kubernetes_executor.py

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# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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"""
KubernetesExecutor.

.. seealso::
    For more information on how the KubernetesExecutor works, take a look at the guide:
    :ref:`executor:KubernetesExecutor`
"""
from __future__ import annotations

import contextlib
import json
import logging
import multiprocessing
import time
from collections import defaultdict
from contextlib import suppress
from datetime import datetime
from queue import Empty, Queue
from typing import TYPE_CHECKING, Any, Sequence

from sqlalchemy import select, update

from airflow.providers.cncf.kubernetes.pod_generator import PodMutationHookException, PodReconciliationError
from airflow.stats import Stats

try:
    from airflow.cli.cli_config import (
        ARG_DAG_ID,
        ARG_EXECUTION_DATE,
        ARG_OUTPUT_PATH,
        ARG_SUBDIR,
        ARG_VERBOSE,
        ActionCommand,
        Arg,
        GroupCommand,
        lazy_load_command,
        positive_int,
    )
except ImportError:
    try:
        from airflow import __version__ as airflow_version
    except ImportError:
        from airflow.version import version as airflow_version

    import packaging.version

    from airflow.exceptions import AirflowOptionalProviderFeatureException

    base_version = packaging.version.parse(airflow_version).base_version

    if packaging.version.parse(base_version) < packaging.version.parse("2.7.0"):
        raise AirflowOptionalProviderFeatureException(
            "Kubernetes Executor from CNCF Provider should only be used with Airflow 2.7.0+.\n"
            f"This is Airflow {airflow_version} and Kubernetes and CeleryKubernetesExecutor are "
            f"available in the 'airflow.executors' package. You should not use "
            f"the provider's executors in this version of Airflow."
        )
    raise
from airflow.configuration import conf
from airflow.executors.base_executor import BaseExecutor
from airflow.providers.cncf.kubernetes.executors.kubernetes_executor_types import (
    ADOPTED,
    POD_EXECUTOR_DONE_KEY,
)
from airflow.providers.cncf.kubernetes.kube_config import KubeConfig
from airflow.providers.cncf.kubernetes.kubernetes_helper_functions import annotations_to_key
from airflow.utils.event_scheduler import EventScheduler
from airflow.utils.log.logging_mixin import remove_escape_codes
from airflow.utils.session import NEW_SESSION, provide_session
from airflow.utils.state import TaskInstanceState

if TYPE_CHECKING:
    import argparse

    from kubernetes import client
    from kubernetes.client import models as k8s
    from sqlalchemy.orm import Session

    from airflow.executors.base_executor import CommandType
    from airflow.models.taskinstance import TaskInstance
    from airflow.models.taskinstancekey import TaskInstanceKey
    from airflow.providers.cncf.kubernetes.executors.kubernetes_executor_types import (
        KubernetesJobType,
        KubernetesResultsType,
    )
    from airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils import (
        AirflowKubernetesScheduler,
    )


# CLI Args
ARG_NAMESPACE = Arg(
    ("--namespace",),
    default=conf.get("kubernetes_executor", "namespace"),
    help="Kubernetes Namespace. Default value is `[kubernetes] namespace` in configuration.",
)

ARG_MIN_PENDING_MINUTES = Arg(
    ("--min-pending-minutes",),
    default=30,
    type=positive_int(allow_zero=False),
    help=(
        "Pending pods created before the time interval are to be cleaned up, "
        "measured in minutes. Default value is 30(m). The minimum value is 5(m)."
    ),
)

# CLI Commands
KUBERNETES_COMMANDS = (
    ActionCommand(
        name="cleanup-pods",
        help=(
            "Clean up Kubernetes pods "
            "(created by KubernetesExecutor/KubernetesPodOperator) "
            "in evicted/failed/succeeded/pending states"
        ),
        func=lazy_load_command("airflow.cli.commands.kubernetes_command.cleanup_pods"),
        args=(ARG_NAMESPACE, ARG_MIN_PENDING_MINUTES, ARG_VERBOSE),
    ),
    ActionCommand(
        name="generate-dag-yaml",
        help="Generate YAML files for all tasks in DAG. Useful for debugging tasks without "
        "launching into a cluster",
        func=lazy_load_command("airflow.cli.commands.kubernetes_command.generate_pod_yaml"),
        args=(ARG_DAG_ID, ARG_EXECUTION_DATE, ARG_SUBDIR, ARG_OUTPUT_PATH, ARG_VERBOSE),
    ),
)


class KubernetesExecutor(BaseExecutor):
    """Executor for Kubernetes."""

