf = context['dag_run'].conf , where **context is given as an argument. However, I'm not sure that it's possible to do it that way when initially defining the DAG in the top level of dag.py.
Related
I have been trying past 2 days to resolve this. There is a DAG python script which I created and saved it in the dags folder in airflow which is being referred to in the "airflow.cfg" file. The other dags are getting updated except for one dag. I tried to restart scheduler and also tried to reset the airflow db using airflow db reset and then tried airflow db init once again but still the same issue exists.
Some ideas on what you could check:
Do all of your DAGs have a unique dag_id? (I lost a few hours to this once, if two dags have the same name, the scheduler will randomly pick one to display with every dag_dir_list_interval)
If you are using a the #dag decorator: are you calling the DAG below its definition? Like so:
from airflow.decorators import dag, task
from pendulum import datetime
#dag(
dag_id="unique_name",
start_date=datetime(2022,12,10),
schedule=None,
catchup=False
)
def my_dag():
#task
def say_hi():
return "hi"
say_hi()
# without this line the DAG will not show up in the UI
my_dag()
What is the output of airflow run dags list and airflow run dags list-import-errors ?
If you have a lot of DAGs in your environment you might want to increase the dagbag_import_timeout.
Does your DAG work if thrown into a new Airflow instance (the easiest way to check is by spinning up a project with the Astro CLI and putting the dag into the dags folder created by astro dev init)
Disclaimer: I work at Astronomer, who develops the Astro CLI as an OS project.
I am trying to write some DAG integrity tests in airflow. The issue I am coming across is the DAG that I am testing, I have references to variables in some of the tasks within that DAG.
eg: Variable.get("AIRFLOW_VAR_BLOB_CONTAINER")
I seem to be getting the error:
sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) no such table: variable
from this because when testing via pytest, those variables (and the variables table) don't exist. Does anyone know any workarounds or suggested methods to handle Variables/Connection references when running DAG Integrity tests?
Thanks,
You can create a local metastore for testing. Running airflow db init without any other settings will create a SQLite metastore in your home directory which you can use during testing. My default additional settings for a local metastore for testing are:
AIRFLOW__CORE__LOAD_DEFAULT_CONNECTIONS=False (to ensure there are no defaults to make things magically work)
AIRFLOW__CORE__LOAD_EXAMPLES=False (to ensure there are no defaults to make things magically work)
AIRFLOW__CORE__UNIT_TEST_MODE=True (Set default test settings, skip certain actions, etc.)
AIRFLOW_HOME=[project root dir] (To avoid Airflow files in your home dir)
Running airflow db init with these settings results in three files in your project root dir:
unittests.db
unittests.cfg
webserver_config.py
It's probably a good idea to add those to your .gitignore. With this set up you can safely test against the local metastore unittests.db during your tests (ensure that when running pytest, the same env vars are set).
Alternatively, if you don't want a local metastore for reasons, you will have to resort to mocking to substitute the call Airflow makes to the metastore. This requires knowledge of the internals of Airflow. An example:
import datetime
from unittest import mock
from airflow.models import DAG
from airflow.operators.bash import BashOperator
def test_bash_operator(tmp_path):
with DAG(dag_id="test_dag", start_date=datetime.datetime(2021, 1, 1), schedule_interval="#daily") as dag:
with mock.patch("airflow.models.variable.Variable.get") as variable_get_mock:
employees = ["Alice", "Bob", "Charlie"]
variable_get_mock.return_value = employees
output_file = tmp_path / "output.txt"
test = BashOperator(task_id="test", bash_command="echo {{ var.json.employees }} > " + str(output_file))
dag.clear()
test.run(
start_date=dag.start_date,
end_date=dag.start_date,
ignore_first_depends_on_past=True,
ignore_ti_state=True,
)
variable_get_mock.assert_called_once()
assert output_file.read_text() == f"[{', '.join(employees)}]\n"
These lines:
with mock.patch("airflow.models.variable.Variable.get") as variable_get_mock:
employees = ["Alice", "Bob", "Charlie"]
variable_get_mock.return_value = employees
Determine that the function airflow.models.variable.Variable.get isn't actually called but instead this list is returned: ["Alice", "Bob", "Charlie"]. Since task.run() doesn't return anything, I made the bash_command write to a tmp_path, and read the file to assert if the content is what I expected.
This avoids the need for a metastore entirely, but mocking can be a lot of work and complex once your tests grow beyond basic examples like these.
I am using airflow v2.0 on windows 10 WSL (Ubuntu 20.04).
