#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, Sequence
from deprecated import deprecated
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.emr import EmrContainerHook, EmrHook, EmrServerlessHook
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.links.emr import EmrLogsLink
from airflow.sensors.base import BaseSensorOperator, poke_mode_only
if TYPE_CHECKING:
from airflow.utils.context import Context
from airflow.compat.functools import cached_property
[docs]class EmrBaseSensor(BaseSensorOperator):
"""
Contains general sensor behavior for EMR.
Subclasses should implement following methods:
- ``get_emr_response()``
- ``state_from_response()``
- ``failure_message_from_response()``
Subclasses should set ``target_states`` and ``failed_states`` fields.
:param aws_conn_id: aws connection to use
"""
def __init__(self, *, aws_conn_id: str = "aws_default", **kwargs):
super().__init__(**kwargs)
self.aws_conn_id = aws_conn_id
self.target_states: Iterable[str] = [] # will be set in subclasses
self.failed_states: Iterable[str] = [] # will be set in subclasses
@deprecated(reason="use `hook` property instead.")
[docs] def get_hook(self) -> EmrHook:
return self.hook
@cached_property
[docs] def hook(self) -> EmrHook:
return EmrHook(aws_conn_id=self.aws_conn_id)
[docs] def poke(self, context: Context):
response = self.get_emr_response(context=context)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
self.log.info("Bad HTTP response: %s", response)
return False
state = self.state_from_response(response)
self.log.info("Job flow currently %s", state)
if state in self.target_states:
return True
if state in self.failed_states:
raise AirflowException(f"EMR job failed: {self.failure_message_from_response(response)}")
return False
[docs] def get_emr_response(self, context: Context) -> dict[str, Any]:
"""
Make an API call with boto3 and get response.
:return: response
"""
raise NotImplementedError("Please implement get_emr_response() in subclass")
@staticmethod
[docs] def state_from_response(response: dict[str, Any]) -> str:
"""
Get state from boto3 response.
:param response: response from AWS API
:return: state
"""
raise NotImplementedError("Please implement state_from_response() in subclass")
@staticmethod
[docs] def failure_message_from_response(response: dict[str, Any]) -> str | None:
"""
Get state from boto3 response.
:param response: response from AWS API
:return: failure message
"""
raise NotImplementedError("Please implement failure_message_from_response() in subclass")
[docs]class EmrServerlessJobSensor(BaseSensorOperator):
"""
Asks for the state of the job run until it reaches a failure state or success state.
If the job run fails, the task will fail.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:EmrServerlessJobSensor`
:param application_id: application_id to check the state of
:param job_run_id: job_run_id to check the state of
:param target_states: a set of states to wait for, defaults to 'SUCCESS'
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
"""
[docs] template_fields: Sequence[str] = (
"application_id",
"job_run_id",
)
def __init__(
self,
*,
application_id: str,
job_run_id: str,
target_states: set | frozenset = frozenset(EmrServerlessHook.JOB_SUCCESS_STATES),
aws_conn_id: str = "aws_default",
**kwargs: Any,
) -> None:
self.aws_conn_id = aws_conn_id
self.target_states = target_states
self.application_id = application_id
self.job_run_id = job_run_id
super().__init__(**kwargs)
[docs] def poke(self, context: Context) -> bool:
response = self.hook.conn.get_job_run(applicationId=self.application_id, jobRunId=self.job_run_id)
state = response["jobRun"]["state"]
if state in EmrServerlessHook.JOB_FAILURE_STATES:
failure_message = f"EMR Serverless job failed: {self.failure_message_from_response(response)}"
raise AirflowException(failure_message)
return state in self.target_states
@cached_property
[docs] def hook(self) -> EmrServerlessHook:
"""Create and return an EmrServerlessHook"""
return EmrServerlessHook(aws_conn_id=self.aws_conn_id)
@staticmethod
[docs] def failure_message_from_response(response: dict[str, Any]) -> str | None:
"""
Get failure message from response dictionary.
:param response: response from AWS API
:return: failure message
"""
return response["jobRun"]["stateDetails"]
[docs]class EmrServerlessApplicationSensor(BaseSensorOperator):
"""
Asks for the state of the application until it reaches a failure state or success state.
