#
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# 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
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import re
import warnings
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.providers.amazon.aws.exceptions import EcsOperatorError, EcsTaskFailToStart
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
from airflow.providers.amazon.aws.hooks.ecs import EcsClusterStates, EcsHook, should_retry_eni
from airflow.providers.amazon.aws.hooks.logs import AwsLogsHook
from airflow.providers.amazon.aws.operators.base_aws import AwsBaseOperator
from airflow.providers.amazon.aws.triggers.ecs import (
ClusterActiveTrigger,
ClusterInactiveTrigger,
TaskDoneTrigger,
)
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
from airflow.providers.amazon.aws.utils.identifiers import generate_uuid
from airflow.providers.amazon.aws.utils.mixins import aws_template_fields
from airflow.providers.amazon.aws.utils.task_log_fetcher import AwsTaskLogFetcher
from airflow.utils.helpers import prune_dict
if TYPE_CHECKING:
import boto3
from airflow.models import TaskInstance
from airflow.utils.context import Context
[docs]class EcsBaseOperator(AwsBaseOperator[EcsHook]):
"""This is the base operator for all Elastic Container Service operators."""
[docs] aws_hook_class = EcsHook
@cached_property
[docs] def client(self) -> boto3.client:
"""Create and return the EcsHook's client."""
return self.hook.conn
[docs] def execute(self, context: Context):
"""Must overwrite in child classes."""
raise NotImplementedError("Please implement execute() in subclass")
def _complete_exec_with_cluster_desc(self, context, event=None):
"""To be used as trigger callback for operators that return the cluster description."""
if event["status"] != "success":
raise AirflowException(f"Error while waiting for operation on cluster to complete: {event}")
cluster_arn = event.get("arn")
# We cannot get the cluster definition from the waiter on success, so we have to query it here.
details = self.hook.conn.describe_clusters(clusters=[cluster_arn])["clusters"][0]
return details
[docs]class EcsCreateClusterOperator(EcsBaseOperator):
"""
Creates an AWS ECS cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EcsCreateClusterOperator`
:param cluster_name: The name of your cluster. If you don't specify a name for your
cluster, you create a cluster that's named default.
:param create_cluster_kwargs: Extra arguments for Cluster Creation.
:param wait_for_completion: If True, waits for creation of the cluster to complete. (default: True)
:param waiter_delay: The amount of time in seconds to wait between attempts,
if not set then the default waiter value will be used.
:param waiter_max_attempts: The maximum number of attempts to be made,
if not set then the default waiter value will be used.
:param deferrable: If True, the operator will wait asynchronously for the job to complete.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
"""
[docs] template_fields: Sequence[str] = aws_template_fields(
"cluster_name",
"create_cluster_kwargs",
"wait_for_completion",
"deferrable",
)
def __init__(
self,
*,
cluster_name: str,
create_cluster_kwargs: dict | None = None,
wait_for_completion: bool = True,
waiter_delay: int = 15,
waiter_max_attempts: int = 60,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.cluster_name = cluster_name
self.create_cluster_kwargs = create_cluster_kwargs or {}
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
[docs] def execute(self, context: Context):
self.log.info(
"Creating cluster %r using the following values: %s",
self.cluster_name,
self.create_cluster_kwargs,
)
result = self.client.create_cluster(clusterName=self.cluster_name, **self.create_cluster_kwargs)
cluster_details = result["cluster"]
cluster_state = cluster_details.get("status")
if cluster_state == EcsClusterStates.ACTIVE:
# In some circumstances the ECS Cluster is created immediately,
# and there is no reason to wait for completion.
self.log.info("Cluster %r in state: %r.", self.cluster_name, cluster_state)
elif self.deferrable:
self.defer(
trigger=ClusterActiveTrigger(
cluster_arn=cluster_details["clusterArn"],
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
region_name=self.region,
),
method_name="_complete_exec_with_cluster_desc",
# timeout is set to ensure that if a trigger dies, the timeout does not restart
# 60 seconds is added to allow the trigger to exit gracefully (i.e. yield TriggerEvent)
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay + 60),
)
elif self.wait_for_completion:
waiter = self.hook.get_waiter("cluster_active")
waiter.wait(
clusters=[cluster_details["clusterArn"]],
WaiterConfig=prune_dict(
{
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
}
),
)
return cluster_details
[docs]class EcsDeleteClusterOperator(EcsBaseOperator):
"""
Deletes an AWS ECS cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EcsDeleteClusterOperator`
:param cluster_name: The short name or full Amazon Resource Name (ARN) of the cluster to delete.
