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from __future__ import annotations
import os
import urllib.parse
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from botocore.exceptions import ClientError
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.glue import GlueDataQualityHook, GlueJobHook
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.links.glue import GlueJobRunDetailsLink
from airflow.providers.amazon.aws.operators.base_aws import AwsBaseOperator
from airflow.providers.amazon.aws.triggers.glue import (
GlueDataQualityRuleRecommendationRunCompleteTrigger,
GlueDataQualityRuleSetEvaluationRunCompleteTrigger,
GlueJobCompleteTrigger,
)
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class GlueJobOperator(BaseOperator):
"""
Create an AWS Glue Job.
AWS Glue is a serverless Spark ETL service for running Spark Jobs on the AWS
cloud. Language support: Python and Scala.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:GlueJobOperator`
:param job_name: unique job name per AWS Account
:param script_location: location of ETL script. Must be a local or S3 path
:param job_desc: job description details
:param concurrent_run_limit: The maximum number of concurrent runs allowed for a job
:param script_args: etl script arguments and AWS Glue arguments (templated)
:param retry_limit: The maximum number of times to retry this job if it fails
:param num_of_dpus: Number of AWS Glue DPUs to allocate to this Job.
:param region_name: aws region name (example: us-east-1)
:param s3_bucket: S3 bucket where logs and local etl script will be uploaded
:param iam_role_name: AWS IAM Role for Glue Job Execution. If set `iam_role_arn` must equal None.
:param iam_role_arn: AWS IAM ARN for Glue Job Execution. If set `iam_role_name` must equal None.
:param create_job_kwargs: Extra arguments for Glue Job Creation
:param run_job_kwargs: Extra arguments for Glue Job Run
: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)
:param verbose: If True, Glue Job Run logs show in the Airflow Task Logs. (default: False)
:param update_config: If True, Operator will update job configuration. (default: False)
:param replace_script_file: If True, the script file will be replaced in S3. (default: False)
:param stop_job_run_on_kill: If True, Operator will stop the job run when task is killed.
"""
[docs] template_fields: Sequence[str] = (
"job_name",
"script_location",
"script_args",
"create_job_kwargs",
"s3_bucket",
"iam_role_name",
"iam_role_arn",
)
[docs] template_ext: Sequence[str] = ()
[docs] template_fields_renderers = {
"script_args": "json",
"create_job_kwargs": "json",
}
def __init__(
self,
*,
job_name: str = "aws_glue_default_job",
job_desc: str = "AWS Glue Job with Airflow",
script_location: str | None = None,
concurrent_run_limit: int | None = None,
script_args: dict | None = None,
retry_limit: int = 0,
num_of_dpus: int | float | None = None,
aws_conn_id: str | None = "aws_default",
region_name: str | None = None,
s3_bucket: str | None = None,
iam_role_name: str | None = None,
iam_role_arn: str | None = None,
create_job_kwargs: dict | None = None,
run_job_kwargs: dict | None = None,
wait_for_completion: bool = True,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
verbose: bool = False,
replace_script_file: bool = False,
update_config: bool = False,
job_poll_interval: int | float = 6,
stop_job_run_on_kill: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.job_name = job_name
self.job_desc = job_desc
self.script_location = script_location
self.concurrent_run_limit = concurrent_run_limit or 1
self.script_args = script_args or {}
self.retry_limit = retry_limit
self.num_of_dpus = num_of_dpus
self.aws_conn_id = aws_conn_id
self.region_name = region_name
self.s3_bucket = s3_bucket
self.iam_role_name = iam_role_name
self.iam_role_arn = iam_role_arn
self.s3_protocol = "s3://"
self.s3_artifacts_prefix = "artifacts/glue-scripts/"
self.create_job_kwargs = create_job_kwargs
self.run_job_kwargs = run_job_kwargs or {}
self.wait_for_completion = wait_for_completion
self.verbose = verbose
self.update_config = update_config
self.replace_script_file = replace_script_file
self.deferrable = deferrable
self.job_poll_interval = job_poll_interval
self.stop_job_run_on_kill = stop_job_run_on_kill
self._job_run_id: str | None = None
@cached_property
[docs] def glue_job_hook(self) -> GlueJobHook:
if self.script_location is None:
s3_script_location = None
elif not self.script_location.startswith(self.s3_protocol):
s3_hook = S3Hook(aws_conn_id=self.aws_conn_id)
script_name = os.path.basename(self.script_location)
s3_hook.load_file(
self.script_location,
self.s3_artifacts_prefix + script_name,
bucket_name=self.s3_bucket,
replace=self.replace_script_file,
)
s3_script_location = f"s3://{self.s3_bucket}/{self.s3_artifacts_prefix}{script_name}"
else:
s3_script_location = self.script_location
return GlueJobHook(
job_name=self.job_name,
desc=self.job_desc,
concurrent_run_limit=self.concurrent_run_limit,
script_location=s3_script_location,
retry_limit=self.retry_limit,
num_of_dpus=self.num_of_dpus,
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
s3_bucket=self.s3_bucket,
iam_role_name=self.iam_role_name,
iam_role_arn=self.iam_role_arn,
create_job_kwargs=self.create_job_kwargs,
update_config=self.update_config,
job_poll_interval=self.job_poll_interval,
)
[docs] def execute(self, context: Context):
"""
Execute AWS Glue Job from Airflow.
