fixup! Swap CeleryExecutor over to use TaskSDK for execution.

airflow/executors/base_executor.py

@@ -59,7 +59,7 @@
     # Command to execute - list of strings
     # the first element is always "airflow".
     # It should be result of TaskInstance.generate_command method.
-    CommandType = list[str]
+    CommandType = Sequence[str]
 
     # Task that is queued. It contains all the information that is
     # needed to run the task.
@@ -223,7 +223,12 @@ def has_task(self, task_instance: TaskInstance) -> bool:
         :param task_instance: TaskInstance
         :return: True if the task is known to this executor
         """
-        return task_instance.key in self.queued_tasks or task_instance.key in self.running
+        return (
+            task_instance.id in self.queued_tasks
+            or task_instance.id in self.running
+            or task_instance.key in self.queued_tasks
+            or task_instance.key in self.running
+        )
 
     def sync(self) -> None:
         """
@@ -319,6 +324,20 @@ def order_queued_tasks_by_priority(self) -> list[tuple[TaskInstanceKey, QueuedTa
 
         :return: List of tuples from the queued_tasks according to the priority.
         """
+        from airflow.executors import workloads
+
+        if not self.queued_tasks:
+            return []
+
+        kind = next(iter(self.queued_tasks.values()))
+        if isinstance(kind, workloads.BaseWorkload):
+            # V3 + new executor that supports workloads
+            return sorted(
+                self.queued_tasks.items(),
+                key=lambda x: x[1].ti.priority_weight,
+                reverse=True,
+            )
+
         return sorted(
             self.queued_tasks.items(),
             key=lambda x: x[1][1],
@@ -332,12 +351,12 @@ def trigger_tasks(self, open_slots: int) -> None:
 
         :param open_slots: Number of open slots
         """
-        span = Trace.get_current_span()
         sorted_queue = self.order_queued_tasks_by_priority()
         task_tuples = []
+        workloads = []
 
         for _ in range(min((open_slots, len(self.queued_tasks)))):
-            key, (command, _, queue, ti) = sorted_queue.pop(0)
+            key, item = sorted_queue.pop(0)
 
             # If a task makes it here but is still understood by the executor
             # to be running, it generally means that the task has been killed
@@ -375,15 +394,19 @@ def trigger_tasks(self, open_slots: int) -> None:
             else:
                 if key in self.attempts:
                     del self.attempts[key]
-                task_tuples.append((key, command, queue, ti.executor_config))
-                if span.is_recording():
-                    span.add_event(
-                        name="task to trigger",
-                        attributes={"command": str(command), "conf": str(ti.executor_config)},
-                    )
+                # TODO: TaskSDK: Compat, remove when KubeExecutor is fully moved over to TaskSDK too.
+                # TODO: TaskSDK: We need to minimum version requirements on executors with Airflow 3.
+                # How/where do we do that? Executor loader?
+                if hasattr(self, "_process_workloads"):
+                    workloads.append(item)
+                else:
+                    (command, _, queue, ti) = item
+                    task_tuples.append((key, command, queue, getattr(ti, "executor_config", None)))
 
         if task_tuples:
             self._process_tasks(task_tuples)
+        elif workloads:
+            self._process_workloads(workloads)  # type: ignore[attr-defined]
 
     @add_span
     def _process_tasks(self, task_tuples: list[TaskTuple]) -> None:
@@ -625,7 +648,7 @@ def slots_occupied(self):
         return len(self.running) + len(self.queued_tasks)
 
     @staticmethod
-    def validate_airflow_tasks_run_command(command: list[str]) -> tuple[str | None, str | None]:
+    def validate_airflow_tasks_run_command(command: Sequence[str]) -> tuple[str | None, str | None]:
         """
         Check if the command to execute is airflow command.
 

airflow/executors/workloads.py

@@ -34,7 +34,7 @@
 ]
 
 
-class BaseActivity(BaseModel):
+class BaseWorkload(BaseModel):
     token: str
     """The identity token for this workload"""
 
@@ -75,7 +75,7 @@ def key(self) -> TaskInstanceKey:
         )
 
 
-class ExecuteTask(BaseActivity):
+class ExecuteTask(BaseWorkload):
     """Execute the given Task."""
 
