I'm trying to see whether or not there is a straightforward way to not start the next dag run if the previous dag run has failures. I already set depends_on_past=True, wait_for_downstream=True, max_active_runs=1.
What i have is tasks 1, 2, 3 where they:
create resources
run job
tear down resources
task 3 always runs with trigger_rule=all_done to make sure we always tear down resources. What i'm seeing is that if task 2 fails, and task 3 then succeeds, the next dag run starts and if i have wait_for_downstream=False it runs task 1 since the previous task 1 was a success and if i have wait_for_downstream=true then it doesn't start the dag as i expect which is perfect.
The problem is that if tasks 1 and 2 succeed but task 3 fails for some reason, now my next dag run starts and task 1 runs immediately because both task 1 and task 2 (due to wait_for_downstream) were successful in the previous run. This is the worst case scenario because task 1 creates resources and then the job is never run so the resources just sit there allocated.
What i ultimately want is for any failure to stop the dag from proceeding to the next dag run. If my previous dag run is marked as fail then the next one should not start at all. Is there any mechanism for doing this?
My current 2 best effort ideas are:
Use a sub dag so that there's only 1 task in the parent dag and therefore the next dag run will never start at all if the previous single task dag failed. This seems like it will work but i've seen mixed reviews on the use of sub dag operators.
Do some sort of logic within the dag as a first task that manually queries the DB to see if the dag has previous failures and fails the task if it does. This seems hacky and not ideal but that it could work as well.
Is there any out of the box solution for this? Seems fairly standard to not want to continue on failure and not want step 1 to start of run 2 if not all steps of run 1 were successful or if run 1 itself was marked as failed.
The reason depends_on_past is not helping your is it's a task parameter not a dag parameter.
Essentially what you're asking for is for the dag to be disabled after a failure.
I can imagine valid use cases for this, and maybe we should add an AirflowDisableDagException that would trigger this.
The problem with this is you risk having your dag disabled and not noticing for days or weeks.
A better solution would be to build recovery or abort logic into your pipeline so that you don't need to disable the dag.
One way you can do this is add a cleanup task to the start of your dag, which can check whether resources were left sitting there and tear them down if appropriate, and just fail the dag run immediately if you get an appropriate error. You can consider using airflow Variable or Xcom to store the state of your resources.
The other option, notwithstanding the risks, is the disable dag approach: if your process fails to tear down resources appropriately, disable the dag. Something along these lines should work:
class MyOp(BaseOperator):
def disable_dag(self):
orm_dag = DagModel(dag_id=self.dag_id)
orm_dag.set_is_paused(is_paused=True)
def execute(self, context):
try:
print('something')
except TeardownFailedError:
self.disable_dag()
The ExternalTaskSensor may work, with an execution_delta of datetime.timedelta(days=1). From the docs:
execution_delta (datetime.timedelta) – time difference with the previous execution to look at, the default is the same execution_date as the current task or DAG. For yesterday, use [positive!] datetime.timedelta(days=1). Either execution_delta or execution_date_fn can be passed to ExternalTaskSensor, but not both.
I've only used it to wait for upstream DAG's to finish, but seems like it should work as self-referencing because the dag_id and task_id are arguments for the sensor. But you'll want to test it first of course.
Is there a way specify that a task can only run once concurrently? So in the tree above where DAG concurrency is 4, Airflow will start task 4 instead of a second instance of task 2?
This DAG is a little special because there is no order between the tasks. These tasks are independent but related in purpose and therefore kept in one DAG so as to new create an excessive number of single task DAGs.
max_active_runs is 2 and dag_concurrency is 4. I would like it start all 4 tasks and only start a task in next if same task in previous run is done.
I may have mis-understood your question, but I believe you are wanting to have all the tasks in a single dagrun finish before the tasks begin in the next dagrun. So a DAG will only execute once the previous execution is complete.
If that is the case, you can make use of the max_active_runs parameter of the dag to limit how many running concurrent instances of a DAG there are allowed to be.
More information here (refer to the last dotpoint): https://airflow.apache.org/faq.html#why-isn-t-my-task-getting-scheduled
max_active_runs defines how many running concurrent instances of a DAG there are allowed to be.
Airflow operator documentation describes argument task_concurrency. Just set it to one.
From the official docs for trigger rules:
depends_on_past (boolean) when set to True, keeps a task from getting triggered if the previous schedule for the task hasn’t succeeded.
