Source code for airflow.providers.amazon.aws.hooks.batch_client

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"""
A client 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 random import uniform
from time import sleep

import botocore.client
import botocore.exceptions
import botocore.waiter

from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
from airflow.typing_compat import Protocol, runtime_checkable


@runtime_checkable
[docs]class BatchProtocol(Protocol): """ A structured Protocol for ``boto3.client('batch') -> botocore.client.Batch``. This is used for type hints on :py:meth:`.BatchClient.client`; it covers only the subset of client methods required. .. seealso:: - https://mypy.readthedocs.io/en/latest/protocols.html - https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html """
[docs] def describe_jobs(self, jobs: list[str]) -> dict: """ Get job descriptions from AWS Batch :param jobs: a list of JobId to describe :return: an API response to describe jobs """ ...
[docs] def get_waiter(self, waiterName: str) -> botocore.waiter.Waiter: """ Get an AWS Batch service waiter :param waiterName: 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). :return: a waiter object for the named AWS Batch service .. note:: AWS Batch might not have any waiters (until botocore PR-1307 is released). .. code-block:: python import boto3 boto3.client("batch").waiter_names == [] .. seealso:: - https://boto3.amazonaws.com/v1/documentation/api/latest/guide/clients.html#waiters - https://github.com/boto/botocore/pull/1307 """ ...
[docs] def submit_job( self, jobName: str, jobQueue: str, jobDefinition: str, arrayProperties: dict, parameters: dict, containerOverrides: dict, tags: dict, ) -> dict: """ Submit a Batch job :param jobName: the name for the AWS Batch job :param jobQueue: the queue name on AWS Batch :param jobDefinition: the job definition name on AWS Batch :param arrayProperties: the same parameter that boto3 will receive :param parameters: the same parameter that boto3 will receive :param containerOverrides: the same parameter that boto3 will receive :param tags: the same parameter that boto3 will receive :return: an API response """ ...
[docs] def terminate_job(self, jobId: str, reason: str) -> dict: """ Terminate a Batch job :param jobId: a job ID to terminate :param reason: a reason to terminate job ID :return: an API response """ ...
# Note that the use of invalid-name parameters should be restricted to the boto3 mappings only; # all the Airflow wrappers of boto3 clients should not adopt invalid-names to match boto3.
[docs]class BatchClientHook(AwsBaseHook): """ A client for AWS Batch services. :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 .. note:: Several methods use a default random delay to check or poll for job status, i.e. ``random.uniform(DEFAULT_DELAY_MIN, DEFAULT_DELAY_MAX)`` Using a random interval helps to avoid AWS API throttle limits when many concurrent tasks request job-descriptions. To modify the global defaults for the range of jitter allowed when a random delay is used to check Batch job status, modify these defaults, e.g.: .. code-block:: BatchClient.DEFAULT_DELAY_MIN = 0 BatchClient.DEFAULT_DELAY_MAX = 5 When explicit delay values are used, a 1 second random jitter is applied to the delay (e.g. a delay of 0 sec will be a ``random.uniform(0, 1)`` delay. It is generally recommended that random jitter is added to API requests. A convenience method is provided for this, e.g. to get a random delay of 10 sec +/- 5 sec: ``delay = BatchClient.add_jitter(10, width=5, minima=0)`` .. seealso:: - https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html - https://docs.aws.amazon.com/general/latest/gr/api-retries.html - https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ """
[docs] MAX_RETRIES = 4200
[docs] STATUS_RETRIES = 10
# delays are in seconds
[docs] DEFAULT_DELAY_MIN = 1
[docs] DEFAULT_DELAY_MAX = 10
[docs] FAILURE_STATE = "FAILED"
[docs] SUCCESS_STATE = "SUCCEEDED"
[docs] RUNNING_STATE = "RUNNING"
[docs] INTERMEDIATE_STATES = ( "SUBMITTED", "PENDING", "RUNNABLE", "STARTING", RUNNING_STATE,
)
[docs] COMPUTE_ENVIRONMENT_TERMINAL_STATUS = ("VALID", "DELETED")
[docs] COMPUTE_ENVIRONMENT_INTERMEDIATE_STATUS = ("CREATING", "UPDATING", "DELETING")
[docs] JOB_QUEUE_TERMINAL_STATUS = ("VALID", "DELETED")
[docs] JOB_QUEUE_INTERMEDIATE_STATUS = ("CREATING", "UPDATING", "DELETING")
def __init__( self, *args, max_retries: int | None = None, status_retries: int | None = None, **kwargs ) -> None: # https://github.com/python/mypy/issues/6799 hence type: ignore super().__init__(client_type="batch", *args, **kwargs) # type: ignore self.max_retries = max_retries or self.MAX_RETRIES self.status_retries = status_retries or self.STATUS_RETRIES @property
