In my Airflow DAG I have a task that needs to know if it's the first time it's ran or if it's a retry run. I need to adjust my logic in the task if it's a retry attempt.
I have a few ideas on how I could store the number of retries for the task but I'm not sure if any of them are legitimate or if there's an easier built in way to get this information within the task.
I'm wondering if I can just have an integer variable inside the dag that I append every time the task runs. Then if the task if reran I could check the value of the variable to see that it's greater than 1 and hence would be a retry run. But I'm not sure if mutable global variables work that way in Airflow since there can be multiple workers for different tasks (I'm not sure on this though).
Write it in an XCOM variable?
The retry number is available from the task instance, which is available via the macro {{ task_instance }}. https://airflow.apache.org/code.html#default-variables
If you are using the python operator simply add provide_context=True, to your operator kwargs, and then in the callable do kwargs['task_instance'].try_number
Otherwise you can do something like:
t = BashOperator(
task_id='try_number_test',
bash_command='echo "{{ task_instance.try_number }}"',
dag=dag)
Edit:
When the task instance is cleared, it will set the max_retry number to be the current try_number + retry value. So you could do something like:
ti = # whatever method you do to get the task_instance object
is_first = ti.max_tries - ti.task.retries + 1 == ti.try_number
Airflow will increments the try_number by 1 when running, so I imagine you'd need the + 1 when subtracting the max_tries from the configured retry value. But I didn't test that to confirm
#cwurtz answer was spot on. I was able to use it like this:
def _get_actual_try_number(self, context):
'''
Returns the real try_number that you also see in task details or logs.
'''
return context['task_instance'].try_number
def _get_relative_try_number(self, context):
'''
When a task is cleared, the try_numbers continue to increment.
This returns the try number relative to the last clearing.
'''
ti = context['task_instance']
actual_try_number = self._get_actual_try_number(context)
# When the task instance is cleared, it will set the max_retry
# number to be the current try_number + retry value.
# From https://stackoverflow.com/a/51757521
relative_first_try = ti.max_tries - ti.task.retries + 1
return actual_try_number - relative_first_try + 1
Related
I try create graph with chain of dynamic tasks.
First of all, I start with expand function. But problem is program should wait, when all the Add tasks finished and only then start Mul tasks. I need the next Mul to run immediately after each Add. Then I got the code that the graph could make
with DAG(dag_id="simple_maping", schedule='* * * * *', start_date=datetime(2022, 12, 22)) as dag:
#task
def read_conf():
conf = Variable.get('tables', deserialize_json=True)
return conf
#task
def add_one(x: str):
sleep(5)
return x + '1'
#task
def mul_two(x: str):
return x * 2
for i in read_conf():
mul_two(add_one(i))
but now there is an error - 'xcomarg' object is not iterable. I can fix it just remove task decorator from read_conf method, but I am not sure it's the best decision, because in my case list configuration names could contain >1000 elements. Without decorator, method have to read configuration every time when scheduler parsed graph.
Maybe the load without the decorator will not be critical? Or is there a way to make an object iterable? How to do it right?
EDIT: This solution has a bug in 2.5.0 which was solved for 2.5.1 (not released yet).
Yes, when you are chaining dynamically mapped tasks the latter (mul_2) will wait until all mapped instances of the first task (add_one) are done by default because the default trigger rule is all_success. While you can change the trigger rule for example to one_done this will not solve your issue because the second task will only once, when it first starts running, decide how many mapped task instances it creates (with one_done it only creates one mapped task instance, so not helpful for your use-case).
The issue with the for-loop (and why Airflow wont allow you to iterate over an XComArg) is that for-loops are parsed when the DAG code is parsed, which happens outside of runtime, when Airflow does not know yet how many results read_conf() will return. If the number of the configurations only rarely change then having a for-loop like that iterating over list in a separate file is an option, but yes at scale this can cause performance issues.
The best solution in my opinion is to use dynamic task group mapping which was added in Airflow 2.5.0:
All mapped task groups will run in parallel and for every input from read_conf(). So for every add_one its mul_two will run immediately. I put the code for this below.
