#
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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
#
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#
# 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
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# under the License.
from __future__ import annotations
import ast
import warnings
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from uuid import uuid4
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.emr import EmrContainerHook, EmrHook, EmrServerlessHook
from airflow.providers.amazon.aws.links.emr import EmrClusterLink, EmrLogsLink, get_log_uri
from airflow.providers.amazon.aws.triggers.emr import (
EmrAddStepsTrigger,
EmrContainerTrigger,
EmrCreateJobFlowTrigger,
EmrServerlessCancelJobsTrigger,
EmrServerlessCreateApplicationTrigger,
EmrServerlessDeleteApplicationTrigger,
EmrServerlessStartApplicationTrigger,
EmrServerlessStartJobTrigger,
EmrServerlessStopApplicationTrigger,
EmrTerminateJobFlowTrigger,
)
from airflow.providers.amazon.aws.utils.waiter import waiter
from airflow.providers.amazon.aws.utils.waiter_with_logging import wait
from airflow.utils.helpers import exactly_one, prune_dict
from airflow.utils.types import NOTSET, ArgNotSet
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class EmrAddStepsOperator(BaseOperator):
"""
An operator that adds steps to an existing EMR job_flow.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrAddStepsOperator`
:param job_flow_id: id of the JobFlow to add steps to. (templated)
:param job_flow_name: name of the JobFlow to add steps to. Use as an alternative to passing
job_flow_id. will search for id of JobFlow with matching name in one of the states in
param cluster_states. Exactly one cluster like this should exist or will fail. (templated)
:param cluster_states: Acceptable cluster states when searching for JobFlow id by job_flow_name.
(templated)
:param aws_conn_id: aws connection to uses
:param steps: boto3 style steps or reference to a steps file (must be '.json') to
be added to the jobflow. (templated)
:param wait_for_completion: If True, the operator will wait for all the steps to be completed.
:param execution_role_arn: The ARN of the runtime role for a step on the cluster.
:param do_xcom_push: if True, job_flow_id is pushed to XCom with key job_flow_id.
:param wait_for_completion: Whether to wait for job run completion. (default: True)
: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] = (
"job_flow_id",
"job_flow_name",
"cluster_states",
"steps",
"execution_role_arn",
)
[docs] template_ext: Sequence[str] = (".json",)
[docs] template_fields_renderers = {"steps": "json"}
def __init__(
self,
*,
job_flow_id: str | None = None,
job_flow_name: str | None = None,
cluster_states: list[str] | None = None,
aws_conn_id: str = "aws_default",
steps: list[dict] | str | None = None,
wait_for_completion: bool = False,
waiter_delay: int = 30,
waiter_max_attempts: int = 60,
execution_role_arn: str | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
if not exactly_one(job_flow_id is None, job_flow_name is None):
raise AirflowException("Exactly one of job_flow_id or job_flow_name must be specified.")
super().__init__(**kwargs)
cluster_states = cluster_states or []
steps = steps or []
self.aws_conn_id = aws_conn_id
self.job_flow_id = job_flow_id
self.job_flow_name = job_flow_name
self.cluster_states = cluster_states
self.steps = steps
self.wait_for_completion = False if deferrable else wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.execution_role_arn = execution_role_arn
self.deferrable = deferrable
[docs] def execute(self, context: Context) -> list[str]:
emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
job_flow_id = self.job_flow_id or emr_hook.get_cluster_id_by_name(
str(self.job_flow_name), self.cluster_states
)
if not job_flow_id:
raise AirflowException(f"No cluster found for name: {self.job_flow_name}")
if self.do_xcom_push:
context["ti"].xcom_push(key="job_flow_id", value=job_flow_id)
EmrClusterLink.persist(
context=context,
operator=self,
region_name=emr_hook.conn_region_name,
aws_partition=emr_hook.conn_partition,
job_flow_id=job_flow_id,
)
EmrLogsLink.persist(
context=context,
operator=self,
region_name=emr_hook.conn_region_name,
aws_partition=emr_hook.conn_partition,
job_flow_id=self.job_flow_id,
log_uri=get_log_uri(emr_client=emr_hook.conn, job_flow_id=job_flow_id),
)
self.log.info("Adding steps to %s", job_flow_id)
# steps may arrive as a string representing a list
# e.g. if we used XCom or a file then: steps="[{ step1 }, { step2 }]"
steps = self.steps
if isinstance(steps, str):
steps = ast.literal_eval(steps)
step_ids = emr_hook.add_job_flow_steps(
job_flow_id=job_flow_id,
steps=steps,
wait_for_completion=self.wait_for_completion,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
execution_role_arn=self.execution_role_arn,
)
if self.deferrable:
self.defer(
trigger=EmrAddStepsTrigger(
job_flow_id=job_flow_id,
step_ids=step_ids,
aws_conn_id=self.aws_conn_id,
waiter_max_attempts=self.waiter_max_attempts,
waiter_delay=self.waiter_delay,
),
method_name="execute_complete",
)
return step_ids
[docs] def execute_complete(self, context, event=None):
if event["status"] != "success":
raise AirflowException(f"Error while running steps: {event}")
else:
self.log.info("Steps completed successfully")
return event["value"]
[docs]class EmrStartNotebookExecutionOperator(BaseOperator):
"""
An operator that starts an EMR notebook execution.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrStartNotebookExecutionOperator`
:param editor_id: The unique identifier of the EMR notebook to use for notebook execution.
:param relative_path: The path and file name of the notebook file for this execution,
relative to the path specified for the EMR notebook.
:param cluster_id: The unique identifier of the EMR cluster the notebook is attached to.
:param service_role: The name or ARN of the IAM role that is used as the service role
for Amazon EMR (the EMR role) for the notebook execution.
:param notebook_execution_name: Optional name for the notebook execution.
:param notebook_params: Input parameters in JSON format passed to the EMR notebook at
runtime for execution.
:param notebook_instance_security_group_id: The unique identifier of the Amazon EC2
security group to associate with the EMR notebook for this notebook execution.
:param master_instance_security_group_id: Optional unique ID of an EC2 security
group to associate with the master instance of the EMR cluster for this notebook execution.
:param tags: Optional list of key value pair to associate with the notebook execution.
:param waiter_max_attempts: Maximum number of tries before failing.
:param waiter_delay: Number of seconds between polling the state of the notebook.
:param waiter_countdown: Total amount of time the operator will wait for the notebook to stop.