:return: the current Glue job ID.
"""
self.log.info(
"Initializing AWS Glue Job: %s. Wait for completion: %s",
self.job_name,
self.wait_for_completion,
)
glue_job_run = self.glue_job_hook.initialize_job(self.script_args, self.run_job_kwargs)
self._job_run_id = glue_job_run["JobRunId"]
glue_job_run_url = GlueJobRunDetailsLink.format_str.format(
aws_domain=GlueJobRunDetailsLink.get_aws_domain(self.glue_job_hook.conn_partition),
region_name=self.glue_job_hook.conn_region_name,
job_name=urllib.parse.quote(self.job_name, safe=""),
job_run_id=self._job_run_id,
)
GlueJobRunDetailsLink.persist(
context=context,
operator=self,
region_name=self.glue_job_hook.conn_region_name,
aws_partition=self.glue_job_hook.conn_partition,
job_name=urllib.parse.quote(self.job_name, safe=""),
job_run_id=self._job_run_id,
)
self.log.info("You can monitor this Glue Job run at: %s", glue_job_run_url)
if self.deferrable:
self.defer(
trigger=GlueJobCompleteTrigger(
job_name=self.job_name,
run_id=self._job_run_id,
verbose=self.verbose,
aws_conn_id=self.aws_conn_id,
job_poll_interval=self.job_poll_interval,
),
method_name="execute_complete",
)
elif self.wait_for_completion:
glue_job_run = self.glue_job_hook.job_completion(self.job_name, self._job_run_id, self.verbose)
self.log.info(
"AWS Glue Job: %s status: %s. Run Id: %s",
self.job_name,
glue_job_run["JobRunState"],
self._job_run_id,
)
else:
self.log.info("AWS Glue Job: %s. Run Id: %s", self.job_name, self._job_run_id)
return self._job_run_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(f"Error in glue job: {event}")
return event["value"]
[docs] def on_kill(self):
"""Cancel the running AWS Glue Job."""
if self.stop_job_run_on_kill:
self.log.info("Stopping AWS Glue Job: %s. Run Id: %s", self.job_name, self._job_run_id)
response = self.glue_job_hook.conn.batch_stop_job_run(
JobName=self.job_name,
JobRunIds=[self._job_run_id],
)
if not response["SuccessfulSubmissions"]:
self.log.error("Failed to stop AWS Glue Job: %s. Run Id: %s", self.job_name, self._job_run_id)
[docs]class GlueDataQualityOperator(AwsBaseOperator[GlueDataQualityHook]):
"""
Creates a data quality ruleset with DQDL rules applied to a specified Glue table.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:GlueDataQualityOperator`
:param name: A unique name for the data quality ruleset.
:param ruleset: A Data Quality Definition Language (DQDL) ruleset.
For more information, see the Glue developer guide.
:param description: A description of the data quality ruleset.
:param update_rule_set: To update existing ruleset, Set this flag to True. (default: False)
:param data_quality_ruleset_kwargs: Extra arguments for RuleSet.