:param wait_for_completion: If True, waits for creation of the cluster to complete. (default: True)
:param waiter_delay: The amount of time in seconds to wait between attempts,
if not set then the default waiter value will be used.
:param waiter_max_attempts: The maximum number of attempts to be made,
if not set then the default waiter value will be used.
:param deferrable: If True, the operator will wait asynchronously for the job to complete.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
"""
[docs] template_fields: Sequence[str] = ("cluster_name", "wait_for_completion", "deferrable")
def __init__(
self,
*,
cluster_name: str,
wait_for_completion: bool = True,
waiter_delay: int = 15,
waiter_max_attempts: int = 60,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.cluster_name = cluster_name
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
[docs] def execute(self, context: Context):
self.log.info("Deleting cluster %r.", self.cluster_name)
result = self.client.delete_cluster(cluster=self.cluster_name)
cluster_details = result["cluster"]
cluster_state = cluster_details.get("status")
if cluster_state == EcsClusterStates.INACTIVE:
# if the cluster doesn't have capacity providers that are associated with it,
# the deletion is instantaneous, and we don't need to wait for it.
self.log.info("Cluster %r in state: %r.", self.cluster_name, cluster_state)
elif self.deferrable:
self.defer(
trigger=ClusterInactiveTrigger(
cluster_arn=cluster_details["clusterArn"],
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
region_name=self.region,
),
method_name="_complete_exec_with_cluster_desc",
# timeout is set to ensure that if a trigger dies, the timeout does not restart
# 60 seconds is added to allow the trigger to exit gracefully (i.e. yield TriggerEvent)
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay + 60),
)
elif self.wait_for_completion:
waiter = self.hook.get_waiter("cluster_inactive")
waiter.wait(
clusters=[cluster_details["clusterArn"]],
WaiterConfig=prune_dict(
{
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
}
),
)
return cluster_details
[docs]class EcsDeregisterTaskDefinitionOperator(EcsBaseOperator):
"""
Deregister a task definition on AWS ECS.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EcsDeregisterTaskDefinitionOperator`
:param task_definition: The family and revision (family:revision) or full Amazon Resource Name (ARN)
of the task definition to deregister. If you use a family name, you must specify a revision.
"""
[docs] template_fields: Sequence[str] = ("task_definition",)
def __init__(
self,
*,
task_definition: str,
**kwargs,
):
if "wait_for_completion" in kwargs or "waiter_delay" in kwargs or "waiter_max_attempts" in kwargs:
warnings.warn(
"'wait_for_completion' and waiter related params have no effect and are deprecated, "
"please remove them.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
# remove args to not trigger Invalid arguments exception
kwargs.pop("wait_for_completion", None)
kwargs.pop("waiter_delay", None)
kwargs.pop("waiter_max_attempts", None)
super().__init__(**kwargs)
self.task_definition = task_definition
[docs] def execute(self, context: Context):
self.log.info("Deregistering task definition %s.", self.task_definition)
result = self.client.deregister_task_definition(taskDefinition=self.task_definition)
task_definition_details = result["taskDefinition"]
task_definition_arn = task_definition_details["taskDefinitionArn"]
self.log.info(
"Task Definition %r in state: %r.", task_definition_arn, task_definition_details.get("status")
)
return task_definition_arn
[docs]class EcsRegisterTaskDefinitionOperator(EcsBaseOperator):
"""
Register a task definition on AWS ECS.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EcsRegisterTaskDefinitionOperator`
:param family: The family name of a task definition to create.
:param container_definitions: A list of container definitions in JSON format that describe
the different containers that make up your task.
:param register_task_kwargs: Extra arguments for Register Task Definition.
"""
[docs] template_fields: Sequence[str] = (
"family",
"container_definitions",
"register_task_kwargs",
)
def __init__(
self,
*,
family: str,
container_definitions: list[dict],
register_task_kwargs: dict | None = None,
**kwargs,
):
if "wait_for_completion" in kwargs or "waiter_delay" in kwargs or "waiter_max_attempts" in kwargs:
warnings.warn(
"'wait_for_completion' and waiter related params have no effect and are deprecated, "
"please remove them.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
# remove args to not trigger Invalid arguments exception
kwargs.pop("wait_for_completion", None)
kwargs.pop("waiter_delay", None)
kwargs.pop("waiter_max_attempts", None)
super().__init__(**kwargs)
self.family = family
self.container_definitions = container_definitions
self.register_task_kwargs = register_task_kwargs or {}
[docs] def execute(self, context: Context):
self.log.info(
"Registering task definition %s using the following values: %s",
self.family,
self.register_task_kwargs,
)
self.log.info("Using container definition %s", self.container_definitions)
response = self.client.register_task_definition(
family=self.family,
containerDefinitions=self.container_definitions,
**self.register_task_kwargs,
)
task_definition_details = response["taskDefinition"]
task_definition_arn = task_definition_details["taskDefinitionArn"]
self.log.info(
"Task Definition %r in state: %r.", task_definition_arn, task_definition_details.get("status")
)
context["ti"].xcom_push(key="task_definition_arn", value=task_definition_arn)
return task_definition_arn
[docs]class EcsRunTaskOperator(EcsBaseOperator):
"""
Execute a task on AWS ECS (Elastic Container Service).
