# 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:
:ref:`executor:KubernetesExecutor`
"""
import functools
import json
import multiprocessing
import time
from datetime import timedelta
from queue import Empty, Queue
from typing import Any, Dict, List, Optional, Tuple
from kubernetes import client, watch
from kubernetes.client import Configuration, models as k8s
from kubernetes.client.rest import ApiException
from urllib3.exceptions import ReadTimeoutError
from airflow.exceptions import AirflowException
from airflow.executors.base_executor import NOT_STARTED_MESSAGE, BaseExecutor, CommandType
from airflow.kubernetes import pod_generator
from airflow.kubernetes.kube_client import get_kube_client
from airflow.kubernetes.kube_config import KubeConfig
from airflow.kubernetes.kubernetes_helper_functions import annotations_to_key, create_pod_id
from airflow.kubernetes.pod_generator import PodGenerator
from airflow.models.taskinstance import TaskInstance, TaskInstanceKey
from airflow.settings import pod_mutation_hook
from airflow.utils import timezone
from airflow.utils.event_scheduler import EventScheduler
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.session import provide_session
from airflow.utils.state import State
# TaskInstance key, command, configuration, pod_template_file
[docs]KubernetesJobType = Tuple[TaskInstanceKey, CommandType, Any, Optional[str]]
# key, state, pod_id, namespace, resource_version
[docs]KubernetesResultsType = Tuple[TaskInstanceKey, Optional[str], str, str, str]
# pod_id, namespace, state, annotations, resource_version
[docs]KubernetesWatchType = Tuple[str, str, Optional[str], Dict[str, str], str]
[docs]class ResourceVersion:
"""Singleton for tracking resourceVersion from Kubernetes"""
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
[docs]class KubernetesJobWatcher(multiprocessing.Process, LoggingMixin):
"""Watches for Kubernetes jobs"""
def __init__(
self,
namespace: Optional[str],
multi_namespace_mode: bool,
watcher_queue: 'Queue[KubernetesWatchType]',
resource_version: Optional[str],
scheduler_job_id: str,
kube_config: Configuration,
):
super().__init__()
self.namespace = namespace
self.multi_namespace_mode = multi_namespace_mode
self.scheduler_job_id = scheduler_job_id
self.watcher_queue = watcher_queue
self.resource_version = resource_version
self.kube_config = kube_config
[docs] def run(self) -> None:
"""Performs watching"""
kube_client: client.CoreV1Api = get_kube_client()
if not self.scheduler_job_id:
raise AirflowException(NOT_STARTED_MESSAGE)
while True:
try:
self.resource_version = self._run(
kube_client, self.resource_version, self.scheduler_job_id, self.kube_config
)
except ReadTimeoutError:
self.log.warning(
"There was a timeout error accessing the Kube API. Retrying request.", exc_info=True
)
time.sleep(1)
except Exception:
self.log.exception('Unknown error in KubernetesJobWatcher. Failing')
raise
else:
self.log.warning(
'Watch died gracefully, starting back up with: last resource_version: %s',
self.resource_version,
)
def _run(
self,
kube_client: client.CoreV1Api,
resource_version: Optional[str],
scheduler_job_id: str,
kube_config: Any,
) -> Optional[str]:
self.log.info('Event: and now my watch begins starting at resource_version: %s', resource_version)
watcher = watch.Watch()
kwargs = {'label_selector': f'airflow-worker={scheduler_job_id}'}
if resource_version:
kwargs['resource_version'] = resource_version
if kube_config.kube_client_request_args:
for key, value in kube_config.kube_client_request_args.items():
kwargs[key] = value
last_resource_version: Optional[str] = None
if self.multi_namespace_mode:
list_worker_pods = functools.partial(
watcher.stream, kube_client.list_pod_for_all_namespaces, **kwargs
)
else:
list_worker_pods = functools.partial(
watcher.stream, kube_client.list_namespaced_pod, self.namespace, **kwargs
)
for event in list_worker_pods():
task = event['object']
self.log.info('Event: %s had an event of type %s', task.metadata.name, event['type'])
if event['type'] == 'ERROR':
return self.process_error(event)
annotations = task.metadata.annotations
task_instance_related_annotations = {
'dag_id': annotations['dag_id'],
'task_id': annotations['task_id'],
'execution_date': annotations.get('execution_date'),
'run_id': annotations.get('run_id'),
'try_number': annotations['try_number'],