Defaults to 25 * 60 seconds. (Deprecated. Please use waiter_max_attempts.)
:param waiter_check_interval_seconds: Number of seconds between polling the state of the notebook.
Defaults to 60 seconds. (Deprecated. Please use waiter_delay.)
"""
[docs] template_fields: Sequence[str] = (
"editor_id",
"cluster_id",
"relative_path",
"service_role",
"notebook_execution_name",
"notebook_params",
"notebook_instance_security_group_id",
"master_instance_security_group_id",
"tags",
"waiter_delay",
"waiter_max_attempts",
)
def __init__(
self,
editor_id: str,
relative_path: str,
cluster_id: str,
service_role: str,
notebook_execution_name: str | None = None,
notebook_params: str | None = None,
notebook_instance_security_group_id: str | None = None,
master_instance_security_group_id: str | None = None,
tags: list | None = None,
wait_for_completion: bool = False,
aws_conn_id: str = "aws_default",
# TODO: waiter_max_attempts and waiter_delay should default to None when the other two are deprecated.
waiter_max_attempts: int | None | ArgNotSet = NOTSET,
waiter_delay: int | None | ArgNotSet = NOTSET,
waiter_countdown: int = 25 * 60,
waiter_check_interval_seconds: int = 60,
**kwargs: Any,
):
if waiter_max_attempts is NOTSET:
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
waiter_max_attempts = waiter_countdown // waiter_check_interval_seconds
if waiter_delay is NOTSET:
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to "
"standardize naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
waiter_delay = waiter_check_interval_seconds
super().__init__(**kwargs)
self.editor_id = editor_id
self.relative_path = relative_path
self.service_role = service_role
self.notebook_execution_name = notebook_execution_name or f"emr_notebook_{uuid4()}"
self.notebook_params = notebook_params or ""
self.notebook_instance_security_group_id = notebook_instance_security_group_id or ""
self.tags = tags or []
self.wait_for_completion = wait_for_completion
self.cluster_id = cluster_id
self.aws_conn_id = aws_conn_id
self.waiter_max_attempts = waiter_max_attempts
self.waiter_delay = waiter_delay
self.master_instance_security_group_id = master_instance_security_group_id
[docs] def execute(self, context: Context):
execution_engine = {
"Id": self.cluster_id,
"Type": "EMR",
"MasterInstanceSecurityGroupId": self.master_instance_security_group_id or "",
}
emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
response = emr_hook.conn.start_notebook_execution(
EditorId=self.editor_id,
RelativePath=self.relative_path,
NotebookExecutionName=self.notebook_execution_name,
NotebookParams=self.notebook_params,
ExecutionEngine=execution_engine,
ServiceRole=self.service_role,
NotebookInstanceSecurityGroupId=self.notebook_instance_security_group_id,
Tags=self.tags,
)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"Starting notebook execution failed: {response}")
self.log.info("Notebook execution started: %s", response["NotebookExecutionId"])
notebook_execution_id = response["NotebookExecutionId"]
if self.wait_for_completion:
emr_hook.get_waiter("notebook_running").wait(
NotebookExecutionId=notebook_execution_id,
WaiterConfig=prune_dict(
{
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
}
),
)
# The old Waiter method raised an exception if the notebook
# failed, adding that here. This could maybe be deprecated
# later to bring it in line with how other waiters behave.
failure_states = {"FAILED"}
final_status = emr_hook.conn.describe_notebook_execution(
NotebookExecutionId=notebook_execution_id
)["NotebookExecution"]["Status"]
if final_status in failure_states:
raise AirflowException(f"Notebook Execution reached failure state {final_status}.")
return notebook_execution_id
[docs]class EmrStopNotebookExecutionOperator(BaseOperator):
"""
An operator that stops a running EMR notebook execution.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrStopNotebookExecutionOperator`
:param notebook_execution_id: The unique identifier of the notebook execution.
:param wait_for_completion: If True, the operator will wait for the notebook.
to be in a STOPPED or FINISHED state. Defaults to False.
:param aws_conn_id: aws connection to use.
:param waiter_max_attempts: Maximum number of tries before failing.
:param waiter_delay: Number of seconds between polling the state of the notebook.
:param waiter_countdown: Total amount of time the operator will wait for the notebook to stop.
Defaults to 25 * 60 seconds. (Deprecated. Please use waiter_max_attempts.)
:param waiter_check_interval_seconds: Number of seconds between polling the state of the notebook.
Defaults to 60 seconds. (Deprecated. Please use waiter_delay.)
"""
[docs] template_fields: Sequence[str] = (
"notebook_execution_id",
"waiter_delay",
"waiter_max_attempts",
)
def __init__(
self,
notebook_execution_id: str,
wait_for_completion: bool = False,
aws_conn_id: str = "aws_default",
# TODO: waiter_max_attempts and waiter_delay should default to None when the other two are deprecated.
waiter_max_attempts: int | None | ArgNotSet = NOTSET,
waiter_delay: int | None | ArgNotSet = NOTSET,
waiter_countdown: int = 25 * 60,
waiter_check_interval_seconds: int = 60,
**kwargs: Any,
):
if waiter_max_attempts is NOTSET:
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
waiter_max_attempts = waiter_countdown // waiter_check_interval_seconds
if waiter_delay is NOTSET:
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to "
"standardize naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
waiter_delay = waiter_check_interval_seconds
super().__init__(**kwargs)
self.notebook_execution_id = notebook_execution_id
self.wait_for_completion = wait_for_completion
self.aws_conn_id = aws_conn_id
self.waiter_max_attempts = waiter_max_attempts
self.waiter_delay = waiter_delay
[docs] def execute(self, context: Context) -> None:
emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
emr_hook.conn.stop_notebook_execution(NotebookExecutionId=self.notebook_execution_id)
if self.wait_for_completion:
emr_hook.get_waiter("notebook_stopped").wait(
NotebookExecutionId=self.notebook_execution_id,
WaiterConfig=prune_dict(
{
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
}
),
)
[docs]class EmrEksCreateClusterOperator(BaseOperator):
"""
An operator that creates EMR on EKS virtual clusters.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrEksCreateClusterOperator`
:param virtual_cluster_name: The name of the EMR EKS virtual cluster to create.
:param eks_cluster_name: The EKS cluster used by the EMR virtual cluster.
:param eks_namespace: namespace used by the EKS cluster.
