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# to you under the Apache License, Version 2.0 (the
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#
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#
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# KIND, either express or implied. See the License for the
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"""AWS Batch Executor. Each Airflow task gets delegated out to an AWS Batch Job."""
from __future__ import annotations
import time
from collections import defaultdict, deque
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Dict, List
from botocore.exceptions import ClientError, NoCredentialsError
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.executors.base_executor import BaseExecutor
from airflow.providers.amazon.aws.executors.utils.exponential_backoff_retry import (
calculate_next_attempt_delay,
exponential_backoff_retry,
)
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
from airflow.utils import timezone
from airflow.utils.helpers import merge_dicts
if TYPE_CHECKING:
from airflow.models.taskinstance import TaskInstanceKey
from airflow.providers.amazon.aws.executors.batch.boto_schema import (
BatchDescribeJobsResponseSchema,
BatchSubmitJobResponseSchema,
)
from airflow.providers.amazon.aws.executors.batch.utils import (
CONFIG_DEFAULTS,
CONFIG_GROUP_NAME,
AllBatchConfigKeys,
BatchJob,
BatchJobCollection,
BatchQueuedJob,
)
from airflow.utils.state import State
[docs]ExecutorConfigType = Dict[str, Any]
[docs]INVALID_CREDENTIALS_EXCEPTIONS = [
"ExpiredTokenException",
"InvalidClientTokenId",
"UnrecognizedClientException",
]
[docs]class AwsBatchExecutor(BaseExecutor):
"""
The Airflow Scheduler creates a shell command, and passes it to the executor.
This Batch Executor simply runs said airflow command in a resource provisioned and managed
by AWS Batch. It then periodically checks in with the launched jobs (via job-ids) to
determine the status.
The `submit_job_kwargs` is a dictionary that should match the kwargs for the
SubmitJob definition per AWS' documentation (see below).
For maximum flexibility, individual tasks can specify `executor_config` as a dictionary, with keys that
match the request syntax for the SubmitJob definition per AWS' documentation (see link below). The
`executor_config` will update the `submit_job_kwargs` dictionary when calling the task. This allows
individual jobs to specify CPU, memory, GPU, env variables, etc.
Prerequisite: proper configuration of Boto3 library
.. seealso:: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html for
authentication and access-key management. You can store an environmental variable, setup aws config from
console, or use IAM roles.
.. seealso:: https://docs.aws.amazon.com/batch/latest/APIReference/API_SubmitJob.html for an
Airflow TaskInstance's executor_config.
"""
# Maximum number of retries to submit a Batch Job.
[docs] MAX_SUBMIT_JOB_ATTEMPTS = conf.get(
CONFIG_GROUP_NAME,
AllBatchConfigKeys.MAX_SUBMIT_JOB_ATTEMPTS,
fallback=CONFIG_DEFAULTS[AllBatchConfigKeys.MAX_SUBMIT_JOB_ATTEMPTS],
)
# AWS only allows a maximum number of JOBs in the describe_jobs function
[docs] DESCRIBE_JOBS_BATCH_SIZE = 99
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.active_workers = BatchJobCollection()
self.pending_jobs: deque = deque()
self.attempts_since_last_successful_connection = 0
self.load_batch_connection(check_connection=False)
self.IS_BOTO_CONNECTION_HEALTHY = False
self.submit_job_kwargs = self._load_submit_kwargs()
[docs] def check_health(self):
"""Make a test API call to check the health of the Batch Executor."""
success_status = "succeeded."
status = success_status
try:
invalid_job_id = "a" * 32
self.batch.describe_jobs(jobs=[invalid_job_id])
# If an empty response was received, then that is considered to be the success case.
except ClientError as ex:
error_code = ex.response["Error"]["Code"]
error_message = ex.response["Error"]["Message"]
status = f"failed because: {error_code}: {error_message}. "
except Exception as e:
# Any non-ClientError exceptions. This can include Botocore exceptions for example
status = f"failed because: {e}. "
finally:
msg_prefix = "Batch Executor health check has %s"
if status == success_status:
self.IS_BOTO_CONNECTION_HEALTHY = True
self.log.info(msg_prefix, status)
else:
msg_error_suffix = (
"The Batch executor will not be able to run Airflow tasks until the issue is addressed."
)
raise AirflowException(msg_prefix % status + msg_error_suffix)
[docs] def start(self):
"""Call this when the Executor is run for the first time by the scheduler."""
check_health = conf.getboolean(
CONFIG_GROUP_NAME, AllBatchConfigKeys.CHECK_HEALTH_ON_STARTUP, fallback=False
)
if not check_health:
return
self.log.info("Starting Batch Executor and determining health...")
try:
self.check_health()
except AirflowException:
self.log.error("Stopping the Airflow Scheduler from starting until the issue is resolved.")