    RUNNING_POD_LOG_LINES = 100
    supports_ad_hoc_ti_run: bool = True

    def __init__(self):
        self.kube_config = KubeConfig()
        self._manager = multiprocessing.Manager()
        self.task_queue: Queue[KubernetesJobType] = self._manager.Queue()
        self.result_queue: Queue[KubernetesResultsType] = self._manager.Queue()
        self.kube_scheduler: AirflowKubernetesScheduler | None = None
        self.kube_client: client.CoreV1Api | None = None
        self.scheduler_job_id: str | None = None
        self.event_scheduler: EventScheduler | None = None
        self.last_handled: dict[TaskInstanceKey, float] = {}
        self.kubernetes_queue: str | None = None
        super().__init__(parallelism=self.kube_config.parallelism)

    def _list_pods(self, query_kwargs):
        if self.kube_config.multi_namespace_mode:
            if self.kube_config.multi_namespace_mode_namespace_list:
                pods = []
                for namespace in self.kube_config.multi_namespace_mode_namespace_list:
                    pods.extend(
                        self.kube_client.list_namespaced_pod(namespace=namespace, **query_kwargs).items
                    )
            else:
                pods = self.kube_client.list_pod_for_all_namespaces(**query_kwargs).items
        else:
            pods = self.kube_client.list_namespaced_pod(
                namespace=self.kube_config.kube_namespace, **query_kwargs
            ).items

        return pods

    def _make_safe_label_value(self, input_value: str | datetime) -> str:
        """
        Normalize a provided label to be of valid length and characters.

        See airflow.providers.cncf.kubernetes.pod_generator.make_safe_label_value for more details.
        """
        # airflow.providers.cncf.kubernetes is an expensive import, locally import it here to
        # speed up load times of the kubernetes_executor module.
        from airflow.providers.cncf.kubernetes import pod_generator

        if isinstance(input_value, datetime):
            return pod_generator.datetime_to_label_safe_datestring(input_value)
        return pod_generator.make_safe_label_value(input_value)

    @provide_session
    def clear_not_launched_queued_tasks(self, session: Session = NEW_SESSION) -> None:
        """
        Clear tasks that were not yet launched, but were previously queued.

        Tasks can end up in a "Queued" state when a rescheduled/deferred operator
        comes back up for execution (with the same try_number) before the
        pod of its previous incarnation has been fully removed (we think).

        It's also possible when an executor abruptly shuts down (leaving a non-empty
        task_queue on that executor), but that scenario is handled via normal adoption.

        This method checks each of our queued tasks to see if the corresponding pod
        is around, and if not, and there's no matching entry in our own
        task_queue, marks it for re-execution.
        """
        if TYPE_CHECKING:
            assert self.kube_client
        from airflow.models.taskinstance import TaskInstance

        with Stats.timer("kubernetes_executor.clear_not_launched_queued_tasks.duration"):
            self.log.debug("Clearing tasks that have not been launched")
            query = select(TaskInstance).where(
                TaskInstance.state == TaskInstanceState.QUEUED, TaskInstance.queued_by_job_id == self.job_id
            )
            if self.kubernetes_queue:
                query = query.where(TaskInstance.queue == self.kubernetes_queue)
            queued_tis: list[TaskInstance] = session.scalars(query).all()
            self.log.info("Found %s queued task instances", len(queued_tis))