:param virtual_cluster_id: The EMR on EKS virtual cluster id.
:param aws_conn_id: The Airflow connection used for AWS credentials.
:param tags: The tags assigned to created cluster.
Defaults to None
"""
[docs] template_fields: Sequence[str] = (
"virtual_cluster_name",
"eks_cluster_name",
"eks_namespace",
)
def __init__(
self,
*,
virtual_cluster_name: str,
eks_cluster_name: str,
eks_namespace: str,
virtual_cluster_id: str = "",
aws_conn_id: str = "aws_default",
tags: dict | None = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.virtual_cluster_name = virtual_cluster_name
self.eks_cluster_name = eks_cluster_name
self.eks_namespace = eks_namespace
self.virtual_cluster_id = virtual_cluster_id
self.aws_conn_id = aws_conn_id
self.tags = tags
@cached_property
[docs] def hook(self) -> EmrContainerHook:
"""Create and return an EmrContainerHook."""
return EmrContainerHook(self.aws_conn_id)
[docs] def execute(self, context: Context) -> str | None:
"""Create EMR on EKS virtual Cluster."""
self.virtual_cluster_id = self.hook.create_emr_on_eks_cluster(
self.virtual_cluster_name, self.eks_cluster_name, self.eks_namespace, self.tags
)
return self.virtual_cluster_id
[docs]class EmrContainerOperator(BaseOperator):
"""
An operator that submits jobs to EMR on EKS virtual clusters.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrContainerOperator`
:param name: The name of the job run.
:param virtual_cluster_id: The EMR on EKS virtual cluster ID
:param execution_role_arn: The IAM role ARN associated with the job run.
:param release_label: The Amazon EMR release version to use for the job run.
:param job_driver: Job configuration details, e.g. the Spark job parameters.
:param configuration_overrides: The configuration overrides for the job run,
specifically either application configuration or monitoring configuration.
:param client_request_token: The client idempotency token of the job run request.
Use this if you want to specify a unique ID to prevent two jobs from getting started.
If no token is provided, a UUIDv4 token will be generated for you.
:param aws_conn_id: The Airflow connection used for AWS credentials.
:param wait_for_completion: Whether or not to wait in the operator for the job to complete.
:param poll_interval: Time (in seconds) to wait between two consecutive calls to check query status on EMR
:param max_tries: Deprecated - use max_polling_attempts instead.
:param max_polling_attempts: Maximum number of times to wait for the job run to finish.
Defaults to None, which will poll until the job is *not* in a pending, submitted, or running state.
:param tags: The tags assigned to job runs.
Defaults to None
:param deferrable: Run operator in the deferrable mode.
"""
[docs] template_fields: Sequence[str] = (
"name",
"virtual_cluster_id",
"execution_role_arn",
"release_label",
"job_driver",
"configuration_overrides",
)
def __init__(
self,
*,
name: str,
virtual_cluster_id: str,
execution_role_arn: str,
release_label: str,
job_driver: dict,
configuration_overrides: dict | None = None,
client_request_token: str | None = None,
aws_conn_id: str = "aws_default",
wait_for_completion: bool = True,
poll_interval: int = 30,
max_tries: int | None = None,
tags: dict | None = None,
max_polling_attempts: int | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.name = name
self.virtual_cluster_id = virtual_cluster_id
self.execution_role_arn = execution_role_arn
self.release_label = release_label
self.job_driver = job_driver
self.configuration_overrides = configuration_overrides or {}
self.aws_conn_id = aws_conn_id
self.client_request_token = client_request_token or str(uuid4())
self.wait_for_completion = wait_for_completion
self.poll_interval = poll_interval
self.max_polling_attempts = max_polling_attempts
self.tags = tags
self.job_id: str | None = None
self.deferrable = deferrable
if max_tries:
warnings.warn(
f"Parameter `{self.__class__.__name__}.max_tries` is deprecated and will be removed "
"in a future release. Please use method `max_polling_attempts` instead.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
if max_polling_attempts and max_polling_attempts != max_tries:
raise Exception("max_polling_attempts must be the same value as max_tries")
else:
self.max_polling_attempts = max_tries
@cached_property
[docs] def hook(self) -> EmrContainerHook:
"""Create and return an EmrContainerHook."""
return EmrContainerHook(
self.aws_conn_id,
virtual_cluster_id=self.virtual_cluster_id,
)
[docs] def execute(self, context: Context) -> str | None:
"""Run job on EMR Containers."""
self.job_id = self.hook.submit_job(
self.name,
self.execution_role_arn,
self.release_label,
self.job_driver,
self.configuration_overrides,
self.client_request_token,
self.tags,
)
if self.deferrable:
query_status = self.hook.check_query_status(job_id=self.job_id)
self.check_failure(query_status)
if query_status in EmrContainerHook.SUCCESS_STATES:
return self.job_id
timeout = (
timedelta(seconds=self.max_polling_attempts * self.poll_interval)
if self.max_polling_attempts
else self.execution_timeout
)
self.defer(
timeout=timeout,
trigger=EmrContainerTrigger(
virtual_cluster_id=self.virtual_cluster_id,
job_id=self.job_id,
aws_conn_id=self.aws_conn_id,
waiter_delay=self.poll_interval,
),
method_name="execute_complete",
)
if self.wait_for_completion:
query_status = self.hook.poll_query_status(
self.job_id,
max_polling_attempts=self.max_polling_attempts,
poll_interval=self.poll_interval,
)
self.check_failure(query_status)
if not query_status or query_status in EmrContainerHook.INTERMEDIATE_STATES:
raise AirflowException(
f"Final state of EMR Containers job is {query_status}. "
f"Max tries of poll status exceeded, query_execution_id is {self.job_id}."
)
return self.job_id
[docs] def check_failure(self, query_status):
if query_status in EmrContainerHook.FAILURE_STATES:
error_message = self.hook.get_job_failure_reason(self.job_id)
raise AirflowException(
f"EMR Containers job failed. Final state is {query_status}. "
f"query_execution_id is {self.job_id}. Error: {error_message}"
)
[docs] def execute_complete(self, context, event=None):
if event["status"] != "success":
raise AirflowException(f"Error while running job: {event}")
return event["job_id"]
[docs] def on_kill(self) -> None:
"""Cancel the submitted job run."""
if self.job_id:
self.log.info("Stopping job run with jobId - %s", self.job_id)
response = self.hook.stop_query(self.job_id)
http_status_code = None
try:
http_status_code = response["ResponseMetadata"]["HTTPStatusCode"]
except Exception as ex:
self.log.error("Exception while cancelling query: %s", ex)
finally:
if http_status_code is None or http_status_code != 200:
self.log.error("Unable to request query cancel on EMR. Exiting")
else:
self.log.info(
"Polling EMR for query with id %s to reach final state",
self.job_id,
)
self.hook.poll_query_status(self.job_id)
[docs]class EmrCreateJobFlowOperator(BaseOperator):
"""
Creates an EMR JobFlow, reading the config from the EMR connection.
