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"""
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
from typing import TYPE_CHECKING, Any, Sequence
from airflow.compat.functools 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
from airflow.providers.amazon.aws.utils import trim_none_values
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] 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")
