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"""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 warnings
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.models import BaseOperator
from airflow.models.mappedoperator import MappedOperator
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.triggers.batch import (
BatchCreateComputeEnvironmentTrigger,
BatchJobTrigger,
)
from airflow.providers.amazon.aws.utils import trim_none_values, validate_execute_complete_event
from airflow.providers.amazon.aws.utils.task_log_fetcher import AwsTaskLogFetcher
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: DEPRECATED, use container_overrides instead with the same value.
:param container_overrides: the `containerOverrides` parameter for boto3 (templated)
:param node_overrides: the `nodeOverrides` parameter for boto3 (templated)
:param share_identifier: The share identifier for the job. Don't specify this parameter if the job queue
doesn't have a scheduling policy.
:param scheduling_priority_override: The scheduling priority for the job.
Jobs with a higher scheduling priority are scheduled before jobs with a lower scheduling priority.
This overrides any scheduling priority in the job definition
: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
:param deferrable: Run operator in the deferrable mode.
:param awslogs_enabled: Specifies whether logs from CloudWatch
should be printed or not, False.
If it is an array job, only the logs of the first task will be printed.
:param awslogs_fetch_interval: The interval with which cloudwatch logs are to be fetched, 30 sec.
:param poll_interval: (Deferrable mode only) Time in seconds to wait between polling.
.. 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",
"container_overrides",
"array_properties",
"node_overrides",
"parameters",
"retry_strategy",
"waiters",
"tags",
"wait_for_completion",
"awslogs_enabled",
"awslogs_fetch_interval",
)
[docs] template_fields_renderers = {
"container_overrides": "json",
"parameters": "json",
"node_overrides": "json",
"retry_strategy": "json",
}
@property
def __init__(
self,
*,
job_name: str,
job_definition: str,
job_queue: str,
overrides: dict | None = None, # deprecated
container_overrides: dict | None = None,
array_properties: dict | None = None,
node_overrides: dict | None = None,
share_identifier: str | None = None,
scheduling_priority_override: int | None = None,
parameters: dict | None = None,
retry_strategy: dict | None = None,
job_id: str | None = None,
waiters: Any | None = None,
max_retries: int = 4200,
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,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: int = 30,
awslogs_enabled: bool = False,
awslogs_fetch_interval: timedelta = timedelta(seconds=30),
**kwargs,
) -> None:
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.container_overrides = container_overrides
# handle `overrides` deprecation in favor of `container_overrides`
if overrides:
if container_overrides:
# disallow setting both old and new params
raise AirflowException(
"'container_overrides' replaces the 'overrides' parameter. "
"You cannot specify both. Please remove assignation to the deprecated 'overrides'."
)
self.container_overrides = overrides
warnings.warn(
"Parameter `overrides` is deprecated, Please use `container_overrides` instead.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.node_overrides = node_overrides
self.share_identifier = share_identifier
self.scheduling_priority_override = scheduling_priority_override
self.array_properties = array_properties
self.parameters = parameters or {}
self.retry_strategy = retry_strategy or {}
if not self.retry_strategy.get("attempts", None):
self.retry_strategy["attempts"] = 1
self.waiters = waiters
self.tags = tags or {}
self.wait_for_completion = wait_for_completion
self.deferrable = deferrable
self.poll_interval = poll_interval
self.awslogs_enabled = awslogs_enabled
self.awslogs_fetch_interval = awslogs_fetch_interval
# params for hook
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) -> 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) -> str | None:
"""Submit and monitor an AWS Batch job.
:raises: AirflowException
"""
self.submit_job(context)
if self.deferrable:
if not self.job_id:
raise AirflowException("AWS Batch job - job_id was not found")
job = self.hook.get_job_description(self.job_id)
job_status = job.get("status")
if job_status == self.hook.SUCCESS_STATE:
self.log.info("Job completed.")
return self.job_id
elif job_status == self.hook.FAILURE_STATE:
raise AirflowException(f"Error while running job: {self.job_id} is in {job_status} state")
elif job_status in self.hook.INTERMEDIATE_STATES:
self.defer(
timeout=self.execution_timeout,
trigger=BatchJobTrigger(
job_id=self.job_id,
waiter_max_attempts=self.max_retries,
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
waiter_delay=self.poll_interval,
),
method_name="execute_complete",
)
raise AirflowException(f"Unexpected status: {job_status}")
if self.wait_for_completion:
self.monitor_job(context)
return self.job_id
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(f"Error while running job: {event}")
self.log.info("Job completed.")
return event["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,
)
if self.container_overrides:
self.log.info("AWS Batch job - container overrides: %s", self.container_overrides)
if self.array_properties:
self.log.info("AWS Batch job - array properties: %s", self.array_properties)
if self.node_overrides:
self.log.info("AWS Batch job - node properties: %s", self.node_overrides)
args = {
"jobName": self.job_name,
"jobQueue": self.job_queue,
"jobDefinition": self.job_definition,
"arrayProperties": self.array_properties,
"parameters": self.parameters,
"tags": self.tags,
"containerOverrides": self.container_overrides,
"nodeOverrides": self.node_overrides,
"retryStrategy": self.retry_strategy,
"shareIdentifier": self.share_identifier,
"schedulingPriorityOverride": self.scheduling_priority_override,
}
try:
response = self.hook.client.submit_job(**trim_none_values(args))
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.