raise
[docs] def load_batch_connection(self, check_connection: bool = True):
self.log.info("Loading Connection information")
aws_conn_id = conf.get(
CONFIG_GROUP_NAME,
AllBatchConfigKeys.AWS_CONN_ID,
fallback=CONFIG_DEFAULTS[AllBatchConfigKeys.AWS_CONN_ID],
)
region_name = conf.get(CONFIG_GROUP_NAME, AllBatchConfigKeys.REGION_NAME, fallback=None)
self.batch = BatchClientHook(aws_conn_id=aws_conn_id, region_name=region_name).conn
self.attempts_since_last_successful_connection += 1
self.last_connection_reload = timezone.utcnow()
if check_connection:
self.check_health()
self.attempts_since_last_successful_connection = 0
[docs] def sync(self):
"""Sync will get called periodically by the heartbeat method in the scheduler."""
if not self.IS_BOTO_CONNECTION_HEALTHY:
exponential_backoff_retry(
self.last_connection_reload,
self.attempts_since_last_successful_connection,
self.load_batch_connection,
)
if not self.IS_BOTO_CONNECTION_HEALTHY:
return
try:
self.sync_running_jobs()
self.attempt_submit_jobs()
except (ClientError, NoCredentialsError) as error:
error_code = error.response["Error"]["Code"]
if error_code in INVALID_CREDENTIALS_EXCEPTIONS:
self.IS_BOTO_CONNECTION_HEALTHY = False
self.log.warning(
"AWS credentials are either missing or expired: %s.\nRetrying connection", error
)
except Exception:
# We catch any and all exceptions because otherwise they would bubble
# up and kill the scheduler process
self.log.exception("Failed to sync %s", self.__class__.__name__)
[docs] def sync_running_jobs(self):
all_job_ids = self.active_workers.get_all_jobs()
if not all_job_ids:
self.log.debug("No active Airflow tasks, skipping sync")
return
describe_job_response = self._describe_jobs(all_job_ids)
self.log.debug("Active Workers: %s", describe_job_response)
for job in describe_job_response:
if job.get_job_state() == State.FAILED:
self._handle_failed_job(job)
elif job.get_job_state() == State.SUCCESS:
task_key = self.active_workers.pop_by_id(job.job_id)
self.success(task_key)
def _handle_failed_job(self, job):
"""
Handle a failed AWS Batch job.
If an API failure occurs when running a Batch job, the job is rescheduled.
"""
# A failed job here refers to a job that has been marked Failed by AWS Batch, which is not
# necessarily the same as being marked Failed by Airflow. AWS Batch will mark a job Failed
# if the job fails before the Airflow process on the container has started. These failures
# can be caused by a Batch API failure, container misconfiguration etc.
# If the container is able to start up and run the Airflow process, any failures after that
# (i.e. DAG failures) will not be marked as Failed by AWS Batch, because Batch on assumes
# responsibility for ensuring the process started. Failures in the DAG will be caught by
# Airflow, which will be handled separately.
job_info = self.active_workers.id_to_job_info[job.job_id]
task_key = self.active_workers.id_to_key[job.job_id]
task_cmd = job_info.cmd
queue = job_info.queue
exec_info = job_info.config
failure_count = self.active_workers.failure_count_by_id(job_id=job.job_id)
if int(failure_count) < int(self.__class__.MAX_SUBMIT_JOB_ATTEMPTS):
self.log.warning(
"Airflow task %s failed due to %s. Failure %s out of %s occurred on %s. Rescheduling.",
task_key,
job.status_reason,
failure_count,
self.__class__.MAX_SUBMIT_JOB_ATTEMPTS,
job.job_id,
)
self.active_workers.increment_failure_count(job_id=job.job_id)
self.active_workers.pop_by_id(job.job_id)
self.pending_jobs.append(
BatchQueuedJob(
task_key,
task_cmd,
queue,
exec_info,
failure_count + 1,
timezone.utcnow() + calculate_next_attempt_delay(failure_count),
)
)
else:
self.log.error(
"Airflow task %s has failed a maximum of %s times. Marking as failed",
task_key,
failure_count,
)
self.active_workers.pop_by_id(job.job_id)
self.fail(task_key)
[docs] def attempt_submit_jobs(self):
"""
Attempt to submit all jobs submitted to the Executor.
For each iteration of the sync() method, every pending job is submitted to Batch.
If a job fails validation, it will be put at the back of the queue to be reattempted
in the next iteration of the sync() method, unless it has exceeded the maximum number of
attempts. If a job exceeds the maximum number of attempts, it is removed from the queue.
