Source code for airflow.executors.dask_executor

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.. seealso::
    For more information on how the DaskExecutor works, take a look at the guide:
from __future__ import annotations

import subprocess
from typing import Any

from distributed import Client, Future, as_completed
from import Security

from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.executors.base_executor import NOT_STARTED_MESSAGE, BaseExecutor, CommandType
from airflow.models.taskinstance import TaskInstanceKey

# queue="default" is a special case since this is the base config default queue name,
# with respect to DaskExecutor, treat it as if no queue is provided
_UNDEFINED_QUEUES = {None, 'default'}

[docs]class DaskExecutor(BaseExecutor): """DaskExecutor submits tasks to a Dask Distributed cluster.""" def __init__(self, cluster_address=None): super().__init__(parallelism=0) if cluster_address is None: cluster_address = conf.get('dask', 'cluster_address') if not cluster_address: raise ValueError('Please provide a Dask cluster address in airflow.cfg') self.cluster_address = cluster_address # ssl / tls parameters self.tls_ca = conf.get('dask', 'tls_ca') self.tls_key = conf.get('dask', 'tls_key') self.tls_cert = conf.get('dask', 'tls_cert') self.client: Client | None = None self.futures: dict[Future, TaskInstanceKey] | None = None
[docs] def start(self) -> None: if self.tls_ca or self.tls_key or self.tls_cert: security = Security( tls_client_key=self.tls_key, tls_client_cert=self.tls_cert, tls_ca_file=self.tls_ca, require_encryption=True, ) else: security = None self.client = Client(self.cluster_address, security=security) self.futures = {}
[docs] def execute_async( self, key: TaskInstanceKey, command: CommandType, queue: str | None = None, executor_config: Any | None = None, ) -> None: self.validate_airflow_tasks_run_command(command) def airflow_run(): return subprocess.check_call(command, close_fds=True) if not self.client: raise AirflowException(NOT_STARTED_MESSAGE) resources = None if queue not in _UNDEFINED_QUEUES: scheduler_info = self.client.scheduler_info() avail_queues = { resource for d in scheduler_info['workers'].values() for resource in d['resources'] } if queue not in avail_queues: raise AirflowException(f"Attempted to submit task to an unavailable queue: '{queue}'") resources = {queue: 1} future = self.client.submit(subprocess.check_call, command, pure=False, resources=resources) self.futures[future] = key # type: ignore
def _process_future(self, future: Future) -> None: if not self.futures: raise AirflowException(NOT_STARTED_MESSAGE) if future.done(): key = self.futures[future] if future.exception(): self.log.error("Failed to execute task: %s", repr(future.exception())) elif future.cancelled(): self.log.error("Failed to execute task") else: self.success(key) self.futures.pop(future)
[docs] def sync(self) -> None: if self.futures is None: raise AirflowException(NOT_STARTED_MESSAGE) # make a copy so futures can be popped during iteration for future in self.futures.copy(): self._process_future(future)
[docs] def end(self) -> None: if not self.client: raise AirflowException(NOT_STARTED_MESSAGE) if self.futures is None: raise AirflowException(NOT_STARTED_MESSAGE) self.client.cancel(list(self.futures.keys())) for future in as_completed(self.futures.copy()): self._process_future(future)
[docs] def terminate(self): if self.futures is None: raise AirflowException(NOT_STARTED_MESSAGE) self.client.cancel(self.futures.keys()) self.end()

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