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from __future__ import annotations
import os.path
import urllib.parse
from typing import TYPE_CHECKING, Sequence
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.glue import GlueJobHook
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.links.glue import GlueJobRunDetailsLink
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class GlueJobOperator(BaseOperator):
"""
Creates 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
: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 or not wait for job run completion. (default: True)
: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)
"""
[docs] template_fields: Sequence[str] = (
"job_name",
"script_location",
"script_args",
"create_job_kwargs",
"s3_bucket",
"iam_role_name",
)
[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 = "aws_default",
region_name: str | None = None,
s3_bucket: str | None = None,
iam_role_name: str | None = None,
create_job_kwargs: dict | None = None,
run_job_kwargs: dict | None = None,
wait_for_completion: bool = True,
verbose: bool = False,
update_config: 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.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
[docs] def execute(self, context: Context):
"""
Executes AWS Glue Job from Airflow
:return: the id of the current glue job.
"""
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
)
s3_script_location = f"s3://{self.s3_bucket}/{self.s3_artifacts_prefix}{script_name}"
else:
s3_script_location = self.script_location
glue_job = 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,
create_job_kwargs=self.create_job_kwargs,
update_config=self.update_config,
)
self.log.info(
"Initializing AWS Glue Job: %s. Wait for completion: %s",
self.job_name,
self.wait_for_completion,
)
glue_job_run = glue_job.initialize_job(self.script_args, self.run_job_kwargs)
glue_job_run_url = GlueJobRunDetailsLink.format_str.format(
aws_domain=GlueJobRunDetailsLink.get_aws_domain(glue_job.conn_partition),
region_name=glue_job.conn_region_name,
job_name=urllib.parse.quote(self.job_name, safe=""),
job_run_id=glue_job_run["JobRunId"],
)
GlueJobRunDetailsLink.persist(
context=context,
operator=self,
region_name=glue_job.conn_region_name,
aws_partition=glue_job.conn_partition,
job_name=urllib.parse.quote(self.job_name, safe=""),
job_run_id=glue_job_run["JobRunId"],
)
self.log.info("You can monitor this Glue Job run at: %s", glue_job_run_url)
if self.wait_for_completion:
glue_job_run = glue_job.job_completion(self.job_name, glue_job_run["JobRunId"], self.verbose)
self.log.info(
"AWS Glue Job: %s status: %s. Run Id: %s",
self.job_name,
glue_job_run["JobRunState"],
glue_job_run["JobRunId"],
)
else:
self.log.info("AWS Glue Job: %s. Run Id: %s", self.job_name, glue_job_run["JobRunId"])
return glue_job_run["JobRunId"]
