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from __future__ import annotations
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowSkipException
from airflow.providers.amazon.aws.hooks.glue import GlueDataQualityHook, GlueJobHook
from airflow.providers.amazon.aws.sensors.base_aws import AwsBaseSensor
from airflow.providers.amazon.aws.triggers.glue import (
GlueDataQualityRuleRecommendationRunCompleteTrigger,
GlueDataQualityRuleSetEvaluationRunCompleteTrigger,
)
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
from airflow.providers.amazon.aws.utils.mixins import aws_template_fields
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class GlueJobSensor(BaseSensorOperator):
"""
Waits for an AWS Glue Job to reach any of the status below.
'FAILED', 'STOPPED', 'SUCCEEDED'
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:GlueJobSensor`
:param job_name: The AWS Glue Job unique name
:param run_id: The AWS Glue current running job identifier
:param verbose: If True, more Glue Job Run logs show in the Airflow Task Logs. (default: False)
"""
[docs] template_fields: Sequence[str] = ("job_name", "run_id")
def __init__(
self,
*,
job_name: str,
run_id: str,
verbose: bool = False,
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.job_name = job_name
self.run_id = run_id
self.verbose = verbose
self.aws_conn_id = aws_conn_id
self.success_states: list[str] = ["SUCCEEDED"]
self.errored_states: list[str] = ["FAILED", "STOPPED", "TIMEOUT"]
self.next_log_tokens = GlueJobHook.LogContinuationTokens()
@cached_property
[docs] def hook(self):
return GlueJobHook(aws_conn_id=self.aws_conn_id)
[docs] def poke(self, context: Context):
self.log.info("Poking for job run status :for Glue Job %s and ID %s", self.job_name, self.run_id)
job_state = self.hook.get_job_state(job_name=self.job_name, run_id=self.run_id)
try:
if job_state in self.success_states:
self.log.info("Exiting Job %s Run State: %s", self.run_id, job_state)
return True
elif job_state in self.errored_states:
job_error_message = "Exiting Job %s Run State: %s", self.run_id, job_state
self.log.info(job_error_message)
# TODO: remove this if block when min_airflow_version is set to higher than 2.7.1
if self.soft_fail:
raise AirflowSkipException(job_error_message)
raise AirflowException(job_error_message)
else:
return False
finally:
if self.verbose:
self.hook.print_job_logs(
job_name=self.job_name,
run_id=self.run_id,
continuation_tokens=self.next_log_tokens,
)
[docs]class GlueDataQualityRuleSetEvaluationRunSensor(AwsBaseSensor[GlueDataQualityHook]):
"""
Waits for an AWS Glue data quality ruleset evaluation run to reach any of the status below.
'FAILED', 'STOPPED', 'STOPPING', 'TIMEOUT', 'SUCCEEDED'
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:GlueDataQualityRuleSetEvaluationRunSensor`
:param evaluation_run_id: The AWS Glue data quality ruleset evaluation run identifier.
:param verify_result_status: Validate all the ruleset rules evaluation run results,
If any of the rule status is Fail or Error then an exception is thrown. (default: True)
:param show_results: Displays all the ruleset rules evaluation run results. (default: True)
:param deferrable: If True, the sensor will operate in deferrable mode. This mode requires aiobotocore
module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
:param poke_interval: Polling period in seconds to check for the status of the job. (default: 120)
:param max_retries: Number of times before returning the current state. (default: 60)
: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 region_name: AWS region_name. If not specified then the default boto3 behaviour is used.
:param verify: Whether to verify SSL certificates. See:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
:param botocore_config: Configuration dictionary (key-values) for botocore client. See:
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
"""
[docs] SUCCESS_STATES = ("SUCCEEDED",)
[docs] FAILURE_STATES = ("FAILED", "STOPPED", "STOPPING", "TIMEOUT")
[docs] aws_hook_class = GlueDataQualityHook
[docs] template_fields: Sequence[str] = aws_template_fields("evaluation_run_id")
def __init__(
self,
*,
evaluation_run_id: str,
show_results: bool = True,
verify_result_status: bool = True,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poke_interval: int = 120,
max_retries: int = 60,
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.evaluation_run_id = evaluation_run_id
self.show_results = show_results
self.verify_result_status = verify_result_status
self.aws_conn_id = aws_conn_id
self.max_retries = max_retries
self.poke_interval = poke_interval
self.deferrable = deferrable
[docs] def execute(self, context: Context) -> Any:
if self.deferrable:
self.defer(
trigger=GlueDataQualityRuleSetEvaluationRunCompleteTrigger(
evaluation_run_id=self.evaluation_run_id,
waiter_delay=int(self.poke_interval),
waiter_max_attempts=self.max_retries,
aws_conn_id=self.aws_conn_id,
),
method_name="execute_complete",
)
else:
super().execute(context=context)
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> None:
event = validate_execute_complete_event(event)
if event["status"] != "success":
message = f"Error: AWS Glue data quality ruleset evaluation run: {event}"
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
self.hook.validate_evaluation_run_results(
evaluation_run_id=event["evaluation_run_id"],
show_results=self.show_results,
verify_result_status=self.verify_result_status,
)
self.log.info("AWS Glue data quality ruleset evaluation run completed.")
