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from __future__ import annotations
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.glue_databrew import GlueDataBrewHook
from airflow.providers.amazon.aws.operators.base_aws import AwsBaseOperator
from airflow.providers.amazon.aws.triggers.glue_databrew import GlueDataBrewJobCompleteTrigger
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
from airflow.providers.amazon.aws.utils.mixins import aws_template_fields
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class GlueDataBrewStartJobOperator(AwsBaseOperator[GlueDataBrewHook]):
"""
Start an AWS Glue DataBrew job.
AWS Glue DataBrew is a visual data preparation tool that makes it easier
for data analysts and data scientists to clean and normalize data
to prepare it for analytics and machine learning (ML).
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:GlueDataBrewStartJobOperator`
:param job_name: unique job name per AWS Account
: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 waiter_delay: Time in seconds to wait between status checks. Default is 30.
:param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 60)
:return: dictionary with key run_id and value of the resulting job's run_id.
: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 = GlueDataBrewHook
[docs] template_fields: Sequence[str] = aws_template_fields(
"job_name",
"wait_for_completion",
"waiter_delay",
"waiter_max_attempts",
"deferrable",
)
def __init__(
self,
job_name: str,
wait_for_completion: bool = True,
delay: int | None = None,
waiter_delay: int = 30,
waiter_max_attempts: int = 60,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
super().__init__(**kwargs)
self.job_name = job_name
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
[docs] def execute(self, context: Context):
job = self.hook.conn.start_job_run(Name=self.job_name)
run_id = job["RunId"]
self.log.info("AWS Glue DataBrew Job: %s. Run Id: %s submitted.", self.job_name, run_id)
if self.deferrable:
self.log.info("Deferring job %s with run_id %s", self.job_name, run_id)
self.defer(
trigger=GlueDataBrewJobCompleteTrigger(
job_name=self.job_name,
run_id=run_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
verify=self.verify,
botocore_config=self.botocore_config,
),
method_name="execute_complete",
)
elif self.wait_for_completion:
self.log.info(
"Waiting for AWS Glue DataBrew Job: %s. Run Id: %s to complete.", self.job_name, run_id
)
status = self.hook.job_completion(
job_name=self.job_name,
delay=self.waiter_delay,
run_id=run_id,
max_attempts=self.waiter_max_attempts,
)
self.log.info("Glue DataBrew Job: %s status: %s", self.job_name, status)
return {"run_id": run_id}
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> dict[str, str]:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException("Error while running AWS Glue DataBrew job: %s", event)
run_id = event.get("run_id", "")
status = event.get("status", "")
self.log.info("AWS Glue DataBrew runID: %s completed with status: %s", run_id, status)
return {"run_id": run_id}