we want to read cli input pass to dag from UI during Dagtrigger in Dag.
i tried below code but its not working. here i am passing input as {""kpi":"ID123"}
and i want to print this ip value in my function get_data_from_bq
from airflow import DAG
from airflow.utils.dates import days_ago
from airflow.operators.python_operator import PythonOperator
from airflow import models
from airflow.models import Variable
from google.cloud import bigquery
from airflow.configuration import conf
LOCATION = Variable.get("HDM_PROJECT_LOCATION")
PROJECT_ID = Variable.get("HDM_PROJECT_ID")
client = bigquery.Client()
kpi='{{ kpi}}'
# default arguments
default_dag_args = {
'start_date':days_ago(0),
'retries': 0,
'project_id': PROJECT_ID
}
# Setting airflow environment varriable,getting hdm_batch_details data and updating it
def get_data_from_bq(**kwargs):
print("op is:")
print(kpi)
#Dag Defination
with models.DAG(
'00_test_sql1',
schedule_interval=None,
default_args=default_dag_args) as dag:
v_run_sql_01 = PythonOperator(
task_id='Run_SQL',
provide_context=True,
python_callable=get_data_from_bq,
location=LOCATION,
use_legacy_sql=False)
v_run_sql_01
Note: I don't want to use any operator to read data passed from cli
Note: I don't want to use any operator to read data passed from cli
This is impossible. Dag Run is only created when there are tasks to run.
You should understand that :
DAG + its top level code - builds DAG structure consisting of Tasks
DAG Run -> is single instance of DAG run which contains Task Instances to be executed. Dag Run simply consists of task instancess that belong to the DAG run with the given "dag run".
The configuration that you pass is "dag_run.conf" not "dag.conf" - which meanss that it is only specified for the DagRun, which is valid only for all Task Instances that belong to it.
Only Task Instances have access to dag_run.conf
Related
Trying to implement a simple deferrable operator based on this example, nothing seems to appear after the manual triggering of my DAG (same case with the exact code of example).
class TestDefer(BaseOperator):
def execute(self, context):
print("--- execute --")
self.defer(
trigger=TimeDeltaTrigger(delta=timedelta(seconds=1)),
method_name="func",
)
def func(self, context, event=None):
print("--- func ----")
pass
with DAG(
"def_dag", schedule_interval=None, start_date=datetime.now(),
) as dag:
t = TestDefer(task_id="defer_task")
and then :
airflow dags test def_dag now
airflow triggerer
Result : func is never called.
Thanks in advance for your help.
Your deferrable operator code is correct. I tested it with the DAG below in Airflow 2.5.1 (only changed the print statements to logs and the start_date because datetime.now() can lead to issues when scheduling, but it should work manually as you had it).
Is the issue the same when you run the DAG manually from the UI? Using airflow dags test... I get an output without "--- func ----" but manually running the DAG from the UI the line prints and the DAG works as expected. (might be loosely related to this issue).
If manually running from the UI does not work: what is the output of docker ps?
from airflow import DAG
from datetime import timedelta, datetime
from airflow.triggers.temporal import TimeDeltaTrigger
from airflow.models.baseoperator import BaseOperator
import logging
# get Airflow logger
log = logging.getLogger('airflow.task')
class TestDefer(BaseOperator):
def execute(self, context):
log.info("--- execute --")
self.defer(
trigger=TimeDeltaTrigger(delta=timedelta(seconds=1)),
method_name="func",
)
def func(self, context, event=None):
log.info("--- func ----")
pass
with DAG(
"def_dag",
schedule_interval=None,
start_date=datetime(2023, 1, 1),
catchup=False
) as dag:
t = TestDefer(task_id="defer_task")
After few tests, with Airflow 2.5.1, and your advices, my deferrable operator works following these steps :
launching of airflow scheduler
launching of airflow triggerer
airflow dags test... or from the UI
Thanks for the help.
example_trigger_controller_dag.py
import pendulum
from airflow import DAG
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
with DAG(
dag_id="example_trigger_controller_dag",
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
schedule="#once",
tags=["example"],
) as dag:
trigger = TriggerDagRunOperator(
task_id="test_trigger_dagrun",
trigger_dag_id="example_trigger_target_dag", # Ensure this equals the dag_id of the DAG to trigger
conf={"message": "Hello World"},
)
example_trigger_target_dag.py
import pendulum
from airflow import DAG
from airflow.decorators import task
from airflow.operators.bash import BashOperator
#task(task_id="run_this")
def run_this_func(dag_run=None):
"""
Print the payload "message" passed to the DagRun conf attribute.
