Source code for airflow.contrib.operators.awsbatch_operator

# -*- coding: utf-8 -*-
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import sys

from math import pow
from time import sleep

from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.utils import apply_defaults

from airflow.contrib.hooks.aws_hook import AwsHook

[docs]class AWSBatchOperator(BaseOperator): """ Execute a job on AWS Batch Service .. warning: the queue parameter was renamed to job_queue to segregate the internal CeleryExecutor queue from the AWS Batch internal queue. :param job_name: the name for the job that will run on AWS Batch :type job_name: str :param job_definition: the job definition name on AWS Batch :type job_definition: str :param job_queue: the queue name on AWS Batch :type job_queue: str :param overrides: the same parameter that boto3 will receive on containerOverrides (templated): :type overrides: dict :param max_retries: exponential backoff retries while waiter is not merged, 4200 = 48 hours :type max_retries: int :param aws_conn_id: connection id of AWS credentials / region name. If None, credential boto3 strategy will be used ( :type aws_conn_id: str :param region_name: region name to use in AWS Hook. Override the region_name in connection (if provided) :type region_name: str """
[docs] ui_color = '#c3dae0'
[docs] client = None
[docs] arn = None
[docs] template_fields = ('overrides',)
@apply_defaults def __init__(self, job_name, job_definition, job_queue, overrides, max_retries=4200, aws_conn_id=None, region_name=None, **kwargs): super(AWSBatchOperator, self).__init__(**kwargs) self.job_name = job_name self.aws_conn_id = aws_conn_id self.region_name = region_name self.job_definition = job_definition self.job_queue = job_queue self.overrides = overrides self.max_retries = max_retries self.jobId = None self.jobName = None self.hook = self.get_hook()
[docs] def execute(self, context): 'Running AWS Batch Job - Job definition: %s - on queue %s', self.job_definition, self.job_queue )'AWSBatchOperator overrides: %s', self.overrides) self.client = self.hook.get_client_type( 'batch', region_name=self.region_name ) try: response = self.client.submit_job( jobName=self.job_name, jobQueue=self.job_queue, jobDefinition=self.job_definition, containerOverrides=self.overrides)'AWS Batch Job started: %s', response) self.jobId = response['jobId'] self.jobName = response['jobName'] self._wait_for_task_ended() self._check_success_task()'AWS Batch Job has been successfully executed: %s', response) except Exception as e:'AWS Batch Job has failed executed') raise AirflowException(e)
[docs] def _wait_for_task_ended(self): """ Try to use a waiter from the below pull request * If the waiter is not available apply a exponential backoff * """ try: waiter = self.client.get_waiter('job_execution_complete') waiter.config.max_attempts = sys.maxsize # timeout is managed by airflow waiter.wait(jobs=[self.jobId]) except ValueError: # If waiter not available use expo retry = True retries = 0 while retries < self.max_retries and retry:'AWS Batch retry in the next %s seconds', retries) response = self.client.describe_jobs( jobs=[self.jobId] ) if response['jobs'][-1]['status'] in ['SUCCEEDED', 'FAILED']: retry = False sleep(1 + pow(retries * 0.1, 2)) retries += 1
[docs] def _check_success_task(self): response = self.client.describe_jobs( jobs=[self.jobId], )'AWS Batch stopped, check status: %s', response) if len(response.get('jobs')) < 1: raise AirflowException('No job found for {}'.format(response)) for job in response['jobs']: job_status = job['status'] if job_status == 'FAILED': reason = job['statusReason'] raise AirflowException('Job failed with status {}'.format(reason)) elif job_status in [ 'SUBMITTED', 'PENDING', 'RUNNABLE', 'STARTING', 'RUNNING' ]: raise AirflowException( 'This task is still pending {}'.format(job_status))
[docs] def get_hook(self): return AwsHook( aws_conn_id=self.aws_conn_id
[docs] def on_kill(self): response = self.client.terminate_job( jobId=self.jobId, reason='Task killed by the user')