            # Go through the "last seen" dictionary and clean out old entries
            allowed_age = self.kube_config.worker_pods_queued_check_interval * 3
            for key, timestamp in list(self.last_handled.items()):
                if time.time() - timestamp > allowed_age:
                    del self.last_handled[key]

            if not queued_tis:
                return

            # airflow worker label selector batch call
            kwargs = {"label_selector": f"airflow-worker={self._make_safe_label_value(str(self.job_id))}"}
            if self.kube_config.kube_client_request_args:
                kwargs.update(self.kube_config.kube_client_request_args)
            pod_list = self._list_pods(kwargs)

            # create a set against pod query label fields
            label_search_set = set()
            for pod in pod_list:
                dag_id = pod.metadata.labels.get("dag_id", None)
                task_id = pod.metadata.labels.get("task_id", None)
                airflow_worker = pod.metadata.labels.get("airflow-worker", None)
                map_index = pod.metadata.labels.get("map_index", None)
                run_id = pod.metadata.labels.get("run_id", None)
                execution_date = pod.metadata.labels.get("execution_date", None)
                if dag_id is None or task_id is None or airflow_worker is None:
                    continue
                label_search_base_str = f"dag_id={dag_id},task_id={task_id},airflow-worker={airflow_worker}"
                if map_index is not None:
                    label_search_base_str += f",map_index={map_index}"
                if run_id is not None:
                    label_search_str = f"{label_search_base_str},run_id={run_id}"
                    label_search_set.add(label_search_str)
                if execution_date is not None:
                    label_search_str = f"{label_search_base_str},execution_date={execution_date}"
                    label_search_set.add(label_search_str)

            for ti in queued_tis:
                self.log.debug("Checking task instance %s", ti)

                # Check to see if we've handled it ourselves recently
                if ti.key in self.last_handled:
                    continue

                # Build the pod selector
                base_label_selector = (
                    f"dag_id={self._make_safe_label_value(ti.dag_id)},"
                    f"task_id={self._make_safe_label_value(ti.task_id)},"
                    f"airflow-worker={self._make_safe_label_value(str(ti.queued_by_job_id))}"
                )
                if ti.map_index >= 0:
                    # Old tasks _couldn't_ be mapped, so we don't have to worry about compat
                    base_label_selector += f",map_index={ti.map_index}"

                # Try run_id first
                label_search_str = f"{base_label_selector},run_id={self._make_safe_label_value(ti.run_id)}"
                if label_search_str in label_search_set:
                    continue
                # Fallback to old style of using execution_date
                label_search_str = (
                    f"{base_label_selector},execution_date={self._make_safe_label_value(ti.execution_date)}"
                )
                if label_search_str in label_search_set:
                    continue
                self.log.info("TaskInstance: %s found in queued state but was not launched, rescheduling", ti)
                session.execute(
                    update(TaskInstance)
                    .where(
                        TaskInstance.dag_id == ti.dag_id,
                        TaskInstance.task_id == ti.task_id,
                        TaskInstance.run_id == ti.run_id,
                        TaskInstance.map_index == ti.map_index,
                    )
                    .values(state=TaskInstanceState.SCHEDULED)
                )

    def start(self) -> None:
        """Start the executor."""
        self.log.info("Start Kubernetes executor")
        self.scheduler_job_id = str(self.job_id)
        self.log.debug("Start with scheduler_job_id: %s", self.scheduler_job_id)
        from airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils import (
            AirflowKubernetesScheduler,
        )
        from airflow.providers.cncf.kubernetes.kube_client import get_kube_client

        self.kube_client = get_kube_client()
        self.kube_scheduler = AirflowKubernetesScheduler(
            kube_config=self.kube_config,
            result_queue=self.result_queue,
            kube_client=self.kube_client,
            scheduler_job_id=self.scheduler_job_id,
        )
        self.event_scheduler = EventScheduler()

        self.event_scheduler.call_regular_interval(
            self.kube_config.worker_pods_queued_check_interval,
            self.clear_not_launched_queued_tasks,
        )
        # We also call this at startup as that's the most likely time to see
        # stuck queued tasks
        self.clear_not_launched_queued_tasks()

