How can I dynamically set the execution_timeout for Task 5 in an Airflow DAG using GKEPodOperator?

Airflow’s execution_timeout is determined when the task is instantiated in the DAG. This means it’s a static property that won’t update dynamically during runtime based on when previous tasks actually finished. In your case, trying to compute the remaining time in Task 5 by looking up Task 4’s end time isn’t supported by the operator’s design—especially inside a GKEPodOperator container where DAG metadata isn’t directly available.

Here are a few alternative approaches:

• Pre-compute Timeout Outside Task 5:
  Create an earlier Python task (or use a Python callable) that retrieves the DAG start time and Task 4’s end time, computes the remaining time following your logic (DAG start + 60 minutes minus Task 4 finish minus 2 minutes buffer), and passes that as a parameter (via XCom or as an environment variable) to Task 5. Then modify Task 5’s internal logic (the run command inside the container) to respect that timeout value. This keeps the heavy lifting in the application logic rather than relying on Airflow’s built-in execution_timeout.

• Internal Timeout Logic in Task 5:
  Remove the static execution_timeout for Task 5 and instead build your own timeout mechanism within the container’s application code. You can pass the computed timeout duration from a prior task (as above) to the container. In your application, check the elapsed time periodically and perform a graceful shutdown if it exceeds the allowed duration.

• Modify the DAG Structure:
  If possible, re-think whether the overall DAG execution window can be statically defined. For instance, if Task 5 is known sometimes to run longer, you might consider adjusting individual task timeouts or splitting Task 5 into two parts so that you can set different timeouts on each.

Unfortunately, you can’t define a dynamic execution_timeout directly on Task 5 that references runtime values from earlier tasks. Instead, shifting the timeout logic into a separate step or within the container’s application logic is the most reliable solution.