# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
KubernetesExecutor.
.. seealso::
For more information on how the KubernetesExecutor works, take a look at the guide:
:doc:`/kubernetes_executor`
"""
from __future__ import annotations
import contextlib
import json
import logging
import multiprocessing
import time
from collections import Counter, defaultdict
from contextlib import suppress
from datetime import datetime
from queue import Empty, Queue
from typing import TYPE_CHECKING, Any, Sequence
from kubernetes.dynamic import DynamicClient
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
[docs] 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
[docs]ARG_NAMESPACE = Arg(
("--namespace",),
default=conf.get("kubernetes_executor", "namespace"),
help="Kubernetes Namespace. Default value is `[kubernetes] namespace` in configuration.",
)
[docs]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
[docs]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),
),
)
[docs]class KubernetesExecutor(BaseExecutor):
"""Executor for Kubernetes."""
[docs] RUNNING_POD_LOG_LINES = 100
[docs] 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
self.task_publish_retries: Counter[TaskInstanceKey] = Counter()
self.task_publish_max_retries = conf.getint("kubernetes", "task_publish_max_retries", fallback=0)
super().__init__(parallelism=self.kube_config.parallelism)
def _list_pods(self, query_kwargs):
query_kwargs["header_params"] = {
"Accept": "application/json;as=PartialObjectMetadataList;v=v1;g=meta.k8s.io"
}
dynamic_client = DynamicClient(self.kube_client.api_client)
pod_resource = dynamic_client.resources.get(api_version="v1", kind="Pod")
if self.kube_config.multi_namespace_mode:
if self.kube_config.multi_namespace_mode_namespace_list:
namespaces = self.kube_config.multi_namespace_mode_namespace_list
else:
namespaces = [None]
else:
namespaces = [self.kube_config.kube_namespace]
pods = []
for namespace in namespaces:
# Dynamic Client list pods is throwing TypeError when there are no matching pods to return
# This bug was fixed in MR https://github.com/kubernetes-client/python/pull/2155
# TODO: Remove the try-except clause once we upgrade the K8 Python client version which
# includes the above MR
try:
pods.extend(
dynamic_client.get(resource=pod_resource, namespace=namespace, **query_kwargs).items
)
except TypeError:
continue
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
[docs] 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)
)
[docs] 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()
[docs] 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()
[docs] 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:
key, command, kube_executor_config, pod_template_file = task
self.kube_scheduler.run_next(task)
self.task_publish_retries.pop(key, None)
except PodReconciliationError as e:
self.log.exception(
"Pod reconciliation failed, likely due to kubernetes library upgrade. "
"Try clearing the task to re-run.",
)
self.fail(task[0], e)
except ApiException as e:
body = json.loads(e.body)
retries = self.task_publish_retries[key]
# In case of exceeded quota errors, requeue the task as per the task_publish_max_retries
if (
str(e.status) == "403"
and "exceeded quota" in body["message"]
and (self.task_publish_max_retries == -1 or retries < self.task_publish_max_retries)
):
self.log.warning(
"[Try %s of %s] Kube ApiException for Task: (%s). Reason: %r. Message: %s",
self.task_publish_retries[key] + 1,
self.task_publish_max_retries,
key,
e.reason,
body["message"],
)
self.task_queue.put(task)
self.task_publish_retries[key] = retries + 1
else:
self.log.error("Pod creation failed with reason %r. Failing task", e.reason)
key, _, _, _ = task
self.fail(key, e)
self.task_publish_retries.pop(key, None)
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) if state else None
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")
[docs] 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)]
[docs] 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
[docs] 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
[docs] 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()
[docs] 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()
[docs] def terminate(self):
"""Terminate the executor is not doing anything."""
@staticmethod
[docs] 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()