}
map_index = annotations.get('map_index')
if map_index is not None:
task_instance_related_annotations['map_index'] = map_index
self.process_status(
pod_id=task.metadata.name,
namespace=task.metadata.namespace,
status=task.status.phase,
annotations=task_instance_related_annotations,
resource_version=task.metadata.resource_version,
event=event,
)
last_resource_version = task.metadata.resource_version
return last_resource_version
[docs] def process_error(self, event: Any) -> str:
"""Process error response"""
self.log.error('Encountered Error response from k8s list namespaced pod stream => %s', event)
raw_object = event['raw_object']
if raw_object['code'] == 410:
self.log.info(
'Kubernetes resource version is too old, must reset to 0 => %s', (raw_object['message'],)
)
# Return resource version 0
return '0'
raise AirflowException(
f"Kubernetes failure for {raw_object['reason']} with code {raw_object['code']} and message: "
f"{raw_object['message']}"
)
[docs] def process_status(
self,
pod_id: str,
namespace: str,
status: str,
annotations: Dict[str, str],
resource_version: str,
event: Any,
) -> None:
"""Process status response"""
if status == 'Pending':
if event['type'] == 'DELETED':
self.log.info('Event: Failed to start pod %s', pod_id)
self.watcher_queue.put((pod_id, namespace, State.FAILED, annotations, resource_version))
else:
self.log.info('Event: %s Pending', pod_id)
elif status == 'Failed':
self.log.error('Event: %s Failed', pod_id)
self.watcher_queue.put((pod_id, namespace, State.FAILED, annotations, resource_version))
elif status == 'Succeeded':
self.log.info('Event: %s Succeeded', pod_id)
self.watcher_queue.put((pod_id, namespace, None, annotations, resource_version))
elif status == 'Running':
if event['type'] == 'DELETED':
self.log.info('Event: Pod %s deleted before it could complete', pod_id)
self.watcher_queue.put((pod_id, namespace, State.FAILED, annotations, resource_version))
else:
self.log.info('Event: %s is Running', pod_id)
else:
self.log.warning(
'Event: Invalid state: %s on pod: %s in namespace %s with annotations: %s with '
'resource_version: %s',
status,
pod_id,
namespace,
annotations,
resource_version,
)
[docs]class AirflowKubernetesScheduler(LoggingMixin):
"""Airflow Scheduler for Kubernetes"""
def __init__(
self,
kube_config: Any,
task_queue: 'Queue[KubernetesJobType]',
result_queue: 'Queue[KubernetesResultsType]',
kube_client: client.CoreV1Api,
scheduler_job_id: str,
):
super().__init__()
self.log.debug("Creating Kubernetes executor")
self.kube_config = kube_config
self.task_queue = task_queue
self.result_queue = result_queue
self.namespace = self.kube_config.kube_namespace
self.log.debug("Kubernetes using namespace %s", self.namespace)
self.kube_client = kube_client
self._manager = multiprocessing.Manager()
self.watcher_queue = self._manager.Queue()
self.scheduler_job_id = scheduler_job_id
self.kube_watcher = self._make_kube_watcher()
[docs] def run_pod_async(self, pod: k8s.V1Pod, **kwargs):
"""Runs POD asynchronously"""
pod_mutation_hook(pod)
sanitized_pod = self.kube_client.api_client.sanitize_for_serialization(pod)
json_pod = json.dumps(sanitized_pod, indent=2)
self.log.debug('Pod Creation Request: \n%s', json_pod)
try:
resp = self.kube_client.create_namespaced_pod(
body=sanitized_pod, namespace=pod.metadata.namespace, **kwargs
)
self.log.debug('Pod Creation Response: %s', resp)
except Exception as e:
self.log.exception('Exception when attempting to create Namespaced Pod: %s', json_pod)
raise e
return resp
def _make_kube_watcher(self) -> KubernetesJobWatcher:
resource_version = ResourceVersion().resource_version
watcher = KubernetesJobWatcher(
watcher_queue=self.watcher_queue,
namespace=self.kube_config.kube_namespace,
multi_namespace_mode=self.kube_config.multi_namespace_mode,
resource_version=resource_version,
scheduler_job_id=self.scheduler_job_id,
kube_config=self.kube_config,
)
watcher.start()
return watcher
def _health_check_kube_watcher(self):
if self.kube_watcher.is_alive():
self.log.debug("KubeJobWatcher alive, continuing")
else:
self.log.error(
'Error while health checking kube watcher process. Process died for unknown reasons'
)
self.kube_watcher = self._make_kube_watcher()
[docs] def run_next(self, next_job: KubernetesJobType) -> None:
"""
The run_next command will check the task_queue for any un-run jobs.