If the application fails, the task will fail.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:EmrServerlessApplicationSensor`
:param application_id: application_id to check the state of
:param target_states: a set of states to wait for, defaults to {'CREATED', 'STARTED'}
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
"""
[docs] template_fields: Sequence[str] = ("application_id",)
def __init__(
self,
*,
application_id: str,
target_states: set | frozenset = frozenset(EmrServerlessHook.APPLICATION_SUCCESS_STATES),
aws_conn_id: str = "aws_default",
**kwargs: Any,
) -> None:
self.aws_conn_id = aws_conn_id
self.target_states = target_states
self.application_id = application_id
super().__init__(**kwargs)
[docs] def poke(self, context: Context) -> bool:
response = self.hook.conn.get_application(applicationId=self.application_id)
state = response["application"]["state"]
if state in EmrServerlessHook.APPLICATION_FAILURE_STATES:
failure_message = f"EMR Serverless job failed: {self.failure_message_from_response(response)}"
raise AirflowException(failure_message)
return state in self.target_states
@cached_property
[docs] def hook(self) -> EmrServerlessHook:
"""Create and return an EmrServerlessHook"""
return EmrServerlessHook(aws_conn_id=self.aws_conn_id)
@staticmethod
[docs] def failure_message_from_response(response: dict[str, Any]) -> str | None:
"""
Get failure message from response dictionary.
:param response: response from AWS API
:return: failure message
"""
return response["application"]["stateDetails"]
[docs]class EmrContainerSensor(BaseSensorOperator):
"""
Asks for the state of the job run until it reaches a failure state or success state.
If the job run fails, the task will fail.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:EmrContainerSensor`
:param job_id: job_id to check the state of
:param max_retries: Number of times to poll for query state before
returning the current state, defaults to None
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param poll_interval: Time in seconds to wait between two consecutive call to
check query status on athena, defaults to 10
"""
)
[docs] FAILURE_STATES = (
"FAILED",
"CANCELLED",
"CANCEL_PENDING",
)
[docs] SUCCESS_STATES = ("COMPLETED",)
[docs] template_fields: Sequence[str] = ("virtual_cluster_id", "job_id")
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
virtual_cluster_id: str,
job_id: str,
max_retries: int | None = None,
aws_conn_id: str = "aws_default",
poll_interval: int = 10,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.aws_conn_id = aws_conn_id
self.virtual_cluster_id = virtual_cluster_id
self.job_id = job_id
self.poll_interval = poll_interval
self.max_retries = max_retries
[docs] def poke(self, context: Context) -> bool:
state = self.hook.poll_query_status(
self.job_id,
max_polling_attempts=self.max_retries,
poll_interval=self.poll_interval,
)
if state in self.FAILURE_STATES:
raise AirflowException("EMR Containers sensor failed")
if state in self.INTERMEDIATE_STATES:
return False
return True
@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]class EmrNotebookExecutionSensor(EmrBaseSensor):
"""
Polls the state of the EMR notebook execution until it reaches
any of the target states.
If a failure state is reached, the sensor throws an error, and fails the task.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:EmrNotebookExecutionSensor`
:param notebook_execution_id: Unique id of the notebook execution to be poked.
:target_states: the states the sensor will wait for the execution to reach.
Default target_states is ``FINISHED``.
:failed_states: if the execution reaches any of the failed_states, the sensor will fail.
Default failed_states is ``FAILED``.
"""
[docs] template_fields: Sequence[str] = ("notebook_execution_id",)
[docs] FAILURE_STATES = {"FAILED"}
[docs] COMPLETED_STATES = {"FINISHED"}
def __init__(
self,
notebook_execution_id: str,
target_states: Iterable[str] | None = None,
failed_states: Iterable[str] | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.notebook_execution_id = notebook_execution_id
self.target_states = target_states or self.COMPLETED_STATES
self.failed_states = failed_states or self.FAILURE_STATES
[docs] def get_emr_response(self, context: Context) -> dict[str, Any]:
emr_client = self.hook.conn
self.log.info("Poking notebook %s", self.notebook_execution_id)
return emr_client.describe_notebook_execution(NotebookExecutionId=self.notebook_execution_id)
@staticmethod
[docs] def state_from_response(response: dict[str, Any]) -> str:
"""
Make an API call with boto3 and get cluster-level details.
.. seealso::
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr.html#EMR.Client.describe_cluster
:return: response
"""
return response["NotebookExecution"]["Status"]
@staticmethod
[docs] def failure_message_from_response(response: dict[str, Any]) -> str | None:
"""
Get failure message from response dictionary.
:param response: response from AWS API
:return: failure message
"""
cluster_status = response["NotebookExecution"]
return cluster_status.get("LastStateChangeReason", None)
[docs]class EmrJobFlowSensor(EmrBaseSensor):
"""
Asks for the state of the EMR JobFlow (Cluster) until it reaches
any of the target states.