A dictionary of JobFlow overrides can be passed that override the config from the connection.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrCreateJobFlowOperator`
:param aws_conn_id: The Airflow connection used for AWS credentials.
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 default boto3 configuration would be used (and must be
maintained on each worker node)
:param emr_conn_id: :ref:`Amazon Elastic MapReduce Connection <howto/connection:emr>`.
Use to receive an initial Amazon EMR cluster configuration:
``boto3.client('emr').run_job_flow`` request body.
If this is None or empty or the connection does not exist,
then an empty initial configuration is used.
:param job_flow_overrides: boto3 style arguments or reference to an arguments file
(must be '.json') to override specific ``emr_conn_id`` extra parameters. (templated)
:param region_name: Region named passed to EmrHook
:param wait_for_completion: Whether to finish task immediately after creation (False) or wait for jobflow
completion (True)
:param waiter_max_attempts: Maximum number of tries before failing.
:param waiter_delay: Number of seconds between polling the state of the notebook.
:param waiter_countdown: Max. seconds to wait for jobflow completion (only in combination with
wait_for_completion=True, None = no limit) (Deprecated. Please use waiter_max_attempts.)
:param waiter_check_interval_seconds: Number of seconds between polling the jobflow state. Defaults to 60
seconds. (Deprecated. Please use waiter_delay.)
:param deferrable: If True, the operator will wait asynchronously for the crawl to complete.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
"""
[docs] template_fields: Sequence[str] = (
"job_flow_overrides",
"waiter_delay",
"waiter_max_attempts",
)
[docs] template_ext: Sequence[str] = (".json",)
[docs] template_fields_renderers = {"job_flow_overrides": "json"}
def __init__(
self,
*,
aws_conn_id: str = "aws_default",
emr_conn_id: str | None = "emr_default",
job_flow_overrides: str | dict[str, Any] | None = None,
region_name: str | None = None,
wait_for_completion: bool = False,
# TODO: waiter_max_attempts and waiter_delay should default to None when the other two are deprecated.
waiter_max_attempts: int | None | ArgNotSet = NOTSET,
waiter_delay: int | None | ArgNotSet = NOTSET,
waiter_countdown: int | None = None,
waiter_check_interval_seconds: int = 60,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs: Any,
):
if waiter_max_attempts is NOTSET:
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
# waiter_countdown defaults to never timing out, which is not supported
# by boto waiters, so we will set it here to "a very long time" for now.
waiter_max_attempts = (waiter_countdown or 999) // waiter_check_interval_seconds
if waiter_delay is NOTSET:
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to "
"standardize naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
waiter_delay = waiter_check_interval_seconds
super().__init__(**kwargs)
self.aws_conn_id = aws_conn_id
self.emr_conn_id = emr_conn_id
self.job_flow_overrides = job_flow_overrides or {}
self.region_name = region_name
self.wait_for_completion = wait_for_completion
self.waiter_max_attempts = int(waiter_max_attempts) # type: ignore[arg-type]
self.waiter_delay = int(waiter_delay) # type: ignore[arg-type]
self.deferrable = deferrable
@cached_property
def _emr_hook(self) -> EmrHook:
"""Create and return an EmrHook."""
return EmrHook(
aws_conn_id=self.aws_conn_id, emr_conn_id=self.emr_conn_id, region_name=self.region_name
)
[docs] def execute(self, context: Context) -> str | None:
self.log.info(
"Creating job flow using aws_conn_id: %s, emr_conn_id: %s", self.aws_conn_id, self.emr_conn_id
)
if isinstance(self.job_flow_overrides, str):
job_flow_overrides: dict[str, Any] = ast.literal_eval(self.job_flow_overrides)
self.job_flow_overrides = job_flow_overrides
else:
job_flow_overrides = self.job_flow_overrides
response = self._emr_hook.create_job_flow(job_flow_overrides)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"Job flow creation failed: {response}")
self._job_flow_id = response["JobFlowId"]
self.log.info("Job flow with id %s created", self._job_flow_id)
EmrClusterLink.persist(
context=context,
operator=self,
region_name=self._emr_hook.conn_region_name,
aws_partition=self._emr_hook.conn_partition,
job_flow_id=self._job_flow_id,
)
if self._job_flow_id:
EmrLogsLink.persist(
context=context,
operator=self,
region_name=self._emr_hook.conn_region_name,
aws_partition=self._emr_hook.conn_partition,
job_flow_id=self._job_flow_id,
log_uri=get_log_uri(emr_client=self._emr_hook.conn, job_flow_id=self._job_flow_id),
)
if self.deferrable:
self.defer(
trigger=EmrCreateJobFlowTrigger(
job_flow_id=self._job_flow_id,
aws_conn_id=self.aws_conn_id,
poll_interval=self.waiter_delay,
max_attempts=self.waiter_max_attempts,
),
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),
)
if self.wait_for_completion:
self._emr_hook.get_waiter("job_flow_waiting").wait(
ClusterId=self._job_flow_id,
WaiterConfig=prune_dict(
{
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
}
),
)
return self._job_flow_id
[docs] def execute_complete(self, context, event=None):
if event["status"] != "success":
raise AirflowException(f"Error creating jobFlow: {event}")
else:
self.log.info("JobFlow created successfully")
return event["job_flow_id"]
[docs] def on_kill(self) -> None:
"""Terminate the EMR cluster (job flow) unless TerminationProtected is enabled on the cluster."""
if self._job_flow_id:
self.log.info("Terminating job flow %s", self._job_flow_id)
self._emr_hook.conn.terminate_job_flows(JobFlowIds=[self._job_flow_id])
[docs]class EmrModifyClusterOperator(BaseOperator):
"""
An operator that modifies an existing EMR cluster.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrModifyClusterOperator`
:param cluster_id: cluster identifier
:param step_concurrency_level: Concurrency of the cluster
:param aws_conn_id: aws connection to uses
:param do_xcom_push: if True, cluster_id is pushed to XCom with key cluster_id.