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EcsRunTaskOperator`
:param task_definition: the task definition name on Elastic Container Service
:param cluster: the cluster name on Elastic Container Service
:param overrides: the same parameter that boto3 will receive (templated):
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/ecs.html#ECS.Client.run_task
:param aws_conn_id: connection id of AWS credentials / region name. If None,
credential boto3 strategy will be used
(https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html).
:param region: region name to use in AWS Hook.
Override the region in connection (if provided)
:param launch_type: the launch type on which to run your task ('EC2', 'EXTERNAL', or 'FARGATE')
:param capacity_provider_strategy: the capacity provider strategy to use for the task.
When capacity_provider_strategy is specified, the launch_type parameter is omitted.
If no capacity_provider_strategy or launch_type is specified,
the default capacity provider strategy for the cluster is used.
:param group: the name of the task group associated with the task
:param placement_constraints: an array of placement constraint objects to use for
the task
:param placement_strategy: an array of placement strategy objects to use for
the task
:param platform_version: the platform version on which your task is running
:param network_configuration: the network configuration for the task
:param tags: a dictionary of tags in the form of {'tagKey': 'tagValue'}.
:param awslogs_group: the CloudWatch group where your ECS container logs are stored.
Only required if you want logs to be shown in the Airflow UI after your job has
finished.
:param awslogs_region: the region in which your CloudWatch logs are stored.
If None, this is the same as the `region` parameter. If that is also None,
this is the default AWS region based on your connection settings.
:param awslogs_stream_prefix: the stream prefix that is used for the CloudWatch logs.
This is usually based on some custom name combined with the name of the container.
Only required if you want logs to be shown in the Airflow UI after your job has
finished.
:param awslogs_fetch_interval: the interval that the ECS task log fetcher should wait
in between each Cloudwatch logs fetches.
If deferrable is set to True, that parameter is ignored and waiter_delay is used instead.
:param quota_retry: Config if and how to retry the launch of a new ECS task, to handle
transient errors.
:param reattach: If set to True, will check if the task previously launched by the task_instance
is already running. If so, the operator will attach to it instead of starting a new task.
This is to avoid relaunching a new task when the connection drops between Airflow and ECS while
the task is running (when the Airflow worker is restarted for example).
:param number_logs_exception: Number of lines from the last Cloudwatch logs to return in the
AirflowException if an ECS task is stopped (to receive Airflow alerts with the logs of what
failed in the code running in ECS).
:param wait_for_completion: If True, waits for creation of the cluster to complete. (default: True)
:param waiter_delay: The amount of time in seconds to wait between attempts,
if not set then the default waiter value will be used.
:param waiter_max_attempts: The maximum number of attempts to be made,
if not set then the default waiter value will be used.