So the future DAGs will wait for the previous ones to finish successfully before executing.
On airflow.cfg under [core]. You will find
dag_concurrency = 16
//The number of task instances allowed to run concurrently by the scheduler
you're free to change this to what you desire.
I have an Airflow DAG that runs once daily at a specific time. The DAG runs a bunch of SQL scripts to create and load tables in a database, and the very last task updates permissions so that users can access the tables. Currently the permissions task requires that all previous SQL tasks have completed, so this means that none of the tables' permissions are updated if any of the table tasks fail.
To fix this I'd like to create another permissions task (i.e., a backup task) that runs at a preset time regardless of the status of any of the previous tasks (doesn't hurt to update permissions multiple times). If I don't specify a time different from the DAG's time, then because the new task has no dependencies, the task will try updating permissions before any of the tables have been updated. Is there a setting for me to pass a cron string to a specific task? Or is there an option to pass a timedelta on top of the task's DAG time? I need to run the task some amount of time after the DAG time.
If your permissions task can run no matter what the result of the upstream tasks, I think the best option is simply to change the trigger_rule of your permissions task to all_done (default is all_success).
If you need to do some specific stuffs when there is a failure, you could consider creating a secondary DAG which first step is a sensor that waits for the main DAG to complete with State.FAILED, then run your permissions task.
Have a look at ExternalTaskSensor when you want to establish a dependency between DAGs.
I haven't checked but you might also need to use soft_fail on the sensor to prevent the secondary DAG to show up as failed when the main DAG completes successfully.
We have a long dag (~60 tasks), and quite frequently we see a dagrun for this dag in a state of failed. When looking at the tasks in the DAG they are all in a state of either success or null (i.e. not even queued yet). It appears that the dag has got into a state of failed prematurely.
Under what circumstances can this happen, and what should people do to protect against it?
If it's helpful for context we're running Airflow using the Celery executor and currently running on version 1.9.0. If we set the state of the dag in question back to running then all the tasks (and the dag as a whole) complete successfully.
The only way that a DAG can fail without a task failing is through something not connected to any of the tasks. Besides manual intervention (check that nobody on the team is manually failing the dags!) the only thing that fails DAGs outside of considering task states is the timeout checker.
This runs inside the scheduler, while considering whether it needs to schedule a new dag_run. If it finds another active run, which has been running longer than the dagrun_timeout argument of the DAG, then it will get killed. As far as I can see this isn't logged anywhere, so the best way to diagnose this is to look at the time that the DAG started and the time that the last task finished to see if it's roughly the length of dagrun_timeout.
You can see the code in action here: https://github.com/apache/incubator-airflow/blob/e9f3fdc52cb53f3ac3e9721e5128d17d1c5c418c/airflow/jobs.py#L800
Airflow is randomly not running queued tasks some tasks dont even get queued status. I keep seeing below in the scheduler logs
[2018-02-28 02:24:58,780] {jobs.py:1077} INFO - No tasks to consider for execution.
I do see tasks in database that either have no status or queued status but they never get started.
The airflow setup is running https://github.com/puckel/docker-airflow on ECS with Redis. There are 4 scheduler threads and 4 Celery worker tasks. For the tasks that are not running are showing in queued state (grey icon) when hovering over the task icon operator is null and task details says:
All dependencies are met but the task instance is not running. In most cases this just means that the task will probably be scheduled soon unless:- The scheduler is down or under heavy load
Metrics on scheduler do not show heavy load. The dag is very simple with 2 independent tasks only dependent on last run. There are also tasks in the same dag that are stuck with no status (white icon).
Interesting thing to notice is when I restart the scheduler tasks change to running state.
Airflow can be a bit tricky to setup.
Do you have the airflow scheduler running?
Do you have the airflow webserver running?
Have you checked that all DAGs you want to run are set to On in the web ui?
Do all the DAGs you want to run have a start date which is in the past?
Do all the DAGs you want to run have a proper schedule which is shown in the web ui?
If nothing else works, you can use the web ui to click on the dag, then on Graph View. Now select the first task and click on Task Instance. In the paragraph Task Instance Details you will see why a DAG is waiting or not running.
I've had for instance a DAG which was wrongly set to depends_on_past: True which forbid the current instance to start correctly.
Also a great resource directly in the docs, which has a few more hints: Why isn't my task getting scheduled?.