: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 or not 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] aws_hook_class = GlueDataQualityHook
[docs] template_fields: Sequence[str] = ("name", "ruleset", "description", "data_quality_ruleset_kwargs")
[docs] template_fields_renderers = {
"data_quality_ruleset_kwargs": "json",
}
def __init__(
self,
*,
name: str,
ruleset: str,
description: str = "AWS Glue Data Quality Rule Set With Airflow",
update_rule_set: bool = False,
data_quality_ruleset_kwargs: dict | None = None,
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.name = name
self.ruleset = ruleset.strip()
self.description = description
self.update_rule_set = update_rule_set
self.data_quality_ruleset_kwargs = data_quality_ruleset_kwargs or {}
self.aws_conn_id = aws_conn_id
[docs] def execute(self, context: Context):
self.validate_inputs()
config = {
"Name": self.name,
"Ruleset": self.ruleset,
"Description": self.description,
**self.data_quality_ruleset_kwargs,
}
try:
if self.update_rule_set:
self.hook.conn.update_data_quality_ruleset(**config)
self.log.info("AWS Glue data quality ruleset updated successfully")
else:
self.hook.conn.create_data_quality_ruleset(**config)
self.log.info("AWS Glue data quality ruleset created successfully")
except ClientError as error:
raise AirflowException(
f"AWS Glue data quality ruleset failed: {error.response['Error']['Message']}"
)
[docs]class GlueDataQualityRuleSetEvaluationRunOperator(AwsBaseOperator[GlueDataQualityHook]):
"""
Evaluate a ruleset against a data source (Glue table).
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:GlueDataQualityRuleSetEvaluationRunOperator`
:param datasource: The data source (Glue table) associated with this run. (templated)
:param role: IAM role supplied for job execution. (templated)
:param rule_set_names: A list of ruleset names for evaluation. (templated)
:param number_of_workers: The number of G.1X workers to be used in the run. (default: 5)
:param timeout: The timeout for a run in minutes. This is the maximum time that a run can consume resources
before it is terminated and enters TIMEOUT status. (default: 2,880)
: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 rule_set_evaluation_run_kwargs: Extra arguments for evaluation run. (templated)
:param wait_for_completion: Whether to wait for job to stop. (default: True)
:param waiter_delay: Time in seconds to wait between status checks. (default: 60)
:param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 20)
:param deferrable: If True, the operator will wait asynchronously for the job to stop.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
: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 or not 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] aws_hook_class = GlueDataQualityHook
[docs] template_fields: Sequence[str] = (
"datasource",
"role",
"rule_set_names",
"rule_set_evaluation_run_kwargs",
)
[docs] template_fields_renderers = {"datasource": "json", "rule_set_evaluation_run_kwargs": "json"}
def __init__(
self,
*,
datasource: dict,
role: str,
rule_set_names: list[str],
number_of_workers: int = 5,
timeout: int = 2880,
verify_result_status: bool = True,
show_results: bool = True,
rule_set_evaluation_run_kwargs: dict[str, Any] | None = None,
wait_for_completion: bool = True,
waiter_delay: int = 60,
waiter_max_attempts: int = 20,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.datasource = datasource
self.role = role
self.rule_set_names = rule_set_names
self.number_of_workers = number_of_workers
self.timeout = timeout
self.verify_result_status = verify_result_status
self.show_results = show_results
self.rule_set_evaluation_run_kwargs = rule_set_evaluation_run_kwargs or {}
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
self.aws_conn_id = aws_conn_id
[docs] def execute(self, context: Context) -> str:
self.validate_inputs()
self.log.info(
"Submitting AWS Glue data quality ruleset evaluation run for RulesetNames %s", self.rule_set_names
)
response = self.hook.conn.start_data_quality_ruleset_evaluation_run(
DataSource=self.datasource,
Role=self.role,
NumberOfWorkers=self.number_of_workers,
Timeout=self.timeout,
RulesetNames=self.rule_set_names,
**self.rule_set_evaluation_run_kwargs,
)
evaluation_run_id = response["RunId"]
message_description = (
f"AWS Glue data quality ruleset evaluation run RunId: {evaluation_run_id} to complete."