It will then create a unique job-id, launch that job in the cluster,
and store relevant info in the current_jobs map so we can track the job's
status
"""
self.log.info('Kubernetes job is %s', str(next_job).replace("\n", " "))
key, command, kube_executor_config, pod_template_file = next_job
dag_id, task_id, run_id, try_number, map_index = key
if command[0:3] != ["airflow", "tasks", "run"]:
raise ValueError('The command must start with ["airflow", "tasks", "run"].')
base_worker_pod = get_base_pod_from_template(pod_template_file, self.kube_config)
if not base_worker_pod:
raise AirflowException(
f"could not find a valid worker template yaml at {self.kube_config.pod_template_file}"
)
pod = PodGenerator.construct_pod(
namespace=self.namespace,
scheduler_job_id=self.scheduler_job_id,
pod_id=create_pod_id(dag_id, task_id),
dag_id=dag_id,
task_id=task_id,
kube_image=self.kube_config.kube_image,
try_number=try_number,
map_index=map_index,
date=None,
run_id=run_id,
args=command,
pod_override_object=kube_executor_config,
base_worker_pod=base_worker_pod,
)
# Reconcile the pod generated by the Operator and the Pod
# generated by the .cfg file
self.log.debug("Kubernetes running for command %s", command)
self.log.debug("Kubernetes launching image %s", pod.spec.containers[0].image)
# the watcher will monitor pods, so we do not block.
self.run_pod_async(pod, **self.kube_config.kube_client_request_args)
self.log.debug("Kubernetes Job created!")
[docs] def delete_pod(self, pod_id: str, namespace: str) -> None:
"""Deletes POD"""
try:
self.log.debug("Deleting pod %s in namespace %s", pod_id, namespace)
self.kube_client.delete_namespaced_pod(
pod_id,
namespace,
body=client.V1DeleteOptions(**self.kube_config.delete_option_kwargs),
**self.kube_config.kube_client_request_args,
)
except ApiException as e:
# If the pod is already deleted
if e.status != 404:
raise
[docs] def sync(self) -> None:
"""
The sync function checks the status of all currently running kubernetes jobs.
If a job is completed, its status is placed in the result queue to
be sent back to the scheduler.
:return:
"""
self.log.debug("Syncing KubernetesExecutor")
self._health_check_kube_watcher()
while True:
try:
task = self.watcher_queue.get_nowait()
try:
self.log.debug("Processing task %s", task)
self.process_watcher_task(task)
finally:
self.watcher_queue.task_done()
except Empty:
break
[docs] def process_watcher_task(self, task: KubernetesWatchType) -> None:
"""Process the task by watcher."""
