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

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""
An Airflow operator for AWS Batch services

.. seealso::

    - https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html
    - https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html
    - https://docs.aws.amazon.com/batch/latest/APIReference/Welcome.html
"""
from __future__ import annotations

import sys
from typing import TYPE_CHECKING, Any, Sequence

from airflow.providers.amazon.aws.utils import trim_none_values

if sys.version_info >= (3, 8):
    from functools import cached_property
else:
    from cached_property import cached_property

from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
from airflow.providers.amazon.aws.links.batch import (
    BatchJobDefinitionLink,
    BatchJobDetailsLink,
    BatchJobQueueLink,
)
from airflow.providers.amazon.aws.links.logs import CloudWatchEventsLink

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class BatchOperator(BaseOperator): """ Execute a job on AWS Batch .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:BatchOperator` :param job_name: the name for the job that will run on AWS Batch (templated) :param job_definition: the job definition name on AWS Batch :param job_queue: the queue name on AWS Batch :param overrides: the `containerOverrides` parameter for boto3 (templated) :param array_properties: the `arrayProperties` parameter for boto3 :param parameters: the `parameters` for boto3 (templated) :param job_id: the job ID, usually unknown (None) until the submit_job operation gets the jobId defined by AWS Batch :param waiters: an :py:class:`.BatchWaiters` object (see note below); if None, polling is used with max_retries and status_retries. :param max_retries: exponential back-off retries, 4200 = 48 hours; polling is only used when waiters is None :param status_retries: number of HTTP retries to get job status, 10; polling is only used when waiters is None :param aws_conn_id: connection id of AWS credentials / region name. If None, credential boto3 strategy will be used. :param region_name: region name to use in AWS Hook. Override the region_name in connection (if provided) :param tags: collection of tags to apply to the AWS Batch job submission if None, no tags are submitted .. note:: Any custom waiters must return a waiter for these calls: .. code-block:: python waiter = waiters.get_waiter("JobExists") waiter = waiters.get_waiter("JobRunning") waiter = waiters.get_waiter("JobComplete") """
[docs] ui_color = "#c3dae0"
[docs] arn: str | None = None
[docs] template_fields: Sequence[str] = ( "job_id", "job_name", "job_definition", "job_queue", "overrides", "array_properties", "parameters", "waiters", "tags", "wait_for_completion",
)
[docs] template_fields_renderers = {"overrides": "json", "parameters": "json"}
@property def __init__( self, *, job_name: str, job_definition: str, job_queue: str, overrides: dict, array_properties: dict | None = None, parameters: dict | None = None, job_id: str | None = None, waiters: Any | None = None, max_retries: int | None = None, status_retries: int | None = None, aws_conn_id: str | None = None, region_name: str | None = None, tags: dict | None = None, wait_for_completion: bool = True, **kwargs, ): BaseOperator.__init__(self, **kwargs) self.job_id = job_id self.job_name = job_name self.job_definition = job_definition self.job_queue = job_queue self.overrides = overrides or {} self.array_properties = array_properties or {} self.parameters = parameters or {} self.waiters = waiters self.tags = tags or {} self.wait_for_completion = wait_for_completion self.hook = BatchClientHook( max_retries=max_retries, status_retries=status_retries, aws_conn_id=aws_conn_id, region_name=region_name, )
[docs] def execute(self, context: Context): """ Submit and monitor an AWS Batch job :raises: AirflowException """ self.submit_job(context) if self.wait_for_completion: self.monitor_job(context) return self.job_id
[docs] def on_kill(self): response = self.hook.client.terminate_job(jobId=self.job_id, reason="Task killed by the user") self.log.info("AWS Batch job (%s) terminated: %s", self.job_id, response)
[docs] def submit_job(self, context: Context): """ Submit an AWS Batch job :raises: AirflowException """ self.log.info( "Running AWS Batch job - job definition: %s - on queue %s", self.job_definition, self.job_queue, ) self.log.info("AWS Batch job - container overrides: %s", self.overrides) try: response = self.hook.client.submit_job( jobName=self.job_name, jobQueue=self.job_queue, jobDefinition=self.job_definition, arrayProperties=self.array_properties, parameters=self.parameters, containerOverrides=self.overrides, tags=self.tags, ) except Exception as e: self.log.error( "AWS Batch job failed submission - job definition: %s - on queue %s", self.job_definition, self.job_queue, ) raise AirflowException(e) self.job_id = response["jobId"] self.log.info("AWS Batch job (%s) started: %s", self.job_id, response) BatchJobDetailsLink.persist( context=context, operator=self, region_name=self.hook.conn_region_name, aws_partition=self.hook.conn_partition, job_id=self.job_id,