"""
[docs] template_fields: Sequence[str] = ("cluster_id", "step_concurrency_level")
[docs] template_ext: Sequence[str] = ()
def __init__(
self, *, cluster_id: str, step_concurrency_level: int, aws_conn_id: str = "aws_default", **kwargs
):
super().__init__(**kwargs)
self.aws_conn_id = aws_conn_id
self.cluster_id = cluster_id
self.step_concurrency_level = step_concurrency_level
[docs] def execute(self, context: Context) -> int:
emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
emr = emr_hook.get_conn()
if self.do_xcom_push:
context["ti"].xcom_push(key="cluster_id", value=self.cluster_id)
EmrClusterLink.persist(
context=context,
operator=self,
region_name=emr_hook.conn_region_name,
aws_partition=emr_hook.conn_partition,
job_flow_id=self.cluster_id,
)
EmrLogsLink.persist(
context=context,
operator=self,
region_name=emr_hook.conn_region_name,
aws_partition=emr_hook.conn_partition,
job_flow_id=self.cluster_id,
log_uri=get_log_uri(emr_client=emr_hook.conn, job_flow_id=self.cluster_id),
)
self.log.info("Modifying cluster %s", self.cluster_id)
response = emr.modify_cluster(
ClusterId=self.cluster_id, StepConcurrencyLevel=self.step_concurrency_level
)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"Modify cluster failed: {response}")
else:
self.log.info("Steps concurrency level %d", response["StepConcurrencyLevel"])
return response["StepConcurrencyLevel"]
[docs]class EmrTerminateJobFlowOperator(BaseOperator):
"""
Operator to terminate EMR JobFlows.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrTerminateJobFlowOperator`
:param job_flow_id: id of the JobFlow to terminate. (templated)
:param aws_conn_id: aws connection to uses
:param waiter_delay: Time (in seconds) to wait between two consecutive calls to check JobFlow status
:param waiter_max_attempts: The maximum number of times to poll for JobFlow status.
:param deferrable: If True, the operator will wait asynchronously for the crawl to complete.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
"""
[docs] template_fields: Sequence[str] = ("job_flow_id",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
job_flow_id: str,
aws_conn_id: str = "aws_default",
waiter_delay: int = 60,
waiter_max_attempts: int = 20,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
super().__init__(**kwargs)
self.job_flow_id = job_flow_id
self.aws_conn_id = aws_conn_id
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
[docs] def execute(self, context: Context) -> None:
emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
emr = emr_hook.get_conn()
EmrClusterLink.persist(
context=context,
operator=self,
region_name=emr_hook.conn_region_name,
aws_partition=emr_hook.conn_partition,
job_flow_id=self.job_flow_id,
)
EmrLogsLink.persist(
context=context,
operator=self,
region_name=emr_hook.conn_region_name,
aws_partition=emr_hook.conn_partition,
job_flow_id=self.job_flow_id,
log_uri=get_log_uri(emr_client=emr, job_flow_id=self.job_flow_id),
)
self.log.info("Terminating JobFlow %s", self.job_flow_id)
response = emr.terminate_job_flows(JobFlowIds=[self.job_flow_id])
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"JobFlow termination failed: {response}")
self.log.info("Terminating JobFlow with id %s", self.job_flow_id)
if self.deferrable:
self.defer(
trigger=EmrTerminateJobFlowTrigger(
job_flow_id=self.job_flow_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
),
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),
)
[docs] def execute_complete(self, context, event=None):
if event["status"] != "success":
raise AirflowException(f"Error terminating JobFlow: {event}")
else:
self.log.info("Jobflow terminated successfully.")
return
[docs]class EmrServerlessCreateApplicationOperator(BaseOperator):
"""
Operator to create Serverless EMR Application.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrServerlessCreateApplicationOperator`
:param release_label: The EMR release version associated with the application.
:param job_type: The type of application you want to start, such as Spark or Hive.
:param wait_for_completion: If true, wait for the Application to start before returning. Default to True.
If set to False, ``waiter_max_attempts`` and ``waiter_delay`` will only be applied when
waiting for the application to be in the ``CREATED`` state.
:param client_request_token: The client idempotency token of the application to create.
Its value must be unique for each request.
:param config: Optional dictionary for arbitrary parameters to the boto API create_application call.
:param aws_conn_id: AWS connection to use
:param waiter_countdown: (deprecated) Total amount of time, in seconds, the operator will wait for
the application to start. Defaults to 25 minutes.
:param waiter_check_interval_seconds: (deprecated) Number of seconds between polling the state
of the application. Defaults to 60 seconds.
:waiter_max_attempts: Number of times the waiter should poll the application to check the state.
If not set, the waiter will use its default value.
:param waiter_delay: Number of seconds between polling the state of the application.
:param deferrable: If True, the operator will wait asynchronously for application to be created.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
"""
def __init__(
self,
release_label: str,
job_type: str,
client_request_token: str = "",
config: dict | None = None,
wait_for_completion: bool = True,
aws_conn_id: str = "aws_default",
waiter_countdown: int | ArgNotSet = NOTSET,
waiter_check_interval_seconds: int | ArgNotSet = NOTSET,
waiter_max_attempts: int | ArgNotSet = NOTSET,
waiter_delay: int | ArgNotSet = NOTSET,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
if waiter_check_interval_seconds is NOTSET:
waiter_delay = 60 if waiter_delay is NOTSET else waiter_delay
else:
waiter_delay = waiter_check_interval_seconds if waiter_delay is NOTSET else waiter_delay
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to standardize "
"naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
if waiter_countdown is NOTSET:
waiter_max_attempts = 25 if waiter_max_attempts is NOTSET else waiter_max_attempts
else:
if waiter_max_attempts is NOTSET:
# ignoring mypy because it doesn't like ArgNotSet as an operand, but neither variables
# are of type ArgNotSet at this point.
waiter_max_attempts = waiter_countdown // waiter_delay # type: ignore[operator]
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.aws_conn_id = aws_conn_id
self.release_label = release_label
self.job_type = job_type
self.wait_for_completion = wait_for_completion
self.kwargs = kwargs
self.config = config or {}
self.waiter_max_attempts = int(waiter_max_attempts) # type: ignore[arg-type]
self.waiter_delay = int(waiter_delay) # type: ignore[arg-type]
self.deferrable = deferrable
super().__init__(**kwargs)
self.client_request_token = client_request_token or str(uuid4())
@cached_property
[docs] def hook(self) -> EmrServerlessHook:
"""Create and return an EmrServerlessHook."""
