Airflow seems to be skipping the dags I added to /usr/local/airflow/dags.
When I run
airflow list_dags
The output shows
[2017-08-06 17:03:47,220] {models.py:168} INFO - Filling up the DagBag from /usr/local/airflow/dags
-------------------------------------------------------------------
DAGS
-------------------------------------------------------------------
example_bash_operator
example_branch_dop_operator_v3
example_branch_operator
example_http_operator
example_passing_params_via_test_command
example_python_operator
example_short_circuit_operator
example_skip_dag
example_subdag_operator
example_subdag_operator.section-1
example_subdag_operator.section-2
example_trigger_controller_dag
example_trigger_target_dag
example_xcom
latest_only
latest_only_with_trigger
test_utils
tutorial
But this doesn't include the dags in /usr/local/airflow/dags
ls -la /usr/local/airflow/dags/
total 20
drwxr-xr-x 3 airflow airflow 4096 Aug 6 17:08 .
drwxr-xr-x 4 airflow airflow 4096 Aug 6 16:57 ..
-rw-r--r-- 1 airflow airflow 1645 Aug 6 17:03 custom_example_bash_operator.py
drwxr-xr-x 2 airflow airflow 4096 Aug 6 17:08 __pycache__
Is there some other condition that neededs to be satisfied for airflow to identify a DAG and load it?
My dag is being loaded but I had the name of the DAG wrong. I was expecting the dag to be named by the file but the name is determined by the first argument to the DAG constructor
dag = DAG(
'tutorial', default_args=default_args, schedule_interval=timedelta(1))
Try airflow db init before listing the dags. This is because airflow list_dags lists down all the dags present in the database (And not in the folder you mentioned). Airflow initdb will create entry for these dags in the database.
Make sure you have environment variable AIRFLOW_HOME set to /usr/local/airflow. If this variable is not set, airflow looks for dags in the home airflow folder, which might not be existing in your case.
The example files are not in /usr/local/airflow/dags. You can simply mute them by edit airflow.cfg (usually in ~/airflow). set load_examples = False in 'core' section.
There are couple of errors may make your DAG not been listed in list_dags.
Your DAG file has syntax issue. To check this, just run python custom_example_bash_operator.py and see if any issue.
See if the folder is the default dag loading path. For a new bird, I suggest that just create a new .py file and copy the sample from here https://airflow.incubator.apache.org/tutorial.html then see if the testing dag shows up.
Make sure there is dag = DAG('dag_name', default_args=default_args) in the dag file.
dag = DAG(
dag_id='example_bash_operator',
default_args=args,
schedule_interval='0 0 * * *',
dagrun_timeout=timedelta(minutes=60))
When a DAG is instantiated it pops up by the name you specify in the dag_id attribute. dag_id serves as a unique identifier for your DAG
It will be the case if airflow.cfg config is pointed to an incorrect path.
STEP 1: Go to {basepath}/src/config/
STEP 2: Open airflow.cfg file
STEP 3: Check the path it should point to the dags folder you have created
dags_folder = /usr/local/airflow/dags
I find that I have to restart the scheduler for the UI to pick up the new dags, When I make changes to a dag in my dags folder. I find that when I update the dags they appear in the list when I run airflow list_dags just not in the UI until I restart the scheduler.
First try running:
airflow scheduler
There can be two issues:
1. Check the Dag name given at the time of DAG object creation in the DAG python program
dag = DAG(
dag_id='Name_Of_Your_DAG',
....)
Note that many of the times the name given may be the same as the already present name in the list of DAGs (since if you copied the DAG code). If this is not the case then
2. Check the path set to the DAG folder in Airflow's config file.
You can create DAG file anywhere on your system but you need to set the path to that DAG folder/directory in Airflow's config file.
For example, I have created my DAG folder in the Home directory then I have to edit airflow.cfg file using the following commands in the terminal:
creating a DAG folder at home or root directory
$mkdir ~/DAG
Editing airflow.cfg present in the airflow directory where I have installed the airflow
~/$cd airflow
~/airflow$nano airflow.cfg
In this file change dags_folder path to DAG folder that we have created.
If you still facing the problem then reinstall the Airflow and refer this link for the installation of Apache Airflow.
Are your
custom_example_bash_operator.py
has a DAG name different from the others?
If yes, try restart the scheduler or even resetdb. I usually mistook the filename to be the dag name as well, so better to name them the same.
Can you share what is in custom_example_bash_operator.py? Airflow scans for certain magic inside a file to determine whether is a DAG or not. It scans for airflow and for DAG.
