Made WinRMOperator deferrable

[dabla]

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Motivation

Recently, we encountered an issue where TaskInstances were being prematurely killed by the scheduler.

We initially tried the fix proposed in PR #60330 made by @ephraimbuddy, but unfortunately this did not help in our case. After further investigation, we discovered that this behaviour only occurred in DAGs using the WinRMOperator.

Problem

We use the WinRMOperator to launch remote processes on Windows servers. Some of these processes can take a significant amount of time to complete.

The root cause is that the WinRMOperator currently performs polling synchronously inside the worker, via the run method of WinRMHook. This has several drawbacks:

• The worker is blocked while waiting for the remote command to complete.
• Long-running polling increases the risk of scheduler heartbeats being missed.
• This can lead to TaskInstances being marked as failed or killed prematurely.

Overall, this is not an efficient or scalable execution model in Airflow.

Solution

This PR refactors the WinRMOperator to support deferrable execution.

When deferrable=True:

• The worker is only responsible for launching the remote command on the Windows server.
• The operator retrieves the shell_id and command_id from the WinRM session.
• Polling for command output and the final result_code is deferred to a new WinRMCommandTrigger.
• The triggerer performs this polling asynchronously, freeing the worker immediately.

This aligns the WinRMOperator with Airflow’s recommended architecture for long-running or polling-based operations.

Benefits

Workers are no longer blocked by long-running WinRM commands.

• Polling is handled asynchronously by the triggerer.
• Prevents scheduler-induced task termination for long-running WinRM jobs.
• Improves scalability and resource utilization.
• Matches the deferrable operator pattern used across Airflow providers.

Example Usage

WinRMOperator(
    task_id="run_job",
    ssh_conn_id="ssh.winrm",
    command="python C:\\Temp\\python_scripts\\job_trigger.py -j TEST",
    pool="winrm",
    poll_interval=10,
    execution_timeout=timedelta(minutes=360),  # Avoid infinite polling if something goes wrong
    deferrable=True,
)

Conclussion

With this refactoring in place, we no longer experience TaskInstances being prematurely killed. Polling is handled asynchronously by the triggerer, which is the preferred and more robust approach for this type of workload in Airflow and we don't block workers for polling unnecessarily.

---

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[kaxil]

# type: ignore :) We should avoid them