I'm running a fork of the puckel/docker-airflow repo as well, mostly on Airflow 1.8 for about a year with 10M+ task instances. I think the issue persists in 1.9, but I'm not positive.
For whatever reason, there seems to be a long-standing issue with the Airflow scheduler where performance degrades over time. I've reviewed the scheduler code, but I'm still unclear on what exactly happens differently on a fresh start to kick it back into scheduling normally. One major difference is that scheduled and queued task states are rebuilt.
Scheduler Basics in the Airflow wiki provides a concise reference on how the scheduler works and its various states.
Most people solve the scheduler diminishing throughput problem by restarting the scheduler regularly. I've found success at a 1-hour interval personally, but have seen as frequently as every 5-10 minutes used too. Your task volume, task duration, and parallelism settings are worth considering when experimenting with a restart interval.
For more info see:
Airflow: Tips, Tricks, and Pitfalls (section "The scheduler should be restarted frequently")
Bug 1286825 - Airflow scheduler stopped working silently
Airflow at WePay (section "Restart everything when deploying DAG changes.")
This used to be addressed by restarting every X runs using the SCHEDULER_RUNS config setting, although that setting was recently removed from the default systemd scripts.
You might also consider posting to the Airflow dev mailing list. I know this has been discussed there a few times and one of the core contributors may be able to provide additional context.
Related Questions
Airflow tasks get stuck at "queued" status and never gets running (especially see Bolke's answer here)
Jobs not executing via Airflow that runs celery with RabbitMQ
Make sure you don't have datetime.now() as your start_date
It's intuitive to think that if you tell your DAG to start "now" that it'll execute "now." BUT, that doesn't take into account how Airflow itself actually reads datetime.now().
For a DAG to be executed, the start_date must be a time in the past, otherwise Airflow will assume that it's not yet ready to execute. When Airflow evaluates your DAG file, it interprets datetime.now() as the current timestamp (i.e. NOT a time in the past) and decides that it's not ready to run. Since this will happen every time Airflow heartbeats (evaluates your DAG) every 5-10 seconds, it'll never run.
To properly trigger your DAG to run, make sure to insert a fixed time in the past (e.g. datetime(2019,1,1)) and set catchup=False (unless you're looking to run a backfill).
By design, an Airflow DAG will execute at the completion of its schedule_interval
That means one schedule_interval AFTER the start date. An hourly DAG, for example, will execute its 2pm run when the clock strikes 3pm. The reasoning here is that Airflow can't ensure that all data corresponding to the 2pm interval is present until the end of that hourly interval.
This is a peculiar aspect to Airflow, but an important one to remember - especially if you're using default variables and macros.
Time in Airflow is in UTC by default
This shouldn't come as a surprise given that the rest of your databases and APIs most likely also adhere to this format, but it's worth clarifying.
Full article and source here
I also had a similar issue, but it is mostly related to SubDagOperator with more than 3000 task instances in total (30 tasks * 44 subdag tasks).
What I found out is that airflow scheduler mainly responsible for putting your scheduled tasks in to "Queued Slots" (pool), while airflow celery workers is the one who pick up your queued task and put it into the "Used Slots" (pool) and run it.
Based on your description, your scheduler should work fine. I suggest you check your "celery workers" log to see whether there is any error, or restart it to see whether it helps or not. I experienced some issues that celery workers normally go on strike for a few minutes then start working again (especially on SubDagOperator)
One of the very silly reasons could be that the DAG is "paused" which is the default state for the first time. I lost around 2 hrs fighting it. If you are using Airflow Web interface, then this shows up as a toggle next to your DAG in the list
I am facing the issue today and found that bullet point 4 from tobi6 answer below worked out and resolved the issue
*'Do all the DAGs you want to run have a start date which is in the past?'*
I am using airflow version v1.10.3
My problem was one step further, in addition to my tasks being queued, I couldn't see any of my celery workers on the Flower UI. The solution was that, since I was running my celery worker as root I had to make changes in my ~/.bashrc file.
The following steps made it work:
Add export C_FORCE_ROOT=true to your ~/.bashrc file
source ~/.bashrc
Run worker : nohup airflow worker $* >> ~/airflow/logs/worker.logs &
Check your Flower UI at http://{HOST}:5555
I think it's worth mentioning that there's an open issue that can cause tasks to fail to run with no obvious reason: https://issues.apache.org/jira/browse/AIRFLOW-5506
The problem seems to occur when using LocalScheduler connected to a PostgreSQL airflow db, and results in the scheduler logging a number of "Killing PID xxxx" lines. Check the scheduler logs after the DAGs have been stalled without starting any new tasks for a while.