pod_id, namespace, state, annotations, resource_version = task
self.log.info(
'Attempting to finish pod; pod_id: %s; state: %s; annotations: %s', pod_id, state, annotations
)
key = annotations_to_key(annotations=annotations)
if key:
self.log.debug('finishing job %s - %s (%s)', key, state, pod_id)
self.result_queue.put((key, state, pod_id, namespace, resource_version))
def _flush_watcher_queue(self) -> None:
self.log.debug('Executor shutting down, watcher_queue approx. size=%d', self.watcher_queue.qsize())
while True:
try:
task = self.watcher_queue.get_nowait()
# Ignoring it since it can only have either FAILED or SUCCEEDED pods
self.log.warning('Executor shutting down, IGNORING watcher task=%s', task)
self.watcher_queue.task_done()
except Empty:
break
[docs] def terminate(self) -> None:
"""Terminates the watcher."""
self.log.debug("Terminating kube_watcher...")
self.kube_watcher.terminate()
self.kube_watcher.join()
self.log.debug("kube_watcher=%s", self.kube_watcher)
self.log.debug("Flushing watcher_queue...")
self._flush_watcher_queue()
# Queue should be empty...
self.watcher_queue.join()
self.log.debug("Shutting down manager...")
self._manager.shutdown()
[docs]def get_base_pod_from_template(pod_template_file: Optional[str], kube_config: Any) -> k8s.V1Pod:
"""
Reads either the pod_template_file set in the executor_config or the base pod_template_file
set in the airflow.cfg to craft a "base pod" that will be used by the KubernetesExecutor
:param pod_template_file: absolute path to a pod_template_file.yaml or None
:param kube_config: The KubeConfig class generated by airflow that contains all kube metadata
:return: a V1Pod that can be used as the base pod for k8s tasks
"""
if pod_template_file:
return PodGenerator.deserialize_model_file(pod_template_file)
else:
return PodGenerator.deserialize_model_file(kube_config.pod_template_file)
[docs]class KubernetesExecutor(BaseExecutor):
"""Executor for Kubernetes"""
[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: Optional[AirflowKubernetesScheduler] = None
self.kube_client: Optional[client.CoreV1Api] = None
self.scheduler_job_id: Optional[str] = None
self.event_scheduler: Optional[EventScheduler] = None
self.last_handled: Dict[TaskInstanceKey, float] = {}
super().__init__(parallelism=self.kube_config.parallelism)
@provide_session
[docs] def clear_not_launched_queued_tasks(self, session=None) -> None:
"""
Tasks can end up in a "Queued" state through either the executor being
abruptly shut down (leaving a non-empty task_queue on this executor)
or 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).
This method checks each of those 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.
"""
self.log.debug("Clearing tasks that have not been launched")
if not self.kube_client:
raise AirflowException(NOT_STARTED_MESSAGE)
queued_tis: List[TaskInstance] = (
session.query(TaskInstance).filter(TaskInstance.state == State.QUEUED).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]
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={pod_generator.make_safe_label_value(ti.dag_id)},"
f"task_id={pod_generator.make_safe_label_value(ti.task_id)},"
f"airflow-worker={pod_generator.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}'
kwargs = dict(label_selector=base_label_selector)
if self.kube_config.kube_client_request_args:
kwargs.update(**self.kube_config.kube_client_request_args)
# Try run_id first
kwargs['label_selector'] += ',run_id=' + pod_generator.make_safe_label_value(ti.run_id)
pod_list = self.kube_client.list_namespaced_pod(self.kube_config.kube_namespace, **kwargs)
if pod_list.items:
continue
# Fallback to old style of using execution_date
kwargs['label_selector'] = (
f'{base_label_selector},'
f'execution_date={pod_generator.datetime_to_label_safe_datestring(ti.execution_date)}'
)
pod_list = self.kube_client.list_namespaced_pod(self.kube_config.kube_namespace, **kwargs)
if pod_list.items:
continue
self.log.info('TaskInstance: %s found in queued state but was not launched, rescheduling', ti)
session.query(TaskInstance).filter(
TaskInstance.dag_id == ti.dag_id,