     ti: TaskInstance

airflow/jobs/scheduler_job_runner.py

@@ -836,7 +836,6 @@ def process_executor_events(
                 )
                 if info is not None:
                     msg += " Extra info: %s" % info  # noqa: RUF100, UP031, flynt
-                cls.logger().error(msg)
                 session.add(Log(event="state mismatch", extra=msg, task_instance=ti.key))
 
                 # Get task from the Serialized DAG
@@ -849,6 +848,9 @@ def process_executor_events(
                     continue
                 ti.task = task
                 if task.on_retry_callback or task.on_failure_callback:
+                    # Only log the error/extra info here, since the `ti.handle_failure()` path will log it
+                    # too, which would lead to double logging
+                    cls.logger().error(msg)
                     request = TaskCallbackRequest(
                         full_filepath=ti.dag_model.fileloc,
                         ti=ti,

generated/provider_dependencies.json

@@ -335,7 +335,7 @@
   "celery": {
     "deps": [
       "apache-airflow>=2.9.0",
-      "celery[redis]>=5.3.0,<6,!=5.3.3,!=5.3.2",
+      "celery[redis]>=5.4.0,<6",
       "flower>=1.0.0",
       "google-re2>=1.0"
     ],

providers/celery/README.rst

@@ -51,14 +51,14 @@ The package supports the following python versions: 3.9,3.10,3.11,3.12
 Requirements
 ------------
 
-==================  ==============================
+==================  ==================
 PIP package         Version required
-==================  ==============================
+==================  ==================
 ``apache-airflow``  ``>=2.9.0``
-``celery[redis]``   ``>=5.3.0,!=5.3.2,!=5.3.3,<6``
+``celery[redis]``   ``>=5.4.0,<6``
 ``flower``          ``>=1.0.0``
 ``google-re2``      ``>=1.0``
-==================  ==============================
+==================  ==================
 
 Cross provider package dependencies
 -----------------------------------

providers/celery/pyproject.toml

@@ -59,9 +59,7 @@ dependencies = [
     # The Celery is known to introduce problems when upgraded to a MAJOR version. Airflow Core
     # Uses Celery for CeleryExecutor, and we also know that Kubernetes Python client follows SemVer
     # (https://docs.celeryq.dev/en/stable/contributing.html?highlight=semver#versions).
-    # Make sure that the limit here is synchronized with [celery] extra in the airflow core
-    # The 5.3.3/5.3.2 limit comes from https://github.com/celery/celery/issues/8470
-    "celery[redis]>=5.3.0,<6,!=5.3.3,!=5.3.2",
+    "celery[redis]>=5.4.0,<6",
     "flower>=1.0.0",
     "google-re2>=1.0",
 ]

providers/celery/src/airflow/providers/celery/cli/celery_command.py

@@ -154,6 +154,11 @@ def worker(args):
     # This needs to be imported locally to not trigger Providers Manager initialization
     from airflow.providers.celery.executors.celery_executor import app as celery_app
 
+    if AIRFLOW_V_3_0_PLUS:
+        from airflow.sdk.log import configure_logging
+
+        configure_logging(output=sys.stdout.buffer)
+
     # Disable connection pool so that celery worker does not hold an unnecessary db connection
     settings.reconfigure_orm(disable_connection_pool=True)
     if not settings.validate_session():

providers/celery/src/airflow/providers/celery/executors/celery_executor.py

@@ -33,7 +33,7 @@
 from collections.abc import Sequence
 from concurrent.futures import ProcessPoolExecutor
 from multiprocessing import cpu_count
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 from deprecated import deprecated
 
@@ -53,7 +53,7 @@
 from airflow.configuration import conf
 from airflow.exceptions import AirflowProviderDeprecationWarning, AirflowTaskTimeout
 from airflow.executors.base_executor import BaseExecutor
-from airflow.providers.celery.version_compat import AIRFLOW_V_2_8_PLUS
+from airflow.providers.celery.version_compat import AIRFLOW_V_2_8_PLUS, AIRFLOW_V_3_0_PLUS
 from airflow.stats import Stats
 from airflow.utils.state import TaskInstanceState
 from celery import states as celery_states
@@ -67,14 +67,13 @@
 if TYPE_CHECKING:
     import argparse
 
