Source code for airflow.providers.amazon.aws.operators.glue_databrew
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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.models import BaseOperator
from airflow.providers.amazon.aws.hooks.glue_databrew import GlueDataBrewHook
from airflow.providers.amazon.aws.triggers.glue_databrew import GlueDataBrewJobCompleteTrigger
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class GlueDataBrewStartJobOperator(BaseOperator):
"""
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 delay: Time in seconds to wait between status checks. Default is 30.
:return: dictionary with key run_id and value of the resulting job's run_id.
"""
[docs] template_fields: Sequence[str] = (
"job_name",
"wait_for_completion",
"delay",
"deferrable",
)
def __init__(
self,
job_name: str,
wait_for_completion: bool = True,
delay: int = 30,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
aws_conn_id: str | None = "aws_default",
**kwargs,
):
super().__init__(**kwargs)
self.job_name = job_name
self.wait_for_completion = wait_for_completion
self.deferrable = deferrable
self.delay = delay
self.aws_conn_id = aws_conn_id
@cached_property
[docs] def hook(self) -> GlueDataBrewHook:
return GlueDataBrewHook(aws_conn_id=self.aws_conn_id)
[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(
aws_conn_id=self.aws_conn_id, job_name=self.job_name, run_id=run_id, delay=self.delay
),
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.delay, run_id=run_id)
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)
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}