"""
failure_reasons = defaultdict(int)
for _ in range(len(self.pending_jobs)):
batch_job = self.pending_jobs.popleft()
key = batch_job.key
cmd = batch_job.command
queue = batch_job.queue
exec_config = batch_job.executor_config
attempt_number = batch_job.attempt_number
_failure_reason = []
if timezone.utcnow() < batch_job.next_attempt_time:
self.pending_jobs.append(batch_job)
continue
try:
submit_job_response = self._submit_job(key, cmd, queue, exec_config or {})
except NoCredentialsError:
self.pending_jobs.append(batch_job)
raise
except ClientError as e:
error_code = e.response["Error"]["Code"]
if error_code in INVALID_CREDENTIALS_EXCEPTIONS:
self.pending_jobs.append(batch_job)
raise
_failure_reason.append(str(e))
except Exception as e:
_failure_reason.append(str(e))
if _failure_reason:
for reason in _failure_reason:
failure_reasons[reason] += 1
if attempt_number >= int(self.__class__.MAX_SUBMIT_JOB_ATTEMPTS):
self.log.error(
"This job has been unsuccessfully attempted too many times (%s). Dropping the task.",
attempt_number,
)
self.fail(key=key)
else:
batch_job.next_attempt_time = timezone.utcnow() + calculate_next_attempt_delay(
attempt_number
)
batch_job.attempt_number += 1
self.pending_jobs.append(batch_job)
else:
# Success case
self.active_workers.add_job(
job_id=submit_job_response["job_id"],
airflow_task_key=key,
airflow_cmd=cmd,
queue=queue,
exec_config=exec_config,
attempt_number=attempt_number,
)
if failure_reasons:
self.log.error(
"Pending Batch jobs failed to launch for the following reasons: %s. Retrying later.",
dict(failure_reasons),
)
def _describe_jobs(self, job_ids) -> list[BatchJob]:
all_jobs = []
for i in range(0, len(job_ids), self.__class__.DESCRIBE_JOBS_BATCH_SIZE):
batched_job_ids = job_ids[i : i + self.__class__.DESCRIBE_JOBS_BATCH_SIZE]
if not batched_job_ids:
continue
boto_describe_tasks = self.batch.describe_jobs(jobs=batched_job_ids)
describe_tasks_response = BatchDescribeJobsResponseSchema().load(boto_describe_tasks)
all_jobs.extend(describe_tasks_response["jobs"])
return all_jobs
[docs] def execute_async(self, key: TaskInstanceKey, command: CommandType, queue=None, executor_config=None):
"""Save the task to be executed in the next sync using Boto3's RunTask API."""
if executor_config and "command" in executor_config:
raise ValueError('Executor Config should never override "command"')
self.pending_jobs.append(
BatchQueuedJob(
key=key,
command=command,
queue=queue,
executor_config=executor_config or {},
attempt_number=1,
next_attempt_time=timezone.utcnow(),
)
)
def _submit_job(
self, key: TaskInstanceKey, cmd: CommandType, queue: str, exec_config: ExecutorConfigType
) -> str:
"""
Override the submit_job_kwargs, and calls the boto3 API submit_job endpoint.
The command and executor config will be placed in the container-override section of the JSON request,
before calling Boto3's "submit_job" function.
"""
submit_job_api = self._submit_job_kwargs(key, cmd, queue, exec_config)
boto_submit_job = self.batch.submit_job(**submit_job_api)
submit_job_response = BatchSubmitJobResponseSchema().load(boto_submit_job)
return submit_job_response
def _submit_job_kwargs(
self, key: TaskInstanceKey, cmd: CommandType, queue: str, exec_config: ExecutorConfigType
) -> dict:
"""
Override the Airflow command to update the container overrides so kwargs are specific to this task.
One last chance to modify Boto3's "submit_job" kwarg params before it gets passed into the Boto3
client. For the latest kwarg parameters:
.. seealso:: https://docs.aws.amazon.com/batch/latest/APIReference/API_SubmitJob.html
"""
submit_job_api = deepcopy(self.submit_job_kwargs)
submit_job_api = merge_dicts(submit_job_api, exec_config)
submit_job_api["containerOverrides"]["command"] = cmd
if "environment" not in submit_job_api["containerOverrides"]:
submit_job_api["containerOverrides"]["environment"] = []
submit_job_api["containerOverrides"]["environment"].append(
{"name": "AIRFLOW_IS_EXECUTOR_CONTAINER", "value": "true"}
)
return submit_job_api
[docs] def end(self, heartbeat_interval=10):
"""Wait for all currently running tasks to end and prevent any new jobs from running."""
try:
while True:
self.sync()
if not self.active_workers:
break
time.sleep(heartbeat_interval)
except Exception:
# This should never happen because sync() should never raise an exception.
self.log.exception("Failed to end %s", self.__class__.__name__)
[docs] def terminate(self):
"""Kill all Batch Jobs by calling Boto3's TerminateJob API."""
try:
for job_id in self.active_workers.get_all_jobs():
self.batch.terminate_job(jobId=job_id, reason="Airflow Executor received a SIGTERM")
self.end()
except Exception:
# We catch any and all exceptions because otherwise they would bubble
# up and kill the scheduler process.
self.log.exception("Failed to terminate %s", self.__class__.__name__)
@staticmethod
def _load_submit_kwargs() -> dict:
from airflow.providers.amazon.aws.executors.batch.batch_executor_config import build_submit_kwargs
submit_kwargs = build_submit_kwargs()
if "containerOverrides" not in submit_kwargs or "command" not in submit_kwargs["containerOverrides"]:
raise KeyError(
'SubmitJob API needs kwargs["containerOverrides"]["command"] field,'
" and value should be NULL or empty."
)
return submit_kwargs