I am trying to package my Repository with my Dag in a Zip file like it states here in the documentation.
So i have followed the convention in the documentation, which is to keep the dag in the root of the zip, and the sub directories are viewed as packages by airflow.
My zip file has the following contents:
$ unzip -l $AIRFLOW_HOME/dags/test_with_zip.zip
Archive: /home/arjunc/Tutorials/airflow/dags/test_with_zip.zip
Length Date Time Name
--------- ---------- ----- ----
0 2018-03-29 17:46 helloworld/
189 2018-03-29 17:22 helloworld/hello.py
0 2018-03-29 17:18 helloworld/__init__.py
461 2018-03-29 17:24 test_with_zip_dag.py
--------- -------
650 4 files
Where test_with_zip_dag.py is the file in the root directory with the Dag definitions as follows:
from datetime import datetime
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from helloworld.hello import HelloWorld
def run():
return HelloWorld().run()
dag = DAG('test_with_zip', description='Test Dependencies With Zipping',
schedule_interval='0 12 * * *',
start_date=datetime(2017, 3, 20), catchup=False)
hello_operator = PythonOperator(task_id='hello_task', python_callable=run, dag=dag)
I have placed this zip in the default dags directory $AIRFLOW_HOME/dags, but my dag isn't recognized!
What am I doing wrong?
Update
When I restarted the webserver, the task test_with_zip has popped up, but it is not runnable because the Scheduler doesn't seem to recognize it. I get the following error for it (from the web interface):
This DAG seems to be existing only locally. The master scheduler doesn't seem to be aware of its existence.
Which version of airflow are you on? Airflow 1.8.1 had problems with loading dags from zips. This issue was fixed in 1.8.3. https://issues.apache.org/jira/browse/AIRFLOW-1357
I recommend that you should update to the latest version of Airflow ie 1.9.0
You mention only to restart the webserver.
You also need to start the scheduler with airflow scheduler.
Also, see more steps to check here: Airflow 1.9.0 is queuing but not launching tasks
The DAG python file has to be in the root of the zip package. See https://airflow.apache.org/docs/stable/concepts.html#packaged-dags
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 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 try to run a Apache Beam pipeline (Python) within Google Cloud Dataflow, triggered by a DAG in Google Cloud Coomposer.
The structure of my dags folder in the respective GCS bucket is as follows:
/dags/
dataflow.py <- DAG
dataflow/
pipeline.py <- pipeline
setup.py
my_modules/
__init__.py
commons.py <- the module I want to import in the pipeline
The setup.py is very basic, but according to the Apache Beam docs and answers on SO:
import setuptools
setuptools.setup(setuptools.find_packages())
In the DAG file (dataflow.py) I set the setup_file option and pass it to Dataflow:
default_dag_args = {
... ,
'dataflow_default_options': {
... ,
'runner': 'DataflowRunner',
'setup_file': os.path.join(configuration.get('core', 'dags_folder'), 'dataflow', 'setup.py')
}
}
Within the pipeline file (pipeline.py) I try to use
from my_modules import commons
but this fails. The log in Google Cloud Composer (Apache Airflow) says:
gcp_dataflow_hook.py:132} WARNING - b' File "/home/airflow/gcs/dags/dataflow/dataflow.py", line 11\n from my_modules import commons\n ^\nSyntaxError: invalid syntax'
The basic idea behind the setup.py file is documented here
Also, there are similar questions on SO which helped me:
Google Dataflow - Failed to import custom python modules
Dataflow/apache beam: manage custom module dependencies
I'm actually wondering why my pipelines fails with a Syntax Error and not a module not found kind of error...
I tried to reproduce your issue and then try to solve it, so I created the same folder structure you already have:
/dags/
dataflow.py
dataflow/
pipeline.py -> pipeline
setup.py
my_modules/
__init__.py
common.py
Therefore, to make it work, the change I made is to copy these folders to a place where the instance is running the code is able to find it, for example in the /tmp/ folder of the instance.