    def execute_async(
        self,
        key: TaskInstanceKey,
        command: CommandType,
        queue: str | None = None,
        executor_config: Any | None = None,
    ) -> None:
        """Execute task asynchronously."""
        if TYPE_CHECKING:
            assert self.task_queue

        if self.log.isEnabledFor(logging.DEBUG):
            self.log.debug("Add task %s with command %s, executor_config %s", key, command, executor_config)
        else:
            self.log.info("Add task %s with command %s", key, command)

        from airflow.providers.cncf.kubernetes.pod_generator import PodGenerator

        try:
            kube_executor_config = PodGenerator.from_obj(executor_config)
        except Exception:
            self.log.error("Invalid executor_config for %s. Executor_config: %s", key, executor_config)
            self.fail(key=key, info="Invalid executor_config passed")
            return

        if executor_config:
            pod_template_file = executor_config.get("pod_template_file", None)
        else:
            pod_template_file = None
        self.event_buffer[key] = (TaskInstanceState.QUEUED, self.scheduler_job_id)
        self.task_queue.put((key, command, kube_executor_config, pod_template_file))
        # We keep a temporary local record that we've handled this so we don't
        # try and remove it from the QUEUED state while we process it
        self.last_handled[key] = time.time()

    def sync(self) -> None:
        """Synchronize task state."""
        if TYPE_CHECKING:
            assert self.scheduler_job_id
            assert self.kube_scheduler
            assert self.kube_config
            assert self.result_queue
            assert self.task_queue
            assert self.event_scheduler

        if self.running:
            self.log.debug("self.running: %s", self.running)
        if self.queued_tasks:
            self.log.debug("self.queued: %s", self.queued_tasks)
        self.kube_scheduler.sync()

        last_resource_version: dict[str, str] = defaultdict(lambda: "0")
        with contextlib.suppress(Empty):
            while True:
                results = self.result_queue.get_nowait()
                try:
                    key, state, pod_name, namespace, resource_version = results
                    last_resource_version[namespace] = resource_version
                    self.log.info("Changing state of %s to %s", results, state)
                    try:
                        self._change_state(key, state, pod_name, namespace)
                    except Exception as e:
                        self.log.exception(
                            "Exception: %s when attempting to change state of %s to %s, re-queueing.",
                            e,
                            results,
                            state,
                        )
                        self.result_queue.put(results)
                finally:
                    self.result_queue.task_done()

        from airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils import ResourceVersion

        resource_instance = ResourceVersion()
        for ns in resource_instance.resource_version:
            resource_instance.resource_version[ns] = (
                last_resource_version[ns] or resource_instance.resource_version[ns]
            )

        from kubernetes.client.rest import ApiException

        with contextlib.suppress(Empty):
            for _ in range(self.kube_config.worker_pods_creation_batch_size):
                task = self.task_queue.get_nowait()

                try:
                    self.kube_scheduler.run_next(task)
                except PodReconciliationError as e:
                    self.log.error(
                        "Pod reconciliation failed, likely due to kubernetes library upgrade. "
                        "Try clearing the task to re-run.",
                        exc_info=True,
                    )
                    self.fail(task[0], e)
                except ApiException as e:
                    # These codes indicate something is wrong with pod definition; otherwise we assume pod
                    # definition is ok, and that retrying may work
                    if e.status in (400, 422):
                        self.log.error("Pod creation failed with reason %r. Failing task", e.reason)
                        key, _, _, _ = task
                        self.change_state(key, TaskInstanceState.FAILED, e)
                    else:
                        self.log.warning(
                            "ApiException when attempting to run task, re-queueing. Reason: %r. Message: %s",
                            e.reason,
                            json.loads(e.body)["message"],
                        )
                        self.task_queue.put(task)
                except PodMutationHookException as e:
                    key, _, _, _ = task
                    self.log.error(
                        "Pod Mutation Hook failed for the task %s. Failing task. Details: %s",
                        key,
                        e.__cause__,
                    )
                    self.fail(key, e)
                finally:
                    self.task_queue.task_done()