)
[docs] def monitor_job(self, context: Context): """ Monitor an AWS Batch job monitor_job can raise an exception or an AirflowTaskTimeout can be raised if execution_timeout is given while creating the task. These exceptions should be handled in taskinstance.py instead of here like it was previously done :raises: AirflowException """ if not self.job_id: raise AirflowException("AWS Batch job - job_id was not found") try: job_desc = self.hook.get_job_description(self.job_id) job_definition_arn = job_desc["jobDefinition"] job_queue_arn = job_desc["jobQueue"] self.log.info( "AWS Batch job (%s) Job Definition ARN: %r, Job Queue ARN: %r", self.job_id, job_definition_arn, job_queue_arn, ) except KeyError: self.log.warning("AWS Batch job (%s) can't get Job Definition ARN and Job Queue ARN", self.job_id) else: BatchJobDefinitionLink.persist( context=context, operator=self, region_name=self.hook.conn_region_name, aws_partition=self.hook.conn_partition, job_definition_arn=job_definition_arn, ) BatchJobQueueLink.persist( context=context, operator=self, region_name=self.hook.conn_region_name, aws_partition=self.hook.conn_partition, job_queue_arn=job_queue_arn, ) if self.waiters: self.waiters.wait_for_job(self.job_id) else: self.hook.wait_for_job(self.job_id) awslogs = self.hook.get_job_awslogs_info(self.job_id) if awslogs: self.log.info("AWS Batch job (%s) CloudWatch Events details found: %s", self.job_id, awslogs) CloudWatchEventsLink.persist( context=context, operator=self, region_name=self.hook.conn_region_name, aws_partition=self.hook.conn_partition, **awslogs, ) self.hook.check_job_success(self.job_id) self.log.info("AWS Batch job (%s) succeeded", self.job_id)
[docs]class BatchCreateComputeEnvironmentOperator(BaseOperator): """ Create an AWS Batch compute environment .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:BatchCreateComputeEnvironmentOperator` :param compute_environment_name: the name of the AWS batch compute environment (templated) :param environment_type: the type of the compute-environment :param state: the state of the compute-environment :param compute_resources: details about the resources managed by the compute-environment (templated). See more details here https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html#Batch.Client.create_compute_environment :param unmanaged_v_cpus: the maximum number of vCPU for an unmanaged compute environment. This parameter is only supported when the ``type`` parameter is set to ``UNMANAGED``. :param service_role: the IAM role that allows Batch to make calls to other AWS services on your behalf (templated) :param tags: the tags that you apply to the compute-environment to help you categorize and organize your resources :param max_retries: exponential back-off retries, 4200 = 48 hours; polling is only used when waiters is None :param status_retries: number of HTTP retries to get job status, 10; polling is only used when waiters is None :param aws_conn_id: connection id of AWS credentials / region name. If None, credential boto3 strategy will be used. :param region_name: region name to use in AWS Hook. Override the region_name in connection (if provided) """
[docs] template_fields: Sequence[str] = ( "compute_environment_name", "compute_resources", "service_role",
)
[docs] template_fields_renderers = {"compute_resources": "json"}
def __init__( self, compute_environment_name: str, environment_type: str, state: str, compute_resources: dict, unmanaged_v_cpus: int | None = None, service_role: str | None = None, tags: dict | None = None, max_retries: int | None = None, status_retries: int | None = None, aws_conn_id: str | None = None, region_name: str | None = None, **kwargs, ): super().__init__(**kwargs) self.compute_environment_name = compute_environment_name self.environment_type = environment_type self.state = state self.unmanaged_v_cpus = unmanaged_v_cpus self.compute_resources = compute_resources self.service_role = service_role self.tags = tags or {} self.max_retries = max_retries self.status_retries = status_retries self.aws_conn_id = aws_conn_id self.region_name = region_name @cached_property
[docs] def hook(self): """Create and return a BatchClientHook""" return BatchClientHook( max_retries=self.max_retries, status_retries=self.status_retries, aws_conn_id=self.aws_conn_id, region_name=self.region_name,
)
[docs] def execute(self, context: Context): """Create an AWS batch compute environment""" kwargs: dict[str, Any] = { "computeEnvironmentName": self.compute_environment_name, "type": self.environment_type, "state": self.state, "unmanagedvCpus": self.unmanaged_v_cpus, "computeResources": self.compute_resources, "serviceRole": self.service_role, "tags": self.tags, } self.hook.client.create_compute_environment(**trim_none_values(kwargs)) self.log.info("AWS Batch compute environment created successfully")

Was this entry helpful?