The warning message is :
/home/jainri/.local/lib/python3.8/site-packages/airflow/models/dag.py:1342: PendingDeprecationWarning: The requested task could not be added to the DAG because a task with task_id create_tag_template_field_result is already in the DAG. Starting in Airflow 2.0, trying to overwrite a task will raise an exception.
warnings.warn(
Done.
Due to this warning, the dags showing in web UI are also some example dags included with apache airflow. I have setup **AIRFLOW_HOME** and it also picks up dags from there. But the list of example dags also displayed. I have posted the image of WEB UI also.
WebUI
This is the dag below that I am trying to run:
import datetime
import logging
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
#
# TODO: Define a function for the python operator to call
#
def greet():
logging.info("Hello Rishabh!!")
dag = DAG(
'lesson1.demo1',
start_date = datetime.datetime.now()
end_date
)
#
# TODO: Define the task below using PythonOperator
#
greet_task = PythonOperator(
task_id='greet_task',
python_callable=greet,
dag=dag
)
Also, the main issue is like the list of dags showing in webUI is some example dags. That shows up a huge list along with my own dags. Which makes it cumbersome to look for my own dags.
I found the issue, the error you are seeing is because of airflow/example_dags/example_complex.py (one of the example_dags) that is shipped with Airflow.
Disable loading of example_dags by setting AIRFLOW__CORE__LOAD_EXAMPLES=False as an environment variable or set [core] load_examples = False in airflow.cfg (docs).
I have a dag that we'll deploy to multiple different airflow instances and in our airflow.cfg we have dags_are_paused_at_creation = True but for this specific dag we want it to be turned on without having to do so manually by clicking on the UI. Is there a way to do it programmatically?
I created the following function to do so if anyone else runs into this issue:
import airflow.settings
from airflow.models import DagModel
def unpause_dag(dag):
"""
A way to programatically unpause a DAG.
:param dag: DAG object
:return: dag.is_paused is now False
"""
session = airflow.settings.Session()
try:
qry = session.query(DagModel).filter(DagModel.dag_id == dag.dag_id)
d = qry.first()
d.is_paused = False
session.commit()
except:
session.rollback()
finally:
session.close()
airflow-rest-api-plugin plugin can also be used to programmatically pause tasks.
Pauses a DAG
Available in Airflow Version: 1.7.0 or greater
GET - http://{HOST}:{PORT}/admin/rest_api/api?api=pause
Query Arguments:
dag_id - string - The id of the dag
subdir (optional) - string - File location or directory from which to
look for the dag
Examples:
http://{HOST}:{PORT}/admin/rest_api/api?api=pause&dag_id=test_id
See for more details:
https://github.com/teamclairvoyant/airflow-rest-api-plugin
supply your dag_id and run this command on your command line.
airflow pause dag_id.
For more information on the airflow command line interface: https://airflow.incubator.apache.org/cli.html
I think you are looking for unpause ( not pause)
airflow unpause DAG_ID
The following cli command should work per the recent docs.
airflow dags unpause dag_id
https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#unpause
Airflow's REST API provides a way using the DAG patch API: we need to update the dag with query parameter ?update_mask=is_paused and send boolean as request body.
Ref: https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/patch_dag
airflow pause dag_id.
has been discontinued.
You will have to use:
airflow dags pause dag_id
You can do this using in the python operator of any dag to pause and unpause the dags programatically . This is the best approch i found instead of using cli just pass the list of dags and rest is take care
from airflow.models import DagModel
dag_id = "dag_name"
dag = DagModel.get_dagmodel(dag_id)
dag.set_is_paused(is_paused=False)
And just if you want to check if it is paused or not it will return boolean
dag.is_paused()
I am looking to add another DAG to an existing Airflow server. The server is currently using LocalExecutor but I might want my DAG to use CeleryExecutor. It seems like the configuration file airflow.cfg only allows one executor:
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor
executor = LocalExecutor
Is it possible to configure Airflow such that the existing DAGs can continue to use LocalExecutor and my new DAG can use CeleryExecutor or a custom executor class? I haven't found any examples of people doing this nor come across anything in the Airflow documentation.
If you have a SubDAG within your DAG, you can pass in a specific executor to that SubDagOperator. For instance, to use a SequentialExecutor:
bar_subdag = SubDagOperator(
task_id='bar',
subdag=my_subdag('foo', 'bar', default_args),
default_args=default_args,
dag=foo_dag,
executor=SequentialExecutor()
)
This is on 1.8, not sure if 1.9 is different.
Seems the scheduler will only start one instance of the executor.