        # Run any pending timed events
        next_event = self.event_scheduler.run(blocking=False)
        self.log.debug("Next timed event is in %f", next_event)

    @provide_session
    def _change_state(
        self,
        key: TaskInstanceKey,
        state: TaskInstanceState | str | None,
        pod_name: str,
        namespace: str,
        session: Session = NEW_SESSION,
    ) -> None:
        if TYPE_CHECKING:
            assert self.kube_scheduler

        if state == ADOPTED:
            # When the task pod is adopted by another executor,
            # then remove the task from the current executor running queue.
            try:
                self.running.remove(key)
            except KeyError:
                self.log.debug("TI key not in running: %s", key)
            return

        if state == TaskInstanceState.RUNNING:
            self.event_buffer[key] = state, None
            return

        if self.kube_config.delete_worker_pods:
            if state != TaskInstanceState.FAILED or self.kube_config.delete_worker_pods_on_failure:
                self.kube_scheduler.delete_pod(pod_name=pod_name, namespace=namespace)
                self.log.info("Deleted pod: %s in namespace %s", key, namespace)
        else:
            self.kube_scheduler.patch_pod_executor_done(pod_name=pod_name, namespace=namespace)
            self.log.info("Patched pod %s in namespace %s to mark it as done", key, namespace)

        try:
            self.running.remove(key)
        except KeyError:
            self.log.debug("TI key not in running, not adding to event_buffer: %s", key)

        # If we don't have a TI state, look it up from the db. event_buffer expects the TI state
        if state is None:
            from airflow.models.taskinstance import TaskInstance

            state = session.scalar(select(TaskInstance.state).where(TaskInstance.filter_for_tis([key])))
            state = TaskInstanceState(state)

        self.event_buffer[key] = state, None

    @staticmethod
    def _get_pod_namespace(ti: TaskInstance):
        pod_override = ti.executor_config.get("pod_override")
        namespace = None
        with suppress(Exception):
            namespace = pod_override.metadata.namespace
        return namespace or conf.get("kubernetes_executor", "namespace")

    def get_task_log(self, ti: TaskInstance, try_number: int) -> tuple[list[str], list[str]]:
        messages = []
        log = []
        try:
            from airflow.providers.cncf.kubernetes.kube_client import get_kube_client
            from airflow.providers.cncf.kubernetes.pod_generator import PodGenerator

            client = get_kube_client()

            messages.append(f"Attempting to fetch logs from pod {ti.hostname} through kube API")
            selector = PodGenerator.build_selector_for_k8s_executor_pod(
                dag_id=ti.dag_id,
                task_id=ti.task_id,
                try_number=try_number,
                map_index=ti.map_index,
                run_id=ti.run_id,
                airflow_worker=ti.queued_by_job_id,
            )
            namespace = self._get_pod_namespace(ti)
            pod_list = client.list_namespaced_pod(
                namespace=namespace,
                label_selector=selector,
            ).items
            if not pod_list:
                raise RuntimeError("Cannot find pod for ti %s", ti)
            elif len(pod_list) > 1:
                raise RuntimeError("Found multiple pods for ti %s: %s", ti, pod_list)
            res = client.read_namespaced_pod_log(
                name=pod_list[0].metadata.name,
                namespace=namespace,
                container="base",
                follow=False,
                tail_lines=self.RUNNING_POD_LOG_LINES,
                _preload_content=False,
            )
            for line in res:
                log.append(remove_escape_codes(line.decode()))
            if log:
                messages.append("Found logs through kube API")
        except Exception as e:
            messages.append(f"Reading from k8s pod logs failed: {e}")
        return messages, ["\n".join(log)]

