Troubleshooting

Operator remains in polling

Check the following:

1. Is the Airflow scheduler running with MesosExecutor?
2. Is airflow_scheduler_url correct and reachable on port 11000?
3. Is the framework registered with the Mesos master?
4. Are there matching CPU, memory, and attribute offers?

The executor API returns HTTP 200 during queueing only to confirm acceptance into the queue. The operator then continues waiting for the Mesos status.

TASK_FAILED or TASK_ERROR

Check Mesos agent logs and the task details in the Mesos UI. Common causes include an unavailable image, insufficient resources, unmatched attributes, or an incorrect container/network mode.

TASK_LOST

The agent or framework connection was lost. Check whether the framework has reconnected and whether the agent is active.

API returns 401

Check the API configuration and the endpoint being used. /v0/dags is protected; the operator uses /v0/queue_command and /v0/task/<task_id>. Do not expose the API through a public reverse proxy without suitable authentication.

DAG is not loaded

First check imports in isolation:

airflow dags list-import-errors

Then make sure dags_folder points to the directory containing the DAG and that the provider is installed in the same Python environment as Airflow.

Resources do not match

cpus, mem_limit, and disk must fit the available Mesos offers. For attributes, at least one active agent must satisfy every constraint. Global attributes from mesos_attributes and task-specific attributes are used together.