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#
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#
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"""This module contains Amazon EKS operators."""
import warnings
from ast import literal_eval
from time import sleep
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union, cast
from airflow import AirflowException
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.eks import ClusterStates, EksHook, FargateProfileStates
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]CHECK_INTERVAL_SECONDS = 15
[docs]TIMEOUT_SECONDS = 25 * 60
[docs]DEFAULT_COMPUTE_TYPE = 'nodegroup'
[docs]DEFAULT_CONN_ID = 'aws_default'
[docs]DEFAULT_FARGATE_PROFILE_NAME = 'profile'
[docs]DEFAULT_NAMESPACE_NAME = 'default'
[docs]DEFAULT_NODEGROUP_NAME = 'nodegroup'
[docs]ABORT_MSG = "{compute} are still active after the allocated time limit. Aborting."
[docs]CAN_NOT_DELETE_MSG = "A cluster can not be deleted with attached {compute}. Deleting {count} {compute}."
[docs]MISSING_ARN_MSG = "Creating an {compute} requires {requirement} to be passed in."
[docs]SUCCESS_MSG = "No {compute} remain, deleting cluster."
[docs]SUPPORTED_COMPUTE_VALUES = frozenset({'nodegroup', 'fargate'})
[docs]NODEGROUP_FULL_NAME = 'Amazon EKS managed node groups'
[docs]FARGATE_FULL_NAME = 'AWS Fargate profiles'
[docs]class EksCreateClusterOperator(BaseOperator):
"""
Creates an Amazon EKS Cluster control plane.
Optionally, can also create the supporting compute architecture:
- If argument 'compute' is provided with a value of 'nodegroup', will also
attempt to create an Amazon EKS Managed Nodegroup for the cluster.
See :class:`~airflow.providers.amazon.aws.operators.EksCreateNodegroupOperator`
documentation for requirements.
- If argument 'compute' is provided with a value of 'fargate', will also attempt to create an AWS
Fargate profile for the cluster.
See :class:`~airflow.providers.amazon.aws.operators.EksCreateFargateProfileOperator`
documentation for requirements.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksCreateClusterOperator`
:param cluster_name: The unique name to give to your Amazon EKS Cluster. (templated)
:param cluster_role_arn: The Amazon Resource Name (ARN) of the IAM role that provides permissions for the
Kubernetes control plane to make calls to AWS API operations on your behalf. (templated)
:param resources_vpc_config: The VPC configuration used by the cluster control plane. (templated)
:param compute: The type of compute architecture to generate along with the cluster. (templated)
Defaults to 'nodegroup' to generate an EKS Managed Nodegroup.
:param create_cluster_kwargs: Optional parameters to pass to the CreateCluster API (templated)
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
If compute is assigned the value of 'nodegroup':
:param nodegroup_name: *REQUIRED* The unique name to give your Amazon EKS managed node group. (templated)
:param nodegroup_role_arn: *REQUIRED* The Amazon Resource Name (ARN) of the IAM role to associate with
the Amazon EKS managed node group. (templated)
:param create_nodegroup_kwargs: Optional parameters to pass to the CreateNodegroup API (templated)
If compute is assigned the value of 'fargate':
:param fargate_profile_name: *REQUIRED* The unique name to give your AWS Fargate profile. (templated)
:param fargate_pod_execution_role_arn: *REQUIRED* The Amazon Resource Name (ARN) of the pod execution
role to use for pods that match the selectors in the AWS Fargate profile. (templated)
:param fargate_selectors: The selectors to match for pods to use this AWS Fargate profile. (templated)
:param create_fargate_profile_kwargs: Optional parameters to pass to the CreateFargateProfile API
(templated)
"""
[docs] template_fields: Sequence[str] = (
"cluster_name",
"cluster_role_arn",
"resources_vpc_config",
"create_cluster_kwargs",
"compute",
"nodegroup_name",
"nodegroup_role_arn",
"create_nodegroup_kwargs",
"fargate_profile_name",
"fargate_pod_execution_role_arn",