    def try_adopt_task_instances(self, tis: Sequence[TaskInstance]) -> Sequence[TaskInstance]:
        with Stats.timer("kubernetes_executor.adopt_task_instances.duration"):
            # Always flush TIs without queued_by_job_id
            tis_to_flush = [ti for ti in tis if not ti.queued_by_job_id]
            scheduler_job_ids = {ti.queued_by_job_id for ti in tis}
            tis_to_flush_by_key = {ti.key: ti for ti in tis if ti.queued_by_job_id}
            kube_client: client.CoreV1Api = self.kube_client
            for scheduler_job_id in scheduler_job_ids:
                scheduler_job_id = self._make_safe_label_value(str(scheduler_job_id))
                # We will look for any pods owned by the no-longer-running scheduler,
                # but will exclude only successful pods, as those TIs will have a terminal state
                # and not be up for adoption!
                # Those workers that failed, however, are okay to adopt here as their TI will
                # still be in queued.
                query_kwargs = {
                    "field_selector": "status.phase!=Succeeded",
                    "label_selector": (
                        "kubernetes_executor=True,"
                        f"airflow-worker={scheduler_job_id},{POD_EXECUTOR_DONE_KEY}!=True"
                    ),
                }
                pod_list = self._list_pods(query_kwargs)
                for pod in pod_list:
                    self.adopt_launched_task(kube_client, pod, tis_to_flush_by_key)
            self._adopt_completed_pods(kube_client)
            tis_to_flush.extend(tis_to_flush_by_key.values())
            return tis_to_flush

    def cleanup_stuck_queued_tasks(self, tis: list[TaskInstance]) -> list[str]:
        """
        Handle remnants of tasks that were failed because they were stuck in queued.

        Tasks can get stuck in queued. If such a task is detected, it will be marked
        as `UP_FOR_RETRY` if the task instance has remaining retries or marked as `FAILED`
        if it doesn't.

        :param tis: List of Task Instances to clean up
        :return: List of readable task instances for a warning message
        """
        from airflow.providers.cncf.kubernetes.pod_generator import PodGenerator

        if TYPE_CHECKING:
            assert self.kube_client
            assert self.kube_scheduler
        readable_tis = []
        for ti in tis:
            selector = PodGenerator.build_selector_for_k8s_executor_pod(
                dag_id=ti.dag_id,
                task_id=ti.task_id,
                try_number=ti.try_number,
                map_index=ti.map_index,
                run_id=ti.run_id,
                airflow_worker=ti.queued_by_job_id,
            )
            namespace = self._get_pod_namespace(ti)
            pod_list = self.kube_client.list_namespaced_pod(
                namespace=namespace,
                label_selector=selector,
            ).items
            if not pod_list:
                self.log.warning("Cannot find pod for ti %s", ti)
                continue
            elif len(pod_list) > 1:
                self.log.warning("Found multiple pods for ti %s: %s", ti, pod_list)
                continue
            readable_tis.append(repr(ti))
            self.kube_scheduler.delete_pod(pod_name=pod_list[0].metadata.name, namespace=namespace)
        return readable_tis

    def adopt_launched_task(
        self,
        kube_client: client.CoreV1Api,
        pod: k8s.V1Pod,
        tis_to_flush_by_key: dict[TaskInstanceKey, k8s.V1Pod],
    ) -> None:
        """
        Patch existing pod so that the current KubernetesJobWatcher can monitor it via label selectors.

        :param kube_client: kubernetes client for speaking to kube API
        :param pod: V1Pod spec that we will patch with new label
        :param tis_to_flush_by_key: TIs that will be flushed if they aren't adopted
        """
        if TYPE_CHECKING:
            assert self.scheduler_job_id

        self.log.info("attempting to adopt pod %s", pod.metadata.name)
        ti_key = annotations_to_key(pod.metadata.annotations)
        if ti_key not in tis_to_flush_by_key:
            self.log.error("attempting to adopt taskinstance which was not specified by database: %s", ti_key)
            return

        new_worker_id_label = self._make_safe_label_value(self.scheduler_job_id)
        from kubernetes.client.rest import ApiException

        try:
            kube_client.patch_namespaced_pod(
                name=pod.metadata.name,
                namespace=pod.metadata.namespace,
                body={"metadata": {"labels": {"airflow-worker": new_worker_id_label}}},
            )
        except ApiException as e:
            self.log.info("Failed to adopt pod %s. Reason: %s", pod.metadata.name, e)
            return

        del tis_to_flush_by_key[ti_key]
        self.running.add(ti_key)

    def _adopt_completed_pods(self, kube_client: client.CoreV1Api) -> None:
        """
        Patch completed pods so that the KubernetesJobWatcher can delete them.

