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#
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#
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from __future__ import annotations
import asyncio
import itertools as it
from functools import cached_property
from typing import Any
from botocore.exceptions import WaiterError
from deprecated import deprecated
from airflow.providers.amazon.aws.hooks.base_aws import AwsGenericHook
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
from airflow.providers.amazon.aws.triggers.base import AwsBaseWaiterTrigger
from airflow.triggers.base import BaseTrigger, TriggerEvent
@deprecated(reason="use BatchJobTrigger instead")
[docs]class BatchOperatorTrigger(BaseTrigger):
"""
Asynchronously poll the boto3 API and wait for the Batch job to be in the `SUCCEEDED` state.
:param job_id: A unique identifier for the cluster.
:param max_retries: The maximum number of attempts to be made.
:param aws_conn_id: The Airflow connection used for AWS credentials.
:param region_name: region name to use in AWS Hook
:param poll_interval: The amount of time in seconds to wait between attempts.
"""
def __init__(
self,
job_id: str | None = None,
max_retries: int = 10,
aws_conn_id: str | None = "aws_default",
region_name: str | None = None,
poll_interval: int = 30,
):
super().__init__()
self.job_id = job_id
self.max_retries = max_retries
self.aws_conn_id = aws_conn_id
self.region_name = region_name
self.poll_interval = poll_interval
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BatchOperatorTrigger arguments and classpath."""
return (
"airflow.providers.amazon.aws.triggers.batch.BatchOperatorTrigger",
{
"job_id": self.job_id,
"max_retries": self.max_retries,
"aws_conn_id": self.aws_conn_id,
"region_name": self.region_name,
"poll_interval": self.poll_interval,
},
)
@cached_property
[docs] def hook(self) -> BatchClientHook:
return BatchClientHook(aws_conn_id=self.aws_conn_id, region_name=self.region_name)
[docs] async def run(self):
async with self.hook.async_conn as client:
waiter = self.hook.get_waiter("batch_job_complete", deferrable=True, client=client)
attempt = 0
while attempt < self.max_retries:
attempt = attempt + 1
try:
await waiter.wait(
jobs=[self.job_id],
WaiterConfig={
"Delay": self.poll_interval,
"MaxAttempts": 1,
},
)
break
except WaiterError as error:
if "terminal failure" in str(error):
yield TriggerEvent(
{"status": "failure", "message": f"Delete Cluster Failed: {error}"}
)
break
self.log.info(
"Job status is %s. Retrying attempt %s/%s",
error.last_response["jobs"][0]["status"],
attempt,
self.max_retries,
)
await asyncio.sleep(int(self.poll_interval))
if attempt >= self.max_retries:
yield TriggerEvent({"status": "failure", "message": "Job Failed - max attempts reached."})
else:
yield TriggerEvent({"status": "success", "job_id": self.job_id})
@deprecated(reason="use BatchJobTrigger instead")
[docs]class BatchSensorTrigger(BaseTrigger):
"""
Checks for the status of a submitted job_id to AWS Batch until it reaches a failure or a success state.
BatchSensorTrigger is fired as deferred class with params to poll the job state in Triggerer.
:param job_id: the job ID, to poll for job completion or not
:param region_name: AWS region name to use
Override the region_name in connection (if provided)
:param aws_conn_id: connection id of AWS credentials / region name. If None,
credential boto3 strategy will be used
:param poke_interval: polling period in seconds to check for the status of the job
"""
def __init__(
self,
job_id: str,
region_name: str | None,
aws_conn_id: str | None = "aws_default",
poke_interval: float = 5,
):
super().__init__()
self.job_id = job_id
self.aws_conn_id = aws_conn_id
self.region_name = region_name
self.poke_interval = poke_interval
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes BatchSensorTrigger arguments and classpath."""
return (
"airflow.providers.amazon.aws.triggers.batch.BatchSensorTrigger",
{
"job_id": self.job_id,
"aws_conn_id": self.aws_conn_id,
"region_name": self.region_name,
"poke_interval": self.poke_interval,
},
)
@cached_property
[docs] def hook(self) -> BatchClientHook:
return BatchClientHook(aws_conn_id=self.aws_conn_id, region_name=self.region_name)
[docs] async def run(self):
"""
Make async connection using aiobotocore library to AWS Batch, periodically poll for the job status.