TaskInstance.task_id == ti.task_id,
TaskInstance.run_id == ti.run_id,
TaskInstance.map_index == ti.map_index,
).update({TaskInstance.state: State.SCHEDULED})
[docs] def start(self) -> None:
"""Starts the executor"""
self.log.info('Start Kubernetes executor')
if not self.job_id:
raise AirflowException("Could not get scheduler_job_id")
self.scheduler_job_id = str(self.job_id)
self.log.debug('Start with scheduler_job_id: %s', self.scheduler_job_id)
self.kube_client = get_kube_client()
self.kube_scheduler = AirflowKubernetesScheduler(
self.kube_config, self.task_queue, self.result_queue, self.kube_client, self.scheduler_job_id
)
self.event_scheduler = EventScheduler()
self.event_scheduler.call_regular_interval(
self.kube_config.worker_pods_pending_timeout_check_interval,
self._check_worker_pods_pending_timeout,
)
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: Optional[str] = None,
executor_config: Optional[Any] = None,
) -> None:
"""Executes task asynchronously"""
self.log.info('Add task %s with command %s with executor_config %s', key, command, executor_config)
try:
kube_executor_config = PodGenerator.from_obj(executor_config)
except Exception:
self.log.error("Invalid executor_config for %s", key)
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
if not self.task_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
self.event_buffer[key] = (State.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 self.running:
self.log.debug('self.running: %s', self.running)
if self.queued_tasks:
self.log.debug('self.queued: %s', self.queued_tasks)
if not self.scheduler_job_id:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.kube_scheduler:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.kube_config:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.result_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.task_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.event_scheduler:
raise AirflowException(NOT_STARTED_MESSAGE)
self.kube_scheduler.sync()
last_resource_version = None
while True:
try:
results = self.result_queue.get_nowait()
try:
key, state, pod_id, namespace, resource_version = results
last_resource_version = resource_version
self.log.info('Changing state of %s to %s', results, state)
try:
self._change_state(key, state, pod_id, 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()
except Empty:
break
resource_instance = ResourceVersion()
resource_instance.resource_version = last_resource_version or resource_instance.resource_version
for _ in range(self.kube_config.worker_pods_creation_batch_size):
try:
task = self.task_queue.get_nowait()
try:
self.kube_scheduler.run_next(task)
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, State.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)
finally:
self.task_queue.task_done()
except Empty:
break
# Run any pending timed events
next_event = self.event_scheduler.run(blocking=False)
self.log.debug("Next timed event is in %f", next_event)
def _check_worker_pods_pending_timeout(self):
"""Check if any pending worker pods have timed out"""
if not self.scheduler_job_id:
raise AirflowException(NOT_STARTED_MESSAGE)
timeout = self.kube_config.worker_pods_pending_timeout
self.log.debug('Looking for pending worker pods older than %d seconds', timeout)
kwargs = {
'limit': self.kube_config.worker_pods_pending_timeout_batch_size,
'field_selector': 'status.phase=Pending',
'label_selector': f'airflow-worker={self.scheduler_job_id}',
**self.kube_config.kube_client_request_args,
}
if self.kube_config.multi_namespace_mode:
pending_pods = functools.partial(self.kube_client.list_pod_for_all_namespaces, **kwargs)
else:
pending_pods = functools.partial(
self.kube_client.list_namespaced_pod, self.kube_config.kube_namespace, **kwargs
)
cutoff = timezone.utcnow() - timedelta(seconds=timeout)
for pod in pending_pods().items:
self.log.debug(
'Found a pending pod "%s", created "%s"', pod.metadata.name, pod.metadata.creation_timestamp
)
if pod.metadata.creation_timestamp < cutoff:
self.log.error(
(
'Pod "%s" has been pending for longer than %d seconds.'
'It will be deleted and set to failed.'
