airflow.providers.amazon.aws.hooks.batch_waiters
¶
AWS Batch service waiters
See also
Module Contents¶
Classes¶
A utility to manage waiters for AWS Batch services. |
- class airflow.providers.amazon.aws.hooks.batch_waiters.BatchWaitersHook(*args, waiter_config=None, **kwargs)[source]¶
Bases:
airflow.providers.amazon.aws.hooks.batch_client.BatchClientHook
A utility to manage waiters for AWS Batch services.
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])
See also
- Parameters
waiter_config (dict | None) – a custom waiter configuration for AWS Batch services
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).
region_name – region name to use in AWS client. Override the AWS region in connection (if provided)
- property default_config: dict[source]¶
An immutable default waiter configuration
- Returns
a waiter configuration for AWS Batch services
- Return type
- property waiter_config: dict[source]¶
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.- Returns
a waiter configuration for AWS Batch services
- Return type
- property waiter_model: botocore.waiter.WaiterModel[source]¶
A configured waiter model used to generate waiters on AWS Batch services.
- Returns
a waiter model for AWS Batch services
- Return type
botocore.waiter.WaiterModel
- get_waiter(waiter_name, _=None)[source]¶
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.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.
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])
- Parameters
- Returns
a waiter object for the named AWS Batch service
- Return type
botocore.waiter.Waiter
- wait_for_job(job_id, delay=None)[source]¶
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”.- Parameters
- 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 thesys.maxsize
, which allows Airflow to manage the timeout on waiting.