Source code for airflow.providers.amazon.aws.operators.glue_databrew

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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}

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