One note: You will not be able to see the mapped task groups in the Airflow UI or be able to access their logs just yet, the feature is still quite new and the UI extension should come in 2.5.1. That is why I added a task downstream of the mapped task groups that prints out the list of results of the mul_two tasks, so you can check if it is working.
from airflow import DAG
from airflow.decorators import task, task_group
from datetime import datetime
from time import sleep
with DAG(
dag_id="simple_mapping",
schedule=None,
start_date=datetime(2022, 12, 22),
catchup=False
) as dag:
#task
def read_conf():
return [10, 20, 30]
#task_group
def calculations(x):
#task
def add_one(x: int):
sleep(x)
return x + 1
#task()
def mul_two(x: int):
return x * 2
mul_two(add_one(x))
#task
def pull_xcom(**context):
pulled_xcom = context["ti"].xcom_pull(
task_ids=['calculations.mul_two'],
key="return_value"
)
print(pulled_xcom)
calculations.expand(x=read_conf()) >> pull_xcom()
Hope this helps! :)
PS: you might want to set catchup=False unless you want to backfill a few weeks of tasks.
By transactional-like I mean - etither all tasks in a set are successful or none of them are and should be retried from the first one.
Consider two operators, A and B, A downloads a file, B reads it and performs some actions.
A successfully executes, but before B comes into play a blackout/corruption occurs, and B cannot process the file, so it fails.
To pass, it needs A to re-download the file, but since A is in success status, there is no direct way to do that automatically. A human must go and clear A`s status
Now, if wonder, if there is a known way to clear the statuses of task instances up to a certain task, if some task fails?
I know I can use a hook, as in clear an upstream task in airflow within the dag, but that looks a bit ugly
This feature is not supported by airflow, but you can use on_failure_callback to achieve that:
def clear_task_A(context):
execution_date = context["execution_date"]
clear_task_A = BashOperator(
task_id='clear_task_A',
bash_command=f'airflow tasks clear -s {execution_date} -t A -d -y <dag_id>'
) # -s: start date, -t: task_id regex -d: include downstream?, -y: yes?
return clear_task_A.execute(context=context)
with DAG(...) as dag:
A = DummyOperator(
task_id='A'
)
B = BashOperator(
task_id='B',
bash_command='exit 1',
on_failure_callback=clear_task_A
)
A >> B
Currently I have two DAGs: DAG_A and DAG_B. Both runs with schedule_interval=timedelta(days=1)
DAG_A has a Task1 which usually takes 7 hours to run. And DAG_B only takes 3 hours.
DAG_B has a ExternalTaskSensor(external_dag_id="DAG_A", external_task_id="Task1") but also uses some other information X that is generated hourly.
What is the best way to increase the frequency of DAG_B so that it runs at least 4 times a day? As far as I know, both DAGs must have the same schedule_interval. However, I want to update X on DAG_B as much as I can.
One possibility is to create another DAG that has a ExternalTaskSensor for DAG_B. But I don't think it's the best way.
If I understood you correctly, your conditions are:
Keep running DAG_A daily
Run DAG_B n times a day
Every time DAG_B runs it will wait for DAG_A__Task_1 to be completed
I think you could easily adapt your current design by instructing ExternalTaskSensor to wait for the desired execution date of DAG_A.
From the ExternalTaskSensor operator defnition:
Waits for a different DAG or a task in a different DAG to complete for a specific execution_date
That execution_date could be defined using execution_date_fn parameter:
execution_date_fn (Optional[Callable]) – function that receives the current execution date as the first positional argument and optionally any number of keyword arguments available in the context dictionary, and returns the desired execution dates to query. Either execution_delta or execution_date_fn can be passed to ExternalTaskSensor, but not both.
You could define the sensor like this:
wait_for_dag_a = ExternalTaskSensor(
task_id='wait_for_dag_a',
external_task_id="external_task_1",
external_dag_id='dag_a_id',
allowed_states=['success', 'failed'],
execution_date_fn=_get_execution_date_of_dag_a,
poke_interval=30
)
Where _get_execution_date_of_dag_a performs a query to the DB using get_last_dagrun allowing you to get the last execution_date of DAG_A.
from airflow.utils.db import provide_session
from airflow.models.dag import get_last_dagrun
#provide_session
def _get_execution_date_of_dag_a(exec_date, session=None, **kwargs):
dag_a_last_run = get_last_dagrun(
'dag_a_id', session)
return dag_a_last_run.execution_date
I hope this approach helps you out. You can find a working example in this answer.
Combining #Gonza Piotti's comment with #NicoE's answer:
from airflow.utils.db import provide_session
from airflow.models.dag import get_last_dagrun
def _get_execution_date_of(dag_id):
#provide_session
def _get_last_execution_date(exec_date, session=None, **kwargs):
dag_a_last_run = get_last_dagrun(dag_id, session)
return dag_a_last_run.execution_date
return _get_last_execution_date
we get a function that will yield another function which computes the last execution date of a given dag_id, use it like:
wait_for_dag_a = ExternalTaskSensor(
task_id='wait_for_dag_a',
external_task_id='external_task_1',
external_dag_id='dag_a',
allowed_states=['success', 'failed'],
execution_date_fn=_get_execution_date_of('dag_a'),
poke_interval=30
)
I need the status of the task like if it is running or upforretry or failed within the same dag. So i tried to get it using the below code, though i got no output...