"fargate_selectors",
"create_fargate_profile_kwargs",
"aws_conn_id",
"region",
)
def __init__(
self,
cluster_name: str,
cluster_role_arn: str,
resources_vpc_config: Dict[str, Any],
compute: Optional[str] = DEFAULT_COMPUTE_TYPE,
create_cluster_kwargs: Optional[Dict] = None,
nodegroup_name: str = DEFAULT_NODEGROUP_NAME,
nodegroup_role_arn: Optional[str] = None,
create_nodegroup_kwargs: Optional[Dict] = None,
fargate_profile_name: str = DEFAULT_FARGATE_PROFILE_NAME,
fargate_pod_execution_role_arn: Optional[str] = None,
fargate_selectors: Optional[List] = None,
create_fargate_profile_kwargs: Optional[Dict] = None,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
**kwargs,
) -> None:
self.compute = compute
self.cluster_name = cluster_name
self.cluster_role_arn = cluster_role_arn
self.resources_vpc_config = resources_vpc_config
self.create_cluster_kwargs = create_cluster_kwargs or {}
self.nodegroup_name = nodegroup_name
self.nodegroup_role_arn = nodegroup_role_arn
self.create_nodegroup_kwargs = create_nodegroup_kwargs or {}
self.fargate_profile_name = fargate_profile_name
self.fargate_pod_execution_role_arn = fargate_pod_execution_role_arn
self.fargate_selectors = fargate_selectors or [{"namespace": DEFAULT_NAMESPACE_NAME}]
self.create_fargate_profile_kwargs = create_fargate_profile_kwargs or {}
self.aws_conn_id = aws_conn_id
self.region = region
super().__init__(**kwargs)
[docs] def execute(self, context: 'Context'):
if self.compute:
if self.compute not in SUPPORTED_COMPUTE_VALUES:
raise ValueError("Provided compute type is not supported.")
elif (self.compute == 'nodegroup') and not self.nodegroup_role_arn:
raise ValueError(
MISSING_ARN_MSG.format(compute=NODEGROUP_FULL_NAME, requirement='nodegroup_role_arn')
)
elif (self.compute == 'fargate') and not self.fargate_pod_execution_role_arn:
raise ValueError(
MISSING_ARN_MSG.format(
compute=FARGATE_FULL_NAME, requirement='fargate_pod_execution_role_arn'
)
)
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
eks_hook.create_cluster(
name=self.cluster_name,
roleArn=self.cluster_role_arn,
resourcesVpcConfig=self.resources_vpc_config,
**self.create_cluster_kwargs,
)
if not self.compute:
return None
self.log.info("Waiting for EKS Cluster to provision. This will take some time.")
countdown = TIMEOUT_SECONDS
while eks_hook.get_cluster_state(clusterName=self.cluster_name) != ClusterStates.ACTIVE:
if countdown >= CHECK_INTERVAL_SECONDS:
countdown -= CHECK_INTERVAL_SECONDS
self.log.info(
"Waiting for cluster to start. Checking again in %d seconds", CHECK_INTERVAL_SECONDS
)
sleep(CHECK_INTERVAL_SECONDS)
else:
message = (
"Cluster is still inactive after the allocated time limit. "
"Failed cluster will be torn down."
)
self.log.error(message)
# If there is something preventing the cluster for activating, tear it down and abort.
eks_hook.delete_cluster(name=self.cluster_name)
raise RuntimeError(message)
if self.compute == 'nodegroup':
eks_hook.create_nodegroup(
clusterName=self.cluster_name,
nodegroupName=self.nodegroup_name,
subnets=cast(List[str], self.resources_vpc_config.get('subnetIds')),
nodeRole=self.nodegroup_role_arn,
**self.create_nodegroup_kwargs,
)
elif self.compute == 'fargate':
eks_hook.create_fargate_profile(
clusterName=self.cluster_name,
fargateProfileName=self.fargate_profile_name,
podExecutionRoleArn=self.fargate_pod_execution_role_arn,
selectors=self.fargate_selectors,
**self.create_fargate_profile_kwargs,
)
[docs]class EksCreateNodegroupOperator(BaseOperator):
"""
Creates an Amazon EKS managed node group for an existing Amazon EKS Cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksCreateNodegroupOperator`
:param cluster_name: The name of the Amazon EKS Cluster to create the managed nodegroup in. (templated)
:param nodegroup_name: The unique name to give your managed nodegroup. (templated)
:param nodegroup_subnets:
The subnets to use for the Auto Scaling group that is created for the managed nodegroup. (templated)
:param nodegroup_role_arn:
