#
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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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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 typing import TYPE_CHECKING, Any, Sequence
from uuid import uuid4
from airflow.exceptions import AirflowException
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
if TYPE_CHECKING:
from airflow.utils.context import Context
from airflow.compat.functools import cached_property
[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 do_xcom_push: if True, job_flow_id is pushed to XCom with key job_flow_id.
"""
[docs] template_fields: Sequence[str] = ("job_flow_id", "job_flow_name", "cluster_states", "steps")
[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,
**kwargs,
):
if not (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
[docs] def execute(self, context: Context) -> list[str]:
emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
emr = emr_hook.get_conn()
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,
)
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)
response = emr.add_job_flow_steps(JobFlowId=job_flow_id, Steps=steps)
if not response["ResponseMetadata"]["HTTPStatusCode"] == 200:
raise AirflowException(f"Adding steps failed: {response}")
else:
self.log.info("Steps %s added to JobFlow", response["StepIds"])
return response["StepIds"]
[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
"""
[docs] template_fields: Sequence[str] = (
"name",
"virtual_cluster_id",
"execution_role_arn",
"release_label",
"job_driver",
)
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,
**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
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.",
DeprecationWarning,
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.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,
)
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}"
)
elif 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 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
"""
[docs] template_fields: Sequence[str] = ("job_flow_overrides",)
[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,
**kwargs,
):
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
[docs] def execute(self, context: Context) -> str:
emr = EmrHook(
aws_conn_id=self.aws_conn_id, emr_conn_id=self.emr_conn_id, region_name=self.region_name
)
self.log.info(
"Creating JobFlow 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 = emr.create_job_flow(job_flow_overrides)
if not response["ResponseMetadata"]["HTTPStatusCode"] == 200:
raise AirflowException(f"JobFlow creation failed: {response}")
else:
job_flow_id = response["JobFlowId"]
self.log.info("JobFlow with id %s created", job_flow_id)
EmrClusterLink.persist(
context=context,
operator=self,
region_name=emr.conn_region_name,
aws_partition=emr.conn_partition,
job_flow_id=job_flow_id,
)
return 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,
)
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
"""
[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", **kwargs):
super().__init__(**kwargs)
self.job_flow_id = job_flow_id
self.aws_conn_id = aws_conn_id
[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,
)
self.log.info("Terminating JobFlow %s", self.job_flow_id)
response = emr.terminate_job_flows(JobFlowIds=[self.job_flow_id])
if not response["ResponseMetadata"]["HTTPStatusCode"] == 200:
raise AirflowException(f"JobFlow termination failed: {response}")
else:
self.log.info("JobFlow with id %s terminated", self.job_flow_id)
[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
: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
"""
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",
**kwargs,
):
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 {}
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):
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)
# This should be replaced with a boto waiter when available.
self.hook.waiter(
get_state_callable=self.hook.conn.get_application,
get_state_args={"applicationId": application_id},
parse_response=["application", "state"],
desired_state={"CREATED"},
failure_states=EmrServerlessHook.APPLICATION_FAILURE_STATES,
object_type="application",
action="created",
)
self.log.info("Starting application %s", application_id)
self.hook.conn.start_application(applicationId=application_id)
if self.wait_for_completion:
# This should be replaced with a boto waiter when available.
self.hook.waiter(
get_state_callable=self.hook.conn.get_application,
get_state_args={"applicationId": application_id},
parse_response=["application", "state"],
desired_state={"STARTED"},
failure_states=EmrServerlessHook.APPLICATION_FAILURE_STATES,
object_type="application",
action="started",
)
return 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.
: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.
"""
[docs] template_fields: Sequence[str] = (
"application_id",
"execution_role_arn",
"job_driver",
"configuration_overrides",
)
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,
**kwargs,
):
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 or self.config.pop("name", f"emr_serverless_job_airflow_{uuid4()}")
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) -> dict:
self.log.info("Starting job on Application: %s", self.application_id)
app_state = self.hook.conn.get_application(applicationId=self.application_id)["application"]["state"]
if app_state not in EmrServerlessHook.APPLICATION_SUCCESS_STATES:
self.hook.conn.start_application(applicationId=self.application_id)
self.hook.waiter(
get_state_callable=self.hook.conn.get_application,
get_state_args={"applicationId": self.application_id},
parse_response=["application", "state"],
desired_state={"STARTED"},
failure_states=EmrServerlessHook.APPLICATION_FAILURE_STATES,
object_type="application",
action="started",
)
response = self.hook.conn.start_job_run(
clientToken=self.client_request_token,
applicationId=self.application_id,
executionRoleArn=self.execution_role_arn,
jobDriver=self.job_driver,
configurationOverrides=self.configuration_overrides,
name=self.name,
**self.config,
)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"EMR serverless job failed to start: {response}")
self.log.info("EMR serverless job started: %s", response["jobRunId"])
if self.wait_for_completion:
# This should be replaced with a boto waiter when available.
self.hook.waiter(
get_state_callable=self.hook.conn.get_job_run,
get_state_args={
"applicationId": self.application_id,
"jobRunId": response["jobRunId"],
},
parse_response=["jobRun", "state"],
desired_state=EmrServerlessHook.JOB_SUCCESS_STATES,
failure_states=EmrServerlessHook.JOB_FAILURE_STATES,
object_type="job",
action="run",
)
return response["jobRunId"]
[docs]class EmrServerlessDeleteApplicationOperator(BaseOperator):
"""
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 start before returning. Default to True
:param aws_conn_id: AWS connection to use
"""
[docs] template_fields: Sequence[str] = ("application_id",)
def __init__(
self,
application_id: str,
wait_for_completion: bool = True,
aws_conn_id: str = "aws_default",
**kwargs,
):
self.aws_conn_id = aws_conn_id
self.application_id = application_id
self.wait_for_completion = wait_for_completion
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)
self.hook.conn.stop_application(applicationId=self.application_id)
# This should be replaced with a boto waiter when available.
self.hook.waiter(
get_state_callable=self.hook.conn.get_application,
get_state_args={
"applicationId": self.application_id,
},
parse_response=["application", "state"],
desired_state=EmrServerlessHook.APPLICATION_FAILURE_STATES,
failure_states=set(),
object_type="application",
action="stopped",
)
self.log.info("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.wait_for_completion:
# This should be replaced with a boto waiter when available.
self.hook.waiter(
get_state_callable=self.hook.conn.get_application,
get_state_args={"applicationId": self.application_id},
parse_response=["application", "state"],
desired_state={"TERMINATED"},
failure_states=EmrServerlessHook.APPLICATION_FAILURE_STATES,
object_type="application",
action="deleted",
)
self.log.info("EMR serverless application deleted")