So, my DAG would be something like this:
1 - Fist of all I declare my arguments:
default_args = {
'start_date': datetime(xxxx, x, x),
'retries': 1,
'retry_delay': timedelta(minutes=5),
'dataflow_default_options': {
'project': '<project>',
'region': '<region>',
'stagingLocation': 'gs://<bucket>/stage',
'tempLocation': 'gs://<bucket>/temp',
'setup_file': <setup.py>,
'runner': 'DataflowRunner'
}
}
2- After this, I created the DAG and before running the Dataflow task, I copied the whole folder directory, above created, into the /tmp/ folder of the instance Task t1, and after this, I run the pipeline from the /tmp/ directory Task t2:
with DAG(
'composer_df',
default_args=default_args,
description='datflow dag',
schedule_interval="xxxx") as dag:
def copy_dependencies():
process = subprocess.Popen(['gsutil','cp', '-r' ,'gs://<bucket>/dags/*',
'/tmp/'])
process.communicate()
t1 = python_operator.PythonOperator(
task_id='copy_dependencies',
python_callable=copy_dependencies,
provide_context=False
)
t2 = DataFlowPythonOperator(task_id="composer_dataflow",
py_file='/tmp/dataflow/pipeline.py', job_name='job_composer')
t1 >> t2
That's how I created the DAG file dataflow.py, and then, in the pipeline.py the package to import would be like:
from my_modules import commons
It should work fine, since the folder directory is understandable for the VM.
i am a newbie to Airflow. i have some .jar jobs generated with Talend Open Studio for Big Data, and i want to schedule and manage those with Airflow my question is , does Airflow support .jar file or generated by TOS as DAG ?
and if it does how ?
or is there any alternative to run .jar on Airlow ?
im using Airflow v1.10.3
the jobs are mainly to extract and process data from a mongodb database then update the database with the new processed data.
Thanks !
Airflow does support running jar files. You do this through the BashOperator.
Quick example:
from airflow import DAG
from airflow.operators import BashOperator
from datetime import datetime
import os
import sys
args = {
'owner': 'you',
'start_date': datetime(2019, 4, 24),
'provide_context': True
}
dag = DAG(
task_id = 'runjar',
schedule_interval = None, #manually triggered
default_args = args)
run_jar_task= BashOperator(
task_id = 'runjar',
dag = dag,
bash_command = 'java -cp /path/to/your/jar.jar param1 param2'
)
Airflow will happily run .jar files. There is a few examples kicking about for you to have a look at.
Running a standard .jar file: run_jar.py
Running a "built" Talend jobl loan_application_data.py
Obviously with both these examples the .jar or Talend file(s) will need to be on the server Airflow is executing on (as well as Java).
I have created a python_scripts/ folder under my dags/ folder.
I have 2 different dags running the same python_operator - calling to 2 different python scripts located in the python_scripts/ folder.
They both write output files BUT:
one of them creates the file under the dags/ folder, and one of them creates it in the plugins/ folder.
How does Airflow determine the working path?
How can I get Airflow to write all outputs to the same folder?
One thing you could try, that I use in my dags, would be to set you working path by adding os.chdir('some/path') in your DAG.
This only works if you do not put it into an operator, as those are run in subprocesses and therefore do not change the working path of the parent process.
The other solution I could think of would be using absolute paths when specifying your output.
For the approach with os.chdir try the following and you should see both files get created in the folder defined with path='/home/chr/test/':
from datetime import datetime
import os
import logging
from airflow import DAG
from airflow.exceptions import AirflowException
from airflow.operators.python_operator import PythonOperator
log = logging.getLogger(__name__)
default_args = {
'owner': 'admin',
'depends_on_past': False,
'retries': 0
}
dag = DAG('test_dag',
description='Test DAG',
catchup=False,
schedule_interval='0 0 * * *',
default_args=default_args,
start_date=datetime(2018, 8, 8))
path = '/home/chr/test'
if os.path.isdir(path):
os.chdir(path)
else:
os.mkdir(path)
os.chdir(path)
def write_some_file():
try:
with open("/home/chr/test/absolute_testfile.txt", "wt") as fout:
fout.write('test1\n')
with open("relative_testfile.txt", "wt") as fout:
fout.write('test2\n')
except Exception as e:
log.error(e)
raise AirflowException(e)
write_file_task = PythonOperator(
task_id='write_some_file',
python_callable=write_some_file,
dag=dag
)
Also, please try to provide code next time you ask a question, as it is almost impossible to find out what the problem is, just by reading your question.