Made WinRMOperator deferrable

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)

[dabla]

---

Was generative AI tooling used to co-author this PR?

• Yes (please specify the tool below)

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.

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst or {issue_number}.significant.rst, in airflow-core/newsfragments.

[Copilot]

Pull request overview

This PR adds deferrable execution support to the WinRMOperator to address issues where long-running remote Windows processes were causing TaskInstances to be prematurely killed by the scheduler. The refactoring introduces asynchronous polling via a new trigger, freeing workers from blocking on command completion.

Changes:

• Introduced WinRMCommandOutputTrigger for asynchronous polling of WinRM command output
• Refactored WinRMOperator to support deferrable mode with new execute_complete callback
• Restructured WinRMHook to separate command execution from output polling

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py	New trigger implementation for async command output polling
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/operators/winrm.py	Added deferrable mode support and refactored execute logic
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/hooks/winrm.py	Extracted command execution and output polling into separate methods
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/test_winrm.py	Unit tests for the new trigger
providers/microsoft/winrm/tests/unit/microsoft/winrm/operators/test_winrm.py	Updated operator tests with deferrable mode coverage
providers/microsoft/winrm/tests/unit/microsoft/winrm/hooks/test_winrm.py	Simplified import statement
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/get_provider_info.py	Registered the new trigger module
providers/microsoft/winrm/provider.yaml	Added trigger configuration
providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/__init__.py	New init file for triggers module
providers/microsoft/winrm/tests/unit/microsoft/winrm/triggers/__init__.py	New init file for trigger tests

---

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[kaxil]

Thanks, I have a few comments below.

[dabla]

@kaxil just have to check one more thing with triggerer

[kaxil]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

[dabla]

Could you also squash your commits in one (or <5) please :) Currently t is 128 commits :D

Will do that once CI/CD is green again, thx for reviewing Kaxil :-)