[docs] def client(self) -> BatchProtocol | botocore.client.BaseClient: """ An AWS API client for Batch services. :return: a boto3 'batch' client for the ``.region_name`` """ return self.conn
[docs] def terminate_job(self, job_id: str, reason: str) -> dict: """ Terminate a Batch job :param job_id: a job ID to terminate :param reason: a reason to terminate job ID :return: an API response """ response = self.get_conn().terminate_job(jobId=job_id, reason=reason) self.log.info(response) return response
[docs] def check_job_success(self, job_id: str) -> bool: """ Check the final status of the Batch job; return True if the job 'SUCCEEDED', else raise an AirflowException :param job_id: a Batch job ID :raises: AirflowException """ job = self.get_job_description(job_id) job_status = job.get("status") if job_status == self.SUCCESS_STATE: self.log.info("AWS Batch job (%s) succeeded: %s", job_id, job) return True if job_status == self.FAILURE_STATE: raise AirflowException(f"AWS Batch job ({job_id}) failed: {job}") if job_status in self.INTERMEDIATE_STATES: raise AirflowException(f"AWS Batch job ({job_id}) is not complete: {job}") raise AirflowException(f"AWS Batch job ({job_id}) has unknown status: {job}")
[docs] def wait_for_job(self, job_id: str, delay: int | float | None = None) -> None: """ Wait for Batch job to complete :param job_id: a Batch job ID :param delay: a delay before polling for job status :raises: AirflowException """ self.delay(delay) self.poll_for_job_running(job_id, delay) self.poll_for_job_complete(job_id, delay) self.log.info("AWS Batch job (%s) has completed", job_id)
[docs] def poll_for_job_running(self, job_id: str, delay: int | float | None = None) -> None: """ Poll for job running. The status that indicates a job is running or already complete are: 'RUNNING'|'SUCCEEDED'|'FAILED'. So the status options that this will wait for are the transitions from: 'SUBMITTED'>'PENDING'>'RUNNABLE'>'STARTING'>'RUNNING'|'SUCCEEDED'|'FAILED' The completed status options are included for cases where the status changes too quickly for polling to detect a RUNNING status that moves quickly from STARTING to RUNNING to completed (often a failure). :param job_id: a Batch job ID :param delay: a delay before polling for job status :raises: AirflowException """ self.delay(delay) running_status = [self.RUNNING_STATE, self.SUCCESS_STATE, self.FAILURE_STATE] self.poll_job_status(job_id, running_status)
[docs] def poll_for_job_complete(self, job_id: str, delay: int | float | None = None) -> None: """ Poll for job completion. The status that indicates job completion are: 'SUCCEEDED'|'FAILED'. So the status options that this will wait for are the transitions from: 'SUBMITTED'>'PENDING'>'RUNNABLE'>'STARTING'>'RUNNING'>'SUCCEEDED'|'FAILED' :param job_id: a Batch job ID :param delay: a delay before polling for job status :raises: AirflowException """ self.delay(delay) complete_status = [self.SUCCESS_STATE, self.FAILURE_STATE] self.poll_job_status(job_id, complete_status)
[docs] def poll_job_status(self, job_id: str, match_status: list[str]) -> bool: """ Poll for job status using an exponential back-off strategy (with max_retries). :param job_id: a Batch job ID :param match_status: a list of job status to match; the Batch job status are: 'SUBMITTED'|'PENDING'|'RUNNABLE'|'STARTING'|'RUNNING'|'SUCCEEDED'|'FAILED' :raises: AirflowException """ retries = 0 while True: job = self.get_job_description(job_id) job_status = job.get("status") self.log.info( "AWS Batch job (%s) check status (%s) in %s", job_id, job_status, match_status, ) if job_status in match_status: return True if retries >= self.max_retries: raise AirflowException(f"AWS Batch job ({job_id}) status checks exceed max_retries") retries += 1 pause = self.exponential_delay(retries) self.log.info( "AWS Batch job (%s) status check (%d of %d) in the next %.2f seconds", job_id, retries, self.max_retries, pause, ) self.delay(pause)
[docs] def get_job_description(self, job_id: str) -> dict: """ Get job description (using status_retries). :param job_id: a Batch job ID :return: an API response for describe jobs :raises: AirflowException """ retries = 0 while True: try: response = self.get_conn().describe_jobs(jobs=[job_id]) return self.parse_job_description(job_id, response) except botocore.exceptions.ClientError as err: # Allow it to retry in case of exceeded quota limit of requests to AWS API if err.response.get("Error", {}).get("Code") != "TooManyRequestsException": raise self.log.warning( "Ignored TooManyRequestsException error, original message: %r. " "Please consider to setup retries mode in boto3, " "check Amazon Provider AWS Connection documentation for more details.", str(err), ) retries += 1 if retries >= self.status_retries: raise AirflowException( f"AWS Batch job ({job_id}) description error: exceeded status_retries " f"({self.status_retries})" ) pause = self.exponential_delay(retries) self.log.info( "AWS Batch job (%s) description retry (%d of %d) in the next %.2f seconds", job_id, retries, self.status_retries, pause, ) self.delay(pause)