The Amazon Resource Name (ARN) of the IAM role to associate with the managed nodegroup. (templated)
:param create_nodegroup_kwargs: Optional parameters to pass to the Create Nodegroup API (templated)
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
"""
[docs] template_fields: Sequence[str] = (
"cluster_name",
"nodegroup_subnets",
"nodegroup_role_arn",
"nodegroup_name",
"create_nodegroup_kwargs",
"aws_conn_id",
"region",
)
def __init__(
self,
cluster_name: str,
nodegroup_subnets: Union[List[str], str],
nodegroup_role_arn: str,
nodegroup_name: str = DEFAULT_NODEGROUP_NAME,
create_nodegroup_kwargs: Optional[Dict] = None,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
**kwargs,
) -> None:
self.cluster_name = cluster_name
self.nodegroup_role_arn = nodegroup_role_arn
self.nodegroup_name = nodegroup_name
self.create_nodegroup_kwargs = create_nodegroup_kwargs or {}
self.aws_conn_id = aws_conn_id
self.region = region
self.nodegroup_subnets = nodegroup_subnets
super().__init__(**kwargs)
[docs] def execute(self, context: 'Context'):
if isinstance(self.nodegroup_subnets, str):
nodegroup_subnets_list: List[str] = []
if self.nodegroup_subnets != "":
try:
nodegroup_subnets_list = cast(List, literal_eval(self.nodegroup_subnets))
except ValueError:
self.log.warning(
"The nodegroup_subnets should be List or string representing "
"Python list and is %s. Defaulting to []",
self.nodegroup_subnets,
)
self.nodegroup_subnets = nodegroup_subnets_list
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
eks_hook.create_nodegroup(
clusterName=self.cluster_name,
nodegroupName=self.nodegroup_name,
subnets=self.nodegroup_subnets,
nodeRole=self.nodegroup_role_arn,
**self.create_nodegroup_kwargs,
)
[docs]class EksCreateFargateProfileOperator(BaseOperator):
"""
Creates an AWS Fargate profile for an Amazon EKS cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksCreateFargateProfileOperator`
:param cluster_name: The name of the Amazon EKS cluster to apply the AWS Fargate profile to. (templated)
:param pod_execution_role_arn: The Amazon Resource Name (ARN) of the pod execution role to
use for pods that match the selectors in the AWS Fargate profile. (templated)
:param selectors: The selectors to match for pods to use this AWS Fargate profile. (templated)
:param fargate_profile_name: The unique name to give your AWS Fargate profile. (templated)
:param create_fargate_profile_kwargs: Optional parameters to pass to the CreateFargate Profile API
(templated)
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
"""
[docs] template_fields: Sequence[str] = (
"cluster_name",
"pod_execution_role_arn",
"selectors",
"fargate_profile_name",
"create_fargate_profile_kwargs",
"aws_conn_id",
"region",
)
def __init__(
self,
cluster_name: str,
pod_execution_role_arn: str,
selectors: List,
fargate_profile_name: Optional[str] = DEFAULT_FARGATE_PROFILE_NAME,
create_fargate_profile_kwargs: Optional[Dict] = None,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
**kwargs,
) -> None:
self.cluster_name = cluster_name
self.pod_execution_role_arn = pod_execution_role_arn
self.selectors = selectors
self.fargate_profile_name = fargate_profile_name
self.create_fargate_profile_kwargs = create_fargate_profile_kwargs or {}
self.aws_conn_id = aws_conn_id
self.region = region
super().__init__(**kwargs)
[docs] def execute(self, context: 'Context'):
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
eks_hook.create_fargate_profile(
clusterName=self.cluster_name,
fargateProfileName=self.fargate_profile_name,
podExecutionRoleArn=self.pod_execution_role_arn,
selectors=self.selectors,
**self.create_fargate_profile_kwargs,
)
[docs]class EksDeleteClusterOperator(BaseOperator):
"""
Deletes the Amazon EKS Cluster control plane and all nodegroups attached to it.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksDeleteClusterOperator`
:param cluster_name: The name of the Amazon EKS Cluster to delete. (templated)
:param force_delete_compute: If True, will delete any attached resources. (templated)
Defaults to False.