You can try to stop the webserver and the scheduler:
ps -ef | grep airflow #show the process id
kill 1234 #kill the webserver
kill 5678 #kill the scheduler
Remove the files from the airflow folder if they exist (they will be created again):
airflow-scheduler.err
airflow-scheduler.pid
airflow-webserver.err
airflow-webserver.pid
Start the webserver and the scheduler again.
airflow webserver -D
airflow scheduler -D
-D will make the services run in the background.
I had a similar issue of a triggered DAG "running" indefinitely because its first task stuck in "queued" state.
I realized this was because of a "ghost" DAG that actually changed name. It seems that since the DAG has run in the past (had data in the postgresDG) and was referenced as child-DAG in other DAGs, the trigger of the parent DAGs referencing the old name would "resurrect" the old DAG name, but with the new code. Indeed the old DAG name and new DAG code did not match, thus producing an "infinite queued execution" bug.
Solution:
Delete the all the previous DAG runs of the previous DAG-runs with the old name
Restart everything (webserver, worker, executor,...) OR Delete relevant DAGs (with the "delete DAG" button in the UI).
The interpretation of the bug can vary but this fix worked in my case.
One more thing to check is whether "the concurrency parameter of your DAG reached?".
I'd experienced the same situation when some task was shown as NO STATUS.
It turned out that my File_Sensor tasks were run with timeout set up to 1 week, while DAG time out was only 5 hours. That leaded to the case when the Files were missing, many sensors tasked were running at the same time. Which results the concurrency overloaded!
The depending tasks couldn't be started before the sensor task succeed, when the dag timeout, they got NO STATUS.
My solution:
Carefully set tasks and DAG timeout
Increase dag_concurrency in airflow.cfg file in AIRFLOW_HOME folder.
Please refer to the docs.
https://airflow.apache.org/faq.html#why-isn-t-my-task-getting-scheduled
I believe this is an issue with celery version 4.2.1 and redis 3.0.1 as described here:
https://github.com/celery/celery/issues/3808
we resolved the issue by downgrading our redis version 2.10.6:
redis==2.10.6
In my case, tasks were not being launched because I had for all operators a pool configured and hadn't created it, hence, tasks were not even scheduled. An operator looks like:
foo = DummyOperator(
task_id='foo',
dag=dag,
pool='capser'
)
To create a pool go to Admin > Pools > Create and set slots, for example, 128, which runs successfully for me. You can also configure by using the CLI.
counter intuitive UI message!
I have spent days on this. So want to elaborate on my specific issue (s).
Each dag has a state. By default the state could be 'pause' or 'not pause'.
The first confusion arises from - what is the default state on startup? The UI message attached seems to indicate that the state is 'not pause' and on clicking the toggle, it pauses.
In reality, the default state is 'pause'. This state can be controlled by settings, environment variables, parameters and UI. I have detailed them below.
The second confusion arises because of the UI again. When we manually trigger a dag which is in the pause state. The UI shows the dag as running (green circle)! But the dag is actually in the 'pause' state. The tasks will not execute unless it is 'un-paused'.
If we read the task instance details. The message would be
Task is in the 'None' state which is not a valid state for execution. The task must be cleared in order to be run.
What is the 'None' state!? And clear which task?!
The actual problem is that the dag is in the pause state. On toggling the dag state the tasks would start to execute.
The pause state of the dag can be changed by
clicking the button on the UI.
set your particular dag to run, by adding the below parameter to your dag
DAG(dag_id='your-dag', is_paused_upon_creation=True)
setting the config variable in airflow.cfg file. (caution: this will start all your dags including the example ones)
dags_are_paused_at_creation = FALSE
configuring an environment variable before starting up the scheduler/webserver.(caution: this will start all your dags including the example ones)
AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=False
Make sure that your task is assigned to the same queue, that your workers is listening to. This means that in your DAG file you have to set 'queue': 'queue_name' and in your worker configuration you have to set either default_queue = 'queue_name' in the airflow.cfg or AIRFLOW__OPERATORS__DEFAULT_QUEUE: 'queue_name' in the docker-compose.yaml (in case you're using Docker).