return EmrServerlessHook(aws_conn_id=self.aws_conn_id)
[docs] def execute(self, context: Context) -> str | None:
response = self.hook.conn.create_application(
clientToken=self.client_request_token,
releaseLabel=self.release_label,
type=self.job_type,
**self.config,
)
application_id = response["applicationId"]
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"Application Creation failed: {response}")
self.log.info("EMR serverless application created: %s", application_id)
if self.deferrable:
self.defer(
trigger=EmrServerlessCreateApplicationTrigger(
application_id=application_id,
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
),
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
method_name="start_application_deferred",
)
waiter = self.hook.get_waiter("serverless_app_created")
wait(
waiter=waiter,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
args={"applicationId": application_id},
failure_message="Serverless Application creation failed",
status_message="Serverless Application status is",
status_args=["application.state", "application.stateDetails"],
)
self.log.info("Starting application %s", application_id)
self.hook.conn.start_application(applicationId=application_id)
if self.wait_for_completion:
waiter = self.hook.get_waiter("serverless_app_started")
wait(
waiter=waiter,
waiter_max_attempts=self.waiter_max_attempts,
waiter_delay=self.waiter_delay,
args={"applicationId": application_id},
failure_message="Serverless Application failed to start",
status_message="Serverless Application status is",
status_args=["application.state", "application.stateDetails"],
)
return application_id
[docs] def start_application_deferred(self, context: Context, event: dict[str, Any] | None = None) -> None:
if event is None:
self.log.error("Trigger error: event is None")
raise AirflowException("Trigger error: event is None")
elif event["status"] != "success":
raise AirflowException(f"Application {event['application_id']} failed to create")
self.log.info("Starting application %s", event["application_id"])
self.hook.conn.start_application(applicationId=event["application_id"])
self.defer(
trigger=EmrServerlessStartApplicationTrigger(
application_id=event["application_id"],
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
),
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> None:
if event is None or event["status"] != "success":
raise AirflowException(f"Trigger error: Application failed to start, event is {event}")
self.log.info("Application %s started", event["application_id"])
return event["application_id"]
[docs]class EmrServerlessStartJobOperator(BaseOperator):
"""
Operator to start EMR Serverless job.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrServerlessStartJobOperator`
:param application_id: ID of the EMR Serverless application to start.
:param execution_role_arn: ARN of role to perform action.
:param job_driver: Driver that the job runs on.
:param configuration_overrides: Configuration specifications to override existing configurations.
:param client_request_token: The client idempotency token of the application to create.
Its value must be unique for each request.
:param config: Optional dictionary for arbitrary parameters to the boto API start_job_run call.
:param wait_for_completion: If true, waits for the job to start before returning. Defaults to True.
If set to False, ``waiter_countdown`` and ``waiter_check_interval_seconds`` will only be applied
when waiting for the application be to in the ``STARTED`` state.
:param aws_conn_id: AWS connection to use.
:param name: Name for the EMR Serverless job. If not provided, a default name will be assigned.
:param waiter_countdown: (deprecated) Total amount of time, in seconds, the operator will wait for
the job finish. Defaults to 25 minutes.
:param waiter_check_interval_seconds: (deprecated) Number of seconds between polling the state of the job.
Defaults to 60 seconds.
:waiter_max_attempts: Number of times the waiter should poll the application to check the state.
If not set, the waiter will use its default value.
:param waiter_delay: Number of seconds between polling the state of the job run.
:param deferrable: If True, the operator will wait asynchronously for the crawl to complete.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
"""
[docs] template_fields: Sequence[str] = (
"application_id",
"config",
"execution_role_arn",
"job_driver",
"configuration_overrides",
"name",
)
[docs] template_fields_renderers = {
"config": "json",
"configuration_overrides": "json",
}
def __init__(
self,
application_id: str,
execution_role_arn: str,
job_driver: dict,
configuration_overrides: dict | None,
client_request_token: str = "",
config: dict | None = None,
wait_for_completion: bool = True,
aws_conn_id: str = "aws_default",
name: str | None = None,
waiter_countdown: int | ArgNotSet = NOTSET,
waiter_check_interval_seconds: int | ArgNotSet = NOTSET,
waiter_max_attempts: int | ArgNotSet = NOTSET,
waiter_delay: int | ArgNotSet = NOTSET,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
if waiter_check_interval_seconds is NOTSET:
waiter_delay = 60 if waiter_delay is NOTSET else waiter_delay
else:
waiter_delay = waiter_check_interval_seconds if waiter_delay is NOTSET else waiter_delay
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to standardize "
"naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
if waiter_countdown is NOTSET:
waiter_max_attempts = 25 if waiter_max_attempts is NOTSET else waiter_max_attempts
else:
if waiter_max_attempts is NOTSET:
# ignoring mypy because it doesn't like ArgNotSet as an operand, but neither variables
# are of type ArgNotSet at this point.
waiter_max_attempts = waiter_countdown // waiter_delay # type: ignore[operator]
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.aws_conn_id = aws_conn_id
self.application_id = application_id
self.execution_role_arn = execution_role_arn
self.job_driver = job_driver
self.configuration_overrides = configuration_overrides
self.wait_for_completion = wait_for_completion
self.config = config or {}
self.name = name
self.waiter_max_attempts = int(waiter_max_attempts) # type: ignore[arg-type]
self.waiter_delay = int(waiter_delay) # type: ignore[arg-type]
self.job_id: str | None = None
self.deferrable = deferrable
super().__init__(**kwargs)
self.client_request_token = client_request_token or str(uuid4())
@cached_property
[docs] def hook(self) -> EmrServerlessHook:
"""Create and return an EmrServerlessHook."""
