Source code for airflow.providers.amazon.aws.hooks.batch_waiters
#
# 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.
"""
AWS Batch service waiters
.. seealso::
- https://boto3.amazonaws.com/v1/documentation/api/latest/guide/clients.html#waiters
- https://github.com/boto/botocore/blob/develop/botocore/waiter.py
"""
from __future__ import annotations
import json
import sys
from copy import deepcopy
from pathlib import Path
import botocore.client
import botocore.exceptions
import botocore.waiter
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
[docs]class BatchWaitersHook(BatchClientHook):
"""
A utility to manage waiters for AWS Batch services.
.. code-block:: python
import random
from airflow.providers.amazon.aws.operators.batch_waiters import BatchWaiters
# to inspect default waiters
waiters = BatchWaiters()
config = waiters.default_config # type: Dict
waiter_names = waiters.list_waiters() # -> ["JobComplete", "JobExists", "JobRunning"]
# The default_config is a useful stepping stone to creating custom waiters, e.g.
custom_config = waiters.default_config # this is a deepcopy
# modify custom_config['waiters'] as necessary and get a new instance:
waiters = BatchWaiters(waiter_config=custom_config)
waiters.waiter_config # check the custom configuration (this is a deepcopy)
waiters.list_waiters() # names of custom waiters
# During the init for BatchWaiters, the waiter_config is used to build a waiter_model;
# and note that this only occurs during the class init, to avoid any accidental mutations
# of waiter_config leaking into the waiter_model.
waiters.waiter_model # -> botocore.waiter.WaiterModel object
# The waiter_model is combined with the waiters.client to get a specific waiter
# and the details of the config on that waiter can be further modified without any
# accidental impact on the generation of new waiters from the defined waiter_model, e.g.
waiters.get_waiter("JobExists").config.delay # -> 5
waiter = waiters.get_waiter("JobExists") # -> botocore.waiter.Batch.Waiter.JobExists object
waiter.config.delay = 10
waiters.get_waiter("JobExists").config.delay # -> 5 as defined by waiter_model
# To use a specific waiter, update the config and call the `wait()` method for jobId, e.g.
waiter = waiters.get_waiter("JobExists") # -> botocore.waiter.Batch.Waiter.JobExists object
waiter.config.delay = random.uniform(1, 10) # seconds
waiter.config.max_attempts = 10
waiter.wait(jobs=[jobId])
.. seealso::
- https://www.2ndwatch.com/blog/use-waiters-boto3-write/
- https://github.com/boto/botocore/blob/develop/botocore/waiter.py
- https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/ec2.html#waiters
- https://github.com/boto/botocore/tree/develop/botocore/data/ec2/2016-11-15
- https://github.com/boto/botocore/issues/1915
:param waiter_config: a custom waiter configuration for AWS Batch services
:param aws_conn_id: connection id of AWS credentials / region name. If None,
credential boto3 strategy will be used
(https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html).
:param region_name: region name to use in AWS client.
Override the AWS region in connection (if provided)
"""
def __init__(self, *args, waiter_config: dict | None = None, **kwargs) -> None:
super().__init__(*args, **kwargs)
self._default_config: dict | None = None
self._waiter_config = waiter_config or self.default_config
self._waiter_model = botocore.waiter.WaiterModel(self._waiter_config)
@property
[docs] def default_config(self) -> dict:
"""
An immutable default waiter configuration
:return: a waiter configuration for AWS Batch services
"""
if self._default_config is None:
config_path = Path(__file__).with_name("batch_waiters.json").resolve()
with open(config_path) as config_file:
self._default_config = json.load(config_file)
return deepcopy(self._default_config) # avoid accidental mutation
@property
[docs] def waiter_config(self) -> dict:
"""
An immutable waiter configuration for this instance; a ``deepcopy`` is returned by this
property. During the init for BatchWaiters, the waiter_config is used to build a
waiter_model and this only occurs during the class init, to avoid any accidental
mutations of waiter_config leaking into the waiter_model.
:return: a waiter configuration for AWS Batch services
"""
return deepcopy(self._waiter_config) # avoid accidental mutation
@property
[docs] def waiter_model(self) -> botocore.waiter.WaiterModel:
"""
A configured waiter model used to generate waiters on AWS Batch services.
:return: a waiter model for AWS Batch services
"""
return self._waiter_model
[docs] def get_waiter(self, waiter_name: str, _: dict[str, str] | None = None) -> botocore.waiter.Waiter:
"""
Get an AWS Batch service waiter, using the configured ``.waiter_model``.
The ``.waiter_model`` is combined with the ``.client`` to get a specific waiter and
the properties of that waiter can be modified without any accidental impact on the
generation of new waiters from the ``.waiter_model``, e.g.
.. code-block:: python
waiters.get_waiter("JobExists").config.delay # -> 5
waiter = waiters.get_waiter("JobExists") # a new waiter object
waiter.config.delay = 10
waiters.get_waiter("JobExists").config.delay # -> 5 as defined by waiter_model
To use a specific waiter, update the config and call the `wait()` method for jobId, e.g.
.. code-block:: python
import random
waiter = waiters.get_waiter("JobExists") # a new waiter object
waiter.config.delay = random.uniform(1, 10) # seconds
waiter.config.max_attempts = 10
waiter.wait(jobs=[jobId])
:param waiter_name: The name of the waiter. The name should match
the name (including the casing) of the key name in the waiter
model file (typically this is CamelCasing); see ``.list_waiters``.
:param _: unused, just here to match the method signature in base_aws
:return: a waiter object for the named AWS Batch service
"""
return botocore.waiter.create_waiter_with_client(waiter_name, self.waiter_model, self.client)
[docs] def list_waiters(self) -> list[str]:
"""
List the waiters in a waiter configuration for AWS Batch services.
:return: waiter names for AWS Batch services
"""
return self.waiter_model.waiter_names
[docs] def wait_for_job(self, job_id: str, delay: int | float | None = None) -> None:
"""
Wait for Batch job to complete. This assumes that the ``.waiter_model`` is configured
using some variation of the ``.default_config`` so that it can generate waiters with the
following names: "JobExists", "JobRunning" and "JobComplete".
:param job_id: a Batch job ID
:param delay: A delay before polling for job status
:raises: AirflowException
.. note::
This method adds a small random jitter to the ``delay`` (+/- 2 sec, >= 1 sec).
Using a random interval helps to avoid AWS API throttle limits when many
concurrent tasks request job-descriptions.
It also modifies the ``max_attempts`` to use the ``sys.maxsize``,
which allows Airflow to manage the timeout on waiting.
"""
self.delay(delay)
try:
waiter = self.get_waiter("JobExists")
waiter.config.delay = self.add_jitter(waiter.config.delay, width=2, minima=1)
waiter.config.max_attempts = sys.maxsize # timeout is managed by Airflow
waiter.wait(jobs=[job_id])
waiter = self.get_waiter("JobRunning")
waiter.config.delay = self.add_jitter(waiter.config.delay, width=2, minima=1)
waiter.config.max_attempts = sys.maxsize # timeout is managed by Airflow
waiter.wait(jobs=[job_id])
waiter = self.get_waiter("JobComplete")
waiter.config.delay = self.add_jitter(waiter.config.delay, width=2, minima=1)
waiter.config.max_attempts = sys.maxsize # timeout is managed by Airflow
waiter.wait(jobs=[job_id])
except (botocore.exceptions.ClientError, botocore.exceptions.WaiterError) as err:
raise AirflowException(err)