        :param kube_client: kubernetes client for speaking to kube API
        """
        if TYPE_CHECKING:
            assert self.scheduler_job_id

        new_worker_id_label = self._make_safe_label_value(self.scheduler_job_id)
        query_kwargs = {
            "field_selector": "status.phase=Succeeded",
            "label_selector": (
                "kubernetes_executor=True,"
                f"airflow-worker!={new_worker_id_label},{POD_EXECUTOR_DONE_KEY}!=True"
            ),
        }
        pod_list = self._list_pods(query_kwargs)
        for pod in pod_list:
            self.log.info("Attempting to adopt pod %s", pod.metadata.name)
            from kubernetes.client.rest import ApiException

            try:
                kube_client.patch_namespaced_pod(
                    name=pod.metadata.name,
                    namespace=pod.metadata.namespace,
                    body={"metadata": {"labels": {"airflow-worker": new_worker_id_label}}},
                )
            except ApiException as e:
                self.log.info("Failed to adopt pod %s. Reason: %s", pod.metadata.name, e)
            ti_id = annotations_to_key(pod.metadata.annotations)
            self.running.add(ti_id)

    def _flush_task_queue(self) -> None:
        if TYPE_CHECKING:
            assert self.task_queue

        self.log.debug("Executor shutting down, task_queue approximate size=%d", self.task_queue.qsize())
        with contextlib.suppress(Empty):
            while True:
                task = self.task_queue.get_nowait()
                # This is a new task to run thus ok to ignore.
                self.log.warning("Executor shutting down, will NOT run task=%s", task)
                self.task_queue.task_done()

    def _flush_result_queue(self) -> None:
        if TYPE_CHECKING:
            assert self.result_queue

        self.log.debug("Executor shutting down, result_queue approximate size=%d", self.result_queue.qsize())
        with contextlib.suppress(Empty):
            while True:
                results = self.result_queue.get_nowait()
                self.log.warning("Executor shutting down, flushing results=%s", results)
                try:
                    key, state, pod_name, namespace, resource_version = results
                    self.log.info(
                        "Changing state of %s to %s : resource_version=%d", results, state, resource_version
                    )
                    try:
                        self._change_state(key, state, pod_name, namespace)
                    except Exception as e:
                        self.log.exception(
                            "Ignoring exception: %s when attempting to change state of %s to %s.",
                            e,
                            results,
                            state,
                        )
                finally:
                    self.result_queue.task_done()

    def end(self) -> None:
        """Shut down the executor."""
        if TYPE_CHECKING:
            assert self.task_queue
            assert self.result_queue
            assert self.kube_scheduler

        self.log.info("Shutting down Kubernetes executor")
        try:
            self.log.debug("Flushing task_queue...")
            self._flush_task_queue()
            self.log.debug("Flushing result_queue...")
            self._flush_result_queue()
            # Both queues should be empty...
            self.task_queue.join()
            self.result_queue.join()
        except ConnectionResetError:
            self.log.exception("Connection Reset error while flushing task_queue and result_queue.")
        if self.kube_scheduler:
            self.kube_scheduler.terminate()
        self._manager.shutdown()

    def terminate(self):
        """Terminate the executor is not doing anything."""

    @staticmethod
    def get_cli_commands() -> list[GroupCommand]:
        return [
            GroupCommand(
                name="kubernetes",
                help="Tools to help run the KubernetesExecutor",
                subcommands=KUBERNETES_COMMANDS,
            )
        ]


def _get_parser() -> argparse.ArgumentParser:
    """
    Generate documentation; used by Sphinx.

    :meta private:
    """
    return KubernetesExecutor._get_parser()