[docs] def poke(self, context: Context):
self.log.info(
"Poking for AWS Glue data quality ruleset evaluation run RunId: %s", self.evaluation_run_id
)
response = self.hook.conn.get_data_quality_ruleset_evaluation_run(RunId=self.evaluation_run_id)
status = response.get("Status")
if status in self.SUCCESS_STATES:
self.hook.validate_evaluation_run_results(
evaluation_run_id=self.evaluation_run_id,
show_results=self.show_results,
verify_result_status=self.verify_result_status,
)
self.log.info(
"AWS Glue data quality ruleset evaluation run completed RunId: %s Run State: %s",
self.evaluation_run_id,
response["Status"],
)
return True
elif status in self.FAILURE_STATES:
job_error_message = (
f"Error: AWS Glue data quality ruleset evaluation run RunId: {self.evaluation_run_id} Run "
f"Status: {status}"
f": {response.get('ErrorString')}"
)
self.log.info(job_error_message)
# TODO: remove this if block when min_airflow_version is set to higher than 2.7.1
if self.soft_fail:
raise AirflowSkipException(job_error_message)
raise AirflowException(job_error_message)
else:
return False
[docs]class GlueDataQualityRuleRecommendationRunSensor(AwsBaseSensor[GlueDataQualityHook]):
"""
Waits for an AWS Glue data quality recommendation run to reach any of the status below.
'FAILED', 'STOPPED', 'STOPPING', 'TIMEOUT', 'SUCCEEDED'
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:GlueDataQualityRuleRecommendationRunSensor`
:param recommendation_run_id: The AWS Glue data quality rule recommendation run identifier.
:param show_results: Displays the recommended ruleset (a set of rules), when recommendation run completes. (default: True)
:param deferrable: If True, the sensor will operate in deferrable mode. This mode requires aiobotocore
module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
:param poke_interval: Polling period in seconds to check for the status of the job. (default: 120)
:param max_retries: Number of times before returning the current state. (default: 60)
: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 region_name: AWS region_name. If not specified then the default boto3 behaviour is used.
:param verify: Whether to verify SSL certificates. See:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
:param botocore_config: Configuration dictionary (key-values) for botocore client. See:
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
"""
[docs] SUCCESS_STATES = ("SUCCEEDED",)
[docs] FAILURE_STATES = ("FAILED", "STOPPED", "STOPPING", "TIMEOUT")
[docs] aws_hook_class = GlueDataQualityHook
[docs] template_fields: Sequence[str] = aws_template_fields("recommendation_run_id")
def __init__(
self,
*,
recommendation_run_id: str,
show_results: bool = True,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poke_interval: int = 120,
max_retries: int = 60,
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.recommendation_run_id = recommendation_run_id
self.show_results = show_results
self.deferrable = deferrable
self.poke_interval = poke_interval
self.max_retries = max_retries
self.aws_conn_id = aws_conn_id
[docs] def execute(self, context: Context) -> Any:
if self.deferrable:
self.defer(
trigger=GlueDataQualityRuleRecommendationRunCompleteTrigger(
recommendation_run_id=self.recommendation_run_id,
waiter_delay=int(self.poke_interval),
waiter_max_attempts=self.max_retries,
aws_conn_id=self.aws_conn_id,
),
method_name="execute_complete",
)
else:
super().execute(context=context)
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> None:
event = validate_execute_complete_event(event)
if event["status"] != "success":
message = f"Error: AWS Glue data quality recommendation run: {event}"
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
if self.show_results:
self.hook.log_recommendation_results(run_id=self.recommendation_run_id)
self.log.info("AWS Glue data quality recommendation run completed.")
[docs] def poke(self, context: Context) -> bool:
self.log.info(
"Poking for AWS Glue data quality recommendation run RunId: %s", self.recommendation_run_id
)
response = self.hook.conn.get_data_quality_rule_recommendation_run(RunId=self.recommendation_run_id)
status = response.get("Status")
if status in self.SUCCESS_STATES:
if self.show_results:
self.hook.log_recommendation_results(run_id=self.recommendation_run_id)
self.log.info(
"AWS Glue data quality recommendation run completed RunId: %s Run State: %s",
self.recommendation_run_id,
response["Status"],
)
return True
elif status in self.FAILURE_STATES:
job_error_message = (
f"Error: AWS Glue data quality recommendation run RunId: {self.recommendation_run_id} Run "
f"Status: {status}"
f": {response.get('ErrorString')}"
)
self.log.info(job_error_message)
# TODO: remove this if block when min_airflow_version is set to higher than 2.7.1
if self.soft_fail:
raise AirflowSkipException(job_error_message)
raise AirflowException(job_error_message)
else:
return False