This can raise an exception or an AirflowTaskTimeout if the task was
created with ``execution_timeout``.
"""
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.awslogs_enabled:
if self.waiters:
self.waiters.wait_for_job(self.job_id, get_batch_log_fetcher=self._get_batch_log_fetcher)
else:
self.hook.wait_for_job(self.job_id, get_batch_log_fetcher=self._get_batch_log_fetcher)
else:
if self.waiters:
self.waiters.wait_for_job(self.job_id)
else:
self.hook.wait_for_job(self.job_id)
awslogs = []
try:
awslogs = self.hook.get_job_all_awslogs_info(self.job_id)
except AirflowException as ae:
self.log.warning("Cannot determine where to find the AWS logs for this Batch job: %s", ae)
if awslogs:
self.log.info("AWS Batch job (%s) CloudWatch Events details found. Links to logs:", self.job_id)
link_builder = CloudWatchEventsLink()
for log in awslogs:
self.log.info(link_builder.format_link(**log))
if len(awslogs) > 1:
# there can be several log streams on multi-node jobs
self.log.warning(
"out of all those logs, we can only link to one in the UI. Using the first one."
)
CloudWatchEventsLink.persist(
context=context,
operator=self,
region_name=self.hook.conn_region_name,
aws_partition=self.hook.conn_partition,
**awslogs[0],
)
self.hook.check_job_success(self.job_id)
self.log.info("AWS Batch job (%s) succeeded", self.job_id)
def _get_batch_log_fetcher(self, job_id: str) -> AwsTaskLogFetcher | None:
awslog_info = self.hook.get_job_awslogs_info(job_id)
if not awslog_info:
return None
return AwsTaskLogFetcher(
aws_conn_id=self.aws_conn_id,
region_name=awslog_info["awslogs_region"],
log_group=awslog_info["awslogs_group"],
log_stream_name=awslog_info["awslogs_stream_name"],
fetch_interval=self.awslogs_fetch_interval,
logger=self.log,
)
[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: Name of the AWS batch compute
environment (templated).
:param environment_type: Type of the compute-environment.
:param state: State of the compute-environment.
:param compute_resources: Details about the resources managed by the
compute-environment (templated). More details:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html#Batch.Client.create_compute_environment
:param unmanaged_v_cpus: 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: IAM role that allows Batch to make calls to other AWS
services on your behalf (templated).
:param tags: Tags that you apply to the compute-environment to help you
categorize and organize your resources.
:param poll_interval: How long to wait in seconds between 2 polls at the environment status.
Only useful when deferrable is True.
:param max_retries: How many times to poll for the environment status.
Only useful when deferrable is True.
: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. Overrides the
``region_name`` in connection if provided.
:param deferrable: If True, the operator will wait asynchronously for the environment to be created.
This mode requires aiobotocore module to be installed. (default: False)
"""
[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,
poll_interval: int = 30,
max_retries: int | None = None,
aws_conn_id: str | None = None,
region_name: str | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
if "status_retries" in kwargs:
warnings.warn(
"The `status_retries` parameter is unused and should be removed. "
"It'll be deleted in a future version.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
kwargs.pop("status_retries") # remove before calling super() to prevent unexpected arg error
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.poll_interval = poll_interval
self.max_retries = max_retries or 120
self.aws_conn_id = aws_conn_id
self.region_name = region_name
self.deferrable = deferrable
@cached_property
[docs] def hook(self):
"""Create and return a BatchClientHook."""
return BatchClientHook(
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,
}
response = self.hook.client.create_compute_environment(**trim_none_values(kwargs))
arn = response["computeEnvironmentArn"]
if self.deferrable:
self.defer(
trigger=BatchCreateComputeEnvironmentTrigger(
arn, self.poll_interval, self.max_retries, self.aws_conn_id, self.region_name
),
method_name="execute_complete",
)
self.log.info("AWS Batch compute environment created successfully")
return arn
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> str:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(f"Error while waiting for the compute environment to be ready: {event}")
return event["value"]