@staticmethod
[docs] def parse_job_description(job_id: str, response: dict) -> dict: """ Parse job description to extract description for job_id :param job_id: a Batch job ID :param response: an API response for describe jobs :return: an API response to describe job_id :raises: AirflowException """ jobs = response.get("jobs", []) matching_jobs = [job for job in jobs if job.get("jobId") == job_id] if len(matching_jobs) != 1: raise AirflowException(f"AWS Batch job ({job_id}) description error: response: {response}") return matching_jobs[0]
[docs] def get_job_awslogs_info(self, job_id: str) -> dict[str, str] | None: """ Parse job description to extract AWS CloudWatch information. :param job_id: AWS Batch Job ID """ job_container_desc = self.get_job_description(job_id=job_id).get("container", {}) log_configuration = job_container_desc.get("logConfiguration", {}) # In case if user select other "logDriver" rather than "awslogs" # than CloudWatch logging should be disabled. # If user not specify anything than expected that "awslogs" will use # with default settings: # awslogs-group = /aws/batch/job # awslogs-region = `same as AWS Batch Job region` log_driver = log_configuration.get("logDriver", "awslogs") if log_driver != "awslogs": self.log.warning( "AWS Batch job (%s) uses logDriver (%s). AWS CloudWatch logging disabled.", job_id, log_driver ) return None awslogs_stream_name = job_container_desc.get("logStreamName") if not awslogs_stream_name: # In case of call this method on very early stage of running AWS Batch # there is possibility than AWS CloudWatch Stream Name not exists yet. # AWS CloudWatch Stream Name also not created in case of misconfiguration. self.log.warning("AWS Batch job (%s) doesn't create AWS CloudWatch Stream.", job_id) return None # Try to get user-defined log configuration options log_options = log_configuration.get("options", {}) return { "awslogs_stream_name": awslogs_stream_name, "awslogs_group": log_options.get("awslogs-group", "/aws/batch/job"), "awslogs_region": log_options.get("awslogs-region", self.conn_region_name),
} @staticmethod
[docs] def add_jitter(delay: int | float, width: int | float = 1, minima: int | float = 0) -> float: """ Use delay +/- width for random jitter Adding jitter to status polling can help to avoid AWS Batch API limits for monitoring Batch jobs with a high concurrency in Airflow tasks. :param delay: number of seconds to pause; delay is assumed to be a positive number :param width: delay +/- width for random jitter; width is assumed to be a positive number :param minima: minimum delay allowed; minima is assumed to be a non-negative number :return: uniform(delay - width, delay + width) jitter and it is a non-negative number """ delay = abs(delay) width = abs(width) minima = abs(minima) lower = max(minima, delay - width) upper = delay + width return uniform(lower, upper)
@staticmethod
[docs] def delay(delay: int | float | None = None) -> None: """ Pause execution for ``delay`` seconds. :param delay: a delay to pause execution using ``time.sleep(delay)``; a small 1 second jitter is applied to the delay. .. note:: This method uses a default random delay, i.e. ``random.uniform(DEFAULT_DELAY_MIN, DEFAULT_DELAY_MAX)``; using a random interval helps to avoid AWS API throttle limits when many concurrent tasks request job-descriptions. """ if delay is None: delay = uniform(BatchClientHook.DEFAULT_DELAY_MIN, BatchClientHook.DEFAULT_DELAY_MAX) else: delay = BatchClientHook.add_jitter(delay) sleep(delay)
@staticmethod
[docs] def exponential_delay(tries: int) -> float: """ An exponential back-off delay, with random jitter. There is a maximum interval of 10 minutes (with random jitter between 3 and 10 minutes). This is used in the :py:meth:`.poll_for_job_status` method. :param tries: Number of tries Examples of behavior: .. code-block:: python def exp(tries): max_interval = 600.0 # 10 minutes in seconds delay = 1 + pow(tries * 0.6, 2) delay = min(max_interval, delay) print(delay / 3, delay) for tries in range(10): exp(tries) # 0.33 1.0 # 0.45 1.35 # 0.81 2.44 # 1.41 4.23 # 2.25 6.76 # 3.33 10.00 # 4.65 13.95 # 6.21 18.64 # 8.01 24.04 # 10.05 30.15 .. seealso:: - https://docs.aws.amazon.com/general/latest/gr/api-retries.html - https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ """ max_interval = 600.0 # results in 3 to 10 minute delay delay = 1 + pow(tries * 0.6, 2) delay = min(max_interval, delay) return uniform(delay / 3, delay)

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