The status that indicates job completion are: 'SUCCEEDED'|'FAILED'.
"""
async with self.hook.async_conn as client:
waiter = self.hook.get_waiter("batch_job_complete", deferrable=True, client=client)
for attempt in it.count(1):
try:
await waiter.wait(
jobs=[self.job_id],
WaiterConfig={
"Delay": int(self.poke_interval),
"MaxAttempts": 1,
},
)
except WaiterError as error:
if "error" in str(error):
yield TriggerEvent({"status": "failure", "message": f"Job Failed: {error}"})
break
self.log.info(
"Job response is %s. Retrying attempt %s",
error.last_response["Error"]["Message"],
attempt,
)
await asyncio.sleep(int(self.poke_interval))
else:
break
yield TriggerEvent(
{
"status": "success",
"job_id": self.job_id,
"message": f"Job {self.job_id} Succeeded",
}
)
[docs]class BatchJobTrigger(AwsBaseWaiterTrigger):
"""
Checks for the status of a submitted job_id to AWS Batch until it reaches a failure or a success state.
:param job_id: the job ID, to poll for job completion or not
:param region_name: AWS region name to use
Override the region_name in connection (if provided)
:param aws_conn_id: connection id of AWS credentials / region name. If None,
credential boto3 strategy will be used
:param waiter_delay: polling period in seconds to check for the status of the job
:param waiter_max_attempts: The maximum number of attempts to be made.
"""
def __init__(
self,
job_id: str | None,
region_name: str | None = None,
aws_conn_id: str | None = "aws_default",
waiter_delay: int = 5,
waiter_max_attempts: int = 720,
):
super().__init__(
serialized_fields={"job_id": job_id},
waiter_name="batch_job_complete",
waiter_args={"jobs": [job_id]},
failure_message=f"Failure while running batch job {job_id}",
status_message=f"Batch job {job_id} not ready yet",
status_queries=["jobs[].status", "computeEnvironments[].statusReason"],
return_key="job_id",
return_value=job_id,
waiter_delay=waiter_delay,
waiter_max_attempts=waiter_max_attempts,
aws_conn_id=aws_conn_id,
region_name=region_name,
)
[docs] def hook(self) -> AwsGenericHook:
return BatchClientHook(aws_conn_id=self.aws_conn_id, region_name=self.region_name)
[docs]class BatchCreateComputeEnvironmentTrigger(AwsBaseWaiterTrigger):
"""
Asynchronously poll the boto3 API and wait for the compute environment to be ready.
:param compute_env_arn: The ARN of the compute env.
:param waiter_max_attempts: The maximum number of attempts to be made.
:param aws_conn_id: The Airflow connection used for AWS credentials.
:param region_name: region name to use in AWS Hook
:param waiter_delay: The amount of time in seconds to wait between attempts.
"""
def __init__(
self,
compute_env_arn: str,
waiter_delay: int = 30,
waiter_max_attempts: int = 10,
aws_conn_id: str | None = "aws_default",
region_name: str | None = None,
):
super().__init__(
serialized_fields={"compute_env_arn": compute_env_arn},
waiter_name="compute_env_ready",
waiter_args={"computeEnvironments": [compute_env_arn]},
failure_message="Failure while creating Compute Environment",
status_message="Compute Environment not ready yet",
status_queries=["computeEnvironments[].status", "computeEnvironments[].statusReason"],
return_value=compute_env_arn,
waiter_delay=waiter_delay,
waiter_max_attempts=waiter_max_attempts,
aws_conn_id=aws_conn_id,
region_name=region_name,
)
[docs] def hook(self) -> AwsGenericHook:
return BatchClientHook(aws_conn_id=self.aws_conn_id, region_name=self.region_name)