Auto = PythonOperator(
task_id='test_sleep',
python_callable=execute_on_emr,
op_kwargs={'cmd':'python /home/hadoop/test/testsleep.py'},
dag=dag)
logger.info(Auto)
The intention is to kill certain running tasks once a particular task on airflow completes.
Question is how do i get the state of a task like is it in the running state or failed or success
I am doing something similar. I need to check for one task if the previous 10 runs of another task were successful.
taky2 sent me on the right path. It is actually fairly easy:
from airflow.models import TaskInstance
ti = TaskInstance(*your_task*, execution_date)
state = ti.current_state()
As I want to check that within the dag, it is not neccessary to specify the dag.
I simply created a function to loop through the past n_days and check the status.
def check_status(**kwargs):
last_n_days = 10
for n in range(0,last_n_days):
date = kwargs['execution_date']- timedelta(n)
ti = TaskInstance(*my_task*, date) #my_task is the task you defined within the DAG rather than the task_id (as in the example below: check_success_task rather than 'check_success_days_before')
state = ti.current_state()
if state != 'success':
raise ValueError('Not all previous tasks successfully completed.')
When you call the function make sure to set provide_context.
check_success_task = PythonOperator(
task_id='check_success_days_before',
python_callable= check_status,
provide_context=True,
dag=dag
)
UPDATE:
When you want to call a task from another dag, you need to call it like this:
from airflow import configuration as conf
from airflow.models import DagBag, TaskInstance
dag_folder = conf.get('core','DAGS_FOLDER')
dagbag = DagBag(dag_folder)
check_dag = dagbag.dags[*my_dag_id*]
my_task = check_dag.get_task(*my_task_id*)
ti = TaskInstance(my_task, date)
Apparently there is also an api-call by now doing the same thing:
from airflow.api.common.experimental.get_task_instance import get_task_instance
ti = get_task_instance(*my_dag_id*, *my_task_id*, date)
Take a look at the code responsible for the command line interface operation suggested by Priyank.
https://github.com/apache/incubator-airflow/blob/2318cea74d4f71fba353eaca9bb3c4fd3cdb06c0/airflow/bin/cli.py#L581
def task_state(args):
dag = get_dag(args)
task = dag.get_task(task_id=args.task_id)
ti = TaskInstance(task, args.execution_date)
print(ti.current_state())
Hence, it seem you should easily be able to accomplish this within your DAG codebase using similar code.
Alternatively you could execute these CLI operations from within your code using python's subprocess library.
Okay, I think I know what you're doing and I don't really agree with it, but I'll start with an answer.
A straightforward, but hackish, way would be to query the task_instance table. I'm in postgres, but the structure should be the same. Start by grabbing the task_ids and state of the task you're interested in with a db call.
SELECT task_id, state
FROM task_instance
WHERE dag_id = '<dag_id_attrib>'
AND execution_date = '<execution_date_attrib>'
AND task_id = '<task_to_check>'
That should give you the state (and name, for reference) of the task you're trying to monitor. State is stored as a simple lowercase string.
You can use the command line Interface for this:
airflow task_state [-h] [-sd SUBDIR] dag_id task_id execution_date
For more on this you can refer official airflow documentation:
http://airflow.incubator.apache.org/cli.html
I am really a newbie in this forum. But I have been playing with airflow, for sometime, for our company. Sorry if this question sounds really dumb.
I am writing a pipeline using bunch of BashOperators.
Basically, for each Task, I want to simply call a REST api using 'curl'
This is what my pipeline looks like(very simplified version):
from airflow import DAG
from airflow.operators import BashOperator, PythonOperator
from dateutil import tz
import datetime
datetime_obj = datetime.datetime
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime.datetime.combine(datetime_obj.today() - datetime.timedelta(1), datetime_obj.min.time()),
'email': ['xxxx#xxx.xxx'],
'email_on_failure': True,
'email_on_retry': False,
'retries': 2,
'retry_delay': datetime.timedelta(minutes=5),
}
current_datetime = datetime_obj.now(tz=tz.tzlocal())
dag = DAG(
'test_run', default_args=default_args, schedule_interval=datetime.timedelta(minutes=60))
curl_cmd='curl -XPOST "'+hostname+':8000/run?st='+current_datetime +'"'
t1 = BashOperator(
task_id='rest-api-1',
bash_command=curl_cmd,
dag=dag)
If you notice I am doing current_datetime= datetime_obj.now(tz=tz.tzlocal())
Instead what I want here is 'execution_date'
How do I use 'execution_date' directly and assign it to a variable in my python file?