)
if self.deferrable:
self.log.info("Deferring %s", message_description)
self.defer(
trigger=GlueDataQualityRuleSetEvaluationRunCompleteTrigger(
evaluation_run_id=response["RunId"],
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
),
method_name="execute_complete",
)
elif self.wait_for_completion:
self.log.info("Waiting for %s", message_description)
self.hook.get_waiter("data_quality_ruleset_evaluation_run_complete").wait(
RunId=evaluation_run_id,
WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts},
)
self.log.info(
"AWS Glue data quality ruleset evaluation run completed RunId: %s", evaluation_run_id
)
self.hook.validate_evaluation_run_results(
evaluation_run_id=evaluation_run_id,
show_results=self.show_results,
verify_result_status=self.verify_result_status,
)
else:
self.log.info("AWS Glue data quality ruleset evaluation run runId: %s.", evaluation_run_id)
return evaluation_run_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(f"Error: AWS Glue data quality ruleset evaluation run: {event}")
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,
)
return event["evaluation_run_id"]
[docs]class GlueDataQualityRuleRecommendationRunOperator(AwsBaseOperator[GlueDataQualityHook]):
"""
Starts a recommendation run that is used to generate rules, Glue Data Quality analyzes the data and comes up with recommendations for a potential ruleset.
Recommendation runs are automatically deleted after 90 days.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:GlueDataQualityRuleRecommendationRunOperator`
:param datasource: The data source (Glue table) associated with this run. (templated)
:param role: IAM role supplied for job execution. (templated)
:param number_of_workers: The number of G.1X workers to be used in the run. (default: 5)
:param timeout: The timeout for a run in minutes. This is the maximum time that a run can consume resources
before it is terminated and enters TIMEOUT status. (default: 2,880)
:param show_results: Displays the recommended ruleset (a set of rules), when recommendation run completes. (default: True)
:param recommendation_run_kwargs: Extra arguments for recommendation run. (templated)
:param wait_for_completion: Whether to wait for job to stop. (default: True)
:param waiter_delay: Time in seconds to wait between status checks. (default: 60)
:param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 20)
:param deferrable: If True, the operator will wait asynchronously for the job to stop.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
: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 or not 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] aws_hook_class = GlueDataQualityHook
[docs] template_fields: Sequence[str] = (
"datasource",
"role",
"recommendation_run_kwargs",
)
[docs] template_fields_renderers = {"datasource": "json", "recommendation_run_kwargs": "json"}
def __init__(
self,
*,
datasource: dict,
role: str,
number_of_workers: int = 5,
timeout: int = 2880,
show_results: bool = True,
recommendation_run_kwargs: dict[str, Any] | None = None,
wait_for_completion: bool = True,
waiter_delay: int = 60,
waiter_max_attempts: int = 20,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.datasource = datasource
self.role = role
self.number_of_workers = number_of_workers
self.timeout = timeout
self.show_results = show_results
self.recommendation_run_kwargs = recommendation_run_kwargs or {}
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
self.aws_conn_id = aws_conn_id
[docs] def execute(self, context: Context) -> str:
glue_table = self.datasource.get("GlueTable", {})
if not glue_table.get("DatabaseName") or not glue_table.get("TableName"):
raise AttributeError("DataSource glue table must have DatabaseName and TableName")
self.log.info("Submitting AWS Glue data quality recommendation run with %s", self.datasource)
try:
response = self.hook.conn.start_data_quality_rule_recommendation_run(
DataSource=self.datasource,
Role=self.role,
NumberOfWorkers=self.number_of_workers,
Timeout=self.timeout,
**self.recommendation_run_kwargs,
)
except ClientError as error:
raise AirflowException(
f"AWS Glue data quality recommendation run failed: {error.response['Error']['Message']}"
)
recommendation_run_id = response["RunId"]
message_description = (
f"AWS Glue data quality recommendation run RunId: {recommendation_run_id} to complete."
)
if self.deferrable:
self.log.info("Deferring %s", message_description)
self.defer(
trigger=GlueDataQualityRuleRecommendationRunCompleteTrigger(
recommendation_run_id=recommendation_run_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
),
method_name="execute_complete",
)
elif self.wait_for_completion:
self.log.info("Waiting for %s", message_description)
self.hook.get_waiter("data_quality_rule_recommendation_run_complete").wait(
RunId=recommendation_run_id,
WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts},
)
self.log.info(
"AWS Glue data quality recommendation run completed RunId: %s", recommendation_run_id
)
if self.show_results:
self.hook.log_recommendation_results(run_id=recommendation_run_id)
else:
self.log.info(message_description)
return recommendation_run_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(f"Error: AWS Glue data quality rule recommendation run: {event}")
if self.show_results:
self.hook.log_recommendation_results(run_id=event["recommendation_run_id"])
return event["recommendation_run_id"]