:param dag_run: The DagRun object
"""
print(f"Remotely received value of {dag_run.conf.get('message')} for key=message")
with DAG(
dag_id="example_trigger_target_dag",
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
schedule=None,
tags=["example"],
) as dag:
run_this = run_this_func()
bash_task = BashOperator(
task_id="bash_task",
bash_command='echo "Here is the message: $message"',
env={"message": '{{ dag_run.conf.get("message") }}'},
)
the task in controller dag successfully ended but the task in target dag stuck in queue. Any ideas about how to solve this problem?
I ran your DAGs (with both of them unpaused) and they work fine in a completely new environment (Airflow 2.5.0, Astro CLI Runtime 7.1.0). So the issue is most likely not with your DAG code.
Tasks stuck in queue is often an issue with the scheduler, mostly with older Airflow versions. I suggest you:
make sure both DAGs are unpaused when the first DAG runs.
make sure all start_dates are in the past (though in this case usually the tasks don't even get queued)
restart your scheduler/Airflow environment
try running the DAGs while no other DAGs are running to check if the issue could be that the parallelism limit is reached. (if you are using K8s executor you should also check worker_pods_creation_batch_size and with the Celery Executor worker_concurrency and stalled_task_timeout)
take a look at your scheduler logs (at $AIRFLOW_HOME/logs/scheduler)
upgrade Airflow if you are running an older version.
Dear Apache Airflow experts,
I am currently trying to make the parallel execution of Apache Airflow 2.3.x DAGs configurable via the DAG run config.
When executing below code the DAG creates two tasks - for the sake of my question it does not matter what the other DAG does.
Because max_active_tis_per_dag is set to 1, the two tasks will be run one after another.
What I want to achieve: I want to provide the result of get_num_max_parallel_runs (which checks the DAG config, if no value is present it falls back to 1 as default) to max_active_tis_per_dag.
I would appreciate any input on this!
Thank you in advance!
from airflow import DAG
from airflow.decorators import task
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
from datetime import datetime
with DAG(
'aaa_test_controller',
schedule_interval=None,
start_date=datetime(2021, 1, 1),
catchup=False
) as dag:
#task
def get_num_max_parallel_runs(dag_run=None):
return dag_run.conf.get("num_max_parallel_runs", 1)
trigger_dag = TriggerDagRunOperator.partial(
task_id="trigger_dependent_dag",
trigger_dag_id="aaa_some_other_dag",
wait_for_completion=True,
max_active_tis_per_dag=1,
poke_interval=5
).expand(conf=['{"some_key": "some_value_1"}', '{"some_key": "some_value_2"}'])
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 created a new DAG using the following code. It is calling a python script.
Code:
from __future__ import print_function
from builtins import range
import airflow
from airflow.operators.python_operator import PythonOperator
from airflow.models import DAG
from airflow.operators.bash_operator import BashOperator
from airflow.operators.dummy_operator import DummyOperator
args = {
'owner': 'admin'
}
dag = DAG(
dag_id='workflow_file_upload', default_args=args,
schedule_interval=None)
t1 = BashOperator(
task_id='testairflow',
bash_command='python /root/DataLake_Scripts/File_Upload_GCP.py',
dag=dag)
I have placed it in $airflowhome/dags folder.
after that I have run :
airflow scheduler
I am trying to see the DAG in WebUI however it is not visible there. There is no error coming.
I've met the same issue.
I figured out that the problem is in initial sqlite db. I suppose it's some feature of airflow 1.10.3
Anyway I solved the problem using postgresql backend.
These links will help you:
link
link
link
All instructions are suitable for python 3.
You'll see your DAG after execution of 'airflow webserver' and 'airflow scheduler' commands.
Also notice that you should call 'sudo service postgresql restart' exactly before 'airflow initdb' command.