Source code for airflow.providers.amazon.aws.operators.glue
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import os.path
from typing import Optional
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.glue import AwsGlueJobHook
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
[docs]class AwsGlueJobOperator(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
:param job_name: unique job name per AWS Account
:type job_name: Optional[str]
:param script_location: location of ETL script. Must be a local or S3 path
:type script_location: Optional[str]
:param job_desc: job description details
:type job_desc: Optional[str]
:param concurrent_run_limit: The maximum number of concurrent runs allowed for a job
:type concurrent_run_limit: Optional[int]
:param script_args: etl script arguments and AWS Glue arguments (templated)
:type script_args: dict
:param retry_limit: The maximum number of times to retry this job if it fails
:type retry_limit: Optional[int]
:param num_of_dpus: Number of AWS Glue DPUs to allocate to this Job.
:type num_of_dpus: int
:param region_name: aws region name (example: us-east-1)
:type region_name: str
:param s3_bucket: S3 bucket where logs and local etl script will be uploaded
:type s3_bucket: Optional[str]
:param iam_role_name: AWS IAM Role for Glue Job Execution
:type iam_role_name: Optional[str]
:param create_job_kwargs: Extra arguments for Glue Job Creation
:type create_job_kwargs: Optional[dict]
"""
[docs] template_fields = ('script_args',)
[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: Optional[str] = None,
concurrent_run_limit: Optional[int] = None,
script_args: Optional[dict] = None,
retry_limit: Optional[int] = None,
num_of_dpus: int = 6,
aws_conn_id: str = 'aws_default',
region_name: Optional[str] = None,
s3_bucket: Optional[str] = None,
iam_role_name: Optional[str] = None,
create_job_kwargs: Optional[dict] = None,
**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
[docs] def execute(self, context):
"""
Executes AWS Glue Job from Airflow
:return: the id of the current glue job.
"""
if self.script_location and 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 = AwsGlueJobHook(
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,
)
self.log.info("Initializing AWS Glue Job: %s", self.job_name)
glue_job_run = glue_job.initialize_job(self.script_args)
glue_job_run = glue_job.job_completion(self.job_name, glue_job_run['JobRunId'])
self.log.info(
"AWS Glue Job: %s status: %s. Run Id: %s",
self.job_name,
glue_job_run['JobRunState'],
glue_job_run['JobRunId'],
)
return glue_job_run['JobRunId']