:param deferrable: If True, the operator will wait asynchronously for the job to complete.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
"""
[docs] template_fields: Sequence[str] = (
"task_definition",
"cluster",
"overrides",
"launch_type",
"capacity_provider_strategy",
"group",
"placement_constraints",
"placement_strategy",
"platform_version",
"network_configuration",
"tags",
"awslogs_group",
"awslogs_region",
"awslogs_stream_prefix",
"awslogs_fetch_interval",
"propagate_tags",
"reattach",
"number_logs_exception",
"wait_for_completion",
"deferrable",
)
[docs] template_fields_renderers = {
"overrides": "json",
"network_configuration": "json",
"tags": "json",
}
def __init__(
self,
*,
task_definition: str,
cluster: str,
overrides: dict,
launch_type: str = "EC2",
capacity_provider_strategy: list | None = None,
group: str | None = None,
placement_constraints: list | None = None,
placement_strategy: list | None = None,
platform_version: str | None = None,
network_configuration: dict | None = None,
tags: dict | None = None,
awslogs_group: str | None = None,
awslogs_region: str | None = None,
awslogs_stream_prefix: str | None = None,
awslogs_fetch_interval: timedelta = timedelta(seconds=30),
propagate_tags: str | None = None,
quota_retry: dict | None = None,
reattach: bool = False,
number_logs_exception: int = 10,
wait_for_completion: bool = True,
waiter_delay: int = 6,
waiter_max_attempts: int = 1000000,
# Set the default waiter duration to 70 days (attempts*delay)
# Airflow execution_timeout handles task timeout
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
super().__init__(**kwargs)
self.task_definition = task_definition
self.cluster = cluster
self.overrides = overrides
self.launch_type = launch_type
self.capacity_provider_strategy = capacity_provider_strategy
self.group = group
self.placement_constraints = placement_constraints
self.placement_strategy = placement_strategy
self.platform_version = platform_version
self.network_configuration = network_configuration
self.tags = tags
self.awslogs_group = awslogs_group
self.awslogs_stream_prefix = awslogs_stream_prefix
self.awslogs_region = awslogs_region
self.awslogs_fetch_interval = awslogs_fetch_interval
self.propagate_tags = propagate_tags
self.reattach = reattach
self.number_logs_exception = number_logs_exception
if self.awslogs_region is None:
self.awslogs_region = self.region
self.arn: str | None = None
self._started_by: str | None = None
self.retry_args = quota_retry
self.task_log_fetcher: AwsTaskLogFetcher | None = None
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
if self._aws_logs_enabled() and not self.wait_for_completion:
self.log.warning(
"Trying to get logs without waiting for the task to complete is undefined behavior."
)
@staticmethod
def _get_ecs_task_id(task_arn: str | None) -> str | None:
if task_arn is None:
return None
return task_arn.split("/")[-1]
[docs] def execute(self, context):
self.log.info(
"Running ECS Task - Task definition: %s - on cluster %s", self.task_definition, self.cluster
)
self.log.info("EcsOperator overrides: %s", self.overrides)
if self.reattach:
# Generate deterministic UUID which refers to unique TaskInstanceKey
ti: TaskInstance = context["ti"]
self._started_by = generate_uuid(*map(str, ti.key.primary))
self.log.info("Try to find run with startedBy=%r", self._started_by)
self._try_reattach_task(started_by=self._started_by)
if not self.arn:
# start the task except if we reattached to an existing one just before.
self._start_task()
if self.do_xcom_push:
self.xcom_push(context, key="ecs_task_arn", value=self.arn)
if self.deferrable:
self.defer(
trigger=TaskDoneTrigger(
cluster=self.cluster,
task_arn=self.arn,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
region=self.region,
log_group=self.awslogs_group,
log_stream=self._get_logs_stream_name(),
),
method_name="execute_complete",
# timeout is set to ensure that if a trigger dies, the timeout does not restart
# 60 seconds is added to allow the trigger to exit gracefully (i.e. yield TriggerEvent)
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay + 60),
)
# self.defer raises a special exception, so execution stops here in this case.
if not self.wait_for_completion:
return
if self._aws_logs_enabled():
self.log.info("Starting ECS Task Log Fetcher")
self.task_log_fetcher = self._get_task_log_fetcher()
self.task_log_fetcher.start()
try:
self._wait_for_task_ended()
finally:
self.task_log_fetcher.stop()
self.task_log_fetcher.join()
else:
self._wait_for_task_ended()
self._after_execution()
if self.do_xcom_push and self.task_log_fetcher:
return self.task_log_fetcher.get_last_log_message()
else:
return None
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str | None:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(f"Error in task execution: {event}")
self.arn = event["task_arn"] # restore arn to its updated value, needed for next steps
self._after_execution()
if self._aws_logs_enabled():