-    from airflow.executors.base_executor import CommandType, TaskTuple
+    from sqlalchemy.orm import Session
+
+    from airflow.executors import workloads
+    from airflow.executors.base_executor import TaskTuple
     from airflow.models.taskinstance import TaskInstance
     from airflow.models.taskinstancekey import TaskInstanceKey
-    from celery import Task
-
-    # Task instance that is sent over Celery queues
-    # TaskInstanceKey, Command, queue_name, CallableTask
-    TaskInstanceInCelery = tuple[TaskInstanceKey, CommandType, Optional[str], Task]
+    from airflow.providers.celery.executors.celery_executor_utils import TaskInstanceInCelery
 
 
 # PEP562
@@ -228,6 +227,11 @@ class CeleryExecutor(BaseExecutor):
     supports_ad_hoc_ti_run: bool = True
     supports_sentry: bool = True
 
+    if TYPE_CHECKING and AIRFLOW_V_3_0_PLUS:
+        # In the v3 path, we store workloads, not commands as strings.
+        # TODO: TaskSDK: move this type change into BaseExecutor
+        queued_tasks: dict[TaskInstanceKey, workloads.All]  # type: ignore[assignment]
+
     def __init__(self):
         super().__init__()
 
@@ -256,10 +260,22 @@ def _num_tasks_per_send_process(self, to_send_count: int) -> int:
         return max(1, math.ceil(to_send_count / self._sync_parallelism))
 
     def _process_tasks(self, task_tuples: list[TaskTuple]) -> None:
+        # Airflow V2 version
         from airflow.providers.celery.executors.celery_executor_utils import execute_command
 
         task_tuples_to_send = [task_tuple[:3] + (execute_command,) for task_tuple in task_tuples]
-        first_task = next(t[3] for t in task_tuples_to_send)
+
+        self._send_tasks(task_tuples_to_send)
+
+    def _process_workloads(self, workloads: list[workloads.All]) -> None:
+        # Airflow V3 version
+        from airflow.providers.celery.executors.celery_executor_utils import execute_workload
+
+        tasks = [(workload.ti.key, workload, workload.ti.queue, execute_workload) for workload in workloads]
+        self._send_tasks(tasks)
+
+    def _send_tasks(self, task_tuples_to_send: Sequence[TaskInstanceInCelery]):
+        first_task = next(t[-1] for t in task_tuples_to_send)
 
         # Celery state queries will stuck if we do not use one same backend
         # for all tasks.
@@ -299,7 +315,7 @@ def _process_tasks(self, task_tuples: list[TaskTuple]) -> None:
                 # which point we don't need the ID anymore anyway
                 self.event_buffer[key] = (TaskInstanceState.QUEUED, result.task_id)
 
-    def _send_tasks_to_celery(self, task_tuples_to_send: list[TaskInstanceInCelery]):
+    def _send_tasks_to_celery(self, task_tuples_to_send: Sequence[TaskInstanceInCelery]):
         from airflow.providers.celery.executors.celery_executor_utils import send_task_to_executor
 
         if len(task_tuples_to_send) == 1 or self._sync_parallelism == 1:
@@ -359,7 +375,7 @@ def update_task_state(self, key: TaskInstanceKey, state: str, info: Any) -> None
                 self.success(key, info)
             elif state in (celery_states.FAILURE, celery_states.REVOKED):
                 self.fail(key, info)
-            elif state in (celery_states.STARTED, celery_states.PENDING):
+            elif state in (celery_states.STARTED, celery_states.PENDING, celery_states.RETRY):
                 pass
             else:
                 self.log.info("Unexpected state for %s: %s", key, state)
@@ -416,6 +432,10 @@ def try_adopt_task_instances(self, tis: Sequence[TaskInstance]) -> Sequence[Task
         for celery_task_id, (state, info) in states_by_celery_task_id.items():
             result, ti = celery_tasks[celery_task_id]
             result.backend = cached_celery_backend
+            if isinstance(result.result, BaseException):
+                e = result.result
+                # Log the exception we got from the remote end
+                self.log.warning("Task %s failed with error", ti.key, exc_info=e)
 
             # Set the correct elements of the state dicts, then update this
             # like we just queried it.
@@ -475,6 +495,10 @@ def get_cli_commands() -> list[GroupCommand]:
             ),
         ]
 
+    def queue_workload(self, workload: workloads.ExecuteTask, session: Session | None) -> None:
+        ti = workload.ti
+        self.queued_tasks[ti.key] = workload
+
 
 def _get_parser() -> argparse.ArgumentParser:
     """