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
"""
[docs] template_fields: Sequence[str] = (
"cluster_name",
"force_delete_compute",
"aws_conn_id",
"region",
)
def __init__(
self,
cluster_name: str,
force_delete_compute: bool = False,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
**kwargs,
) -> None:
self.cluster_name = cluster_name
self.force_delete_compute = force_delete_compute
self.aws_conn_id = aws_conn_id
self.region = region
super().__init__(**kwargs)
[docs] def execute(self, context: 'Context'):
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
if self.force_delete_compute:
self.delete_any_nodegroups(eks_hook)
self.delete_any_fargate_profiles(eks_hook)
eks_hook.delete_cluster(name=self.cluster_name)
[docs] def delete_any_nodegroups(self, eks_hook) -> None:
"""
Deletes all Amazon EKS managed node groups for a provided Amazon EKS Cluster.
Amazon EKS managed node groups can be deleted in parallel, so we can send all
of the delete commands in bulk and move on once the count of nodegroups is zero.
"""
nodegroups = eks_hook.list_nodegroups(clusterName=self.cluster_name)
if nodegroups:
self.log.info(CAN_NOT_DELETE_MSG.format(compute=NODEGROUP_FULL_NAME, count=len(nodegroups)))
for group in nodegroups:
eks_hook.delete_nodegroup(clusterName=self.cluster_name, nodegroupName=group)
# Scaling up the timeout based on the number of nodegroups that are being processed.
additional_seconds = 5 * 60
countdown = TIMEOUT_SECONDS + (len(nodegroups) * additional_seconds)
while eks_hook.list_nodegroups(clusterName=self.cluster_name):
if countdown >= CHECK_INTERVAL_SECONDS:
countdown -= CHECK_INTERVAL_SECONDS
sleep(CHECK_INTERVAL_SECONDS)
self.log.info(
"Waiting for the remaining %s nodegroups to delete. "
"Checking again in %d seconds.",
len(nodegroups),
CHECK_INTERVAL_SECONDS,
)
else:
raise RuntimeError(ABORT_MSG.format(compute=NODEGROUP_FULL_NAME))
self.log.info(SUCCESS_MSG.format(compute=NODEGROUP_FULL_NAME))
[docs] def delete_any_fargate_profiles(self, eks_hook) -> None:
"""
Deletes all EKS Fargate profiles for a provided Amazon EKS Cluster.
EKS Fargate profiles must be deleted one at a time, so we must wait
for one to be deleted before sending the next delete command.
"""
fargate_profiles = eks_hook.list_fargate_profiles(clusterName=self.cluster_name)
if fargate_profiles:
self.log.info(CAN_NOT_DELETE_MSG.format(compute=FARGATE_FULL_NAME, count=len(fargate_profiles)))
for profile in fargate_profiles:
# The API will return a (cluster) ResourceInUseException if you try
# to delete Fargate profiles in parallel the way we can with nodegroups,
# so each must be deleted sequentially
eks_hook.delete_fargate_profile(clusterName=self.cluster_name, fargateProfileName=profile)
countdown = TIMEOUT_SECONDS
while (
eks_hook.get_fargate_profile_state(
clusterName=self.cluster_name, fargateProfileName=profile
)
!= FargateProfileStates.NONEXISTENT
):
if countdown >= CHECK_INTERVAL_SECONDS:
countdown -= CHECK_INTERVAL_SECONDS
sleep(CHECK_INTERVAL_SECONDS)
self.log.info(
"Waiting for the AWS Fargate profile %s to delete. "
"Checking again in %d seconds.",
profile,
CHECK_INTERVAL_SECONDS,
)
else:
raise RuntimeError(ABORT_MSG.format(compute=FARGATE_FULL_NAME))
self.log.info(SUCCESS_MSG.format(compute=FARGATE_FULL_NAME))
[docs]class EksDeleteNodegroupOperator(BaseOperator):
"""
Deletes an Amazon EKS managed node group from an Amazon EKS Cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksDeleteNodegroupOperator`
:param cluster_name: The name of the Amazon EKS Cluster associated with your nodegroup. (templated)
:param nodegroup_name: The name of the nodegroup to delete. (templated)
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
"""
[docs] template_fields: Sequence[str] = (
"cluster_name",
"nodegroup_name",
"aws_conn_id",
"region",
)
def __init__(
self,
cluster_name: str,
nodegroup_name: str,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
**kwargs,
) -> None:
self.cluster_name = cluster_name
self.nodegroup_name = nodegroup_name
self.aws_conn_id = aws_conn_id
self.region = region
super().__init__(**kwargs)
[docs] def execute(self, context: 'Context'):
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
eks_hook.delete_nodegroup(clusterName=self.cluster_name, nodegroupName=self.nodegroup_name)
[docs]class EksDeleteFargateProfileOperator(BaseOperator):
"""