# same behavior as non-deferrable mode, return last line of logs of the task.
logs_client = AwsLogsHook(aws_conn_id=self.aws_conn_id, region_name=self.region).conn
one_log = logs_client.get_log_events(
logGroupName=self.awslogs_group,
logStreamName=self._get_logs_stream_name(),
startFromHead=False,
limit=1,
)
if len(one_log["events"]) > 0:
return one_log["events"][0]["message"]
return None
def _after_execution(self):
self._check_success_task()
def _start_task(self):
run_opts = {
"cluster": self.cluster,
"taskDefinition": self.task_definition,
"overrides": self.overrides,
"startedBy": self._started_by or self.owner,
}
if self.capacity_provider_strategy:
run_opts["capacityProviderStrategy"] = self.capacity_provider_strategy
elif self.launch_type:
run_opts["launchType"] = self.launch_type
if self.platform_version is not None:
run_opts["platformVersion"] = self.platform_version
if self.group is not None:
run_opts["group"] = self.group
if self.placement_constraints is not None:
run_opts["placementConstraints"] = self.placement_constraints
if self.placement_strategy is not None:
run_opts["placementStrategy"] = self.placement_strategy
if self.network_configuration is not None:
run_opts["networkConfiguration"] = self.network_configuration
if self.tags is not None:
run_opts["tags"] = [{"key": k, "value": v} for (k, v) in self.tags.items()]
if self.propagate_tags is not None:
run_opts["propagateTags"] = self.propagate_tags
response = self.client.run_task(**run_opts)
failures = response["failures"]
if len(failures) > 0:
raise EcsOperatorError(failures, response)
self.log.info("ECS Task started: %s", response)
self.arn = response["tasks"][0]["taskArn"]
self.log.info("ECS task ID is: %s", self._get_ecs_task_id(self.arn))
def _try_reattach_task(self, started_by: str):
if not started_by:
raise AirflowException("`started_by` should not be empty or None")
list_tasks_resp = self.client.list_tasks(
cluster=self.cluster, desiredStatus="RUNNING", startedBy=started_by
)
running_tasks = list_tasks_resp["taskArns"]
if running_tasks:
if len(running_tasks) > 1:
self.log.warning("Found more then one previously launched tasks: %s", running_tasks)
self.arn = running_tasks[0]
self.log.info("Reattaching previously launched task: %s", self.arn)
else:
self.log.info("No active previously launched task found to reattach")
def _wait_for_task_ended(self) -> None:
if not self.client or not self.arn:
return
waiter = self.client.get_waiter("tasks_stopped")
waiter.wait(
cluster=self.cluster,
tasks=[self.arn],
WaiterConfig={
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
},
)
def _aws_logs_enabled(self):
return self.awslogs_group and self.awslogs_stream_prefix
def _get_logs_stream_name(self) -> str:
return f"{self.awslogs_stream_prefix}/{self._get_ecs_task_id(self.arn)}"
def _get_task_log_fetcher(self) -> AwsTaskLogFetcher:
if not self.awslogs_group:
raise ValueError("must specify awslogs_group to fetch task logs")
return AwsTaskLogFetcher(
aws_conn_id=self.aws_conn_id,
region_name=self.awslogs_region,
log_group=self.awslogs_group,
log_stream_name=self._get_logs_stream_name(),
fetch_interval=self.awslogs_fetch_interval,
logger=self.log,
)
@AwsBaseHook.retry(should_retry_eni)
def _check_success_task(self) -> None:
if not self.client or not self.arn:
return
response = self.client.describe_tasks(cluster=self.cluster, tasks=[self.arn])
self.log.info("ECS Task stopped, check status: %s", response)
if len(response.get("failures", [])) > 0:
raise AirflowException(response)
for task in response["tasks"]:
if task.get("stopCode", "") == "TaskFailedToStart":
# Reset task arn here otherwise the retry run will not start
# a new task but keep polling the old dead one
# I'm not resetting it for other exceptions here because
# EcsTaskFailToStart is the only exception that's being retried at the moment
self.arn = None
raise EcsTaskFailToStart(f"The task failed to start due to: {task.get('stoppedReason', '')}")
# This is a `stoppedReason` that indicates a task has not
# successfully finished, but there is no other indication of failure
# in the response.
# https://docs.aws.amazon.com/AmazonECS/latest/developerguide/stopped-task-errors.html
if re.match(r"Host EC2 \(instance .+?\) (stopped|terminated)\.", task.get("stoppedReason", "")):
raise AirflowException(
f"The task was stopped because the host instance terminated:"
f" {task.get('stoppedReason', '')}"
)
containers = task["containers"]
for container in containers:
if container.get("lastStatus") == "STOPPED" and container.get("exitCode", 1) != 0:
if self.task_log_fetcher:
last_logs = "\n".join(
self.task_log_fetcher.get_last_log_messages(self.number_logs_exception)
)
raise AirflowException(
f"This task is not in success state - last {self.number_logs_exception} "
f"logs from Cloudwatch:\n{last_logs}"
)
else:
raise AirflowException(f"This task is not in success state {task}")
elif container.get("lastStatus") == "PENDING":
raise AirflowException(f"This task is still pending {task}")
elif "error" in container.get("reason", "").lower():
raise AirflowException(
f"This containers encounter an error during launching: "
f"{container.get('reason', '').lower()}"
)
[docs] def on_kill(self) -> None:
if not self.client or not self.arn:
return
if self.task_log_fetcher:
self.task_log_fetcher.stop()
response = self.client.stop_task(
cluster=self.cluster, task=self.arn, reason="Task killed by the user"
)
self.log.info(response)
