Source code for airflow.providers.amazon.aws.hooks.glue

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import time
from typing import Dict, List, Optional

from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook


[docs]class AwsGlueJobHook(AwsBaseHook): """ Interact with AWS Glue - create job, trigger, crawler :param s3_bucket: S3 bucket where logs and local etl script will be uploaded :type s3_bucket: Optional[str] :param job_name: unique job name per AWS account :type job_name: Optional[str] :param desc: job description :type desc: Optional[str] :param concurrent_run_limit: The maximum number of concurrent runs allowed for a job :type concurrent_run_limit: int :param script_location: path to etl script on s3 :type script_location: Optional[str] :param retry_limit: Maximum number of times to retry this job if it fails :type retry_limit: 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: 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] JOB_POLL_INTERVAL = 6 # polls job status after every JOB_POLL_INTERVAL seconds
def __init__( self, s3_bucket: Optional[str] = None, job_name: Optional[str] = None, desc: Optional[str] = None, concurrent_run_limit: int = 1, script_location: Optional[str] = None, retry_limit: int = 0, num_of_dpus: int = 10, iam_role_name: Optional[str] = None, create_job_kwargs: Optional[dict] = None, *args, **kwargs, ): self.job_name = job_name self.desc = desc self.concurrent_run_limit = concurrent_run_limit self.script_location = script_location self.retry_limit = retry_limit self.num_of_dpus = num_of_dpus self.s3_bucket = s3_bucket self.role_name = iam_role_name self.s3_glue_logs = 'logs/glue-logs/' self.create_job_kwargs = create_job_kwargs or {} kwargs['client_type'] = 'glue' super().__init__(*args, **kwargs)
[docs] def list_jobs(self) -> List: """:return: Lists of Jobs""" conn = self.get_conn() return conn.get_jobs()
[docs] def get_iam_execution_role(self) -> Dict: """:return: iam role for job execution""" iam_client = self.get_client_type('iam', self.region_name) try: glue_execution_role = iam_client.get_role(RoleName=self.role_name) self.log.info("Iam Role Name: %s", self.role_name) return glue_execution_role except Exception as general_error: self.log.error("Failed to create aws glue job, error: %s", general_error) raise
[docs] def initialize_job(self, script_arguments: Optional[dict] = None) -> Dict[str, str]: """ Initializes connection with AWS Glue to run job :return: """ glue_client = self.get_conn() script_arguments = script_arguments or {} try: job_name = self.get_or_create_glue_job() job_run = glue_client.start_job_run(JobName=job_name, Arguments=script_arguments) return job_run except Exception as general_error: self.log.error("Failed to run aws glue job, error: %s", general_error) raise
[docs] def get_job_state(self, job_name: str, run_id: str) -> str: """ Get state of the Glue job. The job state can be running, finished, failed, stopped or timeout. :param job_name: unique job name per AWS account :type job_name: str :param run_id: The job-run ID of the predecessor job run :type run_id: str :return: State of the Glue job """ glue_client = self.get_conn() job_run = glue_client.get_job_run(JobName=job_name, RunId=run_id, PredecessorsIncluded=True) job_run_state = job_run['JobRun']['JobRunState'] return job_run_state
[docs] def job_completion(self, job_name: str, run_id: str) -> Dict[str, str]: """ Waits until Glue job with job_name completes or fails and return final state if finished. Raises AirflowException when the job failed :param job_name: unique job name per AWS account :type job_name: str :param run_id: The job-run ID of the predecessor job run :type run_id: str :return: Dict of JobRunState and JobRunId """ failed_states = ['FAILED', 'TIMEOUT'] finished_states = ['SUCCEEDED', 'STOPPED'] while True: job_run_state = self.get_job_state(job_name, run_id) if job_run_state in finished_states: self.log.info("Exiting Job %s Run State: %s", run_id, job_run_state) return {'JobRunState': job_run_state, 'JobRunId': run_id} if job_run_state in failed_states: job_error_message = "Exiting Job " + run_id + " Run State: " + job_run_state self.log.info(job_error_message) raise AirflowException(job_error_message) else: self.log.info( "Polling for AWS Glue Job %s current run state with status %s", job_name, job_run_state ) time.sleep(self.JOB_POLL_INTERVAL)
[docs] def get_or_create_glue_job(self) -> str: """ Creates(or just returns) and returns the Job name :return:Name of the Job """ glue_client = self.get_conn() try: get_job_response = glue_client.get_job(JobName=self.job_name) self.log.info("Job Already exist. Returning Name of the job") return get_job_response['Job']['Name'] except glue_client.exceptions.EntityNotFoundException: self.log.info("Job doesn't exist. Now creating and running AWS Glue Job") if self.s3_bucket is None: raise AirflowException('Could not initialize glue job, error: Specify Parameter `s3_bucket`') s3_log_path = f's3://{self.s3_bucket}/{self.s3_glue_logs}{self.job_name}' execution_role = self.get_iam_execution_role() try: create_job_response = glue_client.create_job( Name=self.job_name, Description=self.desc, LogUri=s3_log_path, Role=execution_role['Role']['Arn'], ExecutionProperty={"MaxConcurrentRuns": self.concurrent_run_limit}, Command={"Name": "glueetl", "ScriptLocation": self.script_location}, MaxRetries=self.retry_limit, AllocatedCapacity=self.num_of_dpus, **self.create_job_kwargs, ) return create_job_response['Name'] except Exception as general_error: self.log.error("Failed to create aws glue job, error: %s", general_error) raise

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