Deletes an AWS Fargate profile from an Amazon EKS Cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksDeleteFargateProfileOperator`
:param cluster_name: The name of the Amazon EKS cluster associated with your Fargate profile. (templated)
:param fargate_profile_name: The name of the AWS Fargate profile to delete. (templated)
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
"""
[docs] template_fields: Sequence[str] = (
"cluster_name",
"fargate_profile_name",
"aws_conn_id",
"region",
)
def __init__(
self,
cluster_name: str,
fargate_profile_name: str,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.cluster_name = cluster_name
self.fargate_profile_name = fargate_profile_name
self.aws_conn_id = aws_conn_id
self.region = region
[docs] def execute(self, context: 'Context'):
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
eks_hook.delete_fargate_profile(
clusterName=self.cluster_name, fargateProfileName=self.fargate_profile_name
)
[docs]class EksPodOperator(KubernetesPodOperator):
"""
Executes a task in a Kubernetes pod on the specified Amazon EKS Cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EksPodOperator`
:param cluster_name: The name of the Amazon EKS Cluster to execute the task on. (templated)
:param cluster_role_arn: The Amazon Resource Name (ARN) of the IAM role that provides permissions
for the Kubernetes control plane to make calls to AWS API operations on your behalf. (templated)
:param in_cluster: If True, look for config inside the cluster; if False look for a local file path.
:param namespace: The namespace in which to execute the pod. (templated)
:param pod_name: The unique name to give the pod. (templated)
:param aws_profile: The named profile containing the credentials for the AWS CLI tool to use.
:param aws_profile: str
:param region: Which AWS region the connection should use. (templated)
If this is None or empty then the default boto3 behaviour is used.
:param aws_conn_id: The Airflow connection used for AWS credentials. (templated)
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then the default boto3 configuration would be used (and must be
maintained on each worker node).
:param is_delete_operator_pod: What to do when the pod reaches its final
state, or the execution is interrupted. If True, delete the
pod; if False, leave the pod. Current default is False, but this will be
changed in the next major release of this provider.
"""
[docs] template_fields: Sequence[str] = tuple(
{
"cluster_name",
"in_cluster",
"namespace",
"pod_name",
"aws_conn_id",
"region",
}
| set(KubernetesPodOperator.template_fields)
)
def __init__(
self,
cluster_name: str,
# Setting in_cluster to False tells the pod that the config
# file is stored locally in the worker and not in the cluster.
in_cluster: bool = False,
namespace: str = DEFAULT_NAMESPACE_NAME,
pod_context: Optional[str] = None,
pod_name: Optional[str] = None,
pod_username: Optional[str] = None,
aws_conn_id: str = DEFAULT_CONN_ID,
region: Optional[str] = None,
is_delete_operator_pod: Optional[bool] = None,
**kwargs,
) -> None:
if is_delete_operator_pod is None:
warnings.warn(
f"You have not set parameter `is_delete_operator_pod` in class {self.__class__.__name__}. "
"Currently the default for this parameter is `False` but in a future release the default "
"will be changed to `True`. To ensure pods are not deleted in the future you will need to "
"set `is_delete_operator_pod=False` explicitly.",
DeprecationWarning,
stacklevel=2,
)
is_delete_operator_pod = False
self.cluster_name = cluster_name
self.in_cluster = in_cluster
self.namespace = namespace
self.pod_name = pod_name
self.aws_conn_id = aws_conn_id
self.region = region
super().__init__(
in_cluster=self.in_cluster,
namespace=self.namespace,
name=self.pod_name,
is_delete_operator_pod=is_delete_operator_pod,
**kwargs,
)
# There is no need to manage the kube_config file, as it will be generated automatically.
# All Kubernetes parameters (except config_file) are also valid for the EksPodOperator.
if self.config_file:
raise AirflowException("The config_file is not an allowed parameter for the EksPodOperator.")
[docs] def execute(self, context: 'Context'):
eks_hook = EksHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region,
)
with eks_hook.generate_config_file(
eks_cluster_name=self.cluster_name, pod_namespace=self.namespace
) as self.config_file:
return super().execute(context)