),
pod.metadata.name,
timeout,
)
self.kube_scheduler.delete_pod(pod.metadata.name, pod.metadata.namespace)
def _change_state(self, key: TaskInstanceKey, state: Optional[str], pod_id: str, namespace: str) -> None:
if state != State.RUNNING:
if self.kube_config.delete_worker_pods:
if not self.kube_scheduler:
raise AirflowException(NOT_STARTED_MESSAGE)
if state != State.FAILED or self.kube_config.delete_worker_pods_on_failure:
self.kube_scheduler.delete_pod(pod_id, namespace)
self.log.info('Deleted pod: %s in namespace %s', str(key), str(namespace))
try:
self.running.remove(key)
except KeyError:
self.log.debug('Could not find key: %s', str(key))
self.event_buffer[key] = state, None
[docs] def try_adopt_task_instances(self, tis: List[TaskInstance]) -> List[TaskInstance]:
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}
pod_ids = {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 = pod_generator.make_safe_label_value(str(scheduler_job_id))
kwargs = {'label_selector': f'airflow-worker={scheduler_job_id}'}
pod_list = kube_client.list_namespaced_pod(namespace=self.kube_config.kube_namespace, **kwargs)
for pod in pod_list.items:
self.adopt_launched_task(kube_client, pod, pod_ids)
self._adopt_completed_pods(kube_client)
tis_to_flush.extend(pod_ids.values())
return tis_to_flush
[docs] def adopt_launched_task(
self, kube_client: client.CoreV1Api, pod: k8s.V1Pod, pod_ids: 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 pod_ids: pod_ids we expect to patch.
"""
if not self.scheduler_job_id:
raise AirflowException(NOT_STARTED_MESSAGE)
self.log.info("attempting to adopt pod %s", pod.metadata.name)
pod.metadata.labels['airflow-worker'] = pod_generator.make_safe_label_value(self.scheduler_job_id)
pod_id = annotations_to_key(pod.metadata.annotations)
if pod_id not in pod_ids:
self.log.error("attempting to adopt taskinstance which was not specified by database: %s", pod_id)
return
try:
kube_client.patch_namespaced_pod(
name=pod.metadata.name,
namespace=pod.metadata.namespace,
body=PodGenerator.serialize_pod(pod),
)
pod_ids.pop(pod_id)
self.running.add(pod_id)
except ApiException as e:
self.log.info("Failed to adopt pod %s. Reason: %s", pod.metadata.name, e)
def _adopt_completed_pods(self, kube_client: client.CoreV1Api) -> None:
"""
Patch completed pod so that the KubernetesJobWatcher can delete it.
:param kube_client: kubernetes client for speaking to kube API
"""
if not self.scheduler_job_id:
raise AirflowException(NOT_STARTED_MESSAGE)
kwargs = {
'field_selector': "status.phase=Succeeded",
'label_selector': 'kubernetes_executor=True',
}
pod_list = kube_client.list_namespaced_pod(namespace=self.kube_config.kube_namespace, **kwargs)
for pod in pod_list.items:
self.log.info("Attempting to adopt pod %s", pod.metadata.name)
pod.metadata.labels['airflow-worker'] = pod_generator.make_safe_label_value(self.scheduler_job_id)
try:
kube_client.patch_namespaced_pod(
name=pod.metadata.name,
namespace=pod.metadata.namespace,
body=PodGenerator.serialize_pod(pod),
)
except ApiException as e:
self.log.info("Failed to adopt pod %s. Reason: %s", pod.metadata.name, e)
def _flush_task_queue(self) -> None:
if not self.task_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
self.log.debug('Executor shutting down, task_queue approximate size=%d', self.task_queue.qsize())
while True:
try:
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()
except Empty:
break
def _flush_result_queue(self) -> None:
if not self.result_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
self.log.debug('Executor shutting down, result_queue approximate size=%d', self.result_queue.qsize())
while True:
try:
results = self.result_queue.get_nowait()
self.log.warning('Executor shutting down, flushing results=%s', results)
try:
key, state, pod_id, 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_id, 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()
except Empty:
break
[docs] def end(self) -> None:
"""Called when the executor shuts down"""
if not self.task_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.result_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.kube_scheduler:
raise AirflowException(NOT_STARTED_MESSAGE)
self.log.info('Shutting down Kubernetes executor')
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()
if self.kube_scheduler:
self.kube_scheduler.terminate()
self._manager.shutdown()
[docs] def terminate(self):
"""Terminate the executor is not doing anything."""