I have having this general issue of accessing args.
Any help will be genuinely appreciated.
Thanks
The BashOperator's bash_command argument is a template. You can access execution_date in any template as a datetime object using the execution_date variable. In the template, you can use any jinja2 methods to manipulate it.
Using the following as your BashOperator bash_command string:
# pass in the first of the current month
some_command.sh {{ execution_date.replace(day=1) }}
# last day of previous month
some_command.sh {{ execution_date.replace(day=1) - macros.timedelta(days=1) }}
If you just want the string equivalent of the execution date, ds will return a datestamp (YYYY-MM-DD), ds_nodash returns same without dashes (YYYYMMDD), etc. More on macros is available in the Api Docs.
Your final operator would look like:
command = """curl -XPOST '%(hostname)s:8000/run?st={{ ds }}'""" % locals()
t1 = BashOperator( task_id='rest-api-1', bash_command=command, dag=dag)
The PythonOperator constructor takes a 'provide_context' parameter (see https://pythonhosted.org/airflow/code.html). If it's True, then it passes a number of parameters into the python_callable via kwargs. kwargs['execution_date'] is what you want, I believe.
Something like this:
def python_method(ds, **kwargs):
Variable.set('execution_date', kwargs['execution_date'])
return
doit = PythonOperator(
task_id='doit',
provide_context=True,
python_callable=python_method,
dag=dag)
I'm not sure how to do it with the BashOperator, but you might start with this issue: https://github.com/airbnb/airflow/issues/775
I think you can't assign variables with values from the airflow context outside of a task instance, they are only available at run-time. Basically there are 2 different steps when a dag is loaded and executed in airflow :
First your dag file is interpreted and parsed. It has to work and compile and the task definitions must be correct (no syntax error or anything). During this step, if you make function calls to fill some values, these functions won't be able to access airflow context (the execution date for example, even more if you're doing some backfilling).
The second step is the execution of the dag. It's only during this second step that the variables provided by airflow (execution_date, ds, etc...) are available as they are related to an execution of the dag.
So you can't initialize global variables using the Airflow context, however, Airflow gives you multiple mechanisms to achieve the same effect :
Using jinja template in your command (it can be in a string in the code or in a file, both will be processed). You have the list of available templates here : https://airflow.apache.org/macros.html#default-variables. Note that some functions are also available, particularly for computing days delta and date formatting.
Using a PythonOperator in which you pass the context (with the provide_context argument). This will allow you to access the same template with the syntax kwargs['<variable_name']. If you need so, you can return a value from a PythonOperator, this one will be stored in an XCOM variable you can use later in any template. Access to XCOM variables use this syntax : https://airflow.apache.org/concepts.html#xcoms
If you write your own operator, you can access airflow variables with the dict context.
def execute(self, context):
execution_date = context.get("execution_date")
This should be inside the execute() method of Operator
To print execution date inside the callable function of your PythonOperator you can use the following in your Airflow Script and also can add start_time and end_time as follows:
def python_func(**kwargs):
execution_date = kwargs["execution_date"] #<datetime> type with timezone
end_time = str(execution_date)
start_time = str(execution_date.add(minutes=-30))
I have converted the datetime value to string as I need to pass it in a SQL Query. We can use it otherwise also.
You may consider SimpleHttpOperator https://airflow.apache.org/_api/airflow/operators/http_operator/index.html#airflow.operators.http_operator.SimpleHttpOperator. It’s so simple for making http request. you can pass execution_date with endpoint parameter via template.
Here's another way without context. using the dag's last execution time can be very helpful in scheduled ETL jobs. Such as a dag that 'downloads all newly added files'. Instead of hardcoding a datetime.datetime, use the dag's last execution date as your time filter.
Airflow Dags actually have a class called DagRun that can be accessed like so: dag_runs = DagRun.find(dag_id=dag_id)
Here's an easy way to get the most recent run's execution time:
def get_most_recent_dag_run(dag_id):
dag_runs = DagRun.find(dag_id=dag_id)
dag_runs.sort(key=lambda x: x.execution_date, reverse=True)
return dag_runs[1] if len(dag_runs) > 1 else None
Then, within your pythonOperator, you can dynamically access the dag's last execution by calling the function you created above:
last_execution = get_most_recent_dag_run('dag')
Now its a variable!