Fix airflow sensor

plugins/flytekit-airflow/flytekitplugins/airflow/task.py

@@ -85,7 +85,7 @@ class AirflowContainerTask(PythonAutoContainerTask[AirflowObj]):
     The airflow task module, name and parameters are stored in the task config.
 
     Some of the Airflow operators are not deferrable, For example, BeamRunJavaPipelineOperator, BeamRunPythonPipelineOperator.
-    These tasks don't have async method to get the job status, so cannot be used in the Flyte agent. We run these tasks in a container.
+    These tasks don't have an async method to get the job status, so cannot be used in the Flyte agent. We run these tasks in a container.
     """
 
     def __init__(
@@ -164,7 +164,7 @@ def _is_deferrable(cls: Type) -> bool:
     # Only Airflow operators are deferrable.
     if not issubclass(cls, airflow_models.BaseOperator):
         return False
-    # Airflow sensors are not deferrable. Sensor is a subclass of BaseOperator.
+    # Airflow sensors are not deferrable. The Sensor is a subclass of BaseOperator.
     if issubclass(cls, airflow_sensors.BaseSensorOperator):
         return False
     try:
@@ -186,7 +186,7 @@ def _flyte_operator(*args, **kwargs):
     cls = args[0]
     try:
         if FlyteContextManager.current_context().user_space_params.get_original_task:
-            # Return original task when running in the agent.
+            # Return an original task when running in the agent.
             return object.__new__(cls)
     except AssertionError:
         # This happens when the task is created in the dynamic workflow.
@@ -197,7 +197,7 @@ def _flyte_operator(*args, **kwargs):
     task_id = kwargs["task_id"] or cls.__name__
     config = AirflowObj(module=cls.__module__, name=cls.__name__, parameters=kwargs)
 
-    if not _is_deferrable(cls):
+    if not issubclass(cls, airflow_sensors.BaseSensorOperator) and not _is_deferrable(cls):
         # Dataflow operators are not deferrable, so we run them in a container.
         return AirflowContainerTask(name=task_id, task_config=config, container_image=container_image)()
     return AirflowTask(name=task_id, task_config=config)()

plugins/flytekit-airflow/tests/test_task.py

@@ -1,18 +1,23 @@
 import jsonpickle
-from airflow.providers.apache.beam.operators.beam import BeamRunJavaPipelineOperator
+from airflow.operators.bash import BashOperator
+from airflow.providers.apache.beam.operators.beam import BeamRunJavaPipelineOperator, BeamRunPythonPipelineOperator
 from airflow.providers.google.cloud.operators.dataproc import DataprocCreateClusterOperator
 from airflow.sensors.bash import BashSensor
+from airflow.sensors.time_sensor import TimeSensor
 from airflow.utils.context import Context
 from flytekitplugins.airflow.task import (
     AirflowContainerTask,
     AirflowObj,
     AirflowTask,
+    _flyte_operator,
     _is_deferrable,
     airflow_task_resolver,
 )
+from mock import mock
 
 from flytekit import FlyteContextManager
 from flytekit.configuration import ImageConfig, SerializationSettings
+from flytekit.core import context_manager
 
 
 def test_xcom_push():
@@ -87,3 +92,31 @@ def test_airflow_container_task():
     assert isinstance(
         airflow_task_resolver.load_task(t.task_resolver.loader_args(serialization_settings, t)), AirflowContainerTask
     )
+
+
+@mock.patch("flytekitplugins.airflow.task.AirflowContainerTask")
+@mock.patch("flytekitplugins.airflow.task.AirflowTask")
+def test_flyte_operator(airflow_task, airflow_container_task):
+    ctx = context_manager.FlyteContext.current_context()
+    with context_manager.FlyteContextManager.with_context(ctx.new_builder()):
+        params = FlyteContextManager.current_context().user_space_params
+        params.builder().add_attr("GET_ORIGINAL_TASK", False).add_attr("XCOM_DATA", {}).build()
+        _flyte_operator(BashOperator, task_id="BashOperator")
+        airflow_task.assert_called_once()
+        _flyte_operator(BeamRunJavaPipelineOperator, task_id="BeamRunJavaPipelineOperator")
+        airflow_container_task.assert_called_once()
+
+        airflow_task.reset_mock()
+        airflow_container_task.reset_mock()
+
+        _flyte_operator(TimeSensor, task_id="TimeSensor")
+        airflow_task.assert_called_once()
+
+        _flyte_operator(BeamRunPythonPipelineOperator, task_id="BeamRunPythonPipelineOperator")
+        airflow_container_task.assert_called_once()
+
+        airflow_task.reset_mock()
+        airflow_container_task.reset_mock()
+
+        _flyte_operator(DataprocCreateClusterOperator, task_id="DataprocCreateClusterOperator")
+        airflow_task.assert_called_once()