return EmrServerlessHook(aws_conn_id=self.aws_conn_id)
[docs] def execute(self, context: Context, event: dict[str, Any] | None = None) -> str | None:
app_state = self.hook.conn.get_application(applicationId=self.application_id)["application"]["state"]
if app_state not in EmrServerlessHook.APPLICATION_SUCCESS_STATES:
self.log.info("Application state is %s", app_state)
self.log.info("Starting application %s", self.application_id)
self.hook.conn.start_application(applicationId=self.application_id)
waiter = self.hook.get_waiter("serverless_app_started")
if self.deferrable:
self.defer(
trigger=EmrServerlessStartApplicationTrigger(
application_id=self.application_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
),
method_name="execute",
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
)
wait(
waiter=waiter,
waiter_max_attempts=self.waiter_max_attempts,
waiter_delay=self.waiter_delay,
args={"applicationId": self.application_id},
failure_message="Serverless Application failed to start",
status_message="Serverless Application status is",
status_args=["application.state", "application.stateDetails"],
)
self.log.info("Starting job on Application: %s", self.application_id)
self.name = self.name or self.config.pop("name", f"emr_serverless_job_airflow_{uuid4()}")
args = {
"clientToken": self.client_request_token,
"applicationId": self.application_id,
"executionRoleArn": self.execution_role_arn,
"jobDriver": self.job_driver,
"name": self.name,
**self.config,
}
if self.configuration_overrides is not None:
args["configurationOverrides"] = self.configuration_overrides
response = self.hook.conn.start_job_run(
**args,
)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"EMR serverless job failed to start: {response}")
self.job_id = response["jobRunId"]
self.log.info("EMR serverless job started: %s", self.job_id)
if self.deferrable:
self.defer(
trigger=EmrServerlessStartJobTrigger(
application_id=self.application_id,
job_id=self.job_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
),
method_name="execute_complete",
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
)
if self.wait_for_completion:
waiter = self.hook.get_waiter("serverless_job_completed")
wait(
waiter=waiter,
waiter_max_attempts=self.waiter_max_attempts,
waiter_delay=self.waiter_delay,
args={"applicationId": self.application_id, "jobRunId": self.job_id},
failure_message="Serverless Job failed",
status_message="Serverless Job status is",
status_args=["jobRun.state", "jobRun.stateDetails"],
)
return self.job_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> None:
if event is None:
self.log.error("Trigger error: event is None")
raise AirflowException("Trigger error: event is None")
elif event["status"] == "success":
self.log.info("Serverless job completed")
return event["job_id"]
[docs] def on_kill(self) -> None:
"""
Cancel the submitted job run.
Note: this method will not run in deferrable mode.
"""
if self.job_id:
self.log.info("Stopping job run with jobId - %s", self.job_id)
response = self.hook.conn.cancel_job_run(applicationId=self.application_id, jobRunId=self.job_id)
http_status_code = (
response.get("ResponseMetadata", {}).get("HTTPStatusCode") if response else None
)
if http_status_code is None or http_status_code != 200:
self.log.error("Unable to request query cancel on EMR Serverless. Exiting")
return
self.log.info(
"Polling EMR Serverless for query with id %s to reach final state",
self.job_id,
)
# This should be replaced with a boto waiter when available.
waiter(
get_state_callable=self.hook.conn.get_job_run,
get_state_args={
"applicationId": self.application_id,
"jobRunId": self.job_id,
},
parse_response=["jobRun", "state"],
desired_state=EmrServerlessHook.JOB_TERMINAL_STATES,
failure_states=set(),
object_type="job",
action="cancelled",
countdown=self.waiter_delay * self.waiter_max_attempts,
check_interval_seconds=self.waiter_delay,
)
[docs]class EmrServerlessStopApplicationOperator(BaseOperator):
"""
Operator to stop an EMR Serverless application.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrServerlessStopApplicationOperator`
:param application_id: ID of the EMR Serverless application to stop.
:param wait_for_completion: If true, wait for the Application to stop before returning. Default to True
:param aws_conn_id: AWS connection to use
:param waiter_countdown: (deprecated) Total amount of time, in seconds, the operator will wait for
the application be stopped. Defaults to 5 minutes.
:param waiter_check_interval_seconds: (deprecated) Number of seconds between polling the state of the
application. Defaults to 60 seconds.
:param force_stop: If set to True, any job for that app that is not in a terminal state will be cancelled.
Otherwise, trying to stop an app with running jobs will return an error.
If you want to wait for the jobs to finish gracefully, use
:class:`airflow.providers.amazon.aws.sensors.emr.EmrServerlessJobSensor`
:waiter_max_attempts: Number of times the waiter should poll the application to check the state.
Default is 25.
:param waiter_delay: Number of seconds between polling the state of the application.
Default is 60 seconds.
:param deferrable: If True, the operator will wait asynchronously for the application to stop.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
"""
[docs] template_fields: Sequence[str] = ("application_id",)
def __init__(
self,
application_id: str,
wait_for_completion: bool = True,
aws_conn_id: str = "aws_default",
waiter_countdown: int | ArgNotSet = NOTSET,
waiter_check_interval_seconds: int | ArgNotSet = NOTSET,
waiter_max_attempts: int | ArgNotSet = NOTSET,
waiter_delay: int | ArgNotSet = NOTSET,
force_stop: bool = False,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
if waiter_check_interval_seconds is NOTSET:
waiter_delay = 60 if waiter_delay is NOTSET else waiter_delay
else:
waiter_delay = waiter_check_interval_seconds if waiter_delay is NOTSET else waiter_delay
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to standardize "
"naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
if waiter_countdown is NOTSET:
waiter_max_attempts = 25 if waiter_max_attempts is NOTSET else waiter_max_attempts
else:
if waiter_max_attempts is NOTSET:
# ignoring mypy because it doesn't like ArgNotSet as an operand, but neither variables
# are of type ArgNotSet at this point.
waiter_max_attempts = waiter_countdown // waiter_delay # type: ignore[operator]
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.aws_conn_id = aws_conn_id
self.application_id = application_id
self.wait_for_completion = False if deferrable else wait_for_completion
self.waiter_max_attempts = int(waiter_max_attempts) # type: ignore[arg-type]
self.waiter_delay = int(waiter_delay) # type: ignore[arg-type]
self.force_stop = force_stop
self.deferrable = deferrable
super().__init__(**kwargs)
@cached_property
[docs] def hook(self) -> EmrServerlessHook:
"""Create and return an EmrServerlessHook."""