In addition if you are using a duplicate dag_id for a DAG it will be overwritten. As you seem to be deriving from the example bash operator did you keep the name of the DAG example_bash_operator maybe? Try renaming that.
You need to set airflow first and initialise the db
export AIRFLOW_HOME=/myfolder
mkdir /myfolder/dags
airflow db init
You need to create a user too
airflow users create \
--username admin \
--firstname FIRST_NAME \
--lastname LAST_NAME \
--role Admin \
--email admin#example.org
If you have done it correctly you should see airflow.cfg in your folder. There you will find dags_folder which shows the dags folder.
If you have saved your dag inside this folder you should see it in the dag lists
airflow dags list
, or using the UI with
airflow webserver --port 8080
Otherwise, run again airflow db init.
In my case, print(something) in dag file prevented printing dag list on command line.
Check if there is print line in your dag if above solutions are not working.
Try Restarting the scheduler. Scheduler needs to be restarted when new DAGS need to be added to the DAG Bag
Related
I'm encountering a problem where valid dags that show up in airflow dags list don't show up in the UI
The dags_folder points to the right location and I have restarted the webserver and scheduler many times, as well as ran the occasional airflow db reset
I think that my airflow is just ignoring the cfg file, because I changed load_examples to False and all the example dags still show up
I'm on airflow 2.4.0 working in a venv on ubuntu. What could be causing this?
I'm having a few problems with airflow. When I installed airflow and set the airflow home directory to be
my_home/Workspace/airflow_home
But when I start the webserver a new airflow directory is created
my_home/airflow
I thought maybe something in the airflow.cfg file needs to be changed but I'm not really sure. Has anyone had this problem before?
Try doing echo $AIRFLOW_HOME and see if it the correct path you set
you need to set AIRFLOW_HOME to the directory where you save airflow config file.
if the full path of airflow.cfg file is /home/test/bigdata/airflow/airflow.cfg
just run
export AIRFLOW_HOME=/home/test/bigdata/airflow
if AIRFLOW_HOME is not set, it will use ~/airflow as default.
you could also write a shell script to start airflow webserver
it might contain lines below
source ~/.virtualenvs/airflow/bin/activate # if your airflow is installed with virtualenv, this is not necessary
export AIRFLOW_HOME=/home/test/bigdata/airflow # path should be changed according to your environment
airflow webserver -D # start airflow webserver as daemon
I deleted dag from airflow dag_bag and corresponding .pyc file as well. When I try to delete the same dag from airflow UI it is showing this error:
Dag id MY_DAG_ID is still in DagBag. Remove the DAG file first.
The airflow version I am using is 1.10.4
Even after restarting airflow I'm not able to delete from UI. I was using 1.10.3 previously, but I never faced this issue. I was able to delete from UI after deleting from dags folder.
When I click on that dag in UI it is showing :
DAG "MY_DAG_ID" seems to be missing.( this is expected as I deleted dag from folder)
Try stopping the scheduler and the webserver and then deleting the DAG from the command line:
airflow delete_dag 'MY_DAG_ID'
I had the same issues after I upgraded to 1.10.6. Here's what I did:
Before removing the DAG, make sure no instance is on running, retry status. Then Pause it
Delete on UI or using the command airflow delete_dag dag_id
Restart the scheduler and webserver
Try to execute airflow list_dags to see if it really got deleted.
If it doesn't work, try to upgrade to the latest version.
I have a running Airflow server and I am making a config change in airflow.cfg which requires to run airflow initdb .
Will running airflow initdb command for the second time be destructive to existing tables or it will only execute changes according to the new config?
Only destructive command related to airflow database is airflow resetdb.
initdb and upgradedb share the same behavior (except for the first-run).
I think you can run both:
From Airflow source code:
def initdb():
from airflow import models
from airflow.models import Connection
upgradedb()
I suspected that
airflow run dag_id task_id execution_date
would run all upstream tasks, but it does not. It will simply fail when it sees that not all dependent tasks are run. How can I run a specific task and all its dependencies? I am guessing this is not possible because of an airflow design decision, but is there a way to get around this?
You can run a task independently by using -i/-I/-A flags along with the run command.
But yes the design of airflow does not permit running a specific task and all its dependencies.
You can backfill the dag by removing non-related tasks from the DAG for testing purpose
A bit of a workaround but in case you have given your tasks task_id-s consistently you can try the backfilling from Airflow CLI (Command Line Interface):
airflow backfill -t TASK_REGEX ... dag_id
where TASK_REGEX corresponds to the naming pattern of the task you want to rerun and its dependencies.
(remember to add the rest of the command line options, like --start_date).