If it fails the sensor errors, failing the task.
With the default target states, sensor waits cluster to be terminated.
When target_states is set to ['RUNNING', 'WAITING'] sensor waits
until job flow to be ready (after 'STARTING' and 'BOOTSTRAPPING' states)
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:EmrJobFlowSensor`
:param job_flow_id: job_flow_id to check the state of
:param target_states: the target states, sensor waits until
job flow reaches any of these states
:param failed_states: the failure states, sensor fails when
job flow reaches any of these states
"""
[docs] template_fields: Sequence[str] = ("job_flow_id", "target_states", "failed_states")
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
job_flow_id: str,
target_states: Iterable[str] | None = None,
failed_states: Iterable[str] | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.job_flow_id = job_flow_id
self.target_states = target_states or ["TERMINATED"]
self.failed_states = failed_states or ["TERMINATED_WITH_ERRORS"]
[docs] def get_emr_response(self, context: Context) -> dict[str, Any]:
"""
Make an API call with boto3 and get cluster-level details.
.. seealso::
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr.html#EMR.Client.describe_cluster
:return: response
"""
emr_client = self.hook.conn
self.log.info("Poking cluster %s", self.job_flow_id)
response = emr_client.describe_cluster(ClusterId=self.job_flow_id)
log_uri = S3Hook.parse_s3_url(response["Cluster"]["LogUri"])
EmrLogsLink.persist(
context=context,
operator=self,
region_name=self.hook.conn_region_name,
aws_partition=self.hook.conn_partition,
job_flow_id=self.job_flow_id,
log_uri="/".join(log_uri),
)
return response
@staticmethod
[docs] def state_from_response(response: dict[str, Any]) -> str:
"""
Get state from response dictionary.
:param response: response from AWS API
:return: current state of the cluster
"""
return response["Cluster"]["Status"]["State"]
@staticmethod
[docs] def failure_message_from_response(response: dict[str, Any]) -> str | None:
"""
Get failure message from response dictionary.
:param response: response from AWS API
:return: failure message
"""
cluster_status = response["Cluster"]["Status"]
state_change_reason = cluster_status.get("StateChangeReason")
if state_change_reason:
return (
f"for code: {state_change_reason.get('Code', 'No code')} "
f"with message {state_change_reason.get('Message', 'Unknown')}"
)
return None
@poke_mode_only
[docs]class EmrStepSensor(EmrBaseSensor):
"""
Asks for the state of the step until it reaches any of the target states.
If it fails the sensor errors, failing the task.
With the default target states, sensor waits step to be completed.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:EmrStepSensor`
:param job_flow_id: job_flow_id which contains the step check the state of
:param step_id: step to check the state of
:param target_states: the target states, sensor waits until
step reaches any of these states
:param failed_states: the failure states, sensor fails when
step reaches any of these states
"""
[docs] template_fields: Sequence[str] = ("job_flow_id", "step_id", "target_states", "failed_states")
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
job_flow_id: str,
step_id: str,
target_states: Iterable[str] | None = None,
failed_states: Iterable[str] | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.job_flow_id = job_flow_id
self.step_id = step_id
self.target_states = target_states or ["COMPLETED"]
self.failed_states = failed_states or ["CANCELLED", "FAILED", "INTERRUPTED"]
[docs] def get_emr_response(self, context: Context) -> dict[str, Any]:
"""
Make an API call with boto3 and get details about the cluster step.
.. seealso::
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr.html#EMR.Client.describe_step
:return: response
"""
emr_client = self.hook.conn
self.log.info("Poking step %s on cluster %s", self.step_id, self.job_flow_id)
return emr_client.describe_step(ClusterId=self.job_flow_id, StepId=self.step_id)
@staticmethod
[docs] def state_from_response(response: dict[str, Any]) -> str:
"""
Get state from response dictionary.
:param response: response from AWS API
:return: execution state of the cluster step
"""
return response["Step"]["Status"]["State"]
@staticmethod
[docs] def failure_message_from_response(response: dict[str, Any]) -> str | None:
"""
Get failure message from response dictionary.
:param response: response from AWS API
:return: failure message
"""
fail_details = response["Step"]["Status"].get("FailureDetails")
if fail_details:
return (
f"for reason {fail_details.get('Reason')} "
f"with message {fail_details.get('Message')} and log file {fail_details.get('LogFile')}"
)
return None
