We have many DAGs scheduled to run daily using Airflow. Dependencies has been enabled using ExternalTaskSensor, TriggerDagRunOperator and custom operators
Sample:
Task 1 in DAG A are dependent on task 2 in DAG B
Task 3 in DAG A are dependent on task 4 in DAG C
Task 5 in DAG A are dependent on task 6 in DAG D
...
Task 2 in DAG B are dependent on task 7 in DAG E
Task 4 in DAG B are dependent on task 8 in DAG F
...
While checking Task Instance details in UI, only downstream_task_ids and upstream_task_ids belonging to the same dag are displayed.
How can we see the full lineage of a single task across multiple DAGs to the last available level?
Airflow does not currently (v 1.8.1) have a mechanism for viewing cross-dag dependencies.
At this time if you need a visualization of relationships between tasks, they have to be in the same dag. Potentially a view in a custom plugin could show these dependencies, but the stock UI does not do this.
Related
We have two dags : dag1 and dag2. Dag1 should be running for 4 times in a dag and Dag2 should be running only once as per first instance of Dag1 run.
Dag 1 - Schedule interval: 5-20/5 13 * * *
Dag1 as per above schedule interval Dag1 will be running for 4 times but it should trigger only once for Dag2. We are using trigger dag run operator to trigger dag2 from dag1
Is there any way to achieve. I tried External task sensor, branch operator to have exact time dependency and some file dependency but still it may impact in some or other case.
Is there any simpler way.
We have 1000's DAGs scheduled to run daily using Airflow.
Dependencies has been enabled using ExternalTaskSensor.
Is there any way to generate and track execution lineage in graphical/textual format ?
I have just started to explore Apache Airflow.
Is there any way to run a job that will look into the running DAGS and move those tasks in those DAGS to new DAG by creating them and adding those tasks in it.
For Example : DAG A has four tasks, 4th one has been waiting from 7 hours to start - Goal is to create new DAG and move that tasks automatically to new DAG.
Scenario : Actually we have around 40 VM, and each job time varies with its own instance. For Example : Task A will take 2 hours today but might take 12 Hours tomorrow in the same DAG. What i need is to move that task to other DAG if the waiting time of any task exceed certain time to run on other VM instantly.
The main benefi is to keep all the task waiting time minimum as possible by building dynamic DAGs
I have 3 dags A, B and C. Dag C should get triggered only after tasks in dag A and B completes. Is there a way to implement this in airflow? I am able to set dependency between dag A and C using Triggerdagrun Operator. But when I try to set dependency between dag B and C, C is getting triggered when either A or B completes.
Can someone please help me in solving this?
I understand that explains external task sensor Operator can be used. But it continuously polls if task in dag A and B is complete which might create performance hit over a period of time.
You could set two more wait-task in your dagc,
then startop >> [wait-daga, wait-dagb] >> dagc.
Below is the link to airflow doc:
https://airflow.apache.org/docs/stable/concepts.html
I have three task in one dag.
Task A run first. Task B runs if task A is successful.
I have Task C which has run after Task B but it is not depend up Task B or Task A success or failure.
Task C needs to no matter what happen to task A and B. However, it needs to run after task A and B is completed.
Any idea ?
To have a task run after other tasks are done, but regardless of the outcome of their execution, set the trigger_rule parameter to all_done like so:
my_task = MyOperator(task_id='my_task',
trigger_rule='all_done'
See the trigger rule documentation for more options