return EmrServerlessHook(aws_conn_id=self.aws_conn_id)
[docs] def execute(self, context: Context) -> None:
self.log.info("Stopping application: %s", self.application_id)
if self.force_stop:
count = self.hook.cancel_running_jobs(
application_id=self.application_id,
wait_for_completion=False,
)
if count > 0:
self.log.info("now waiting for the %s cancelled job(s) to terminate", count)
if self.deferrable:
self.defer(
trigger=EmrServerlessCancelJobsTrigger(
application_id=self.application_id,
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
),
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
method_name="stop_application",
)
self.hook.get_waiter("no_job_running").wait(
applicationId=self.application_id,
states=list(self.hook.JOB_INTERMEDIATE_STATES.union({"CANCELLING"})),
WaiterConfig={
"Delay": self.waiter_delay,
"MaxAttempts": self.waiter_max_attempts,
},
)
else:
self.log.info("no running jobs found with application ID %s", self.application_id)
self.hook.conn.stop_application(applicationId=self.application_id)
if self.deferrable:
self.defer(
trigger=EmrServerlessStopApplicationTrigger(
application_id=self.application_id,
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
),
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
method_name="execute_complete",
)
if self.wait_for_completion:
waiter = self.hook.get_waiter("serverless_app_stopped")
wait(
waiter=waiter,
waiter_max_attempts=self.waiter_max_attempts,
waiter_delay=self.waiter_delay,
args={"applicationId": self.application_id},
failure_message="Error stopping application",
status_message="Serverless Application status is",
status_args=["application.state", "application.stateDetails"],
)
self.log.info("EMR serverless application %s stopped successfully", self.application_id)
[docs] def stop_application(self, context: Context, event: dict[str, Any] | None = None) -> None:
if event is None:
self.log.error("Trigger error: event is None")
raise AirflowException("Trigger error: event is None")
elif event["status"] == "success":
self.hook.conn.stop_application(applicationId=self.application_id)
self.defer(
trigger=EmrServerlessStopApplicationTrigger(
application_id=self.application_id,
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
),
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> None:
if event is None:
self.log.error("Trigger error: event is None")
raise AirflowException("Trigger error: event is None")
elif event["status"] == "success":
self.log.info("EMR serverless application %s stopped successfully", self.application_id)
[docs]class EmrServerlessDeleteApplicationOperator(EmrServerlessStopApplicationOperator):
"""
Operator to delete EMR Serverless application.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:EmrServerlessDeleteApplicationOperator`
:param application_id: ID of the EMR Serverless application to delete.
:param wait_for_completion: If true, wait for the Application to be deleted before returning.
Defaults to True. Note that this operator will always wait for the application to be STOPPED first.
:param aws_conn_id: AWS connection to use
:param waiter_countdown: (deprecated) Total amount of time, in seconds, the operator will wait for each
step of first,the application to be stopped, and then deleted. Defaults to 25 minutes.
:param waiter_check_interval_seconds: (deprecated) Number of seconds between polling the state
of the application. Defaults to 60 seconds.
:waiter_max_attempts: Number of times the waiter should poll the application to check the state.
Defaults to 25.
:param waiter_delay: Number of seconds between polling the state of the application.
Defaults to 60 seconds.
:param deferrable: If True, the operator will wait asynchronously for application to be deleted.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
:param force_stop: If set to True, any job for that app that is not in a terminal state will be cancelled.
Otherwise, trying to delete an app with running jobs will return an error.
If you want to wait for the jobs to finish gracefully, use
:class:`airflow.providers.amazon.aws.sensors.emr.EmrServerlessJobSensor`
"""
[docs] template_fields: Sequence[str] = ("application_id",)
def __init__(
self,
application_id: str,
wait_for_completion: bool = True,
aws_conn_id: str = "aws_default",
waiter_countdown: int | ArgNotSet = NOTSET,
waiter_check_interval_seconds: int | ArgNotSet = NOTSET,
waiter_max_attempts: int | ArgNotSet = NOTSET,
waiter_delay: int | ArgNotSet = NOTSET,
force_stop: bool = False,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
if waiter_check_interval_seconds is NOTSET:
waiter_delay = 60 if waiter_delay is NOTSET else waiter_delay
else:
waiter_delay = waiter_check_interval_seconds if waiter_delay is NOTSET else waiter_delay
warnings.warn(
"The parameter waiter_check_interval_seconds has been deprecated to standardize "
"naming conventions. Please use waiter_delay instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
if waiter_countdown is NOTSET:
waiter_max_attempts = 25 if waiter_max_attempts is NOTSET else waiter_max_attempts
else:
if waiter_max_attempts is NOTSET:
# ignoring mypy because it doesn't like ArgNotSet as an operand, but neither variables
# are of type ArgNotSet at this point.
waiter_max_attempts = waiter_countdown // waiter_delay # type: ignore[operator]
warnings.warn(
"The parameter waiter_countdown has been deprecated to standardize "
"naming conventions. Please use waiter_max_attempts instead. In the "
"future this will default to None and defer to the waiter's default value.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.wait_for_delete_completion = wait_for_completion
# super stops the app
super().__init__(
application_id=application_id,
# when deleting an app, we always need to wait for it to stop before we can call delete()
wait_for_completion=True,
aws_conn_id=aws_conn_id,
waiter_delay=waiter_delay,
waiter_max_attempts=waiter_max_attempts,
force_stop=force_stop,
**kwargs,
)
self.deferrable = deferrable
self.wait_for_delete_completion = False if deferrable else wait_for_completion
[docs] def execute(self, context: Context) -> None:
# super stops the app (or makes sure it's already stopped)
super().execute(context)
self.log.info("Now deleting application: %s", self.application_id)
response = self.hook.conn.delete_application(applicationId=self.application_id)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"Application deletion failed: {response}")
if self.deferrable:
self.defer(
trigger=EmrServerlessDeleteApplicationTrigger(
application_id=self.application_id,
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
),
timeout=timedelta(seconds=self.waiter_max_attempts * self.waiter_delay),
method_name="execute_complete",
)
elif self.wait_for_delete_completion:
waiter = self.hook.get_waiter("serverless_app_terminated")
wait(
waiter=waiter,
waiter_max_attempts=self.waiter_max_attempts,
waiter_delay=self.waiter_delay,
args={"applicationId": self.application_id},
failure_message="Error terminating application",
status_message="Serverless Application status is",
status_args=["application.state", "application.stateDetails"],
)
self.log.info("EMR serverless application deleted")
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> None:
if event is None:
self.log.error("Trigger error: event is None")
raise AirflowException("Trigger error: event is None")
elif event["status"] == "success":
self.log.info("EMR serverless application %s deleted successfully", self.application_id)
